From 74e116c8ef18871da2a1c4e591d55918d0e3c4aa Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sun, 18 Mar 2018 23:34:18 -0400 Subject: [PATCH] Release 0.6 (#349) --- .gitignore | 74 +- .travis.yml | 10 +- CONTRIBUTING.md | 45 +- DESCRIPTION.rst | 6 +- MAINTAINERS.md | 3 +- Makefile | 11 +- README.md | 82 +- docs/about.rst | 21 +- docs/api/anscombe.rst | 4 +- docs/api/classifier/class_balance.py | 1 - docs/api/classifier/class_balance.rst | 2 + docs/api/classifier/class_prediction_error.py | 63 ++ .../api/classifier/class_prediction_error.rst | 48 + docs/api/classifier/classification_report.py | 58 +- docs/api/classifier/classification_report.rst | 4 +- docs/api/classifier/confusion_matrix.py | 3 - docs/api/classifier/confusion_matrix.rst | 2 + .../images/class_prediction_error.png | Bin 0 -> 26641 bytes .../images/class_prediction_error_credit.png | Bin 0 -> 24426 bytes .../images/classification_report.png | Bin 28263 -> 26356 bytes .../classifier/images/confusion_matrix.png | 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yellowbrick/gridsearch/base.py create mode 100644 yellowbrick/gridsearch/pcolor.py create mode 100644 yellowbrick/utils/nan_warnings.py diff --git a/.gitignore b/.gitignore index ee8149910..9ad8e8cbf 100644 --- a/.gitignore +++ b/.gitignore @@ -49,61 +49,68 @@ coverage.xml *.mo *.pot -# Django stuff: -*.log - # Sphinx documentation docs/_build/ # PyBuilder target/ -#Ipython Notebook +# Jupyter Notebook .ipynb_checkpoints -# Making sure the team plays well together -venv* +# pyenv +.python-version + +# IDE/editor droppings +*.swp +*.swo + +# OS droppings .DS_Store -spad.py -# Created by https://www.gitignore.io/api/pycharm +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject -### PyCharm ### -# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and Webstorm -# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 +# mkdocs documentation +/site -# User-specific stuff: +# mypy +.mypy_cache/ + +# PyTest +.pytest_cache + +# PyCharm .idea/workspace.xml .idea/tasks.xml .idea/dictionaries .idea/vcs.xml .idea/jsLibraryMappings.xml - -# Sensitive or high-churn files: .idea/dataSources.ids .idea/dataSources.xml .idea/dataSources.local.xml .idea/sqlDataSources.xml .idea/dynamic.xml .idea/uiDesigner.xml - -# Gradle: .idea/gradle.xml .idea/libraries - -# Mongo Explorer plugin: .idea/mongoSettings.xml - -## File-based project format: *.iws - -## Plugin-specific files: - -# IntelliJ /out/ - -# mpeltonen/sbt-idea plugin .idea_modules/ +.idea # JIRA plugin atlassian-ide-plugin.xml @@ -114,18 +121,5 @@ crashlytics.properties crashlytics-build.properties fabric.properties -### PyCharm Patch ### -# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 - -# *.iml -# modules.xml -# .idea/misc.xml -# *.ipr - -.idea - -# VisualTestCase Outputs -/tests/actual_images/* - -# Data downloaded from Yellowbrick +# Data downloaded from Yellowbrick data/ diff --git a/.travis.yml b/.travis.yml index 19494b672..4ec8dac99 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,14 +1,13 @@ language: python python: - '2.7' - - '3.5' - '3.6' before_install: - sudo apt-get build-dep python-scipy - - pip install scipy - - pip install nose coverage mock - - pip install coveralls requests + - pip install -r tests/requirements.txt + - python -c 'import nltk; nltk.download("popular");' + - pip install coveralls install: pip install -r requirements.txt @@ -20,7 +19,8 @@ notifications: email: recipients: - bbengfort@districtdatalabs.com - - tojeda@districtdatalabs.com - rbilbro@districtdatalabs.com + - nathan.danielsen@gmail.com + - tojeda@districtdatalabs.com on_success: change on_failure: always diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index dd3bb4b0d..8be88a532 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -8,7 +8,7 @@ For more on the development path, goals, and motivations behind Yellowbrick, che Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick! -Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. +Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with scikit-learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. Beyond creating visualizers, there are many ways to contribute: @@ -74,7 +74,14 @@ Once forked, use the following steps to get your development environment set up $ pip install -r requirements.txt `` - Note that there may be other dependencies required for development and testing, you can simply install them with `pip`. + Note that there may be other dependencies required for development and testing, you can simply install them with `pip`. For example to install + the additional dependencies for building the documentation or to run the + test suite, use the `requirements.txt` files in those directories: + + ``` + $ pip install -r tests/requirements.txt + $ pip install -r docs/requirements.txt + ``` 4. Switch to the develop branch. @@ -124,23 +131,23 @@ Head back to Waffle and checkout another issue! In this section, we'll discuss the basics of developing visualizers. This of course is a big topic, but hopefully these simple tips and tricks will help make sense. -One thing that is necessary is a good understanding of Scikit-Learn and Matplotlib. Because our API is intended to integrate with Scikit-Learn, a good start is to review ["APIs of Scikit-Learn objects"](http://scikit-learn.org/stable/developers/contributing.html#apis-of-scikit-learn-objects) and ["rolling your own estimator"](http://scikit-learn.org/stable/developers/contributing.html#rolling-your-own-estimator). In terms of matplotlib, check out [Nicolas P. Rougier's Matplotlib tutorial](https://www.labri.fr/perso/nrougier/teaching/matplotlib/). +One thing that is necessary is a good understanding of scikit-learn and Matplotlib. Because our API is intended to integrate with scikit-learn, a good start is to review ["APIs of scikit-learn objects"](http://scikit-learn.org/stable/developers/contributing.html#apis-of-scikit-learn-objects) and ["rolling your own estimator"](http://scikit-learn.org/stable/developers/contributing.html#rolling-your-own-estimator). In terms of matplotlib, check out [Nicolas P. Rougier's Matplotlib tutorial](https://www.labri.fr/perso/nrougier/teaching/matplotlib/). ### Visualizer API There are two basic types of Visualizers: - **Feature Visualizers** are high dimensional data visualizations that are essentially transformers. -- **Score Visualizers** wrap a Scikit-Learn regressor, classifier, or clusterer and visualize the behavior or performance of the model on test data. +- **Score Visualizers** wrap a scikit-learn regressor, classifier, or clusterer and visualize the behavior or performance of the model on test data. -These two basic types of visualizers map well to the two basic objects in Scikit-Learn: +These two basic types of visualizers map well to the two basic objects in scikit-learn: - **Transformers** take input data and return a new data set. - **Estimators** are fit to training data and can make predictions. -The Scikit-Learn API is object oriented, and estimators and transformers are initialized with parameters by instantiating their class. Hyperparameters can also be set using the `set_attrs()` method and retrieved with the corresponding `get_attrs()` method. All Scikit-Learn estimators have a `fit(X, y=None)` method that accepts a two dimensional data array, `X`, and optionally a vector `y` of target values. The `fit()` method trains the estimator, making it ready to transform data or make predictions. Transformers have an associated `transform(X)` method that returns a new dataset, `Xprime` and models have a `predict(X)` method that returns a vector of predictions, `yhat`. Models also have a `score(X, y)` method that evaluate the performance of the model. +The scikit-learn API is object oriented, and estimators and transformers are initialized with parameters by instantiating their class. Hyperparameters can also be set using the `set_attrs()` method and retrieved with the corresponding `get_attrs()` method. All scikit-learn estimators have a `fit(X, y=None)` method that accepts a two dimensional data array, `X`, and optionally a vector `y` of target values. The `fit()` method trains the estimator, making it ready to transform data or make predictions. Transformers have an associated `transform(X)` method that returns a new dataset, `Xprime` and models have a `predict(X)` method that returns a vector of predictions, `yhat`. Models also have a `score(X, y)` method that evaluate the performance of the model. -Visualizers interact with Scikit-Learn objects by intersecting with them at the methods defined above. Specifically, visualizers perform actions related to `fit()`, `transform()`, `predict()`, and `score()` then call a `draw()` method which initializes the underlying figure associated with the visualizer. The user calls the visualizer's `poof()` method, which in turn calls a `finalize()` method on the visualizer to draw legends, titles, etc. and then `poof()` renders the figure. The Visualizer API is therefore: +Visualizers interact with scikit-learn objects by intersecting with them at the methods defined above. Specifically, visualizers perform actions related to `fit()`, `transform()`, `predict()`, and `score()` then call a `draw()` method which initializes the underlying figure associated with the visualizer. The user calls the visualizer's `poof()` method, which in turn calls a `finalize()` method on the visualizer to draw legends, titles, etc. and then `poof()` renders the figure. The Visualizer API is therefore: - `draw()`: add visual elements to the underlying axes object - `finalize()`: prepare the figure for rendering, adding final touches such as legends, titles, axis labels, etc. @@ -172,7 +179,7 @@ class MyVisualizer(Visualizer): self.set_title("My Visualizer") ``` -This simple visualizer simply draws a line graph for some input dataset X, intersecting with the Scikit-Learn API at the `fit()` method. A user would use this visualizer in the typical style:: +This simple visualizer simply draws a line graph for some input dataset X, intersecting with the scikit-learn API at the `fit()` method. A user would use this visualizer in the typical style:: ```python visualizer = MyVisualizer() @@ -184,11 +191,13 @@ Score visualizers work on the same principle but accept an additional required ` ### Testing -The test package mirrors the yellowbrick package in structure and also contains several helper methods and base functionality. To add a test to your visualizer, find the corresponding file to add the test case, or create a new test file in the same place you added your code. +The test package mirrors the `yellowbrick` package in structure and also contains several helper methods and base functionality. To add a test to your visualizer, find the corresponding file to add the test case, or create a new test file in the same place you added your code. -Visual tests are notoriously difficult to create --- how do you test a visualization or figure? Moreover, testing Scikit-Learn models with real data can consume a lot of memory. Therefore the primary test you should create is simply to test your visualizer from end to end and make sure that no exceptions occur. To assist with this, we have two primary helpers, `VisualTestCase` and `DatasetMixin`. Create your unittest as follows:: +Visual tests are notoriously difficult to create --- how do you test a visualization or figure? Moreover, testing scikit-learn models with real data can consume a lot of memory. Therefore the primary test you should create is simply to test your visualizer from end to end and make sure that no exceptions occur. To assist with this, we have two primary helpers, `VisualTestCase` and `DatasetMixin`. Create your unit test as follows:: ```python +import pytest + from tests.base import VisualTestCase from tests.dataset import DatasetMixin @@ -212,26 +221,28 @@ class MyVisualizerTests(VisualTestCase, DatasetMixin): visualizer.fit(X) visualizer.poof() except Exception as e: - self.fail("my visualizer didn't work") + pytest.fail("my visualizer didn't work") ``` The entire test suite can be run as follows:: ``` -$ make test +$ pytest ``` You can also run your own test file as follows:: ``` -$ nosetests tests/test_your_visualizer.py +$ pytest tests/test_your_visualizer.py ``` -The Makefile uses the nosetest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The `DatasetMixin` also requires requests.py to fetch data from our Amazon S3 account. +The Makefile uses the pytest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The `DatasetMixin` also requires [requests.py](http://docs.python-requests.org/en/master/) to fetch data from our Amazon S3 account. + +**Note**: Advanced developers can use our _image comparison tests_ to assert that an image generated matches a baseline image. Read more about this in our [testing documentation](http://www.scikit-yb.org/en/latest/contributing.html#testing) ### Documentation -The initial documentation for your visualizer will be a well structured docstring. Yellowbrick uses Sphinx to build documentation, therefore docstrings should be written in reStructuredText in numpydoc format (similar to Scikit-Learn). The primary location of your docstring should be right under the class definition, here is an example:: +The initial documentation for your visualizer will be a well structured docstring. Yellowbrick uses Sphinx to build documentation, therefore docstrings should be written in reStructuredText in numpydoc format (similar to scikit-learn). The primary location of your docstring should be right under the class definition, here is an example:: ```python class MyVisualizer(Visualizer): @@ -245,7 +256,7 @@ class MyVisualizer(Visualizer): Parameters ---------- - model : a Scikit-Learn regressor + model : a scikit-learn regressor Should be an instance of a regressor, and specifically one whose name ends with "CV" otherwise a will raise a YellowbrickTypeError exception on instantiation. To use non-CV regressors see: @@ -273,7 +284,7 @@ class MyVisualizer(Visualizer): """ ``` -You should also add your example to the `examples` directory of the documentation when you have the chance, as well as create a demonstration in a notebook in the `examples` directory of the repository. +This is a very good start to producing a high quality visualizer, but unless it is part of the documentation on our website, it will not be visible. For details on including documentation in the `docs` directory see the [Contributing Documentation](http://www.scikit-yb.org/en/latest/contributing.html#documentation) section in the larger contributing guide. ## Throughput diff --git a/DESCRIPTION.rst b/DESCRIPTION.rst index fd58f830a..4c00c604e 100644 --- a/DESCRIPTION.rst +++ b/DESCRIPTION.rst @@ -13,7 +13,7 @@ Yellowbrick is a suite of visual analysis and diagnostic tools designed to facil Visualizers allow users to steer the model selection process, building intuition around feature engineering, algorithm selection, and hyperparameter tuning. For example, visualizers can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models to more successful results, faster. -Please see the full documentation at: http://scikit-yb.org/ +Please see the full documentation at: http://scikit-yb.org/ particularly the `quick start guide `_ Visualizers ----------- @@ -30,12 +30,14 @@ Feature Visualization - **Parallel Coordinates**: horizontal visualization of instances - **Radial Visualization**: separation of instances around a circular plot - **PCA Projection**: projection of instances based on principal components +- **Feature Importances**: rank features based on their in-model performance - **Scatter and Joint Plots**: direct data visualization with feature selection Classification Visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - **Class Balance**: see how the distribution of classes affects the model +- **Class Prediction Error**: shows error and support in classification - **Classification Report**: visual representation of precision, recall, and F1 - **ROC/AUC Curves**: receiver operator characteristics and area under the curve - **Confusion Matrices**: visual description of class decision making @@ -61,5 +63,5 @@ Text Visualization ... and more! Visualizers are being added all the time; be sure to check the examples_ (or even the develop_ branch) and feel free to contribute your ideas for new Visualizers! -.. _examples: http://www.scikit-yb.org/en/latest/examples/examples.html +.. _examples: http://www.scikit-yb.org/en/latest/api/index.html .. _develop: https://github.com/districtdatalabs/yellowbrick/tree/develop diff --git a/MAINTAINERS.md b/MAINTAINERS.md index 430acbec5..2ec60c6fe 100644 --- a/MAINTAINERS.md +++ b/MAINTAINERS.md @@ -13,9 +13,9 @@ For everyone who has [contributed](https://github.com/DistrictDataLabs/yellowbri This is a list of the primary project maintainers. Feel free to @ message them in issues and converse with them directly. - [bbengfort](https://github.com/bbengfort) +- [ndanielsen](https://github.com/ndanielsen) - [NealHumphrey](https://github.com/NealHumphrey) - [jkeung](https://github.com/jkeung) -- [ndanielsen](https://github.com/ndanielsen) ## Core Contributors @@ -27,3 +27,4 @@ This is a list of the core-contributors of the project. Core contributors set th - [tuulihill](https://github.com/tuulihill) - [balavenkatesan](https://github.com/balavenkatesan) - [morganmendis](https://github.com/morganmendis) +- [lwgray](https://github.com/lwgray) diff --git a/Makefile b/Makefile index c7334eef0..96dbe8fe0 100644 --- a/Makefile +++ b/Makefile @@ -4,11 +4,9 @@ SHELL := /bin/bash # Set important Paths PROJECT := yellowbrick LOCALPATH := $(CURDIR)/$(PROJECT) -PYTHONPATH := $(LOCALPATH)/ -PYTHON_BIN := $(VIRTUAL_ENV)/bin # Export targets not associated with files -.PHONY: test coverage pip virtualenv clean publish uml build deploy +.PHONY: test coverage pip clean publish uml build deploy install # Clean build files clean: @@ -19,6 +17,7 @@ clean: -rm -rf build -rm -rf dist -rm -rf $(PROJECT).egg-info + -rm -rf .eggs -rm -rf site -rm -rf classes_$(PROJECT).png -rm -rf packages_$(PROJECT).png @@ -26,7 +25,7 @@ clean: # Targets for testing test: - $(PYTHON_BIN)/nosetests -v --with-coverage --cover-package=$(PROJECT) --cover-inclusive --cover-erase tests + python setup.py test # Publish to gh-pages publish: @@ -40,6 +39,10 @@ uml: build: python setup.py sdist bdist_wheel +# Install the package from source +install: + python setup.py install + # Deploy to PyPI deploy: python setup.py register diff --git a/README.md b/README.md index 30d3003aa..7ad4df154 100644 --- a/README.md +++ b/README.md @@ -16,58 +16,62 @@ This README is a guide for developers, if you're new to Yellowbrick, get started ## What is Yellowbrick? -Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for _your_ models! +Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for _your_ models! ![Visualizers](docs/images/visualizers.png) ### Visualizers -Visualizers are estimators (objects that learn from data) whose primary objective is to create visualizations that allow insight into the model selection process. In Scikit-Learn terms, they can be similar to transformers when visualizing the data space or wrap an model estimator similar to how the "ModelCV" (e.g. RidgeCV, LassoCV) methods work. The primary goal of Yellowbrick is to create a sensical API similar to Scikit-Learn. Some of our most popular visualizers include: +Visualizers are estimators (objects that learn from data) whose primary objective is to create visualizations that allow insight into the model selection process. In scikit-learn terms, they can be similar to transformers when visualizing the data space or wrapping a model estimator similar to how the "ModelCV" (e.g. RidgeCV, LassoCV) methods work. The primary goal of Yellowbrick is to create a sensical API similar to scikit-learn. Some of our most popular visualizers include: #### Feature Visualization -- Rank2D: pairwise ranking of features to detect relationships -- Parallel Coordinates: horizontal visualization of instances -- Radial Visualization: separation of instances around a circular plot +- **Feature Importances**: rank features based on their in-model performance +- **Parallel Coordinates**: horizontal visualization of instances +- **PCA Projection**: projection of instances based on principal components +- **RadViz**: separation of instances around a circular plot +- **Rank Features**: single or pairwise ranking of features to detect relationships +- **Scatter and Joint Plots**: direct data visualization with feature selection #### Classification Visualization -- Class Balance: see how the distribution of classes affects the model -- Classification Report: visual representation of precision, recall, and F1 -- ROC/AUC Curves: receiver operator characteristics and area under the curve -- Confusion Matrices: visual description of class decision making +- **Class Balance**: see how the distribution of classes affects the model +- **Class Prediction Error**: shows error and support in classification +- **Classification Report**: visual representation of precision, recall, and F1 +- **Confusion Matrices**: visual description of class decision making +- **ROC/AUC Curves**: receiver operator characteristics and area under the curve #### Regression Visualization -- Prediction Error Plots: find model breakdowns along the domain of the target -- Residuals Plot: show the difference in residuals of training and test data -- Alpha Selection: show how the choice of alpha influences regularization +- **Alpha Selection**: show how the choice of alpha influences regularization +- **Prediction Error Plots**: find model breakdowns along the domain of the target +- **Residuals Plot**: show the difference in residuals of training and test data #### Clustering Visualization -- K-Elbow Plot: select k using the elbow method and various metrics -- Silhouette Plot: select k by visualizing silhouette coefficient values +- **K-Elbow Plot**: select k using the elbow method and various metrics +- **Silhouette Plot**: select k by visualizing silhouette coefficient values #### Text Visualization -- Term Frequency: visualize the frequency distribution of terms in the corpus -- TSNE: use stochastic neighbor embedding to project documents. +- **Term Frequency**: visualize the frequency distribution of terms in the corpus +- **TSNE**: use stochastic neighbor embedding to project documents. -And more! Visualizers are being added all the time, be sure to check the examples (or even the develop branch) and feel free to contribute your ideas for Visualizers! +And more! Visualizers are being added all the time, so be sure to check the examples (or even the develop branch) and feel free to contribute your ideas for Visualizers! ## Installing Yellowbrick -Yellowbrick is compatible with Python 2.7 or later but it is preferred to use Python 3.5 or later to take full advantage of all functionality. Yellowbrick also depends on Scikit-Learn 0.18 or later and Matplotlib 1.5 or later. The simplest way to install Yellowbrick is from PyPI with pip, Python's preferred package installer. +Yellowbrick is compatible with Python 2.7 or later but it is preferred to use Python 3.5 or later to take full advantage of all functionality. Yellowbrick also depends on scikit-learn 0.18 or later and matplotlib 1.5 or later. The simplest way to install Yellowbrick is from PyPI with pip, Python's preferred package installer. $ pip install yellowbrick Note that Yellowbrick is an active project and routinely publishes new releases with more visualizers and updates. In order to upgrade Yellowbrick to the latest version, use pip as follows. - $ pip install -u yellowbrick + $ pip install -U yellowbrick -You can also use the `-u` flag to update Scikit-Learn, matplotlib, or any other third party utilities that work well with Yellowbrick to their latest versions. +You can also use the `-U` flag to update scikit-learn, matplotlib, or any other third party utilities that work well with Yellowbrick to their latest versions. -If you're using Windows or Anaconda, you can take advantage of the conda utility to install Yellowbrick: +If you're using Anaconda (recommended for Windows users), you can take advantage of the conda utility to install Yellowbrick: conda install -c districtdatalabs yellowbrick @@ -75,11 +79,11 @@ Note, however, that there is a [known bug](https://github.com/DistrictDataLabs/y ## Using Yellowbrick -The Yellowbrick API is specifically designed to play nicely with Scikit-Learn. Here is an example of a typical workflow sequence with Scikit-Learn and Yellowbrick: +The Yellowbrick API is specifically designed to play nicely with scikit-learn. Here is an example of a typical workflow sequence with scikit-learn and Yellowbrick: ### Feature Visualization -In this example, we see how Rank2D performs pairwise comparisons of each feature in the data set with a specific metric or algorithm, then returns them ranked as a lower left triangle diagram. +In this example, we see how Rank2D performs pairwise comparisons of each feature in the data set with a specific metric or algorithm and then returns them ranked as a lower left triangle diagram. ```python from yellowbrick.features import Rank2D @@ -92,7 +96,7 @@ visualizer.poof() # Draw/show/poof the data ### Model Visualization -In this example, we instantiate a Scikit-Learn classifier, and then we use Yellowbrick's ROCAUC class to visualize the tradeoff between the classifier's sensitivity and specificity. +In this example, we instantiate a scikit-learn classifier and then use Yellowbrick's ROCAUC class to visualize the tradeoff between the classifier's sensitivity and specificity. ```python from sklearn.svm import LinearSVC @@ -113,7 +117,7 @@ We also have a [quick start guide](https://github.com/DistrictDataLabs/yellowbri Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick! -Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. +Principally, Yellowbrick development is about the addition and creation of *visualizers* -- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with scikit-learn estimators, transformers, and pipelines for specific purposes and as a result can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. Beyond creating visualizers, there are many ways to contribute: @@ -129,4 +133,30 @@ Beyond creating visualizers, there are many ways to contribute: As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in the [Python Software Foundation Code of Conduct](https://www.python.org/psf/codeofconduct/). -For more information, checkout [CONTRIBUTING.md](https://github.com/DistrictDataLabs/yellowbrick/blob/develop/CONTRIBUTING.md). +For more information, checkout the `CONTRIBUTING.md` file in the root of the repository or the detailed documentation at [Contributing to Yellowbrick](http://www.scikit-yb.org/en/latest/contributing.html) + +## Development Scripts + +Yellowbrick contains scripts to help with development, including downloading fixture data for tests and managing images for comparison. + +### Images + +The image comparison helper script manages the test directory's `baseline_images` folder by copying files from the `actual_images` folder to setup baselines. To use this script, first run the tests (which will cause image not found errors) then copy the images into baseline as follows: + +``` +$ python -m tests.images tests/test_visualizer.py +``` + +Where `tests/test_visualizer.py` is the test file that contains the image comparison tests. All related tests will be discovered, validated, and copied to the baseline directory. To clear out images from both actual and baseline to reset tests, use the `-C` flag: + +``` +$ python -m tests.images -C tests/test_visualizer.py +``` + +Glob syntax can be used to move multiple files. For example to reset all the classifier tests: + +``` +$ python -m tests.images tests/test_classifier/* +``` + +Though it is recommended that specific test cases are targeted, rather than updating entire directories. diff --git a/docs/about.rst b/docs/about.rst index 6d78ce90f..d5c17cff6 100644 --- a/docs/about.rst +++ b/docs/about.rst @@ -5,28 +5,24 @@ About Image by QuatroCinco_, used with permission, Flickr Creative Commons. -Yellowbrick is an open source, pure Python project that extends the Scikit-Learn API_ with visual analysis and diagnostic tools. The Yellowbrick API also wraps Matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow. +Yellowbrick is an open source, pure Python project that extends the scikit-learn API_ with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models and assist in diagnosing problems throughout the machine learning workflow. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search. By visualizing the model selection process, data scientists can steer towards final, explainable models and avoid pitfalls and traps. -The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. Yellowbrick extends the Scikit-Learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process, providing visual diagnostics throughout the transformation of high dimensional data. +The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data. -The Model Selection Triple --------------------------- +Model Selection +--------------- Discussions of machine learning are frequently characterized by a singular focus on model selection. Be it logistic regression, random forests, Bayesian methods, or artificial neural networks, machine learning practitioners are often quick to express their preference. The reason for this is mostly historical. Though modern third-party machine learning libraries have made the deployment of multiple models appear nearly trivial, traditionally the application and tuning of even one of these algorithms required many years of study. As a result, machine learning practitioners tended to have strong preferences for particular (and likely more familiar) models over others. However, model selection is a bit more nuanced than simply picking the "right" or "wrong" algorithm. In practice, the workflow includes: 1. selecting and/or engineering the smallest and most predictive feature set - 2. choosing a set of algorithms from a model family, and - 3. tuning the algorithm hyperparameters to optimize performance. + 2. choosing a set of algorithms from a model family + 3. tuning the algorithm hyperparameters to optimize performance The **model selection triple** was first described in a 2015 SIGMOD_ paper by Kumar et al. In their paper, which concerns the development of next-generation database systems built to anticipate predictive modeling, the authors cogently express that such systems are badly needed due to the highly experimental nature of machine learning in practice. "Model selection," they explain, "is iterative and exploratory because the space of [model selection triples] is usually infinite, and it is generally impossible for analysts to know a priori which [combination] will yield satisfactory accuracy and/or insights." -Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search. By visualizing the model selection process, data scientists can steer towards final, explainable models and avoid pitfalls and traps. - -The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. Yellowbrick extends the Scikit-Learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process, providing visual diagnostics throughout the transformation of high dimensional data. - Name Origin ----------- The Yellowbrick package gets its name from the fictional element in the 1900 children's novel **The Wonderful Wizard of Oz** by American author L. Frank Baum. In the book, the yellow brick road is the path that the protagonist, Dorothy Gale, must travel in order to reach her destination in the Emerald City. @@ -37,9 +33,9 @@ From Wikipedia_: Team ---- -Yellowbrick is is developed by data scientists who believe in open source and the project enjoys contributions from Python developers all over the world. The project was started by `@rebeccabilbro`_ and `@bbengfort`_ as an attempt to better explain machine learning concepts to their students; they quickly realized, however, that the potential for visual steering could have a large impact on practical data science and developed it into a high-level Python library. +Yellowbrick is developed by data scientists who believe in open source and the project enjoys contributions from Python developers all over the world. The project was started by `@rebeccabilbro`_ and `@bbengfort`_ as an attempt to better explain machine learning concepts to their students; they quickly realized, however, that the potential for visual steering could have a large impact on practical data science and developed it into a high-level Python library. -Yellowbrick is incubated by `District Data Labs`_, an organization that is dedicated to collaboration and open source development. As part of District Data Labs, Yellowbrick was first introduced to the Python Community at `PyCon 2016 `_ in both talks and during the development sprints. The project was then carried on through DDL Research Labs (semester-long sprints where members of the DDL community contribute to various data related projects). +Yellowbrick is incubated by `District Data Labs`_, an organization that is dedicated to collaboration and open source development. As part of District Data Labs, Yellowbrick was first introduced to the Python Community at `PyCon 2016 `_ in both talks and during the development sprints. The project was then carried on through DDL Research Labs (semester-long sprints where members of the DDL community contribute to various data-related projects). License ------- @@ -64,7 +60,6 @@ Yellowbrick has enjoyed the spotlight at a few conferences and in several presen Videos: - `Visual Diagnostics for More Informed Machine Learning: Within and Beyond Scikit-Learn (PyCon 2016) `_ - - `Visual Diagnostics for More Informed Machine Learning (PyData Carolinas 2016) `_ - `Yellowbrick: Steering Machine Learning with Visual Transformers (PyData London 2017) `_ Slides: diff --git a/docs/api/anscombe.rst b/docs/api/anscombe.rst index df9b2070c..74886652c 100644 --- a/docs/api/anscombe.rst +++ b/docs/api/anscombe.rst @@ -1,7 +1,9 @@ +.. -*- mode: rst -*- + Anscombe's Quartet ================== -Yellowbrick has learned Anscombe's lesson - which is why we believe that +Yellowbrick has learned Anscombe's lesson---which is why we believe that visual diagnostics are vital to machine learning. .. code:: python diff --git a/docs/api/classifier/class_balance.py b/docs/api/classifier/class_balance.py index 9847c0bf5..0aa30ff7b 100644 --- a/docs/api/classifier/class_balance.py +++ b/docs/api/classifier/class_balance.py @@ -1,5 +1,4 @@ import pandas as pd -import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split diff --git a/docs/api/classifier/class_balance.rst b/docs/api/classifier/class_balance.rst index d39485b58..7a3fe37b3 100644 --- a/docs/api/classifier/class_balance.rst +++ b/docs/api/classifier/class_balance.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Class Balance ============= diff --git a/docs/api/classifier/class_prediction_error.py b/docs/api/classifier/class_prediction_error.py new file mode 100644 index 000000000..de6bab33b --- /dev/null +++ b/docs/api/classifier/class_prediction_error.py @@ -0,0 +1,63 @@ +# class_prediction_error.py + +""" +Creates the visualizations for the class_prediction_error.rst documentation +""" + +########################################################################## +## Imports +########################################################################## + +import pandas as pd +import matplotlib.pyplot as plt + +from sklearn.datasets import make_classification +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split as tts + +from yellowbrick.classifier import ClassPredictionError + + +def make_fruit_dataset(): + X, y = make_classification( + n_samples=1000, n_classes=5, n_informative=3, n_clusters_per_class=1 + ) + + classes = ['apple', 'kiwi', 'pear', 'banana', 'orange'] + return tts(X, y, test_size=0.20, random_state=42), classes + + +def load_credit_dataset(): + data = pd.read_csv("../../../examples/data/credit/credit.csv") + target = "default" + features = list(data.columns) + features.remove(target) + + X = data[features] + y = data[target] + + classes = ["default", "current"] + return tts(X, y, test_size=0.2, random_state=53), classes + + +def make_cb_pred_error(dataset="fruit", path=None, clf=None): + clf = clf or RandomForestClassifier() + + loader = { + 'fruit': make_fruit_dataset, + 'credit': load_credit_dataset, + }[dataset] + + (X_train, X_test, y_train, y_test), classes = loader() + + _, ax = plt.subplots() + viz = ClassPredictionError(clf, ax=ax, classes=classes) + viz.fit(X_train, y_train) + viz.score(X_test, y_test) + + return viz.poof(outpath=path) + + +if __name__ == '__main__': + make_cb_pred_error("fruit", "images/class_prediction_error.png") + make_cb_pred_error("credit", "images/class_prediction_error_credit.png") diff --git a/docs/api/classifier/class_prediction_error.rst b/docs/api/classifier/class_prediction_error.rst new file mode 100644 index 000000000..38d8e5ce8 --- /dev/null +++ b/docs/api/classifier/class_prediction_error.rst @@ -0,0 +1,48 @@ +.. -*- mode: rst -*- + +Class Prediction Error +====================== +The class prediction error chart provides a way to quickly understand how good your classifier is at predicting the right classes. + +.. code:: python + + from sklearn.datasets import make_classification + + # Create classification dataset + X, y = make_classification( + n_samples=1000, n_classes=5, n_informative=3, n_clusters_per_class=1 + ) + + # Name the classes + classes = ['apple', 'kiwi', 'pear', 'banana', 'orange'] + + # Perform 80/20 training/test split + X_train, X_test, y_train, y_test = tts( + X, y, test_size=0.20, random_state=42 + ) + +.. code:: python + + # Instantiate the classification model and visualizer + visualizer = ClassPredictionError( + RandomForestClassifier(), classes=classes + ) + + # Fit the training data to the visualizer + visualizer.fit(X_train, y_train) + + # Evaluate the model on the test data + visualizer.score(X_test, y_test) + + # Draw visualization + g = visualizer.poof() + +.. image:: images/class_prediction_error.png + +API Reference +------------- + +.. automodule:: yellowbrick.classifier.class_balance + :members: ClassPredictionError + :undoc-members: + :show-inheritance: diff --git a/docs/api/classifier/classification_report.py b/docs/api/classifier/classification_report.py index 4999bb07d..2cabec28a 100644 --- a/docs/api/classifier/classification_report.py +++ b/docs/api/classifier/classification_report.py @@ -1,30 +1,58 @@ +# classification_report +# Generates images for the classification report documentation. +# +# Author: Benjamin Bengfort +# Created: Sun Mar 18 16:35:30 2018 -0400 +# +# ID: classification_report.py [] benjamin@bengfort.com $ + +""" +Generates images for the classification report documentation. +""" + +########################################################################## +## Imports +########################################################################## + import pandas as pd import matplotlib.pyplot as plt from sklearn.naive_bayes import GaussianNB -from sklearn.model_selection import train_test_split +from sklearn.model_selection import train_test_split as tts from yellowbrick.classifier import ClassificationReport -if __name__ == '__main__': - # Load the regression data set +########################################################################## +## Quick Methods +########################################################################## + +def make_dataset(): data = pd.read_csv("../../../examples/data/occupancy/occupancy.csv") - features = ["temperature", "relative humidity", "light", "C02", "humidity"] - classes = ['unoccupied', 'occupied'] + X = data[["temperature", "relative humidity", "light", "C02", "humidity"]] + y = data.occupancy + + return tts(X, y, test_size=0.2) + - # Extract the numpy arrays from the data frame - X = data[features].as_matrix() - y = data.occupancy.as_matrix() +def make_gb_report(path="images/classification_report.png"): + X_train, X_test, y_train, y_test = make_dataset() - # Create the train and test data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + _, ax = plt.subplots() - # Instantiate the classification model and visualizer bayes = GaussianNB() - visualizer = ClassificationReport(bayes, classes=classes) + viz = ClassificationReport(bayes, ax=ax, classes=['unoccupied', 'occupied']) - visualizer.fit(X_train, y_train) # Fit the training data to the visualizer - visualizer.score(X_test, y_test) # Evaluate the model on the test data - g = visualizer.poof(outpath="images/classification_report.png") # Draw/show/poof the data + viz.fit(X_train, y_train) + viz.score(X_test, y_test) + + viz.poof(outpath=path) + + +########################################################################## +## Main Method +########################################################################## + +if __name__ == '__main__': + make_gb_report() diff --git a/docs/api/classifier/classification_report.rst b/docs/api/classifier/classification_report.rst index b537d2459..72d7b0c70 100644 --- a/docs/api/classifier/classification_report.rst +++ b/docs/api/classifier/classification_report.rst @@ -1,5 +1,7 @@ +.. -*- mode: rst -*- + Classification Report -~~~~~~~~~~~~~~~~~~~~~ +===================== The classification report visualizer displays the precision, recall, and F1 scores for the model. In order to support easier interpretation and problem detection, the report integrates numerical scores with a color-coded diff --git a/docs/api/classifier/confusion_matrix.py b/docs/api/classifier/confusion_matrix.py index 2ba1f1cfd..6bbae0cf2 100644 --- a/docs/api/classifier/confusion_matrix.py +++ b/docs/api/classifier/confusion_matrix.py @@ -1,6 +1,3 @@ -import pandas as pd -import matplotlib.pyplot as plt - from sklearn.datasets import load_digits from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split diff --git a/docs/api/classifier/confusion_matrix.rst b/docs/api/classifier/confusion_matrix.rst index fccd438b3..30f18c6f8 100644 --- a/docs/api/classifier/confusion_matrix.rst +++ b/docs/api/classifier/confusion_matrix.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Confusion Matrix ================ diff --git a/docs/api/classifier/images/class_prediction_error.png b/docs/api/classifier/images/class_prediction_error.png new file mode 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zyHIm-ECPg7$)#*8_Q`mq5K^gNWMqVZV*rjgaqSHtY{|h11&R&n05m`zX;C^Y4^0y4 zL1ArzBdQFEsjLT$K$wv-jt;mdZQqk`#&8>*HA!&1q%68or_)ME#_8>Kw4@6;HNWJW zew(mvE)b3=CgLD_5Yjw2;{E84(+jJW6#FlB_7AW3f95{_Z{O(9OH^@?OKkoi9R+`6 NZz)OV+|Yae{{U)1`S}0< literal 0 HcmV?d00001 diff --git a/docs/api/classifier/index.rst b/docs/api/classifier/index.rst index 3517eb8e6..dc63cc234 100644 --- a/docs/api/classifier/index.rst +++ b/docs/api/classifier/index.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Classification Visualizers ========================== @@ -10,6 +12,8 @@ Classification models attempt to predict a target in a discrete space, that is a - :doc:`rocauc`: Presents the graph of receiver operating characteristics along with area under the curve - :doc:`class_balance`: Displays the difference between the class balances and support +- :doc:`class_prediction_error`: An alternative to the confusion matrix that shows both support and the difference between actual and predicted classes +- :doc:`threshold`: Shows the bounds of precision, recall and queue rate after a number of trials. Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods @@ -26,7 +30,7 @@ a Pipeline or VisualPipeline. from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split - from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance + from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance, ThresholdViz .. toctree:: :maxdepth: 2 @@ -35,3 +39,5 @@ a Pipeline or VisualPipeline. confusion_matrix rocauc class_balance + class_prediction_error + threshold diff --git a/docs/api/classifier/rocauc.py b/docs/api/classifier/rocauc.py index 3060eb1db..a0a4593fb 100644 --- a/docs/api/classifier/rocauc.py +++ b/docs/api/classifier/rocauc.py @@ -1,5 +1,4 @@ import pandas as pd -import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split diff --git a/docs/api/classifier/rocauc.rst b/docs/api/classifier/rocauc.rst index d48e41a91..582cc030d 100644 --- a/docs/api/classifier/rocauc.rst +++ b/docs/api/classifier/rocauc.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + ROCAUC ====== diff --git a/docs/api/classifier/threshold.py b/docs/api/classifier/threshold.py new file mode 100644 index 000000000..2004a2408 --- /dev/null +++ b/docs/api/classifier/threshold.py @@ -0,0 +1,23 @@ +import pandas as pd + +from yellowbrick.classifier import ThreshViz +from sklearn.linear_model import LogisticRegression + + +if __name__ == '__main__': + # Load the data set + data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data', header=None) + data.rename(columns={57:'is_spam'}, inplace=True) + + features = [col for col in data.columns if col != 'is_spam'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.is_spam.as_matrix() + + # Instantiate the classification model and visualizer + logistic = LogisticRegression() + visualizer = ThreshViz(logistic) + + visualizer.fit(X, y) # Fit the training data to the visualizer + g = visualizer.poof(outpath="images/thresholdviz.png") # Draw/show/poof the data diff --git a/docs/api/classifier/threshold.rst b/docs/api/classifier/threshold.rst new file mode 100644 index 000000000..912308f75 --- /dev/null +++ b/docs/api/classifier/threshold.rst @@ -0,0 +1,39 @@ +.. -*- mode: rst -*- + +Threshold +========= + +The Threshold visualizer shows the bounds of precision, recall and queue rate for different thresholds for binary targets after a given number of trials. + +.. code:: python + + # Load the data set + data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data', header=None) + data.rename(columns={57:'is_spam'}, inplace=True) + + features = [col for col in data.columns if col != 'is_spam'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.is_spam.as_matrix() + +.. code:: python + + # Instantiate the classification model and visualizer + logistic = LogisticRegression() + visualizer = ThreshViz(logistic) + + visualizer.fit(X, y) # Fit the training data to the visualizer + g = visualizer.poof() # Draw/show/poof the data + + +.. image:: images/thresholdviz.png + + +API Reference +------------- + +.. automodule:: yellowbrick.classifier.threshold + :members: ThreshViz + :undoc-members: + :show-inheritance: diff --git a/docs/api/cluster/elbow.py b/docs/api/cluster/elbow.py index 425ed5f95..06e530ac0 100644 --- a/docs/api/cluster/elbow.py +++ b/docs/api/cluster/elbow.py @@ -1,7 +1,7 @@ # Clustering Evaluation Imports from functools import partial -from sklearn.cluster import KMeans, MiniBatchKMeans +from sklearn.cluster import MiniBatchKMeans from sklearn.datasets import make_blobs as sk_make_blobs from yellowbrick.cluster import KElbowVisualizer diff --git a/docs/api/cluster/elbow.rst b/docs/api/cluster/elbow.rst index 7f4e91c51..297f97cd1 100644 --- a/docs/api/cluster/elbow.rst +++ b/docs/api/cluster/elbow.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Elbow Method ============ diff --git a/docs/api/cluster/index.rst b/docs/api/cluster/index.rst index d7f27f9e7..58bbba7b7 100644 --- a/docs/api/cluster/index.rst +++ b/docs/api/cluster/index.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Clustering Visualizers ====================== diff --git a/docs/api/cluster/silhouette.py b/docs/api/cluster/silhouette.py index 147e2f8de..c62f31751 100644 --- a/docs/api/cluster/silhouette.py +++ b/docs/api/cluster/silhouette.py @@ -1,7 +1,7 @@ # Clustering Evaluation Imports from functools import partial -from sklearn.cluster import KMeans, MiniBatchKMeans +from sklearn.cluster import MiniBatchKMeans from sklearn.datasets import make_blobs as sk_make_blobs from yellowbrick.cluster import SilhouetteVisualizer diff --git a/docs/api/cluster/silhouette.rst b/docs/api/cluster/silhouette.rst index 0aaf458fa..30cca88ce 100644 --- a/docs/api/cluster/silhouette.rst +++ b/docs/api/cluster/silhouette.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Silhouette Visualizer ===================== diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst index e7bedd527..983151fab 100644 --- a/docs/api/datasets.rst +++ b/docs/api/datasets.rst @@ -1,16 +1,18 @@ +.. -*- mode: rst -*- + Example Datasets ----------------- +================ Yellowbrick hosts several datasets wrangled from the `UCI Machine Learning Repository `__ to present the -examples in this section. If you haven't downloaded the data, you can do so by +examples used throughout this documentation. If you haven't downloaded the data, you can do so by running: :: $ python -m yellowbrick.download -This should create a folder called ``data`` in your current working directory with all of the datasets. You can load a specified dataset with ``pandas.read_csv`` as follows: +This should create a folder named ``data`` in your current working directory that contains all of the datasets. You can load a specified dataset with ``pandas.read_csv`` as follows: .. code:: python @@ -18,10 +20,12 @@ This should create a folder called ``data`` in your current working directory wi data = pd.read_csv('data/concrete/concrete.csv') -The following code snippet can be found at the top of the ``examples/examples.ipynb`` notebok in Yellowbrick. Please reference this code when trying to load a specific data set: +The following code snippet can be found at the top of the ``examples/examples.ipynb`` notebook in Yellowbrick. Please reference this code when trying to load a specific data set: .. code:: python + import os + from yellowbrick.download import download_all ## The path to the test data sets @@ -62,8 +66,8 @@ The following code snippet can be found at the top of the ``examples/examples.ip # Return the data frame return pd.read_csv(path) -Note that most of the examples currently use one or more of the listed datasets for their examples (unless specifically shown otherwise). Each dataset has a ``README.md`` with detailed information about the data source, attributes, and target. Here is a complete listing of all datasets in Yellowbrick and their associated analytical tasks: +Unless otherwise specified, most of the examples currently use one or more of the listed datasets. Each dataset has a ``README.md`` with detailed information about the data source, attributes, and target. 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a/docs/api/features/rankd.py b/docs/api/features/rankd.py index d7027682e..8db246fe1 100644 --- a/docs/api/features/rankd.py +++ b/docs/api/features/rankd.py @@ -5,11 +5,10 @@ def rank1d(X, y, outpath, **kwargs): # Create a new figure and axes - fig = plt.figure() - ax = fig.add_subplot(111) + _, ax = plt.subplots() # Create the visualizer - visualizer = Rank1D(**kwargs) + visualizer = Rank1D(ax=ax, **kwargs) visualizer.fit(X, y) visualizer.transform(X) @@ -19,11 +18,10 @@ def rank1d(X, y, outpath, **kwargs): def rank2d(X, y, outpath, **kwargs): # Create a new figure and axes - fig = plt.figure() - ax = fig.add_subplot(111) + _, ax = plt.subplots() # Create the visualizer - visualizer = Rank2D(**kwargs) + visualizer = Rank2D(ax=ax, **kwargs) visualizer.fit(X, y) visualizer.transform(X) diff --git a/docs/api/features/rankd.rst b/docs/api/features/rankd.rst index 9d5dfa0b1..51e73a4d6 100644 --- a/docs/api/features/rankd.rst +++ b/docs/api/features/rankd.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Rank Features ============= diff --git a/docs/api/features/scatter.py b/docs/api/features/scatter.py index 936ca44da..e04ef5a6e 100644 --- a/docs/api/features/scatter.py +++ b/docs/api/features/scatter.py @@ -6,11 +6,10 @@ def scatter(data, target, outpath, **kwargs): # Create a new figure and axes - fig = plt.figure() - ax = fig.add_subplot(111) + _, ax = plt.subplots() # Create the visualizer - visualizer = ScatterVisualizer(**kwargs) + visualizer = ScatterVisualizer(ax=ax, **kwargs) visualizer.fit(data, target) visualizer.transform(data) @@ -25,7 +24,7 @@ def jointplot(X, y, outpath, **kwargs): ax = fig.add_subplot(111) # Create the visualizer - visualizer = JointPlotVisualizer(**kwargs) + visualizer = JointPlotVisualizer(ax=ax, **kwargs) visualizer.fit(X, y) visualizer.transform(X) diff --git a/docs/api/features/scatter.rst b/docs/api/features/scatter.rst index 1d305a827..2ce2c3a05 100644 --- a/docs/api/features/scatter.rst +++ b/docs/api/features/scatter.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Direct Data Visualization ========================= diff --git a/docs/api/index.rst b/docs/api/index.rst index 1b557bbe9..d2dc585c8 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -1,7 +1,9 @@ +.. -*- mode: rst -*- + Visualizers and API =================== -Welcome the API documentation for Yellowbrick! This section contains a complete listing of all currently available, production-ready visualizers along with code examples of how to use them. Use the links below to navigate to the reference for each visualization. +Welcome to the API documentation for Yellowbrick! This section contains a complete listing of the currently available, production-ready visualizers along with code examples of how to use them. You may use the following links to navigate to the reference material for each visualization. .. toctree:: :maxdepth: 2 @@ -15,8 +17,8 @@ Welcome the API documentation for Yellowbrick! This section contains a complete text/index palettes -.. note:: Many examples utilize data from the UCI Machine Learning repository, in order to run the examples, make sure you follow the instructions in :doc:`datasets` to download and load required data. +.. note:: Many examples utilize data from the UCI Machine Learning repository. In order to run the accompanying code, make sure to follow the instructions in :doc:`datasets` to download and load the required data. A guide to finding the visualizer you're looking for: generally speaking, visualizers can be data visualizers which visualize instances relative to the model space; score visualizers which visualize model performance; model selection visualizers which compare multiple model forms against each other; and application specific-visualizers. This can be a bit confusing, so we've grouped visualizers according to the type of analysis they are well suited for. -Feature analysis visualizers are where you'll find the primary implementation of data visualizers. Regression, classification, and clustering analysis visualizers can be found in their respective libraries. Finally visualizers for text analysis are also available in Yellowbrick! Other utilities like styles, best fit lines, and anscombe's visualization can also be found in the links above. +Feature analysis visualizers are where you'll find the primary implementation of data visualizers. Regression, classification, and clustering analysis visualizers can be found in their respective libraries. Finally, visualizers for text analysis are also available in Yellowbrick! Other utilities, such as styles, best fit lines, and Anscombe's visualization, can also be found in the links above. diff --git a/docs/api/palettes.rst b/docs/api/palettes.rst index c13cacac6..1bbc1915e 100644 --- a/docs/api/palettes.rst +++ b/docs/api/palettes.rst @@ -1,9 +1,11 @@ +.. -*- mode: rst -*- + Colors and Style ================ -Yellowbrick believes that visual diagnostics are more effective if visualizations are appealing. As a result, we have borrowed familiar styles from `Seaborn `_ and use the new `Matplotlib 2.0 styles `_. We hope that these out of the box styles will make your visualizations publication ready, though of course you can customize your own look and feel by directly modifying the visualization with matplotlib. +Yellowbrick believes that visual diagnostics are more effective if visualizations are appealing. As a result, we have borrowed familiar styles from `Seaborn `_ and use the new `matplotlib 2.0 styles `_. We hope that these out-of-the-box styles will make your visualizations publication ready, though you can also still customize your own look and feel by directly modifying the visualizations with matplotlib. -Yellowbrick prioritizes color in its visualizations for most visualizers. There are two types of color sets that can be provided to a visualizer: a palette and a sequence. Palettes are discrete color values usually of a fixed length and are typically used for classification or clustering by showing each class, cluster or topic. Sequences are continuous color values that do not have a fixed length but rather a range and are typically used for regression or clustering, showing all possible values in the target or distances between items in clusters. +For most visualizers, Yellowbrick prioritizes color in its visualizations. There are two types of color sets that can be provided to a visualizer: a palette and a sequence. Palettes are discrete color values usually of a fixed length and are typically used for classification or clustering by showing each class, cluster, or topic. Sequences are continuous color values that do not have a fixed length but rather a range and are typically used for regression or clustering, showing all possible values in the target or distances between items in clusters. In order to make the distinction easy, most matplotlib colors (both palettes and sequences) can be referred to by name. A complete listing can be imported as follows: @@ -18,12 +20,12 @@ Palettes and sequences can be passed to visualizers as follows: visualizer = Visualizer(color="bold") -Refer to the API listing of each visualizer for specifications about how each color argument is handled. In the next two sections we will show every possible color palette and sequence currently available in Yellowbrick. +Refer to the API listing of each visualizer for specifications on how the color argument is handled. In the next two sections, we will show every possible color palette and sequence currently available in Yellowbrick. Color Palettes -------------- -Color palettes are discrete color lists that have a fixed length. The most common palettes are ordered as "blue", "green", "red", "maroon", "yellow", "cyan", and an optional "key". This allows you to specify these named colors or by the first character, e.g. 'bgrmyck' for matplotlib visualizations. +Color palettes are discrete color lists that have a fixed length. The most common palettes are ordered as "blue", "green", "red", "maroon", "yellow", "cyan", and an optional "key". This allows you to specify these named colors by the first character, e.g. 'bgrmyck' for matplotlib visualizations. To change the global color palette, use the `set_palette` function as follows: @@ -115,7 +117,7 @@ A complete listing of the Yellowbrick color palettes can be visualized as follow Color Sequences --------------- -Color sequences are continuous representations of color and are usually defined as a fixed number of steps between a minimum and maximal value. Sequences must be created with a total number of bins (or length) before plotting to ensure that values are assigned correctly. In the listing below, each sequence is shown with varying lengths to describe the range of colors in detail. +Color sequences are continuous representations of color and are usually defined as a fixed number of steps between a minimum and maximal value. Sequences must be created with a total number of bins (or length) before plotting to ensure that the values are assigned correctly. In the listing below, each sequence is shown with varying lengths to describe the range of colors in detail. Color sequences are most often used in regressions to show the distribution in the range of target values. They can also be used in clustering and distribution analysis to show distance or histogram data. diff --git a/docs/api/regressor/alphas.py b/docs/api/regressor/alphas.py index 81592d665..256a25472 100644 --- a/docs/api/regressor/alphas.py +++ b/docs/api/regressor/alphas.py @@ -1,7 +1,6 @@ import pandas as pd -import matplotlib.pyplot as plt -from sklearn.linear_model import Ridge, Lasso +from sklearn.linear_model import Lasso from sklearn.model_selection import train_test_split from yellowbrick.regressor import PredictionError diff --git a/docs/api/regressor/alphas.rst b/docs/api/regressor/alphas.rst index 6b2960132..c8484884a 100644 --- a/docs/api/regressor/alphas.rst +++ b/docs/api/regressor/alphas.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Alpha Selection =============== diff --git a/docs/api/regressor/index.rst b/docs/api/regressor/index.rst index 7f6ccafc4..388de01b7 100644 --- a/docs/api/regressor/index.rst +++ b/docs/api/regressor/index.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Regression Visualizers ====================== diff --git a/docs/api/regressor/peplot.py b/docs/api/regressor/peplot.py index 81592d665..256a25472 100644 --- a/docs/api/regressor/peplot.py +++ b/docs/api/regressor/peplot.py @@ -1,7 +1,6 @@ import pandas as pd -import matplotlib.pyplot as plt -from sklearn.linear_model import Ridge, Lasso +from sklearn.linear_model import Lasso from sklearn.model_selection import train_test_split from yellowbrick.regressor import PredictionError diff --git a/docs/api/regressor/peplot.rst b/docs/api/regressor/peplot.rst index b87b68f08..bead98f96 100644 --- a/docs/api/regressor/peplot.rst +++ b/docs/api/regressor/peplot.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Prediction Error Plot ===================== diff --git a/docs/api/regressor/residuals.py b/docs/api/regressor/residuals.py index 77ef08953..a53be6105 100644 --- a/docs/api/regressor/residuals.py +++ b/docs/api/regressor/residuals.py @@ -1,7 +1,6 @@ import pandas as pd -import matplotlib.pyplot as plt -from sklearn.linear_model import Ridge, Lasso +from sklearn.linear_model import Ridge from sklearn.model_selection import train_test_split from yellowbrick.regressor import ResidualsPlot diff --git a/docs/api/regressor/residuals.rst b/docs/api/regressor/residuals.rst index d9148c9c9..32cccb757 100644 --- a/docs/api/regressor/residuals.rst +++ b/docs/api/regressor/residuals.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Residuals Plot ============== diff --git a/docs/api/text/corpus.rst b/docs/api/text/corpus.rst index 1a5731b07..b2f15afc3 100644 --- a/docs/api/text/corpus.rst +++ b/docs/api/text/corpus.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Loading a Text Corpus ===================== diff --git a/docs/api/text/freqdist.py b/docs/api/text/freqdist.py index 067f0223a..3ba42c61d 100644 --- a/docs/api/text/freqdist.py +++ b/docs/api/text/freqdist.py @@ -17,7 +17,7 @@ def freqdist(docs, outpath, corpus_kwargs={}, **kwargs): features = vectorizer.get_feature_names() # Visualize the frequency distribution - visualizer = FreqDistVisualizer(features=features, **kwargs) + visualizer = FreqDistVisualizer(ax=ax, features=features, **kwargs) visualizer.fit(docs) visualizer.poof(outpath=outpath) diff --git a/docs/api/text/freqdist.rst b/docs/api/text/freqdist.rst index 391b557d3..25fbd9f9d 100644 --- a/docs/api/text/freqdist.rst +++ b/docs/api/text/freqdist.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + Token Frequency Distribution ============================ diff --git a/docs/api/text/images/tsne_all_docs.png b/docs/api/text/images/tsne_all_docs.png index 04c55c937dbfa87d392537b55646e7ae7b4e0a7d..e54766369a765cef5b920eaf8fe284b7108e8cc5 100644 GIT binary patch literal 69395 zcmeFYWl&sEw=LSZYY5(G2yO}P4#9#$a0%|g-5U2m0)ZsB2X}XfK!D&7EChFVxT|x% zbKZMZ_us90^?tmrN~M9_Ywxwzm~)OX=IUrQRe2mta!d#Wg7ZQ_MgsytqJcmVpQ59J z-;^2ecZ2_s-K1Vn!P&kX(#p;)}g7W`7sT~^Os)7i@1%j}IM z#L>*%#lhL#!PcC{)AEg*t+Nx&Gmd8*Tx>Kp?(Qx^oSgsH1su+AtT~CVj&&dq8psP7 zNiFY>y9+-4nwA~nha+yPQ+x{sb2Zw`8lEyJ$a0cI2Sax`Vfw$xEUgUHBFUgKh84!v zZdxv~W5OKS2}1h6t>j##)et1nB#E^6B$?Q4$NAWYeSerVs%IsyFE zkAs8{zlJV`0+r_9HAMW_692AYav}}JhhL-q`~M&4|9FkgFg3o*aEACI+2`ZH%=**_3Z(fjjeGcPeW zKOgzWd-mAtw(&w0tbhmb$NJ_3qgGGZ0BEiCh?B=H_!zgxw$|@}i-qE{GT|2f(rjt>u~@9KFTrd0ei4WSR^s_^50B zuim7lrkXz7->D~FUGG$KoA;r&ozK~uHS+WE{aN%o=m*1cdG6{Nx{t}~bp{3uZOnHD z1@$e3yhwR!w*R|%rp{LL#(0-|ds5e=zx`q{bJG%^fI(C`mqfz-OY45ij>}O$Nn}UR z?a^Sh<2)bRtC}b7nZs5{keNMC_?TZuMg~>*7hpX-QUa8?ZP(jnO@Cro4-b3sR(ESh zaD^Yj@Wi5?{}e!ym6hGoA9>55l$4f6a@28ucQfd}d$`i;I&I_?-f?l|f4QFNeAtDA zXL@tK=e<>sMgy~%7BVmAybN@I_l{Nf&kxQ{?AAT%hf6d}Ow5*3pM-(qwI9_!Csx0M z9`3+nL{8ep``msO^@9)6Co-^eX1>xBet2_NzTbNCQ~0PGje2>2!ntp`JM8E?YvuBE z=RL2n|B-AQ+4HCCJ{vzj#g=FmAwoo`@K{(_4sTCJR^h9imnarf`%{SZJXTbW-Kd3V zbujVe>9eS)w^T^@xlVj8%M#d>0$is7o}PTlPamm@9QEl8b{v0abrJfc*ZjtEM)vY_ zYn

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update the Yellowbrick dependencies (2) enhance the Yellowbrick documentation to help orient new users and contributors, and (3) make several small additions and upgrades (e.g. pulling the Yellowbrick utils into a standalone module). -We have updated the Scikit-Learn and SciPy dependencies from version 0.17.1 or later to 0.18 or later. This primarily entails moving from ``from sklearn.cross_validation import train_test_split`` to ``from sklearn.model_selection import train_test_split``. +We have updated the scikit-learn and SciPy dependencies from version 0.17.1 or later to 0.18 or later. This primarily entails moving from ``from sklearn.cross_validation import train_test_split`` to ``from sklearn.model_selection import train_test_split``. -The updates to the documentation include new Quickstart and Installation guides as well as updates to the Contributors documentation, which is modeled on the Scikit-Learn contributing documentation. +The updates to the documentation include new Quickstart and Installation guides, as well as updates to the Contributors documentation, which is modeled on the scikit-learn contributing documentation. -This version also included upgrades to the KMeans visualizer, which now supports not only ``silhouette_score`` but also ``distortion_score`` and ``calinski_harabaz_score``. The ``distortion_score`` computes the mean distortion of all samples as the sum of the squared distances between each observation and its closest centroid. This is the metric that K-Means attempts to minimize as it is fitting the model. The ``calinski_harabaz_score`` is defined as ratio between the within-cluster dispersion and the between-cluster dispersion. +This version also included upgrades to the KMeans visualizer, which now supports not only ``silhouette_score`` but also ``distortion_score`` and ``calinski_harabaz_score``. The ``distortion_score`` computes the mean distortion of all samples as the sum of the squared distances between each observation and its closest centroid. This is the metric that KMeans attempts to minimize as it is fitting the model. The ``calinski_harabaz_score`` is defined as ratio between the within-cluster dispersion and the between-cluster dispersion. -Finally, this release includes a prototype of the ``VisualPipeline``, which extends Scikit-Learn's ``Pipeline`` class, allowing multiple Visualizers to be chained or sequenced together. +Finally, this release includes a prototype of the ``VisualPipeline``, which extends scikit-learn's ``Pipeline`` class, allowing multiple Visualizers to be chained or sequenced together. * Tag: v0.4.1_ * Deployed: Monday, May 22, 2017 @@ -61,7 +92,7 @@ Finally, this release includes a prototype of the ``VisualPipeline``, which exte Changes: - Score and model visualizers now wrap estimators as proxies so that all methods on the estimator can be directly accessed from the visualizer - - Updated Scikit-learn dependency from >=0.17.1 to >=0.18 + - Updated scikit-learn dependency from >=0.17.1 to >=0.18 - Replaced ``sklearn.cross_validation`` with ``model_selection`` - Updated SciPy dependency from >=0.17.1 to >=0.18 - ScoreVisualizer now subclasses ModelVisualizer; towards allowing both fitted and unfitted models passed to Visualizers @@ -72,7 +103,7 @@ Changes: - Replaced the ``self.ax`` property on all of the individual ``draw`` methods with a new property on the ``Visualizer`` class that ensures all visualizers automatically have axes. - Refactored the utils module into a package - Continuing to update the docstrings to conform to Sphinx - - Added a prototype visual pipeline class that extends the Scikit-learn pipeline class to ensure that visualizers get called correctly. + - Added a prototype visual pipeline class that extends the scikit-learn pipeline class to ensure that visualizers get called correctly. Bug Fixes: - Fixed title bug in Rank2D FeatureVisualizer @@ -88,7 +119,7 @@ Notable in this release is the inclusion of two new feature visualizers that use This release also adds support for clustering visualizations, namely the elbow method for selecting K, ``KElbowVisualizer`` and a visualization of cluster size and density using the ``SilhouetteVisualizer``. The release also adds support for regularization analysis using the ``AlphaSelection`` visualizer. Both the text and classification modules were also improved with the inclusion of the ``PosTagVisualizer`` and the ``ConfusionMatrix`` visualizer respectively. -This release also added an Anaconda repository and distribution so that users can ``conda install`` yellowbrick. Even more notable, we got yellowbrick stickers! We've also updated the documentation to make it more friendly and a bit more visual; fixing the API rendering errors. All-in-all, this was a big release with a lot of contributions and we thank everyone that participated in the lab! +This release also added an Anaconda repository and distribution so that users can ``conda install`` yellowbrick. Even more notable, we got Yellowbrick stickers! We've also updated the documentation to make it more friendly and a bit more visual; fixing the API rendering errors. All-in-all, this was a big release with a lot of contributions and we thank everyone that participated in the lab! * Tag: v0.4_ * Deployed: Thursday, May 4, 2017 @@ -127,8 +158,8 @@ The ``FreqDistVisualizer`` implements frequency distribution plot that tells us * Contributors: Rebecca Bilbro, Benjamin Bengfort Changes: - - TSNEVisualizer for 2D projections of vectorized documents - - FreqDistVisualizer for token frequency of text in a corpus + - ``TSNEVisualizer`` for 2D projections of vectorized documents + - ``FreqDistVisualizer`` for token frequency of text in a corpus - Added the user testing evaluation to the documentation - Created scikit-yb.org and host documentation there with RFD - Created a sample corpus and text examples notebook @@ -139,7 +170,7 @@ Changes: Version 0.3.2 ------------- -Hardened the Yellowbrick API to elevate the idea of a Visualizer to a first principle. This included reconciling shifts in the development of the preliminary versions to the new API, formalizing Visualizer methods like `draw()` and `finalize()`, and adding utilities that revolve around Scikit-Learn. To that end we also performed administrative tasks like refreshing the documentation and preparing the repository for more and varied open source contributions. +Hardened the Yellowbrick API to elevate the idea of a Visualizer to a first principle. This included reconciling shifts in the development of the preliminary versions to the new API, formalizing Visualizer methods like ``draw()`` and ``finalize()``, and adding utilities that revolve around scikit-learn. To that end we also performed administrative tasks like refreshing the documentation and preparing the repository for more and varied open source contributions. * Tag: v0.3.2_ * Deployed: Friday, January 20, 2017 @@ -173,40 +204,40 @@ Hotfix to solve pip install issues with Yellowbrick. Version 0.3 ----------- -This release marks a major change from the previous MVP releases as Yellowbrick moves towards direct integration with Scikit-Learn for visual diagnostics and steering of machine learning and could therefore be considered the first alpha release of the library. To that end we have created a Visualizer model which extends sklearn.base.BaseEstimator and can be used directly in the ML Pipeline. There are a number of visualizers that can be used throughout the model selection process, including for feature analysis, model selection, and hyperparameter tuning. +This release marks a major change from the previous MVP releases as Yellowbrick moves towards direct integration with scikit-learn for visual diagnostics and steering of machine learning and could therefore be considered the first alpha release of the library. To that end we have created a Visualizer model which extends ``sklearn.base.BaseEstimator`` and can be used directly in the ML Pipeline. There are a number of visualizers that can be used throughout the model selection process, including for feature analysis, model selection, and hyperparameter tuning. -In this release specifically we focused on visualizers in the data space for feature analysis and visualizers in the model space for scoring and evaluating models. Future releases will extend these base classes and add more functionality. +In this release specifically, we focused on visualizers in the data space for feature analysis and visualizers in the model space for scoring and evaluating models. Future releases will extend these base classes and add more functionality. * Tag: v0.3_ * Deployed: Sunday, October 9, 2016 * Contributors: Benjamin Bengfort, Rebecca Bilbro, Marius van Niekerk Enhancements: - - Created an API for visualization with machine learning: Visualizers that are BaseEstimators. + - Created an API for visualization with machine learning: Visualizers that are ``BaseEstimators``. - Created a class hierarchy for Visualizers throughout the ML process particularly feature analysis and model evaluation - Visualizer interface is draw method which can be called multiple times on data or model spaces and a poof method to finalize the figure and display or save to disk. - - ScoreVisualizers wrap Scikit-Learn estimators and implement fit and predict (pass-throughs to the estimator) and also score which calls draw in order to visually score the estimator. If the estimator isn't appropriate for the scoring method an exception is raised. - - ROCAUC is a ScoreVisualizer that plots the receiver operating characteristic curve and displays the area under the curve score. - - ClassificationReport is a ScoreVisualizer that renders the confusion matrix of a classifier as a heatmap. - - PredictionError is a ScoreVisualizer that plots the actual vs. predicted values and the 45 degree accuracy line for regressors. - - ResidualPlot is a ScoreVisualizer that plots the residuals (y - yhat) across the actual values (y) with the zero accuracy line for both train and test sets. - - ClassBalance is a ScoreVisualizer that displays the support for each class as a bar plot. - - FeatureVisualizers are Scikit-Learn Transformers that implement fit and transform and operate on the data space, calling draw to display instances. - - ParallelCoordinates plots instances with class across each feature dimension as line segments across a horizontal space. - - RadViz plots instances with class in a circular space where each feature dimension is an arc around the circumference and points are plotted relative to the weight of the feature. - - Rank2D plots pairwise scores of features as a heatmap in the space [-1, 1] to show relative importance of features. Currently implemented ranking functions are Pearson correlation and covariance. - - Coordinated and added palettes in the bgrmyck space and implemented a version of the Seaborn set_palette and set_color_codes functions as well as the ColorPalette object and other matplotlib.rc modifications. - - Inherited Seaborn's notebook context and whitegrid axes style but make them the default, don't allow user to modify (if they'd like to, they'll have to import Seaborn). This gives Yellowbrick a consistent look and feel without giving too much work to the user and prepares us for Matplotlib 2.0. + - ``ScoreVisualizers`` wrap scikit-learn estimators and implement ``fit()`` and ``predict()`` (pass-throughs to the estimator) and also score which calls draw in order to visually score the estimator. If the estimator isn't appropriate for the scoring method an exception is raised. + - ``ROCAUC`` is a ``ScoreVisualizer`` that plots the receiver operating characteristic curve and displays the area under the curve score. + - ``ClassificationReport`` is a ``ScoreVisualizer`` that renders the confusion matrix of a classifier as a heatmap. + - ``PredictionError`` is a ``ScoreVisualizer`` that plots the actual vs. predicted values and the 45 degree accuracy line for regressors. + - ``ResidualPlot`` is a ``ScoreVisualizer`` that plots the residuals (y - yhat) across the actual values (y) with the zero accuracy line for both train and test sets. + - ``ClassBalance`` is a ``ScoreVisualizer`` that displays the support for each class as a bar plot. + - ``FeatureVisualizers`` are scikit-learn Transformers that implement ``fit()`` and ``transform()`` and operate on the data space, calling draw to display instances. + - ``ParallelCoordinates`` plots instances with class across each feature dimension as line segments across a horizontal space. + - ``RadViz`` plots instances with class in a circular space where each feature dimension is an arc around the circumference and points are plotted relative to the weight of the feature. + - ``Rank2D`` plots pairwise scores of features as a heatmap in the space [-1, 1] to show relative importance of features. Currently implemented ranking functions are Pearson correlation and covariance. + - Coordinated and added palettes in the bgrmyck space and implemented a version of the Seaborn set_palette and set_color_codes functions as well as the ``ColorPalette`` object and other matplotlib.rc modifications. + - Inherited Seaborn's notebook context and whitegrid axes style but make them the default, don't allow user to modify (if they'd like to, they'll have to import Seaborn). This gives Yellowbrick a consistent look and feel without giving too much work to the user and prepares us for matplotlib 2.0. - Jupyter Notebook with Examples of all Visualizers and usage. Bug Fixes: - Fixed Travis-CI test failures with matplotlib.use('Agg'). - Fixed broken link to Quickstart on README - - Refactor of the original API to the Scikit-Learn Visualizer API + - Refactor of the original API to the scikit-learn Visualizer API Version 0.2 ----------- -Intermediate steps towards a complete API for visualization. Preparatory stages for Scikit-Learn visual pipelines. +Intermediate steps towards a complete API for visualization. Preparatory stages for scikit-learn visual pipelines. * Tag: v0.2_ * Deployed: Sunday, September 4, 2016 diff --git a/docs/contributing.rst b/docs/contributing.rst index 6af90a615..19e22c97a 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -1,9 +1,11 @@ +.. -*- mode: rst -*- + Contributing ============ Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick! -Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. +Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with scikit-learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. Beyond creating visualizers, there are many ways to contribute: @@ -61,11 +63,16 @@ Once forked, use the following steps to get your development environment set up 3. Install dependencies. - Yellowbrick's dependencies are in the `requirements.txt` document at the root of the repository. Open this file and uncomment the dependencies that are for development only. Then install the dependencies with ``pip``:: + Yellowbrick's dependencies are in the ``requirements.txt`` document at the root of the repository. Open this file and uncomment the dependencies that are for development only. Then install the dependencies with ``pip``:: $ pip install -r requirements.txt - Note that there may be other dependencies required for development and testing; you can simply install them with ``pip``. + Note that there may be other dependencies required for development and testing; you can simply install them with ``pip``. For example to install + the additional dependencies for building the documentation or to run the + test suite, use the ``requirements.txt`` files in those directories:: + + $ pip install -r tests/requirements.txt + $ pip install -r docs/requirements.txt 4. Switch to the develop branch. @@ -109,13 +116,13 @@ Then we will "Squash and Merge" your contribution, combining all of your commits Developing Visualizers ---------------------- -In this section, we'll discuss the basics of developing visualizers. This of course is a big topic, but hopefully these simple tips and tricks will help make sense. First thing though, check out this presentation that we put together on yellowbrick development, it discusses the expected user workflow, our integration with Scikit-Learn, our plans and roadmap, etc: +In this section, we'll discuss the basics of developing visualizers. This of course is a big topic, but hopefully these simple tips and tricks will help make sense. First thing though, check out this presentation that we put together on yellowbrick development, it discusses the expected user workflow, our integration with scikit-learn, our plans and roadmap, etc: .. raw:: html -One thing that is necessary is a good understanding of Scikit-Learn and Matplotlib. Because our API is intended to integrate with Scikit-Learn, a good start is to review `"APIs of Scikit-Learn objects" `_ and `"rolling your own estimator" `_. In terms of matplotlib, check out `Nicolas P. Rougier's Matplotlib tutorial `_. +One thing that is necessary is a good understanding of scikit-learn and Matplotlib. Because our API is intended to integrate with scikit-learn, a good start is to review `"APIs of scikit-learn objects" `_ and `"rolling your own estimator" `_. In terms of matplotlib, use Yellowbrick's guide :doc:`matplotlib`. Additional resources include `Nicolas P. Rougier's Matplotlib tutorial `_ and `Chris Moffitt's Effectively Using Matplotlib `_. Visualizer API ~~~~~~~~~~~~~~ @@ -123,16 +130,16 @@ Visualizer API There are two basic types of Visualizers: - **Feature Visualizers** are high dimensional data visualizations that are essentially transformers. -- **Score Visualizers** wrap a Scikit-Learn regressor, classifier, or clusterer and visualize the behavior or performance of the model on test data. +- **Score Visualizers** wrap a scikit-learn regressor, classifier, or clusterer and visualize the behavior or performance of the model on test data. -These two basic types of visualizers map well to the two basic objects in Scikit-Learn: +These two basic types of visualizers map well to the two basic objects in scikit-learn: - **Transformers** take input data and return a new data set. - **Estimators** are fit to training data and can make predictions. -The Scikit-Learn API is object oriented, and estimators and transformers are initialized with parameters by instantiating their class. Hyperparameters can also be set using the ``set_attrs()`` method and retrieved with the corresponding ``get_attrs()`` method. All Scikit-Learn estimators have a ``fit(X, y=None)`` method that accepts a two dimensional data array, ``X``, and optionally a vector ``y`` of target values. The ``fit()`` method trains the estimator, making it ready to transform data or make predictions. Transformers have an associated ``transform(X)`` method that returns a new dataset, ``Xprime`` and models have a ``predict(X)`` method that returns a vector of predictions, ``yhat``. Models also have a ``score(X, y)`` method that evaluate the performance of the model. +The scikit-learn API is object oriented, and estimators and transformers are initialized with parameters by instantiating their class. Hyperparameters can also be set using the ``set_attrs()`` method and retrieved with the corresponding ``get_attrs()`` method. All scikit-learn estimators have a ``fit(X, y=None)`` method that accepts a two dimensional data array, ``X``, and optionally a vector ``y`` of target values. The ``fit()`` method trains the estimator, making it ready to transform data or make predictions. Transformers have an associated ``transform(X)`` method that returns a new dataset, ``Xprime`` and models have a ``predict(X)`` method that returns a vector of predictions, ``yhat``. Models also have a ``score(X, y)`` method that evaluate the performance of the model. -Visualizers interact with Scikit-Learn objects by intersecting with them at the methods defined above. Specifically, visualizers perform actions related to ``fit()``, ``transform()``, ``predict()``, and ``score()`` then call a ``draw()`` method which initializes the underlying figure associated with the visualizer. The user calls the visualizer's ``poof()`` method, which in turn calls a ``finalize()`` method on the visualizer to draw legends, titles, etc. and then ``poof()`` renders the figure. The Visualizer API is therefore: +Visualizers interact with scikit-learn objects by intersecting with them at the methods defined above. Specifically, visualizers perform actions related to ``fit()``, ``transform()``, ``predict()``, and ``score()`` then call a ``draw()`` method which initializes the underlying figure associated with the visualizer. The user calls the visualizer's ``poof()`` method, which in turn calls a ``finalize()`` method on the visualizer to draw legends, titles, etc. and then ``poof()`` renders the figure. The Visualizer API is therefore: - ``draw()``: add visual elements to the underlying axes object - ``finalize()``: prepare the figure for rendering, adding final touches such as legends, titles, axis labels, etc. @@ -162,7 +169,7 @@ Creating a visualizer means defining a class that extends ``Visualizer`` or one def finalize(self): self.set_title("My Visualizer") -This simple visualizer simply draws a line graph for some input dataset X, intersecting with the Scikit-Learn API at the ``fit()`` method. A user would use this visualizer in the typical style:: +This simple visualizer simply draws a line graph for some input dataset X, intersecting with the scikit-learn API at the ``fit()`` method. A user would use this visualizer in the typical style:: visualizer = MyVisualizer() visualizer.fit(X) @@ -175,8 +182,9 @@ Testing The test package mirrors the yellowbrick package in structure and also contains several helper methods and base functionality. To add a test to your visualizer, find the corresponding file to add the test case, or create a new test file in the same place you added your code. -Visual tests are notoriously difficult to create --- how do you test a visualization or figure? Moreover, testing Scikit-Learn models with real data can consume a lot of memory. Therefore the primary test you should create is simply to test your visualizer from end to end and make sure that no exceptions occur. To assist with this, we have two primary helpers, ``VisualTestCase`` and ``DatasetMixin``. Create your unittest as follows:: +Visual tests are notoriously difficult to create --- how do you test a visualization or figure? Moreover, testing scikit-learn models with real data can consume a lot of memory. Therefore the primary test you should create is simply to test your visualizer from end to end and make sure that no exceptions occur. To assist with this, we have two primary helpers, ``VisualTestCase`` and ``DatasetMixin``. Create your unittest as follows:: + import pytest from tests.base import VisualTestCase from tests.dataset import DatasetMixin @@ -200,18 +208,48 @@ Visual tests are notoriously difficult to create --- how do you test a visualiza visualizer.fit(X) visualizer.poof() except Exception as e: - self.fail("my visualizer didn't work") + pytest.fail("my visualizer didn't work") Tests can be run as follows:: $ make test -The Makefile uses the nosetest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The ``DatasetMixin`` also requires requests.py to fetch data from our Amazon S3 account. +The Makefile uses the pytest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The ``DatasetMixin`` also requires `requests.py `_ to fetch data from our Amazon S3 account. + +Image Comparison Tests +~~~~~~~~~~~~~~~~~~~~~~ + +Writing an image based comparison test is only a little more difficult than the simple testcase presented above. We have adapted matplotlib's image comparison test utility into an easy to use assert method : ``self.assert_images_similar(visualizer)`` + +The main consideration is that you must specify the “baseline”, or expected, image in the ``tests/baseline_images/`` folder structure. + +For example, create your unittest located in ``tests/test_regressor/test_myvisualizer.py`` as follows:: + + from tests.base import VisualTestCase + ... + def test_my_visualizer_output(self): + ... + visualizer = MyVisualizer() + visualizer.fit(X) + visualizer.poof() + self.assert_images_similar(visualizer) + +The first time this test is run, there will be no baseline image to compare against, so the test will fail. Copy the output images (in this case ``tests/actual_images/test_regressor/test_myvisualizer/test_my_visualizer_output.png``) to the correct subdirectory of baseline_images tree in the source directory (in this case ``tests/baseline_images/test_regressor/test_myvisualizer/test_my_visualizer_output.png``). Put this new file under source code revision control (with git add). When rerunning the tests, they should now pass. + +We also have a helper script, ``tests/images.py`` to clean up and manage baseline images automatically. It is run using the ``python -m`` command to execute a module as main, and it takes as an argument the path to your *test file*. To copy the figures as above:: + + $ python -m tests.images tests/test_regressor/test_myvisualizer.py + +This will move all related test images from ``actual_images`` to ``baseline_images`` on your behalf (note you'll have had to run the tests at least once to generate the images). You can also clean up images from both actual and baseline as follows:: + + $ python -m tests.images -C tests/test_regressor/test_myvisualizer.py + +This is useful particularly if you're stuck trying to get an image comparison to work. For more information on the images helper script, use ``python -m tests.images --help``. Documentation ~~~~~~~~~~~~~ -The initial documentation for your visualizer will be a well structured docstring. Yellowbrick uses Sphinx to build documentation, therefore docstrings should be written in reStructuredText in numpydoc format (similar to Scikit-Learn). The primary location of your docstring should be right under the class definition, here is an example:: +The initial documentation for your visualizer will be a well structured docstring. Yellowbrick uses Sphinx to build documentation, therefore docstrings should be written in reStructuredText in numpydoc format (similar to scikit-learn). The primary location of your docstring should be right under the class definition, here is an example:: class MyVisualizer(Visualizer): """ @@ -224,7 +262,7 @@ The initial documentation for your visualizer will be a well structured docstrin Parameters ---------- - model : a Scikit-Learn regressor + model : a scikit-learn regressor Should be an instance of a regressor, and specifically one whose name ends with "CV" otherwise a will raise a YellowbrickTypeError exception on instantiation. To use non-CV regressors see: @@ -251,7 +289,59 @@ The initial documentation for your visualizer will be a well structured docstrin In the notes section specify any gotchas or other info. """ -You should also add your example to the ``api`` directory of the documentation when you have the chance. Currently the documentation is undergoing development, notes on how to add your visualizer to the documentation will be updated in the future. +When your visualizer is added to the API section of the documentation, this docstring will be rendered in HTML to show the various options and functionality of your visualizer! + +To add the visualizer to the documentation it needs to be added to the ``docs/api`` folder in the correct subdirectory. For example if your visualizer is a model score visualizer related to regression it would go in the ``docs/api/regressor`` subdirectory. If you have a question where your documentation should be located, please ask the maintainers via your pull request, we'd be happy to help! + +There are two primary files that need to be created: + +1. **mymodule.rst**: the reStructuredText document +2. **mymodule.py**: a python file that generates images for the rst document + +There are quite a few examples in the documentation on which you can base your files of similar types. The primary format for the API section is as follows:: + + .. -*- mode: rst -*- + + My Visualizer + ============= + + Intro to my visualizer + + .. code:: python + + # Example to run MyVisualizer + visualizer = MyVisualizer(LinearRegression()) + + visualizer.fit(X, y) + g = visualizer.poof() + + + .. image:: images/my_visualizer.png + + Discussion about my visualizer + + + API Reference + ------------- + + .. automodule:: yellowbrick.regressor.mymodule + :members: MyVisualizer + :undoc-members: + :show-inheritance: + +This is a pretty good structure for a documentation page; a brief introduction followed by a code example with a visualization included (using the ``mymodule.py`` to generate the images into the local directory's ``images`` subdirectory). The primary section is wrapped up with a discussion about how to interpret the visualizer and use it in practice. Finally the ``API Reference`` section will use ``automodule`` to include the documentation from your docstring. + +At this point there are several places where you can list your visualizer, but to ensure it is included in the documentation it *must be listed in the TOC of the local index*. Find the ``index.rst`` file in your subdirectory and add your rst file (without the ``.rst`` extension) to the ``..toctree::`` directive. This will ensure the documentation is included when it is built. + +Speaking of, you can build your documentation by changing into the ``docs`` directory and running ``make html``, the documentation will be built and rendered in the ``_build/html`` directory. You can view it by opening ``_build/html/index.html`` then navigating to your documentation in the browser. + +There are several other places that you can list your visualizer including: + + - ``docs/index.rst`` for a high level overview of our visualizers + - ``DESCRIPTION.rst`` for inclusion on PyPI + - ``README.md`` for inclusion on GitHub + +Please ask for the maintainer's advice about how to include your visualizer in these pages. Advanced Development -------------------- diff --git a/docs/evaluation.rst b/docs/evaluation.rst index fccd5de05..3defb7ea5 100644 --- a/docs/evaluation.rst +++ b/docs/evaluation.rst @@ -1,3 +1,5 @@ +.. -*- mode: rst -*- + User Testing Instructions ========================= @@ -9,45 +11,32 @@ options and customize as much as possible. After you've exercised the code with your examples, respond to our `alpha testing survey `__! -Step One: Questionaire -~~~~~~~~~~~~~~~~~~~~~~ -Please open the quesionaire, in order to familiarize yourself with the -feedback that we are looking to receive. We are very interested in -identifying any bugs in Yellowbrick. Please include al cells in your -jupyter notebook that produce errors so that we may reproduce the +Step One: Questionnaire +~~~~~~~~~~~~~~~~~~~~~~~ +Please open the questionnaire, in order to familiarize yourself with the +type of feedback we are looking to receive. We are very interested in +identifying any bugs in Yellowbrick. Please include any cells in your +Jupyter notebook that produce errors so that we may reproduce the problem. Step Two: Dataset ~~~~~~~~~~~~~~~~~ -Select a multivariate dataset of your own; the more (e.g. different) -datasets that we can run through Yellowbrick, the more likely we'll -discover edge cases and exceptions! Note that your dataset must be -well-suited to modeling with Scikit-Learn. In particular we recommend -you choose a dataset whose target is suited to the following supervised -learning tasks: +Select a multivariate dataset of your own. The greater the variety of datasets that we can run through Yellowbrick, the more likely we'll discover edge cases and exceptions! Please note that your dataset must be well-suited to modeling with scikit-learn. In particular, we recommend choosing a dataset whose target is suited to one of the following supervised learning tasks: - `Regression `__ (target is a continuous variable) - `Classification `__ (target is a discrete variable) -There are datasets that are well suited to both types of analysis; -either way you can use the testing methodology from this notebook for -either type of task (or both). In order to find a dataset, we recommend -you try the following places: +There are datasets that are well suited to both types of analysis; either way, you can use the testing methodology from this notebook for either type of task (or both). In order to find a dataset, we recommend you try the following places: - `UCI Machine Learning Repository `__ - `MLData.org `__ -- `Awesome Public - Datasets `__ +- `Awesome Public Datasets `__ -You're more than welcome to choose a dataset of your own, but we do ask -that you make at least the notebook containing your testing results -publicly available for us to review. If the data is also public (or -you're willing to share it with the primary contributors) that will help -us figure out bugs and required features much more easily! +You're more than welcome to choose a dataset of your own, but we do ask that you make at least the notebook containing your testing results publicly available for us to review. If the data is also public (or you're willing to share it with the primary contributors) that will help us figure out bugs and required features much more easily! Step Three: Notebook ~~~~~~~~~~~~~~~~~~~~ @@ -55,13 +44,10 @@ Step Three: Notebook Create a notebook in a GitHub repository. We suggest the following: 1. Fork the Yellowbrick repository -2. Under the ``examples`` directory, create a directory named with your - GitHub username +2. Under the ``examples`` directory, create a directory named with your GitHub username 3. Create a notebook named ``testing``, i.e. examples/USERNAME/testing.ipynb -Alternatively, you could just send us a notebook via Gist or your own -repository. However, if you fork Yellowbrick, you can initiate a pull -request to have your example added to our gallery! +Alternatively, you could just send us a notebook via Gist or your own repository. However, if you fork Yellowbrick, you can initiate a pull request to have your example added to our gallery! Step Four: Model with Yellowbrick and Scikit-Learn ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -74,17 +60,12 @@ Add the following to the notebook: Then conduct the following modeling activities: -- Feature analysis using Scikit-Learn and Yellowbrick -- Estimator fitting using Scikit-Learn and Yellowbrick +- Feature analysis using scikit-learn and Yellowbrick +- Estimator fitting using scikit-learn and Yellowbrick -You can follow along with our ``examples`` directory (check out -`examples.ipynb `__) -or even create your own custom visualizers! The goal is that you create -an end-to-end model from data loading to estimator(s) with visualizers -along the way. +You can follow along with our ``examples`` directory (check out `examples.ipynb `__) or even create your own custom visualizers! The goal is that you create an end-to-end model from data loading to estimator(s) with visualizers along the way. -**IMPORTANT**: please make sure you record all errors that you get and -any tracebacks you receive for step three! +**IMPORTANT**: please make sure you record all errors that you get and any tracebacks you receive for step three! Step Five: Feedback ~~~~~~~~~~~~~~~~~~~ @@ -93,16 +74,9 @@ Finally, submit feedback via the Google Form we have created: https://goo.gl/forms/naoPUMFa1xNcafY83 -This form is allowing us to aggregate multiple submissions and bugs so -that we can coordinate the creation and management of issues. If you are -the first to report a bug or feature request, we will make sure you're -notified (we'll tag you using your Github username) about the created -issue! +This form is allowing us to aggregate multiple submissions and bugs so that we can coordinate the creation and management of issues. If you are the first to report a bug or feature request, we will make sure you're notified (we'll tag you using your Github username) about the created issue! Step Six: Thanks! ~~~~~~~~~~~~~~~~~ -Thank you for helping us make Yellowbrick better! We'd love to see pull -requests for features you think would be extend the library. We'll also -be doing a user study that we would love for you to participate in. Stay -tuned for more great things from Yellowbrick! +Thank you for helping us make Yellowbrick better! We'd love to see pull requests for features you think should be added to the library. We'll also be doing a user study that we would love for you to participate in. 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:doc:`Scatter and Joint Plots`: direct data visualization with feature selection Classification Visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - :doc:`api/classifier/class_balance`: see how the distribution of classes affects the model +- :doc:`api/classifier/class_prediction_error`: shows error and support in classification - :doc:`api/classifier/classification_report`: visual representation of precision, recall, and F1 - :doc:`ROC/AUC Curves `: receiver operator characteristics and area under the curve - :doc:`Confusion Matrices `: visual description of class decision making @@ -81,9 +84,10 @@ The following is a complete listing of the Yellowbrick documentation for this ve quickstart tutorial api/index - about evaluation contributing + matplotlib + about changelog Indices and tables diff --git a/docs/matplotlib.rst b/docs/matplotlib.rst new file mode 100644 index 000000000..82484c081 --- /dev/null +++ b/docs/matplotlib.rst @@ -0,0 +1,247 @@ +.. -*- mode: rst -*- + +Effective Matplotlib +==================== + +Yellowbrick generates visualizations by wrapping `matplotlib `_, the most prominent Python scientific visualization library. Because of this, Yellowbrick is able to generate publication-ready images for a variety of GUI backends, image formats, and Jupyter notebooks. Yellowbrick strives to provide well-styled visual diagnostic tools and complete information. However, to customize figures or roll your own visualizers, a strong background in using matplotlib is required. + +With permission, we have included part of `Chris Moffitt's `_ `Effectively Using Matplotlib `_ as a crash course into Matplotlib terminology and usage. For a complete example, please visit his excellent post on creating a visual sales analysis! Additionally we recommend `Nicolas P. Rougier's Matplotlib tutorial `_ for an in-depth dive. + +Figures and Axes +---------------- + +This graphic from the `matplotlib faq is gold `_. Keep it handy to understand the different terminology of a plot. + +.. image:: images/matplotlib_anatomy.png + +Most of the terms are straightforward but the main thing to remember is that the ``Figure`` is the final image that may contain 1 or more axes. The ``Axes`` represent an individual plot. Once you understand what these are and how to access them through the object oriented API, the rest of the process starts to fall into place. + +The other benefit of this knowledge is that you have a starting point when you see things on the web. If you take the time to understand this point, the rest of the matplotlib API will start to make sense. + +Matplotlib keeps a global reference to the global figure and axes objects which can be modified by the ``pyplot`` API. To access this import matplotlib as follows: + +.. code:: python + + import matplotlib.pyplot as plt + + axes = plt.gca() + +The ``plt.gca()`` function gets the current axes so that you can draw on it directly. You can also directly create a figure and axes as follows: + +.. code:: python + + fig = plt.figure() + ax = fig.add_subplot(111) + +Yellowbrick will use ``plt.gca()`` by default to draw on. You can access the ``Axes`` object on a visualizer via its ``ax`` property: + +.. code:: python + + from sklearn.linear_model import LinearRegression + from yellowbrick.regressor import PredictionError + + # Fit the visualizer + model = PredictionError(LinearRegression() ) + model.fit(X_train, y_train) + model.score(X_test, y_test) + + # Call finalize to draw the final yellowbrick-specific elements + model.finalize() + + # Get access to the axes object and modify labels + model.ax.set_xlabel("measured concrete strength") + model.ax.set_ylabel("predicted concrete strength") + plt.savefig("peplot.pdf") + +You can also pass an external ``Axes`` object directly to the visualizer: + +.. code:: python + + model = PredictionError(LinearRegression(), ax=ax) + +Therefore you have complete control of the style and customization of a Yellowbrick visualizer. + +Creating a Custom Plot +---------------------- + +.. image:: images/matplotlib_pbpython_example.png + +The first step with any visualization is to plot the data. Often the simplest way to do this is using the standard pandas plotting function (given a ``DataFrame`` called ``top_10``): + +.. code:: python + + top_10.plot(kind='barh', y="Sales", x="Name") + +The reason I recommend using pandas plotting first is that it is a quick and easy way to prototype your visualization. Since most people are probably already doing some level of data manipulation/analysis in pandas as a first step, go ahead and use the basic plots to get started. + +Assuming you are comfortable with the gist of this plot, the next step is to customize it. Some of the customizations (like adding titles and labels) are very simple to use with the pandas plot function. However, you will probably find yourself needing to move outside of that functionality at some point. That's why it is recommended to create your own ``Axes`` first and pass it to the plotting function in Pandas: + +.. code:: python + + fig, ax = plt.subplots() + top_10.plot(kind='barh', y="Sales", x="Name", ax=ax) + +The resulting plot looks exactly the same as the original but we added an additional call to ``plt.subplots()`` and passed the ``ax`` to the plotting function. Why should you do this? Remember when I said it is critical to get access to the axes and figures in matplotlib? That’s what we have accomplished here. Any future customization will be done via the ``ax`` or ``fig`` objects. + +We have the benefit of a quick plot from pandas but access to all the power from matplotlib now. An example should show what we can do now. Also, by using this naming convention, it is fairly straightforward to adapt others’ solutions to your unique needs. + +Suppose we want to tweak the x limits and change some axis labels? Now that we have the axes in the ``ax`` variable, we have a lot of control: + +.. code:: python + + fig, ax = plt.subplots() + top_10.plot(kind='barh', y="Sales", x="Name", ax=ax) + ax.set_xlim([-10000, 140000]) + ax.set_xlabel('Total Revenue') + ax.set_ylabel('Customer'); + +Here’s another shortcut we can use to change the title and both labels: + +.. code:: python + + fig, ax = plt.subplots() + top_10.plot(kind='barh', y="Sales", x="Name", ax=ax) + ax.set_xlim([-10000, 140000]) + ax.set(title='2014 Revenue', xlabel='Total Revenue', ylabel='Customer') + +To further demonstrate this approach, we can also adjust the size of this image. By using the ``plt.subplots()`` function, we can define the ``figsize`` in inches. We can also remove the legend using ``ax.legend().set_visible(False)``: + +.. code:: python + + fig, ax = plt.subplots(figsize=(5, 6)) + top_10.plot(kind='barh', y="Sales", x="Name", ax=ax) + ax.set_xlim([-10000, 140000]) + ax.set(title='2014 Revenue', xlabel='Total Revenue') + ax.legend().set_visible(False) + +There are plenty of things you probably want to do to clean up this plot. One of the biggest eye sores is the formatting of the Total Revenue numbers. Matplotlib can help us with this through the use of the ``FuncFormatter`` . This versatile function can apply a user defined function to a value and return a nicely formatted string to place on the axis. + +Here is a currency formatting function to gracefully handle US dollars in the several hundred thousand dollar range: + +.. code:: python + + def currency(x, pos): + """ + The two args are the value and tick position + """ + if x >= 1000000: + return '${:1.1f}M'.format(x*1e-6) + return '${:1.0f}K'.format(x*1e-3) + +Now that we have a formatter function, we need to define it and apply it to the x axis. Here is the full code: + +.. code:: python + + fig, ax = plt.subplots() + top_10.plot(kind='barh', y="Sales", x="Name", ax=ax) + ax.set_xlim([-10000, 140000]) + ax.set(title='2014 Revenue', xlabel='Total Revenue', ylabel='Customer') + formatter = FuncFormatter(currency) + ax.xaxis.set_major_formatter(formatter) + ax.legend().set_visible(False) + +That’s much nicer and shows a good example of the flexibility to define your own solution to the problem. + +The final customization feature I will go through is the ability to add annotations to the plot. In order to draw a vertical line, you can use ``ax.axvline()`` and to add custom text, you can use ``ax.text()``. + +For this example, we’ll draw a line showing an average and include labels showing three new customers. Here is the full code with comments to pull it all together. + +.. code:: python + + # Create the figure and the axes + fig, ax = plt.subplots() + + # Plot the data and get the average + top_10.plot(kind='barh', y="Sales", x="Name", ax=ax) + avg = top_10['Sales'].mean() + + # Set limits and labels + ax.set_xlim([-10000, 140000]) + ax.set(title='2014 Revenue', xlabel='Total Revenue', ylabel='Customer') + + # Add a line for the average + ax.axvline(x=avg, color='b', label='Average', linestyle='--', linewidth=1) + + # Annotate the new customers + for cust in [3, 5, 8]: + ax.text(115000, cust, "New Customer") + + # Format the currency + formatter = FuncFormatter(currency) + ax.xaxis.set_major_formatter(formatter) + + # Hide the legend + ax.legend().set_visible(False) + +.. image:: images/matplotlib_single.png + +While this may not be the most exciting plot it does show how much power you have when following this approach. + +Up until now, all the changes we have made have been with the individual plot. Fortunately, we also have the ability to add multiple plots on a figure as well as save the entire figure using various options. + +If we decided that we wanted to put two plots on the same figure, we should have a basic understanding of how to do it. First, create the figure, then the axes, then plot it all together. We can accomplish this using ``plt.subplots()``: + +.. code:: python + + fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(7, 4)) + +In this example, I’m using ``nrows`` and ``ncols`` to specify the size because this is very clear to the new user. In sample code you will frequently just see variables like 1,2. I think using the named parameters is a little easier to interpret later on when you’re looking at your code. + +I am also using ``sharey=True`` so that the y-axis will share the same labels. + +This example is also kind of nifty because the various axes get unpacked to ``ax0`` and ``ax1``. Now that we have these axes, you can plot them like the examples above but put one plot on ``ax0`` and the other on ``ax1``. + +.. code:: python + + # Get the figure and the axes + fig, (ax0, ax1) = plt.subplots(nrows=1,ncols=2, sharey=True, figsize=(7, 4)) + top_10.plot(kind='barh', y="Sales", x="Name", ax=ax0) + ax0.set_xlim([-10000, 140000]) + ax0.set(title='Revenue', xlabel='Total Revenue', ylabel='Customers') + + # Plot the average as a vertical line + avg = top_10['Sales'].mean() + ax0.axvline(x=avg, color='b', label='Average', linestyle='--', linewidth=1) + + # Repeat for the unit plot + top_10.plot(kind='barh', y="Purchases", x="Name", ax=ax1) + avg = top_10['Purchases'].mean() + ax1.set(title='Units', xlabel='Total Units', ylabel='') + ax1.axvline(x=avg, color='b', label='Average', linestyle='--', linewidth=1) + + # Title the figure + fig.suptitle('2014 Sales Analysis', fontsize=14, fontweight='bold'); + + # Hide the legends + ax1.legend().set_visible(False) + ax0.legend().set_visible(False) + +When writing code in a Jupyter notebook you can take advantage of the ``%matplotlib inline`` or ``%matplotlib notebook`` directives to render figures inline. More often, however, you probably want to save your images to disk. Matplotlib supports many different formats for saving files. You can use ``fig.canvas.get_supported_filetypes()`` to see what your system supports: + +.. code:: python + + fig.canvas.get_supported_filetypes() + +.. parsed-literal:: + + {'eps': 'Encapsulated Postscript', + 'jpeg': 'Joint Photographic Experts Group', + 'jpg': 'Joint Photographic Experts Group', + 'pdf': 'Portable Document Format', + 'pgf': 'PGF code for LaTeX', + 'png': 'Portable Network Graphics', + 'ps': 'Postscript', + 'raw': 'Raw RGBA bitmap', + 'rgba': 'Raw RGBA bitmap', + 'svg': 'Scalable Vector Graphics', + 'svgz': 'Scalable Vector Graphics', + 'tif': 'Tagged Image File Format', + 'tiff': 'Tagged Image File Format'} + +Since we have the fig object, we can save the figure using multiple options: + +.. code:: python + + fig.savefig('sales.png', transparent=False, dpi=80, bbox_inches="tight") + +This version saves the plot as a png with opaque background. I have also specified the dpi and bbox_inches="tight" in order to minimize excess white space. diff --git a/docs/quickstart.rst b/docs/quickstart.rst index 1a49c7798..fef589e46 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -3,11 +3,11 @@ Quick Start =========== -If you're new to Yellowbrick, this guide will get you started and help you include visualizers in your machine learning workflow. Before we get started, however, there are several notes about development environments that you should consider. +If you're new to Yellowbrick, this guide will get you started and help you include visualizers in your machine learning workflow. Before we begin, however, there are several notes about development environments that you should consider. -Yellowbrick has two primary dependencies: `Scikit-Learn `_ and `Matplotlib `_. If you do not have these Python packages, they will be installed alongside Yellowbrick. Note that Yellowbrick works best with Scikit-Learn version 0.18 or later and Matplotlib version 2.0 or later. Both of these packages require some C code to be compiled, which can be difficult on some systems, like Windows. If you're having trouble, try using a distribution of Python that includes these packages like `Anaconda `_. +Yellowbrick has two primary dependencies: `scikit-learn `_ and `matplotlib `_. If you do not have these Python packages, they will be installed alongside Yellowbrick. Note that Yellowbrick works best with scikit-learn version 0.18 or later and matplotlib version 2.0 or later. Both of these packages require some C code to be compiled, which can be difficult on some systems, like Windows. If you're having trouble, try using a distribution of Python that includes these packages like `Anaconda `_. -Yellowbrick is also commonly used inside of a `Jupyter Notebook `_ alongside `Pandas `_ data frames. Notebooks make it especially easy to coordinate code and visualizations, however you can also use Yellowbrick inside of regular Python scripts, either saving figures to disk or showing figures in a GUI window. If you're having trouble with this, please consult Matplotlib's `backends documentation `_. +Yellowbrick is also commonly used inside of a `Jupyter Notebook `_ alongside `Pandas `_ data frames. Notebooks make it especially easy to coordinate code and visualizations; however, you can also use Yellowbrick inside of regular Python scripts, either saving figures to disk or showing figures in a GUI window. If you're having trouble with this, please consult matplotlib's `backends documentation `_. .. NOTE:: Jupyter, Pandas, and other ancillary libraries like NLTK for text visualizers are not installed with Yellowbrick and must be installed separately. @@ -27,11 +27,11 @@ Note that Yellowbrick is an active project and routinely publishes new releases .. code-block:: bash - $ pip install -u yellowbrick + $ pip install -U yellowbrick -You can also use the ``-u`` flag to update Scikit-Learn, matplotlib, or any other third party utilities that work well with Yellowbrick to their latest versions. +You can also use the ``-U`` flag to update scikit-learn, matplotlib, or any other third party utilities that work well with Yellowbrick to their latest versions. -If you're using Windows or Anaconda, you can take advantage of the `conda `_ utility to install the `Anaconda Yellowbrick package `_: +If you're using Anaconda, you can take advantage of the `conda `_ utility to install the `Anaconda Yellowbrick package `_: .. code-block:: bash @@ -43,9 +43,9 @@ Once installed, you should be able to import Yellowbrick without an error, both Using Yellowbrick ----------------- -The Yellowbrick API is specifically designed to play nicely with Scikit-Learn. The primary interface is therefore a ``Visualizer`` -- an object that learns from data to produce a visualization. Visualizers are Scikit-Learn `Estimator `_ objects and have a similar interface along with methods for drawing. In order to use visualizers, you simply use the same workflow as with a Scikit-Learn model, import the visualizer, instantiate it, call the visualizer's ``fit()`` method, then in order to render the visualization, call the visualizer's ``poof()`` method, which does the magic! +The Yellowbrick API is specifically designed to play nicely with scikit-learn. The primary interface is therefore a ``Visualizer`` -- an object that learns from data to produce a visualization. Visualizers are scikit-learn `Estimator `_ objects and have a similar interface along with methods for drawing. In order to use visualizers, you simply use the same workflow as with a scikit-learn model, import the visualizer, instantiate it, call the visualizer's ``fit()`` method, then in order to render the visualization, call the visualizer's ``poof()`` method, which does the magic! -For example, there are several visualizers that act as transformers, used to perform feature analysis prior to fitting a model. Here is an example to visualize a high dimensional data set with parallel coordinates: +For example, there are several visualizers that act as transformers, used to perform feature analysis prior to fitting a model. The following example visualizes a high-dimensional data set with parallel coordinates: .. code-block:: python @@ -55,7 +55,7 @@ For example, there are several visualizers that act as transformers, used to per visualizer.fit_transform(X, y) visualizer.poof() -As you can see, the workflow is very similar to using a Scikit-Learn transformer, and visualizers are intended to be integrated along with Scikit-Learn utilities. Arguments that change how the visualization is drawn can be passed into the visualizer upon instantiation, similarly to how hyperparameters are included with Scikit-Learn models. +As you can see, the workflow is very similar to using a scikit-learn transformer, and visualizers are intended to be integrated along with scikit-learn utilities. Arguments that change how the visualization is drawn can be passed into the visualizer upon instantiation, similarly to how hyperparameters are included with scikit-learn models. The ``poof()`` method finalizes the drawing (adding titles, axes labels, etc) and then renders the image on your behalf. If you're in a Jupyter notebook, the image should just appear. If you're in a Python script, a GUI window should open with the visualization in interactive form. However, you can also save the image to disk by passing in a file path as follows: @@ -63,11 +63,11 @@ The ``poof()`` method finalizes the drawing (adding titles, axes labels, etc) an visualizer.poof(outpath="pcoords.png") -The extension of the filename will determine how the image is rendered, in addition to the .png extension, .pdf is also commonly used. +The extension of the filename will determine how the image is rendered. In addition to the .png extension, .pdf is also commonly used. -.. NOTE:: Data input to Yellowbrick is identical to that of Scikit-Learn: a dataset, ``X``, which is a two-dimensional matrix of shape ``(n,m)`` where ``n`` is the number of instances (rows) and ``m`` is the number of features (columns). The dataset ``X`` can be a Pandas DataFrame, a Numpy array, or even a Python list of lists. Optionally, a vector ``y``, which represents the target variable (in supervised learning), can also be supplied as input. The target ``y`` must have length ``n`` -- the same number of elements as rows in ``X`` and can be a Pandas Series, Numpy array, or Python list. +.. NOTE:: Data input to Yellowbrick is identical to that of scikit-learn: a dataset, ``X``, which is a two-dimensional matrix of shape ``(n,m)`` where ``n`` is the number of instances (rows) and ``m`` is the number of features (columns). The dataset ``X`` can be a Pandas DataFrame, a NumPy array, or even a Python list of lists. Optionally, a vector ``y``, which represents the target variable (in supervised learning), can also be supplied as input. The target ``y`` must have length ``n`` -- the same number of elements as rows in ``X`` and can be a Pandas Series, NumPy array, or Python list. -Visualizers can also wrap Scikit-Learn models for evaluation, hyperparameter tuning and algorithm selection. For example, to produce a visual heatmap of a classification report, displaying the precision, recall, F1 score, and support for each class in a classifier, wrap the estimator in a visualizer as follows: +Visualizers can also wrap scikit-learn models for evaluation, hyperparameter tuning and algorithm selection. For example, to produce a visual heatmap of a classification report, displaying the precision, recall, F1 score, and support for each class in a classifier, wrap the estimator in a visualizer as follows: .. code-block:: python @@ -85,7 +85,7 @@ Only two additional lines of code are required to add visual evaluation of the c .. TODO:: Walkthrough visual pipelines and text analysis. -The class-based API is meant to integrate with Scikit-Learn directly, however on occasion there are times when you just need a quick visualization. Yellowbrick supports quick functions for taking advantage of this directly. For example, the two visual diagnostics could have been instead implemented as follows: +The class-based API is meant to integrate with scikit-learn directly, however on occasion there are times when you just need a quick visualization. Yellowbrick supports quick functions for taking advantage of this directly. For example, the two visual diagnostics could have been instead implemented as follows: .. code-block:: python @@ -138,7 +138,7 @@ The machine learning workflow is the art of creating *model selection triples*, This figure shows us the Pearson correlation between pairs of features such that each cell in the grid represents two features identified in order on the x and y axes and whose color displays the magnitude of the correlation. A Pearson correlation of 1.0 means that there is a strong positive, linear relationship between the pairs of variables and a value of -1.0 indicates a strong negative, linear relationship (a value of zero indicates no relationship). Therefore we are looking for dark red and dark blue boxes to identify further. -In this chart we see that features 7 (temperature) and feature 9 (feelslike) have a strong correlation and also that feature 0 (season) has a strong correlation with feature 1 (month). This seems to make sense; the apparent temperature we feel outside depends on the actual temperature and other airquality factors, and the season of the year is described by the month! To dive in deeper, we can use the `JointPlotVisualizer `_ to inspect those relationships. +In this chart, we see that the features ``temp`` and ``feelslike`` have a strong correlation and also that the feature ``season`` has a strong correlation with the feature ``month``. This seems to make sense; the apparent temperature we feel outside depends on the actual temperature and other airquality factors, and the season of the year is described by the month! To dive in deeper, we can use the `JointPlotVisualizer `_ to inspect those relationships. .. code-block:: python @@ -219,6 +219,6 @@ We can now train our final model and visualize it with the ``PredictionError`` v The prediction error visualizer plots the actual (measured) vs. expected (predicted) values against each other. The dotted black line is the 45 degree line that indicates zero error. Like the residuals plot, this allows us to see where error is occurring and in what magnitude. -In this plot we can see that most of the instance density is less than 200 riders. We may want to try orthogonal matching pursuit or splines to fit a regression that takes into account more regionality. We can also note that that weird topology from the residuals plot seems to be fixed using the Ridge regression, and that there is a bit more balance in our model between large and small values. Potentially the Ridge regularization cured a covariance issue we had between two features. As we move forward in our analysis using other model forms, we can continue to utilize visualizers to quickly compare and see our results. +In this plot, we can see that most of the instance density is less than 200 riders. We may want to try orthogonal matching pursuit or splines to fit a regression that takes into account more regionality. We can also note that that weird topology from the residuals plot seems to be fixed using the Ridge regression, and that there is a bit more balance in our model between large and small values. Potentially the Ridge regularization cured a covariance issue we had between two features. As we move forward in our analysis using other model forms, we can continue to utilize visualizers to quickly compare and see our results. -Hopefully this workflow gives you an idea of how to integrate Visualizers into machine learning with Scikit-Learn and inspires you to use them in your work and write your own! For additional information on getting started with Yellowbrick, check out the :doc:`tutorial`. After that you can get up to speed on specific visualizers detailed in the :doc:`api/index`. +Hopefully this workflow gives you an idea of how to integrate Visualizers into machine learning with scikit-learn and inspires you to use them in your work and write your own! For additional information on getting started with Yellowbrick, check out the :doc:`tutorial`. After that you can get up to speed on specific visualizers detailed in the :doc:`api/index`. diff --git a/docs/requirements.txt b/docs/requirements.txt index a08bcbe28..02cff7e57 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,11 +1,11 @@ # Library Dependencies matplotlib>=1.5.1 -scipy>=0.17.1 -scikit-learn>=0.18 -numpy>=1.11.0 +scipy>=0.19 +scikit-learn>=0.19 +numpy>=1.13.0 cycler>=0.10.0 -# Documentation (uncomment to build documentation) -Sphinx>=1.5.5 +# Documentation Dependencies +Sphinx>=1.7.1 sphinx-rtd-theme>=0.2.4 -numpydoc>=0.6.0 +numpydoc>=0.7.0 diff --git a/docs/tutorial.rst b/docs/tutorial.rst index bb9729138..117f20258 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -1,4 +1,4 @@ -.. _examples/yellowbrick-modelselect: +.. -*- mode: rst -*- Model Selection Tutorial ======================== @@ -8,6 +8,24 @@ In this tutorial, we are going to look at scores for a variety of visual diagnostic tools from `Yellowbrick `__ in order to select the best model for our data. +The Model Selection Triple +-------------------------- +Discussions of machine learning are frequently characterized by a singular focus on model selection. Be it logistic regression, random forests, Bayesian methods, or artificial neural networks, machine learning practitioners are often quick to express their preference. The reason for this is mostly historical. Though modern third-party machine learning libraries have made the deployment of multiple models appear nearly trivial, traditionally the application and tuning of even one of these algorithms required many years of study. As a result, machine learning practitioners tended to have strong preferences for particular (and likely more familiar) models over others. + +However, model selection is a bit more nuanced than simply picking the "right" or "wrong" algorithm. In practice, the workflow includes: + + 1. selecting and/or engineering the smallest and most predictive feature set + 2. choosing a set of algorithms from a model family, and + 3. tuning the algorithm hyperparameters to optimize performance. + +The **model selection triple** was first described in a 2015 SIGMOD_ paper by Kumar et al. In their paper, which concerns the development of next-generation database systems built to anticipate predictive modeling, the authors cogently express that such systems are badly needed due to the highly experimental nature of machine learning in practice. "Model selection," they explain, "is iterative and exploratory because the space of [model selection triples] is usually infinite, and it is generally impossible for analysts to know a priori which [combination] will yield satisfactory accuracy and/or insights." + +Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search. By visualizing the model selection process, data scientists can steer towards final, explainable models and avoid pitfalls and traps. + +The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. Yellowbrick extends the Scikit-Learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process, providing visual diagnostics throughout the transformation of high dimensional data. + +.. _SIGMOD: http://cseweb.ucsd.edu/~arunkk/vision/SIGMODRecord15.pdf + About the Data -------------- @@ -371,7 +389,7 @@ in fact poisonous). -.. image:: images/modelselect_linear_svc.png +.. image:: images/tutorial/modelselect_linear_svc.png .. code:: python @@ -380,7 +398,7 @@ in fact poisonous). -.. image:: images/modelselect_nu_svc.png +.. image:: images/tutorial/modelselect_nu_svc.png .. code:: python @@ -389,7 +407,7 @@ in fact poisonous). -.. image:: images/modelselect_svc.png +.. image:: images/tutorial/modelselect_svc.png .. code:: python @@ -398,7 +416,7 @@ in fact poisonous). -.. image:: images/modelselect_sgd_classifier.png +.. image:: images/tutorial/modelselect_sgd_classifier.png .. code:: python @@ -407,7 +425,7 @@ in fact poisonous). -.. image:: images/modelselect_kneighbors_classifier.png +.. image:: images/tutorial/modelselect_kneighbors_classifier.png .. code:: python @@ -416,7 +434,7 @@ in fact poisonous). -.. image:: images/modelselect_logistic_regression_cv.png +.. image:: images/tutorial/modelselect_logistic_regression_cv.png .. code:: python @@ -425,7 +443,7 @@ in fact poisonous). -.. image:: images/modelselect_logistic_regression.png +.. image:: images/tutorial/modelselect_logistic_regression.png .. code:: python @@ -434,7 +452,7 @@ in fact poisonous). -.. image:: images/modelselect_bagging_classifier.png +.. image:: images/tutorial/modelselect_bagging_classifier.png .. code:: python @@ -443,7 +461,7 @@ in fact poisonous). -.. image:: images/modelselect_extra_trees_classifier.png +.. image:: images/tutorial/modelselect_extra_trees_classifier.png .. code:: python @@ -452,7 +470,7 @@ in fact poisonous). -.. image:: images/modelselect_random_forest_classifier.png +.. image:: images/tutorial/modelselect_random_forest_classifier.png Reflection diff --git a/examples/gary-mayfield/.gitignore b/examples/gary-mayfield/.gitignore new file mode 100644 index 000000000..c746e94d1 --- /dev/null +++ b/examples/gary-mayfield/.gitignore @@ -0,0 +1 @@ +train.csv \ No newline at end of file diff --git a/examples/gary-mayfield/testing.ipynb b/examples/gary-mayfield/testing.ipynb new file mode 100644 index 000000000..0bcdfaaae --- /dev/null +++ b/examples/gary-mayfield/testing.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Digit Classification

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.image as mpimg\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "from yellowbrick.classifier import ClassificationReport, ConfusionMatrix, ROCAUC, ClassBalance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Loading the data

\n", + "\n", + "
    \n", + "
  • We use panda's read_csv to read train.csv into a dataframe
  • \n", + "
  • Separate our images and labels for supervised learning.
  • \n", + "
  • Use train_test_split to break data into sets for training and testing
  • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "labeled_images = pd.read_csv('train.csv')\n", + "images = labeled_images.iloc[0:10000,1:].as_matrix()\n", + "labels = labeled_images.iloc[0:10000,:1].as_matrix()\n", + "labels = np.ravel(labels)\n", + "train_images, test_images, train_labels, test_labels = train_test_split(images, labels, train_size=0.75, test_size=0.25, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Viewing an image

" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 5000\n", + "img = train_images[i]\n", + "img=img.reshape((28,28))\n", + "plt.imshow(img, cmap='gray')\n", + "plt.title(train_labels[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EEEJYIAVSCCGEsEAKpBBCCGGBFEghhBDCAimQQgghhAVSIIUQQggLpEAKIYQQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bayes = GaussianNB()\n", + "classes = [0,1,2,3,4,5,6,7,8,9]\n", + "visualizer = ClassificationReport(bayes, classes=classes)\n", + "\n", + "visualizer.fit(train_images, train_labels)\n", + "visualizer.score(test_images, test_labels)\n", + "g = visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jlMep7IpXfqd5Kkb5npqqGPd/yrLiqXGM1ZhZbdQ4xurJXNapbLt9Krtq1arMnDnTXg8v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\nZhERERNRMYuIiJiIillERMRE/g/wnUuNhXSgtwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bayes = GaussianNB()\n", + "visualizer = ConfusionMatrix(bayes)\n", + "\n", + "visualizer.fit(train_images, train_labels)\n", + "visualizer.score(test_images, test_labels)\n", + "g = visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+Uley1pWc0rXNesVCvlRQ0P8/qS4uLlZkZKTCw8NVXFx80fLvFnR1WrVqpXr1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\nI0McAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "forest = RandomForestClassifier()\n", + "visualizer = ClassBalance(forest, classes=classes)\n", + "\n", + "visualizer.fit(train_images, train_labels)\n", + "visualizer.score(test_images, test_labels)\n", + "g = visualizer.poof()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/lwgray/ClassPredictionError.ipynb b/examples/lwgray/ClassPredictionError.ipynb new file mode 100644 index 000000000..bc3a648f3 --- /dev/null +++ b/examples/lwgray/ClassPredictionError.ipynb @@ -0,0 +1,103 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Class Prediction Error Visualizer\n", + "The `ClassPredictionError` visualizer is a ScoreVisualizer that takes a fitted scikit-learn classifier and a set of test X and y values and returns a stacked bar graph showing a color-coded break down of predicted classes compared to their actual classes. This visualizer provides a way to quickly understand how good your classifier is at predicting the right classes.\n", + "\n", + "Below is an example with the visualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from yellowbrick.classifier import ClassPredictionError\n", + "\n", + "from sklearn.datasets import make_classification\n", + "from sklearn.model_selection import train_test_split as tts\n", + "from sklearn.ensemble import RandomForestClassifier\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We create our own classification dataset\n", + "# The data set contains 5 classess and 1000 samples\n", + "# We use RandomForest for modeling the data\n", + "# I came up with arbitrary names for the classes\n", + "\n", + "X, y = make_classification(n_samples=1000, n_classes=5,\n", + " n_informative=3, n_clusters_per_class=1)\n", + "\n", + "# Perform 80/20 training/test split\n", + "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.20,\n", + " random_state=42)\n", + "\n", + "# Pass in model and classes to ClassPredictionError\n", + "visualizer = ClassPredictionError(RandomForestClassifier(),\n", + " classes=['apple', 'kiwi', 'pear',\n", + " 'banana', 'orange'])\n", + "# Fit the model\n", + "visualizer.fit(X_train, y_train)\n", + "\n", + "# Use test data to create visualization\n", + "visualizer.score(X_test, y_test)\n", + "\n", + "# Display visualization\n", + "visualizer.poof()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ndanielsen/ThresholdVisualizer Example.ipynb b/examples/ndanielsen/ThresholdVisualizer Example.ipynb new file mode 100644 index 000000000..71f207feb --- /dev/null +++ b/examples/ndanielsen/ThresholdVisualizer Example.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "sys.path.append(\"./../..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", + " \"This module will be removed in 0.20.\", DeprecationWarning)\n" + ] + } + ], + "source": [ + "%reload_ext yellowbrick\n", + "%matplotlib inline\n", + "# Imports\n", + "import pandas as pd \n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "\n", + "from sklearn.cross_validation import train_test_split \n", + "from sklearn.naive_bayes import BernoulliNB\n", + "from sklearn.metrics import precision_recall_curve \n", + "\n", + "from yellowbrick.style.palettes import get_color_cycle, PALETTES\n", + "from yellowbrick.style.colors import resolve_colors\n", + "from yellowbrick.base import ModelVisualizer\n", + "from yellowbrick.classifier import ThresholdVisualizer, thresholdviz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Retrieve Data Set\n", + "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data', header=None)\n", + "df.rename(columns={57:'is_spam'}, inplace=True)\n", + "\n", + "# Build the classifier and get the predictions\n", + "model = BernoulliNB(3)\n", + "\n", + "X = df[[col for col in df.columns if col != 'is_spam']] \n", + "y = df['is_spam']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tnGS0fHLfxnDYGKYA+Czwac/zfo88xOXPep73M8BV3/d/zfO8/wD4uud5GfAV\n4DeHMQijklts03abn//W+3SilB987gwnals3fJdCcG60woXxCidrJU4ewabPmlyYVN0xqu4YZ8c8\nkjTq2+f74aIqJcsyVPF7VNjsg7jzgD/kOCGFpOqOU91F1nGmsjzcNm4TJJ0i9LZDGLdZ6dxlpXMX\nyH0Oaz6KXmSQ088Mt6SNY1VwzPKeffeUUrTCFVrhCreX36bijFK26zhmGdeqUHbqj6ypHAWGJgB8\n38+AP3ff5rcG9v8s8LPDun+PvgbQ6bDcyTMuz4xsvnI3peCpyRrPnxzlmanarqJyNIeXnlN0J6xl\nMCuSLCSM84msFS7T7C4eWef2sJFCFpnJFUYGtiulCOI2zWCRVrhMWmgNWZbkYbf3ddhbu56Ba1VJ\nU8iWmwMO7oFSGdLElBaWsf1EPqUUrWB5XXfAnuCruePUSxM4ZuWxXPg99rObUc01gKzTphHkK7mN\nTD8nqi5/8OIUHzgx8kh+Ac3jRy8KCMA0LFwrX1Sc4AmSNGa1O083avbylcmylDgN+6/H1f+wW4QQ\neeitXeUET6zb14ui6vl34sLXE8RtunGDTpTHlYStrTOeHbNMya5RcycYK5/cUSRQprK+uej2cl7U\nr+ZOFJpCDdeqPBaRRY+9AJDlngDo0AgLAWCsTfBSCD755Ak++eSJorKkRrN9TMNiovqg43mQLEuJ\ns80FgVIqn/CyvJpoksWkaUyqknXX6ESNx16Y9JP4pAnWgwG3WZYye+c2E5Njaya8bM2M1/vswqRD\nJ2oSFqamu433OVV/ktHyyV2t5AebRUGel1FzJxgtTVEvTT1SBvtB8vgLgGphAup2BjSAfKIfcS1+\n4iMXtTNXM1SkNHBkeesDt6BnOunGzX5EVHtecn7iuY2OJkkjojQkToN+EmCShkfaZCWlgSEsSvbW\n7U6VUoRJh4XmTZbas9xY+g6zq1ep2COU7Xp/FS+FxDQcbMPdtnDIspTVzjyrnXmEELmWUTnFSGnq\nSJUGOToj3SVGzwfQ7dIMYgwhMGT++uMfvqAnf82RYdB00qMk55iont72NfLw2m5esiFq0o2atKPV\nXVUkPewIkWddnx3/ACfqT3C3cY1G9x6r3XlWu/MPHG8ZblFme4J6aXLbWdeD0UVSGn0BU3bqVJ2x\nQ52d/NgLAFlEAWVBl9UgwjbzDMdPPzvNmZFHX5VpNEeJPLw2j8kfLZ8A1oRCuml4rFr3Y7ekKunn\nb3SiBo2l79gGAAAgAElEQVTuwr6ZtGyzxLnx51DqA8RpQDtaJUqCPFxVZblTP1hmqT3LUnsWQ1qM\nl6cZr073fT7bIctSmsFacT0hBGV7hJHSJBVnDNeuYMrDYy567AVAXwMIAhpBjGMaPHdyhO86v73Y\nco3mcacnFPabTKU0u0t0otUNZUuShqx2722z5Mj2EEL0azDdj1KKbtRgpTvPUnuOe60b3GvdoGTX\nGS+fYqR8csfJY70clHa40t9mmy6nRp7c0ne0Hzz2AqCnAahCAJRtgx954dxjGdKl0RwlpDAYKU8x\nUp7a9JhMZbSCJRrBYl97MMQSUhp7ntAnhKDsjFB2Rjg18hSN7j2W2nM0gyVuRw1ur7yNFGa/f0Sl\naOlZtms7igiKkoAbi28QJh2mR54+0LnosRcAwjDAtsnCkGYYM1l19qUgm0ajeXSkkNRLk+uywRu3\nBB88+5EimS/sNwhSZP0yF0Hcph0uE8S7q/ElhWS0nGcJx2nIcvsOrXCZOO1dv0Wje69/bNkepeqM\nUnHHqNgj25rU765eI4w7PDFxaV1znf3ksRcAALgl0iive1LWiV0azZFnswZCZbu+7n2UBDS697iz\n+h7xLh3dluFwov5EP2ehF7bbjlZoB7l5pxUu0QqXoJF3Rxstn2CkfJKKXX+odrDSmUep17kw9cED\nKfV9PGZD1yXr5l2+KvbxeGSNRpPb2ydr5xgtn2Ju9SqLrdsPlEHfKbkfwcU2TzFWzvtwJGmUl6oO\nFlnt3mOhdYuF1q1CO8hNRaPlExs6lFe797ixeIUnJi7te3LZ8ZgNXRfVyPumagGg0Rw/TMPi3Phz\nTFTPstyeY6VzlygJtj5x29e3GSmfYKR8grPKoxks0+jeox2t0AqXaYXL3G28T8mqMV6ZZrx6el2t\noeX2XaQwODf+/L76BI7HbOiWIM7Vv6pzPB5Zo9E8SNmuUbZrnB59pghFvUcjWKQTNvbsHkJI6qUJ\n6qUJAJIsphUssdy+QyNY5PbK26x057k4+UGMgZDQxdYsSinOTTy/b+ag4zEbui4iTTHThJp7eGJw\nNRrNwSCEoOKMUHFGmOZpkoHSG0plzK5cZbXzYLLYbjCltc6hfHv5bVa781yd/xZPTn14XSnqpfYc\ncRpyYeqD+5IvcDyK37h56edSElI/JA1bNBrN4cE0rH7/CdeqcHHygzwx8cKel3WwDIcnJi4xUT1D\nELd45+6rBHF73THNYImrd18l2Ycy5MdEA8iTPtw4pO4e3rRsjUZzOBBCMF49Ta00QRC3SNKYJItp\nh8s0uouPlMEshODMqIclHe403uOdu6/wxMSldaGu3ahFnASY9nAXrMdDAJRyDaAch4xoE5BGo9km\n1n0d1aZq54oG9ct0oxZRGhAlAd2osSOnshCCkyMXsc0SN5ff5P2FbzM98jRTtfN9J/CjRitth+Mh\nAJyeCShgtKQ1AI1Gs3ukkEU7y4l128OkSztYphM3CeN2vopPN+iJOcBY5RSOVebawmvMrV5FkXGy\nnrdKVwy/auvxEACFCagUh4yWtAag0Wj2Hscs4VRLDDbPzJvbRMRJyHzzGqudew+cV7brPHPyZd65\n+wp3Vt+n5k5Stmv7Urb7eDiBS2sCYExrABqNZp+QwsAxS1TdUS5OfohTI09ueJxlOJwbfw5Q3Fz6\nTr8F6dDHN/Q7HAac3IZXjgMmKs4WB2s0Gs3eI4RgevQpLk59aMPoopo7wUTlDEHc5u7qe/tiAjoe\nAqDQANwkZKLsHvBgNBrNcWa0fAJv+qNUnJEH9k2PPo1tlJhvXmepNTv0sRwPAVD4AMraBKTRaA4B\njlnm6ZMvcWrk4rrSD4Y0OT32DAC3l98Z+jiOiQDIV/21JKBk61LQGo3m4JFCMj36NE9OfXidScg2\n8vkqU8PvlnasBEA1CbGM4/HIGo3maFAvTfLsqZdxrLwrW08j2LxF595xPGbDoitYNQ50JzCNRnPo\ncK0qz558mdJAd7FMaQGwN9TyJhHVPSz/qtFoNHuJadhcmHyxXyFUm4D2iLhSA6CSPDwrT6PRaA4S\n16pwZjR3Au91z+ONOBYCoGsVtYAiLQA0Gs3hZrx6BtAmoD2jbeShn2VtAtJoNIecXkSQzgTeI9qZ\nIDBt3FhrABqN5nBjFK0iFVoA7AmtOKVjOrix1gA0Gs3hRkcB7THtOCOwHGytAWg0mkOOEAIpDLLs\nCJeD9jxPAj8HfAgIgZ/yff/qwP4fAH4WEMAM8F/6vj8UnacZpnQsh4l2e+uDNRqN5oCR0jjyGsCP\nAa7v+x8H/nvgM70dnufVgL8D/JDv+x8FrgGTG11kL1iNUrqmixVHw7qFRqPR7BmGMI+8APgE8HkA\n3/e/Drw0sO8PAq8Dn/E878vAXd/3H+yUsEc0o5TAcpBZShZpIaDRaA43UhiofWgIM8yOYHVgdeB9\n6nme6ft+Qr7a/z7gw0AL+LLneV/zff/th13wypUruxpIJ87oWHkfgJkvfwk5Orar6xw1ZmZmDnoI\n+45+5uPB4/7MSZISq2Tdcw7jmYcpABpAbeC9LCZ/gEXgFd/37wB4nvclcmHwUAFw6dIlHGfnDV06\n3/gcXTM/78ULF3CfenrH1zhqzMzMcPny5YMexr6in/l4cBye+fqrv0uUdvvPudtnDsPwoQvnYZqA\nvgr8IIDneR8jN/n0+BZwyfO8Sc/zTOBjwBvDGkg7zugWGkCysjKs22g0Gs2eIKVxeEpBeJ73VzbY\n9re2OO2zQOB53u8Bfw/4S57n/YzneT/i+/488FeA3wC+AfxL3/d3Z9/ZBp0k65eDyJrNYd1Go9Fo\n9gQpjH1pCv9QE5Dnef8LcAL4Ec/znhnYZQEfBf7qZuf6vp8Bf+6+zW8N7P8F4Bd2OuDd0EkyOoUJ\nKGmsbnG0RqPRHCz7FQa6lQ/gXwDPA58C/t3A9gT4m8Ma1F7TiTNCu6cBNA54NBqNRvNwDJELAKXU\nUHuYPFQA+L7/CvCK53m/6vv+kV06d5KM2MkFQKpNQBqN5pAjRVEQjgzB8NrYbjcK6Mc8z/sM0Iuf\nFIDyff9INNjtxCmJk7dbS7UGoNFoDjlS5lNrlqVI4+AFwM8C3ztMR+0w6SQZqVsCtAag0WgOP7Ko\nCJqqdKix+tsNA719VCf/NMvoJgpVzjWATNcD0mg0hxxjQAMYJtsVLjOe5/0K8AWgX1PZ9/3/dyij\n2kNaYZ57pkqFCajdOsjhaDQazZb0NIBhRwJtVwCMAE3g4wPbFHDoBUAjiAEQ5QoAmRYAGo3mkLMm\nAIbbGH5bAsD3/T8L4HnemO/7y0Md0R7TCHMBYFarAGTtzkEOR6PRaLZEFm0h08NgAvI870PALwLl\noqzDl4A/4fv+t4Y5uL2gWQgAoycAOtoHoNFoDjfGPpmAtusE/j+A/xhY9H1/FvjzwD8c2qj2kJ4J\nyKnldemyrtYANBrN4aanAWTZcE1A2xUAZd/33+y98X3/N4Gdl+U8AHoCoFQvNIBu9yCHo9FoNFvS\n0wDSQyIAlgozkALwPO9PAUtDG9Ue0hMAtZKLLJW0ANBoNIeeXiLYsAXAdqOA/jzwz4AXPM9bAd4B\n/vTQRrWH9HwAIyULWa5oAaDRaA498jBpAL7vvwv8UWAcOA/8hO/7/jAHtlf0NIAR10ZWq2RheMAj\n0mg0modzqASA53n/NfBvfd9vk9cD+pzneT891JHtEWUr/yDPj1UwqjVUGGxxhkaj0RwsvUzgdMh5\nANv1Afw08EkA3/evA5eBvzCsQe0lf/F7nuOf/OELPD1ZR1bKWgPQaDSHnl410EOhAZA3gBmcOSMK\nh/Bhx5CS01Ubx5QYlSqkKVkUHfSwNBqNZlPkPmkA23UC/yrwO57n/VLx/seBfzWcIQ0HxzRIK3ko\naNpsIicmDnhEGo1GszH9UhCHRAP4q8A/ADzgSeAf+L7/Pw5tVEPAMWQ/G7jxxd8+4NFoNBrN5hiH\nLAz0Fd/3PwL8yjAHM0xsUyKLgnDNr3wJ9+lnqHzoDxzwqDQajeZB1qKADkcpiLue533S87wjkf27\nEY5pIKu5AFBRzMqv/2uCd68e8Kg0Go3mQXpO4GFXA92uAHiJvCl81/O81PO8zPO84bes3yMEYBsS\nc2QUgPjOLGQZS//yl+n6bx3s4DQajeY+1kxA8VDvs91y0FNDHcWQMQ2BEIKJP/mnmPvM/0rjd38b\n5+KTmGPjLP3KL1L92Mepf++nEEPsvanRaDTbZS0M9BCYgDzPsz3P+6ue5/0zz/Pqnuf9Nc/z7KGO\nbA+xpQDAeeIC0//d/4CKIpY/969QWQZA6+tfY+Hn/znB+++hkuGqXBqNRrMV/abwh6Qj2P8J3CNP\nAEuAp4H/B/hPhjSuPcUsBADA+B/94yz+wv9H8OYbtL76ZWqf/B4AohvXWfz5f46wLJwLF7HPncee\nPo11ahrpugc1dI1GcwxZ6wiWDfU+2xUAl33f/4jneT/g+37H87z/FHh9mAPbS6RYEwBGucLoH/lB\n7t2+RfOrXybrdilffglrYhIAFccE77xN8M7b/XPsM2cpvfhBSs+9gFE0l9doNJphYRwyDUDdZ/KZ\n5IhkAt+PMTqKNX2asR/9cZZ+9V/QnnmF9swrOBeepPLSyzhPPY2Q6y1j0e1bRLdv0fjN38g1gzNn\nsc+cxZycQrouwnEeOEej0Wh2y1oi2OEQAH8f+C3gpOd5f5+8O9jfGNqohoi0bcZ//I+x0Gxw8s//\nBYJ3fNqvvkJ47T3Ca+9hjI5S+QOXcT/wPObo6LpzVZoSXnuf8Nr7D1zXHJ+gdOlFypc+iDk2tl+P\no9FoHkN6TmB1SDSAXwTOkU/6fwH4i8A/Gdagho09fZr6pz7N6hc+T+kDz1P6wPPEd+/Q/tardK+8\nTuN3f5vG7/425vgEzlNPU37xg1gnTz30msnSIs0vfZHml76Ic+Eilcsv4z7rac1Ao9HsmDUn8OHw\nAfwjwCWvASSBPwM8RS4IjiSVl76L8Pp1gqLTpXXyFKM/8EPUv/dTdN/8DsG7V4muX6P9yjdov/IN\n7LPnqFx+CfvcE8hqFTHgV7ifnpZgjIzgPv1MP7xUmBbWqWms6dMYIyMPvYZGozm+7FdT+O0KgI/6\nvv+B3hvP8z4HXBnOkPYHIQRjP/QjLKcJwdV3+ttlqUTlIy9R+chLqCQhfP892jOvEr7/LtGtm/m5\nloUxNoY5No45No4xNo5ZvJe1Wn9iT1dXac+8uuH9petijk+snTteXKc+AoXWIExTRyBpNMeQwxYG\netPzvKd93+/VTjgJ3B7SmPYN6bqM/4mfoP2tV2n81hceyAEQpon7zLO4zzxLsrhI5zuvkywukCwt\nkS4vkczPP3BNYZqYJ07mIaSnz+CcfwKjXn/guCwIiGZvw+zDP0ZjdCx3Ok9Pg5n/uYSUGPWRXPiM\njmozk0bzmCEPmQZgAd/2PO9L5HkAnwDmPM/7HQDf97///hM8z5PAzwEfIu8l8FMDAmTwmH8D/Cvf\n9//hrp/iERBCUL38Ms75CzS/+mWCt99CxQ+mX5sTE9S/+3v775VSZO02yXIhDJaXSJaXSRYXie/M\nEc/ehplX8nOnpnCefBrn/BNYp09jFEXptkO6skx3ZZnudzaJuhViQwGg5uaY/9YrhclpGvvMWawT\nJ3W2s0ZzBFiLAjocPoCfve/9393GOT8GuL7vf9zzvI8BnwF+9L5j/ifyFpMHjjU1xfiP/ThZGBL4\nb9K58noe7aM2jnYVQmBUq3mJ6XPn1+1TcUx89y7R7C3C998nvHGN5Btfo/2NrwF5KKo5Oo4ouUi3\nhFGrrZmSxseRzg5q7imFSh9cJag0JZ6/Szx/F177/XzMVu6DMOojSMdGuG5fkzDHxhHbuK+wLKRl\nbX98Go1mxwghkMIYugYg1CYT3KPied7/BnzT9/1fKN7f9n3/zMD+PwZ8mFyjuPMwDWBmZuYC8GDs\n5ZBRnQ5cvwZzs9BsQKOBSnZRnClJ4O4duDcPC/fg3j14WG9i14VaHUbHYGoKJqegViMvawcYRt9P\ncBAI08zH6DhgO2BZffPUA0gDXAfcEtgDqSRCgm3l5296rszv4ZbANLXTXHOs+E73s1iizLPuH9mL\ny128fPnytfs3blcD2A11YHXgfep5nun7fuJ53iXgJ4E/Bvy17V7w0qVLODtZHRfMzMxw+fLlHZ8H\nwCc/2f/1AbPP4mJu9lleIlm49/A6QufOrXurkoQs6JJ1u6SNxppfobheuriQC4x3/AevJQTGyGju\nOK6PIEul/FWu5JrE+Bh3V1Y5ffr07p55RyiIo/y1Ge3mI99F2Dbm6FjhNB9FuiWE4+TaksiF4Rtv\nvMHzzz/fP0e6LrJSQZbKOzZ9yfLOzzkIHul/+4hyXJ757a//GxzL5vLly7t+5jAMuXJl83idYQqA\nBlAbeC993+/NkH8GOAP8DnABiDzPu+b7/ueHOJ5H5mFmn6zbpf3736L9rVdJV1a2vpZpYlRrGNUa\n1tSJPKh2AJWmJIsLRLO3iWdvk7baa/cKuqTLy4Tvvbv5DUyTu+Uywi1hlCtF1NLYuoglsdnK+xCi\nomjNpLXZMXNzLL+zR+W9hcAYyc1jslJFOjbScRGlEkZPqAxoNNJxMOojCNfVmopmT5DCODSZwLvh\nq8APA79U+AD6Xkzf9/9y73fP8/46uQnoUE/+WyFLJWof/0NUP/pxops3iG7dJLp9m+j2TbJOZ8fX\nE4aBdeIk1omT8OGPbHhMFoakzQZZEKC6XdJms6+dBIuLkKakK8sk83fh2gZjrtaQ5RLSLa1pEcXK\neisTkxACo1bPBcroKGIzv4BhHM0JUSnSlZVtCfNBhG1jVGt9zcOolJGlMrJSWedjkY6b+2N0Pohm\nE6Q0Dk0i2G74LPBpz/N+j9x4/Wc9z/sZ4Krv+782xPseKEJKnCcu4DxxAcjNRmmjQTw3Szw3SzR7\nm+j2rQ0jjXaKdByks3Grhtm5OU5OTwOQRVEuCJaXclPTyjLJ8jLp6grp6uqG4ax7hpSFgHER7oCQ\nMdfMK7JcxhybwBgbW5f3IEwLWcoF0lGZJFUUkSwtwtLito7Pn30ciucThpELjHIp1zJcNzdluaUi\nUGAMaR+ZSuyaR0AKgzgLh3qPoQkA3/cz4M/dt/kB/dz3/b8+rDEcBoQQmCMjmCMjlD7wHAAqy0gW\nF8nCABUEZN1OYftf7oeU7kZr2Axp28ieNrEBKsvIul1U0CXrBmRhsGn00+A56epqPt7VFUg3WKko\nRRaF+TN2umRLS1ted0OEyIVGETU1qG1Ix+mbtQgjus3Gti8ri6goWSrtfEx7RNbpEO3wby3L5b6G\npm7dYu4rXwSKyBG3hCznWsegFidsG+k6a2asciW/ziP4OYxSOc9DOQK+kqOIIQ3C5OiagDSbIKTE\nmnp4k7Ws213LLVhazM0R7RZZp0PW7eSTahjubkLdYDxGpQKV7ecn7AalFCoMyYLugMBQpK1W33SV\nRWuakYrjQih1+w7zeGUFHhIbvbyLcRlj4/nfo5eBLWS+8h4wi8mSiyiV89V4qYR03PUT7D5GZQ0u\nDlS3S9Zq9d+nzUd3uO8ImbdaFQOam1Gt5bknp6Yxxydyh7r2jeyY/QgD1QLgkCJLJezSGezTZzY9\nRimFCgLSxirJUq459CKRhO9TPn26r1Gkje2vjIeFEKJv0hjEnJjsm8y2Qim1TuhlQZd0KTdtrczf\npb5B1vVmZN1ubpabmyV4e4Noqx0grMJc1TdzubkPoHAkG2Njm/tJBjDKlQPVSHZMlpEsL63bFAPB\n/dFrUvYFp3CcQrjmPhJhbyOyT0qk4yAcB3XzBvH585hjY0cqkGGnSGEemkQwzSFECIEoVqn3VysV\nlRpjA2FjWRyTFiGmqheyqRRZFOVaRadNFoSoMCALAhhILsuiiKzbyVeeQ8ob2S5CiL69HPIJ0yhX\nsM+eY2Vujmrh99gJSimybmetw0WWkgVBrnl0u7m2FXTzz6DYroIQ1Tshy/qaTd/p/ijP6JYwx8Zy\nLWPr0dM4fRpzbCIXHAO+BNHzt9gWvRwSYRgI297/1XiWkbXbZO321sdugZqbY/47r+WRWrUaslzp\nO90Ho7XM0bEiLHr8SCYv5k5grQFo9gBpWcgTJ7BOnNj1NVRvogtzQTFouknbG9uxVRTlx6wsH9p+\ny0KIB8pzGLXtaxL3o9JCgHTapCsruW9nZRm1VUifUqTNFunyEvHdOw81dQ3S2qA/xUMRYiBvpBca\nnPtRjPHx/Nm3ISAO3KRTBFhsR7s16vV+tr116lReq+vEyUOtQeQVQdVQI4EO79NrDh1Cyr7GAWBt\n7FPeEJVluWaxlQaRpiRFxFK6utqfBJXK8lDXTrdYrT94HZFm2L38DKVIm438GvuMMAyMSgWjUslz\nPHaBUmprAaAUc9euMWGZuWYXrGWX54mGhbYykKSnkpQsyB3+abNBsnBvV+MDCnNeCemuJeMBfT+J\ncNyNfSPFyr3nvDfGhr9C7wuK69ceGEv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "ThresholdVisualizer(model=None, n_trials=100, quantiles=(0.1, 0.5, 0.9),\n", + " random_state=None, test_size_percent=0.1)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viz = ThresholdVisualizer(model, n_trials=100, title=\"Spam vs Ham Thresholds\", quantiles=(0.10, 0.5, .9))\n", + "viz.fit_poof(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KR5v38kFJKcFwy2+rPreTVLeLGDHe1Xv4aPNerM4j3SZ/UAYXTRvNjNE5NDxmw9EYdaEI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\nGC5Xky7BTdIXDrPCakNxDcp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"pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/pbs929/features-pipeline.ipynb b/examples/pbs929/features-pipeline.ipynb new file mode 100644 index 000000000..5cfa0b729 --- /dev/null +++ b/examples/pbs929/features-pipeline.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking `examples/examples.ipynb` as a starting point. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", + " \"This module will be removed in 0.20.\", DeprecationWarning)\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"..\")\n", + "sys.path.append(\"../..\")\n", + "\n", + "import numpy as np \n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from download import download_all \n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + " \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n", + "}\n", + "\n", + "def load_data(name, download=True):\n", + " \"\"\"\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files. \n", + " \"\"\"\n", + " # Get the path from the datasets \n", + " path = datasets[name]\n", + " \n", + " # Check if the data exists, otherwise download or raise \n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all() \n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + " \n", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20560\n" + ] + }, + { + "data": { + "text/html": [ + "
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42015-02-04 17:55:0023.1027.2000426.0704.500.0047571
\n", + "
" + ], + "text/plain": [ + " datetime temperature relative humidity light C02 \\\n", + "0 2015-02-04 17:51:00 23.18 27.2720 426.0 721.25 \n", + "1 2015-02-04 17:51:59 23.15 27.2675 429.5 714.00 \n", + "2 2015-02-04 17:53:00 23.15 27.2450 426.0 713.50 \n", + "3 2015-02-04 17:54:00 23.15 27.2000 426.0 708.25 \n", + "4 2015-02-04 17:55:00 23.10 27.2000 426.0 704.50 \n", + "\n", + " humidity occupancy \n", + "0 0.004793 1 \n", + "1 0.004783 1 \n", + "2 0.004779 1 \n", + "3 0.004772 1 \n", + "4 0.004757 1 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the classification data set\n", + "data = load_data('occupancy') \n", + "print(len(data))\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target \n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "classes = ['unoccupied', 'occupied']\n", + "\n", + "# Get a small sample for demo-ing\n", + "X = data.head(1000)[features]\n", + "y = data.head(1000).occupancy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot all the things" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from yellowbrick.features import (Rank1D, \n", + " Rank2D,\n", + " ScatterViz,\n", + " RadViz,\n", + " ParallelCoordinates,\n", + " JointPlotVisualizer,\n", + " PCADecomposition\n", + " )\n", + "from yellowbrick.pipeline import VisualPipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "dataframe version" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3, 2, figsize=[12, 16])\n", + "pl = VisualPipeline([\n", + " ('rank1d', Rank1D(features=features, ax=ax[0,0])),\n", + " ('rank2d', Rank2D(features=features, ax=ax[1,0])),\n", + " ('pcoords', ParallelCoordinates(features=features, classes=classes, ax=ax[0,1])),\n", + " ('radviz', RadViz(features=features, classes=classes, ax=ax[1,1])),\n", + " ('scatter', ScatterViz(features=features[:2], classes=classes, ax=ax[2,0])),\n", + "# ('joinplot', JointPlotVisualizer(feature=features[0], ax=ax[2,1])),\n", + " ('pca', PCADecomposition(ax=ax[2,1]))\n", + "])\n", + "pl.fit_transform_poof(X, y);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "numpy version" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3, 2, figsize=[12, 16])\n", + "pl = VisualPipeline([\n", + " ('rank1d', Rank1D(features=features, ax=ax[0,0])),\n", + " ('rank2d', Rank2D(features=features, ax=ax[1,0])),\n", + " ('pcoords', ParallelCoordinates(features=features, classes=classes, ax=ax[0,1])),\n", + " ('radviz', RadViz(features=features, classes=classes, ax=ax[1,1])),\n", + " ('scatter', ScatterViz(x=0, y=1, classes=classes, ax=ax[2,0])),\n", + "# ('joinplot', JointPlotVisualizer(feature=features[0], cvax=ax[2,1])),\n", + " ('pca', PCADecomposition(ax=ax[2,1]))\n", + "])\n", + "pl.fit_transform_poof(X.values, y);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/pbs929/gridsearch.ipynb b/examples/pbs929/gridsearch.ipynb new file mode 100644 index 000000000..4bd3493f2 --- /dev/null +++ b/examples/pbs929/gridsearch.ipynb @@ -0,0 +1,945 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "\n", + "sys.path.append(\"..\")\n", + "sys.path.append(\"../..\")\n", + "\n", + "import numpy as np \n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Occupancy data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "## [from examples/examples.py]\n", + "from download import download_all \n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + " \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n", + "}\n", + "\n", + "def load_data(name, download=True):\n", + " \"\"\"\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files. \n", + " \"\"\"\n", + " # Get the path from the datasets \n", + " path = datasets[name]\n", + " \n", + " # Check if the data exists, otherwise download or raise \n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all() \n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + " \n", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20560\n" + ] + }, + { + "data": { + "text/html": [ + "
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datetimetemperaturerelative humiditylightC02humidityoccupancy
02015-02-04 17:51:0023.1827.2720426.0721.250.0047931
12015-02-04 17:51:5923.1527.2675429.5714.000.0047831
22015-02-04 17:53:0023.1527.2450426.0713.500.0047791
32015-02-04 17:54:0023.1527.2000426.0708.250.0047721
42015-02-04 17:55:0023.1027.2000426.0704.500.0047571
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" + ], + "text/plain": [ + " datetime temperature relative humidity light C02 \\\n", + "0 2015-02-04 17:51:00 23.18 27.2720 426.0 721.25 \n", + "1 2015-02-04 17:51:59 23.15 27.2675 429.5 714.00 \n", + "2 2015-02-04 17:53:00 23.15 27.2450 426.0 713.50 \n", + "3 2015-02-04 17:54:00 23.15 27.2000 426.0 708.25 \n", + "4 2015-02-04 17:55:00 23.10 27.2000 426.0 704.50 \n", + "\n", + " humidity occupancy \n", + "0 0.004793 1 \n", + "1 0.004783 1 \n", + "2 0.004779 1 \n", + "3 0.004772 1 \n", + "4 0.004757 1 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the classification data set\n", + "data = load_data('occupancy') \n", + "print(len(data))\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target \n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "classes = ['unoccupied', 'occupied']\n", + "\n", + "# Searching the whole dataset takes a while (15 mins on my mac)... \n", + "# For demo purposes, we reduce the size\n", + "X = data[features].head(2000)\n", + "y = data.occupancy.head(2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter projection\n", + "\n", + "* Because the visualizer only displays results across two parameters, we need some way of reducing the dimension to 2. \n", + "* Our approach: for each value of the parameters of interest, display the _maximum_ score across all the other parameters.\n", + "\n", + "Here we demo the `param_projection` utility function that does this" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", + " \"This module will be removed in 0.20.\", DeprecationWarning)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.svm import SVC\n", + "\n", + "from yellowbrick.gridsearch.base import param_projection" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit a vanilla grid search... these are the example parameters from sklearn's gridsearch docs.\n", + "svc = SVC()\n", + "grid = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]},\n", + " {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n", + "gs = GridSearchCV(svc, grid, n_jobs=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 147 ms, sys: 28.2 ms, total: 175 ms\n", + "Wall time: 31 s\n" + ] + }, + { + "data": { + "text/plain": [ + "GridSearchCV(cv=None, error_score='raise',\n", + " estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False),\n", + " fit_params=None, iid=True, n_jobs=4,\n", + " param_grid=[{'kernel': ['rbf'], 'gamma': [0.001, 0.0001], 'C': [1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring=None, verbose=0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "gs.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As of Scikit-learn 0.18, `cv_results` has replaced `grid_scores` as the grid search results format" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pschafer/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n", + " warnings.warn(*warn_args, **warn_kwargs)\n", + "/Users/pschafer/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n", + " warnings.warn(*warn_args, **warn_kwargs)\n", + "/Users/pschafer/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n", + " warnings.warn(*warn_args, **warn_kwargs)\n", + "/Users/pschafer/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n", + " warnings.warn(*warn_args, **warn_kwargs)\n", + "/Users/pschafer/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n", + " warnings.warn(*warn_args, **warn_kwargs)\n" + ] + }, + { + "data": { + "text/plain": [ + "{'mean_fit_time': array([0.02446747, 0.01360734, 0.02218564, 0.00889039, 0.02143168,\n", + " 0.0146842 , 0.03453048, 0.02234364, 1.94032502, 3.24313124,\n", + " 4.1275959 , 3.99856925]),\n", + " 'mean_score_time': array([0.00456238, 0.00284052, 0.00362539, 0.00334334, 0.00262896,\n", + " 0.00233173, 0.00262411, 0.00177709, 0.0011073 , 0.00116841,\n", + " 0.00101606, 0.00154575]),\n", + " 'mean_test_score': array([0.895 , 0.891 , 0.9055, 0.8995, 0.8995, 0.914 , 0.9045, 0.9185,\n", + " 0.915 , 0.9135, 0.9105, 0.9115]),\n", + " 'mean_train_score': array([0.9847535 , 0.97950518, 0.990002 , 0.98125506, 0.99450106,\n", + " 0.98475368, 0.99625056, 0.99275119, 0.96525668, 0.96550693,\n", + " 0.96525687, 0.96500699]),\n", + " 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100, 1000, 1000, 1, 10, 100, 1000],\n", + " mask=[False, False, False, False, False, False, False, False,\n", + " False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object),\n", + " 'param_gamma': masked_array(data=[0.001, 0.0001, 0.001, 0.0001, 0.001, 0.0001, 0.001,\n", + " 0.0001, --, --, --, --],\n", + " mask=[False, False, False, False, False, False, False, False,\n", + " True, True, True, True],\n", + " fill_value='?',\n", + " dtype=object),\n", + " 'param_kernel': masked_array(data=['rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf',\n", + " 'linear', 'linear', 'linear', 'linear'],\n", + " mask=[False, False, False, False, False, False, False, False,\n", + " False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object),\n", + " 'params': [{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'},\n", + " {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'},\n", + " {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'},\n", + " {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'},\n", + " {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'},\n", + " {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'},\n", + " {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'},\n", + " {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'},\n", + " {'C': 1, 'kernel': 'linear'},\n", + " {'C': 10, 'kernel': 'linear'},\n", + " {'C': 100, 'kernel': 'linear'},\n", + " {'C': 1000, 'kernel': 'linear'}],\n", + " 'rank_test_score': array([11, 12, 7, 9, 9, 3, 8, 1, 2, 4, 6, 5], dtype=int32),\n", + " 'split0_test_score': array([0.85907046, 0.82158921, 0.89805097, 0.84557721, 0.87256372,\n", + " 0.88905547, 0.89655172, 0.90254873, 0.91004498, 0.93103448,\n", + " 0.90854573, 0.91754123]),\n", + " 'split0_train_score': array([0.98349587, 0.97974494, 0.987997 , 0.98274569, 0.99324831,\n", + " 0.98424606, 0.99474869, 0.99024756, 0.95723931, 0.95873968,\n", + " 0.9579895 , 0.9579895 ]),\n", + " 'split1_test_score': array([0.82608696, 0.85307346, 0.82608696, 0.85307346, 0.82608696,\n", + " 0.85307346, 0.82608696, 0.85307346, 0.89055472, 0.89055472,\n", + " 0.89055472, 0.89055472]),\n", + " 'split1_train_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n", + " 'split2_test_score': array([1. , 0.9984985 , 0.99249249, 1. , 1. ,\n", + " 1. , 0.99099099, 1. , 0.94444444, 0.91891892,\n", + " 0.93243243, 0.92642643]),\n", + " 'split2_train_score': array([0.97076462, 0.95877061, 0.982009 , 0.96101949, 0.99025487,\n", + " 0.97001499, 0.994003 , 0.988006 , 0.93853073, 0.93778111,\n", + " 0.93778111, 0.93703148]),\n", + " 'std_fit_time': array([0.00783314, 0.00750969, 0.00391411, 0.00338101, 0.0082266 ,\n", + " 0.00776113, 0.02039378, 0.01587166, 1.54496874, 2.5505148 ,\n", + " 2.94695329, 2.82673379]),\n", + " 'std_score_time': array([1.04638701e-03, 1.01035138e-03, 1.00287903e-03, 2.10844017e-03,\n", + " 5.38548407e-04, 6.35982442e-04, 6.02011534e-04, 5.75618900e-04,\n", + " 2.99961800e-04, 8.37446367e-05, 3.58759960e-04, 6.80733055e-04]),\n", + " 'std_test_score': array([0.07540321, 0.07703631, 0.06813033, 0.07107689, 0.07350339,\n", + " 0.06251678, 0.06754775, 0.06102718, 0.02227512, 0.01696758,\n", + " 0.01715017, 0.01525357]),\n", + " 'std_train_score': array([0.01196838, 0.01683268, 0.00748038, 0.01594859, 0.00407586,\n", + " 0.01224659, 0.00266867, 0.00520673, 0.02572711, 0.02584756,\n", + " 0.02591536, 0.02618132])}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gs.cv_results_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Demo the use of param_projection... It identifies the unique values of the the two parameter values and gets the best score for each (here taking the max over `gamma` values)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([1, 10, 100, 1000],\n", + " ['linear', 'rbf'],\n", + " array([[0.915 , 0.9135, 0.9105, 0.9115],\n", + " [0.895 , 0.9055, 0.914 , 0.9185]]))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_1 = 'C'\n", + "param_2 = 'kernel'\n", + "param_1_vals, param2_vals, best_scores = param_projection(gs.cv_results_, param_1, param_2)\n", + "param_1_vals, param2_vals, best_scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GridSearchColorPlot\n", + "\n", + "This visualizer wraps the GridSearchCV object and plots the values obtained from `param_projection`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from yellowbrick.gridsearch import GridSearchColorPlot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'C', 'kernel')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'kernel', 'C')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'C', 'gamma')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If there are missing values in the grid, these are filled with a hatch (see https://stackoverflow.com/a/35905483/7637679)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'kernel', 'gamma')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose a different metric..." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'C', 'kernel', metric='mean_fit_time')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quick Method\n", + "\n", + "Because grid search can take a long time and we may want to interactively cut the results a few different ways, by default the quick method assumes that the GridSearchCV object is **already fit** if no X data is passed in. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from yellowbrick.gridsearch import gridsearch_color_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 84.1 ms, sys: 2.95 ms, total: 87.1 ms\n", + "Wall time: 87.4 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "# passing the GridSearchCV object pre-fit\n", + "gridsearch_color_plot(gs, 'C', 'kernel')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 73.3 ms, sys: 3.95 ms, total: 77.2 ms\n", + "Wall time: 79.8 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "# trying a different cut across parameters\n", + "gridsearch_color_plot(gs, 'C', 'gamma')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 206 ms, sys: 35.1 ms, total: 241 ms\n", + "Wall time: 31 s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "# When we provide X, the `fit` method will call fit (takes longer)\n", + "gridsearch_color_plot(gs, 'C', 'kernel', X=X, y=y)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 67.5 ms, sys: 3.4 ms, total: 70.9 ms\n", + "Wall time: 77.7 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "# can also choose a different metric\n", + "gridsearch_color_plot(gs, 'C', 'kernel', metric='mean_fit_time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bad param values" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "YellowbrickKeyError", + "evalue": "\"Parameter 'foo' does not exist in the grid search results\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0mx_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'param_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx_param\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwarn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mwarn_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDeprecationDict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'param_foo'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mYellowbrickKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mgs_viz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchColorPlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'kernel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgs_viz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 187\u001b[0m \"\"\"\n\u001b[1;32m 188\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/pcolor.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;31m# Project the grid search results to 2 dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m x_vals, y_vals, best_scores = self.param_projection(\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m )\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(self, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mArray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mscores\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mdisplayed\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meach\u001b[0m \u001b[0mparameter\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mpair\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \"\"\"\n\u001b[0;32m--> 164\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mparam_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m raise YellowbrickKeyError(\"Parameter '{}' does not exist in the grid \"\n\u001b[0;32m---> 61\u001b[0;31m \"search results\".format(x_param))\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0my_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'param_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my_param\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mYellowbrickKeyError\u001b[0m: \"Parameter 'foo' does not exist in the grid search results\"" + ] + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'foo', 'kernel')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "YellowbrickKeyError", + "evalue": "\"Parameter 'foo' does not exist in the grid search results\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0my_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'param_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my_param\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwarn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mwarn_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDeprecationDict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'param_foo'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mYellowbrickKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mgs_viz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchColorPlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgs_viz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 187\u001b[0m \"\"\"\n\u001b[1;32m 188\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/pcolor.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;31m# Project the grid search results to 2 dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m x_vals, y_vals, best_scores = self.param_projection(\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m )\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(self, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mArray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mscores\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mdisplayed\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meach\u001b[0m \u001b[0mparameter\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mpair\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \"\"\"\n\u001b[0;32m--> 164\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mparam_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m raise YellowbrickKeyError(\"Parameter '{}' does not exist in the grid \"\n\u001b[0;32m---> 66\u001b[0;31m \"search results\".format(y_param))\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mYellowbrickKeyError\u001b[0m: \"Parameter 'foo' does not exist in the grid search results\"" + ] + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'C', 'foo')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bad metric option" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "ename": "YellowbrickKeyError", + "evalue": "\"Metric 'foo' does not exist in the grid search results\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.pyenv/versions/3.6.2/envs/yellowbrick/lib/python3.6/site-packages/sklearn/utils/deprecation.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwarn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mwarn_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDeprecationDict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'foo'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mYellowbrickKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mgs_viz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchColorPlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'kernel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgs_viz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 187\u001b[0m \"\"\"\n\u001b[1;32m 188\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/pcolor.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;31m# Project the grid search results to 2 dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m x_vals, y_vals, best_scores = self.param_projection(\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m )\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(self, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mArray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mscores\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mdisplayed\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meach\u001b[0m \u001b[0mparameter\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mpair\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \"\"\"\n\u001b[0;32m--> 164\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mparam_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m raise YellowbrickKeyError(\"Metric '{}' does not exist in the grid \"\n\u001b[0;32m---> 71\u001b[0;31m \"search results\".format(metric))\n\u001b[0m\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;31m# Get unique, unmasked values of the two display parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mYellowbrickKeyError\u001b[0m: \"Metric 'foo' does not exist in the grid search results\"" + ] + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'C', 'kernel', metric='foo')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Metric option exists in cv_results but is not numeric -> not valid" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "ename": "YellowbrickValueError", + "evalue": "Cannot display grid search results for metric 'param_kernel': result values may not all be numeric", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0mbest_scores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_scores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'linear'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mYellowbrickValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mgs_viz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchColorPlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'kernel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'param_kernel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgs_viz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 187\u001b[0m \"\"\"\n\u001b[1;32m 188\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/pcolor.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;31m# Project the grid search results to 2 dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m x_vals, y_vals, best_scores = self.param_projection(\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m )\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(self, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mArray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mscores\u001b[0m \u001b[0mto\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mdisplayed\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meach\u001b[0m \u001b[0mparameter\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mpair\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \"\"\"\n\u001b[0;32m--> 164\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mparam_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_param\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/yellowbrick/yellowbrick/gridsearch/base.py\u001b[0m in \u001b[0;36mparam_projection\u001b[0;34m(cv_results, x_param, y_param, metric)\u001b[0m\n\u001b[1;32m 107\u001b[0m raise YellowbrickValueError(\n\u001b[1;32m 108\u001b[0m \u001b[0;34m\"Cannot display grid search results for metric '{}': \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m \u001b[0;34m\"result values may not all be numeric\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 110\u001b[0m )\n\u001b[1;32m 111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mYellowbrickValueError\u001b[0m: Cannot display grid search results for metric 'param_kernel': result values may not all be numeric" + ] + } + ], + "source": [ + "gs_viz = GridSearchColorPlot(gs, 'C', 'kernel', metric='param_kernel')\n", + "gs_viz.fit(X, y).poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/requirements.txt b/requirements.txt index a5691b889..d11d4bd01 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,26 +1,30 @@ -# Dependencies +## Dependencies matplotlib>=1.5.1 -scipy>=0.18 -scikit-learn>=0.18 -numpy>=1.11.0 +scipy>=0.19 +scikit-learn>=0.19 +numpy>=1.13.0 cycler>=0.10.0 -# Testing Requirements (uncomment for development) -#nose>=1.3.7 -#coverage>=4.1 -#requests>=2.10.0 +## Testing Requirements (uncomment for development) +#pytest>=3.4.1 +#pytest-cov>=2.5.1 +#pytest-flakes>=2.0.0 +#pytest-spec>=1.1.0 +#coverage>=4.4.1 +#requests>=2.18.3 +#six==1.11.0 -# Python 2 Testing Requirements +## Python 2 Testing Requirements #mock>=2.0.0 -# Optional Testing Dependencies (uncomment for development) -#nltk>=3.0 -#pandas>=0.18 +## Optional Testing Dependencies (uncomment for development) +#nltk>=3.2 +#pandas>=0.20 ## Documentation (uncomment to build documentation) -#Sphinx>=1.5.5 +#Sphinx>=1.7.1 #sphinx-rtd-theme>=0.2.4 -#numpydoc>=0.6.0 +#numpydoc>=0.7.0 -# Build Requirements (uncomment for deployment) +## Build Requirements (uncomment for deployment) #wheel>=0.29.0 diff --git a/setup.cfg b/setup.cfg index c8d57ee9b..46ae23dce 100644 --- a/setup.cfg +++ b/setup.cfg @@ -7,3 +7,21 @@ universal = 1 [test] local_freetype = True tests = True + +[aliases] +test=pytest + +[tool:pytest] +addopts = --verbose --cov=yellowbrick --flakes --spec +python_files = tests/* +flakes-ignore = + __init__.py UnusedImport + __init__.py ImportStarUsed + test_*.py ImportStarUsed + test_*.py ImportStarUsage + examples/* ALL + tests/checks.py ALL +spec_header_format = {class_name} ({path}) +filterwarnings = + once::DeprecationWarning + once::PendingDeprecationWarning diff --git a/setup.py b/setup.py index 5d9deaca8..77cceed09 100755 --- a/setup.py +++ b/setup.py @@ -20,7 +20,6 @@ ########################################################################## import os -import re import codecs from setuptools import setup @@ -127,8 +126,11 @@ def get_requires(path=REQUIRE_PATH): "keywords": KEYWORDS, "zip_safe": False, "scripts": [], + "setup_requires":["pytest-runner"], + "tests_require":["pytest"], } + ########################################################################## ## Run setup script ########################################################################## diff --git a/tests/.gitignore b/tests/.gitignore index a2ded98bb..cd203fbe0 100644 --- a/tests/.gitignore +++ b/tests/.gitignore @@ -1 +1,5 @@ +# Dataset Fixture Downloads fixtures/* + +# VisualTestCase Outputs +actual_images/* diff --git a/tests/__init__.py b/tests/__init__.py index 8d7d4aca3..5317ab596 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -28,7 +28,7 @@ ## Test Constants ########################################################################## -EXPECTED_VERSION = "0.5" +EXPECTED_VERSION = "0.6" ########################################################################## diff --git a/tests/base.py b/tests/base.py index 3557c310f..49c600c71 100644 --- a/tests/base.py +++ b/tests/base.py @@ -52,8 +52,15 @@ def setUp(self): """ Assert tthat the backend is 'Agg' and close all previous plots """ + # Reset the matplotlib environment + plt.cla() # clear current axis + plt.clf() # clear current figure plt.close("all") # close all existing plots - rcParams['font.family'] = 'DejaVu Sans' # Travis-CI does not have san-sarif + + # Travis-CI does not have san-serif + rcParams['font.family'] = 'DejaVu Sans' + + # Assert that the backend is agg self.assertEqual(self._backend, 'agg') super(VisualTestCase, self).setUp() @@ -94,7 +101,7 @@ def _base_img_path(self, extension='.png'): base_img = os.path.join(base_results, test_func_name + extension) return base_img - def assert_images_similar(self, visualizer, tol=0.01): + def assert_images_similar(self, visualizer=None, ax=None, tol=0.01): """Accessible testing method for testing generation of a Visualizer. Requires the placement of a baseline image for comparison in the @@ -118,19 +125,26 @@ def assert_images_similar(self, visualizer, tol=0.01): An instantiated yellowbrick visualizer that has been fitted, transformed and had all operations except for poof called on it. + ax : matplotlib Axes, default: None + The axis to plot the figure on. + tol : float The tolerance (a color value difference, where 255 is the maximal difference). The test fails if the average pixel difference is greater than this value. - """ + if visualizer is None and ax is None: + raise ValueError("must supply either a visualizer or axes") + + ax = ax or visualizer.ax + # inspect is used to locate and organize the baseline images and actual # test generated images for comparison inspect_obj = inspect.stack() module_path, test_func_name = self._setup_imagetest(inspect_obj=inspect_obj) # clean and remove the textual/ formatting elements from the visualizer - remove_ticks_and_titles(visualizer.ax) + remove_ticks_and_titles(ax) plt.savefig(self._actual_img_path()) base_image = self._base_img_path() diff --git a/tests/baseline_images/test_classifier/test_class_prediction_error/test_class_prediction_error_quickmethod.png b/tests/baseline_images/test_classifier/test_class_prediction_error/test_class_prediction_error_quickmethod.png new file mode 100644 index 0000000000000000000000000000000000000000..7c03ed01c39b608f92a515e35de1094ae5074008 GIT 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definitions for Yellowbrick PyTest +# +# Author: Benjamin Bengfort +# Created: Fri Mar 02 11:53:55 2018 -0500 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: conftest.py [] benjamin@bengfort.com $ + +""" +Global definitions for Yellowbrick PyTest +""" + +########################################################################## +## Imports +########################################################################## + +import os + + +########################################################################## +## PyTest Hooks +########################################################################## + +def pytest_itemcollected(item): + """ + A reporting hook that is called when a test item is collected. + + This function can do many things to modify test output and test + handling and in the future can be modified with those tasks. Right now + we're currently only using this function to create node ids that + pytest-spec knows how to parse and display as spec-style output. + + .. seealso:: https://stackoverflow.com/questions/28898919/use-docstrings-to-list-tests-in-py-test + """ + + # Ignore Session and PyFlake tests that are generated automatically + if not hasattr(item.parent, 'obj'): + return + + # Collect test objects to inspect + parent = item.parent.obj + node = item.obj + + # Goal: produce a parsable string of the relative path, parent docstring + # or class name, and the docstring of the test case, then set the nodeid + # so that pytest-spec will correctly parse the information. + path = os.path.relpath(str(item.fspath)) + prefix = parent.__doc__ or getattr(parent, '__name__', parent.__class__.__name__) + suffix = node.__doc__ if node.__doc__ else node.__name__ + + if prefix or suffix: + item._nodeid = '::'.join((path, prefix.strip(), suffix.strip())) diff --git a/tests/dataset.py b/tests/dataset.py index 6a260fb00..f72c6dc05 100644 --- a/tests/dataset.py +++ b/tests/dataset.py @@ -18,7 +18,6 @@ ########################################################################## import os -import sys import shutil import hashlib import zipfile diff --git a/tests/images.py b/tests/images.py new file mode 100644 index 000000000..50d9dc5be --- /dev/null +++ b/tests/images.py @@ -0,0 +1,147 @@ +# tests.images +# Helper utility to manage baseline images for image comparisons. +# +# Author: Benjamin Bengfort +# Created: Fri Mar 02 21:51:56 2018 -0500 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: images.py [] benjamin@bengfort.com $ + +""" +Helper utility to manage baseline images for image comparisons. Usage: + + $ python -m tests.images --help + +The utility uses argparse to manage command line input for specific baseline +image movement or clearing. +""" + +########################################################################## +## Imports +########################################################################## + +import os +import glob +import shutil +import argparse + + +BASE = os.path.dirname(__file__) +BASELINE = os.path.join(BASE, "baseline_images") +ACTUAL = os.path.join(BASE, "actual_images") + + +########################################################################## +## Helper Methods +########################################################################## + +def relpath(path): + """ + Compute the path relative to the test directory. + """ + path = path.rstrip(".py") + path = os.path.relpath(path, start=BASE) + if path.startswith("..") or path.startswith(os.path.sep): + raise ValueError("path must include test directory") + return path + + +def validate(path): + """ + Validates a test directory path by checking if any actual images exist + and creating the mirror baseline directory if required. Returns bool to + show if work should be done on the path or raises an exception if invalid. + """ + actual_path = os.path.join(ACTUAL, path) + if not os.path.exists(actual_path): + return False + + if not os.path.isdir(actual_path): + raise ValueError("{} is not a directory".format(actual_path)) + + baseline_path = os.path.join(BASELINE, path) + if not os.path.exists(baseline_path): + os.makedirs(baseline_path) + + return True + + +def clear(path): + """ + Clear .png files in the actual and baseline directories + """ + for root in (BASELINE, ACTUAL): + for img in glob.glob(os.path.join(root, path, "*.png")): + os.remove(img) + print("removed {}".format(os.path.relpath(img))) + + +def sync(path): + """ + Move all non-diff images from actual to baseline + """ + for fname in os.listdir(os.path.join(ACTUAL, path)): + if fname.endswith('-diff.png'): + continue + + if fname.endswith('.png'): + src = os.path.join(ACTUAL, path, fname) + dst = os.path.join(BASELINE, path, fname) + shutil.copy2(src, dst) + print("synced {}".format(os.path.relpath(src, start=ACTUAL))) + + +########################################################################## +## Main Method +########################################################################## + +def main(args): + # Get directories relative to test dir + test_dirs = list(map(relpath, args.test_dirs)) + + # Validate directories and filter empty ones + test_dirs = filter(validate, test_dirs) + + # If clear, clear the baseline and actual directories + if args.clear: + for path in test_dirs: + clear(path) + return + + # Move images that aren't diffs from actual to baseline + for path in test_dirs: + sync(path) + + +if __name__ == '__main__': + + args = { + ('-C', '--clear'): { + 'action': 'store_true', + 'help': 'clear actual and baseline images in specified dirs', + }, + 'test_dirs' : { + 'metavar': 'DIR', 'nargs': '+', + 'help': 'directories to move images from actual to baseline', + }, + } + + + # Create the parser and add the arguments + parser = argparse.ArgumentParser( + description="utility to manage baseline images for comparisons", + epilog="report any issues on GitHub" + ) + + for pargs, kwargs in args.items(): + if isinstance(pargs, str): + pargs = (pargs,) + parser.add_argument(*pargs, **kwargs) + + args = parser.parse_args() + try: + main(args) + except Exception as e: + parser.error(str(e)) diff --git a/tests/requirements.txt b/tests/requirements.txt index ab5119148..a1f4133d9 100644 --- a/tests/requirements.txt +++ b/tests/requirements.txt @@ -1,18 +1,22 @@ # Library Dependencies matplotlib>=1.5.1 -scipy>=0.17.1 -scikit-learn>=0.18 -numpy>=1.11.0 +scipy>=0.19 +scikit-learn>=0.19 +numpy>=1.13.0 cycler>=0.10.0 # Testing Requirements -nose>=1.3.7 -coverage>=4.1 -requests>=2.10.0 +pytest>=3.4.1 +pytest-cov>=2.5.1 +pytest-flakes>=2.0.0 +pytest-spec>=1.1.0 +coverage>=4.4.1 +requests>=2.18.3 +six==1.11.0 # Python 2 Testing Requirements mock>=2.0.0 # Optional Testing Dependencies -nltk>=3.0 -pandas>=0.18 +nltk>=3.2 +pandas>=0.20 diff --git a/tests/test_base.py b/tests/test_base.py index 9c5fbc68e..ab2adfb36 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -22,11 +22,6 @@ from yellowbrick.base import * -try: - from unittest import mock -except ImportError: - import mock - ########################################################################## ## Imports @@ -70,3 +65,19 @@ def test_finalize_interface(self): """ visualizer = Visualizer() self.assertIs(visualizer.finalize(), visualizer.ax) + + def test_size_property(self): + """ + Test the size property on the base Visualizer + """ + fig = plt.figure(figsize =(1,2)) + visualizer = Visualizer() + self.assertIsNone(visualizer._size) + self.assertIsNotNone(visualizer.size) + figure_size = fig.get_size_inches() * fig.get_dpi() + self.assertEqual(all(visualizer.size), all(figure_size)) + visualizer.size = (1080, 720) + figure_size = fig.get_size_inches() * fig.get_dpi() + self.assertEqual(all(visualizer.size), all(figure_size)) + self.assertEqual(visualizer._size, (1080, 720)) + self.assertEqual(visualizer.size, (1080, 720)) diff --git a/tests/test_bestfit.py b/tests/test_bestfit.py index 121ffa340..8641b4a91 100644 --- a/tests/test_bestfit.py +++ b/tests/test_bestfit.py @@ -17,7 +17,7 @@ ## Imports ########################################################################## -import unittest +import pytest import numpy as np import matplotlib.pyplot as plt @@ -61,6 +61,7 @@ def test_ensure_same_length(self): with self.assertRaises(YellowbrickValueError): draw_best_fit(X[:,np.newaxis], y, axe, 'linear') + @pytest.mark.filterwarnings('ignore') def testdraw_best_fit(self): """ Test that drawing a best fit line works. diff --git a/tests/test_classifier/test_boundaries.py b/tests/test_classifier/test_boundaries.py index e2b3eaf24..a105f613f 100644 --- a/tests/test_classifier/test_boundaries.py +++ b/tests/test_classifier/test_boundaries.py @@ -16,28 +16,31 @@ # Imports ########################################################################## +import six +import pytest +import numpy as np + +from tests.base import VisualTestCase + +from yellowbrick.classifier import * +from yellowbrick.exceptions import YellowbrickTypeError +from yellowbrick.exceptions import YellowbrickValueError + +from sklearn import datasets +from sklearn import neighbors +from sklearn import naive_bayes + try: from unittest import mock except ImportError: import mock -from collections import OrderedDict -import numpy as np - try: import pandas as pd except ImportError: pd = None -import unittest -from tests.base import VisualTestCase -from yellowbrick.classifier import * -from yellowbrick.exceptions import YellowbrickTypeError -from yellowbrick.exceptions import YellowbrickValueError -from sklearn import datasets -from sklearn import neighbors -from sklearn import naive_bayes ########################################################################## # Data ########################################################################## @@ -66,19 +69,34 @@ ########################################################################## +@pytest.mark.filterwarnings('ignore') class DecisionBoundariesVisualizerTest(VisualTestCase): - """Testcases for the DecisionBoundariesVisualizers """ + """ + DecisionBoundariesVisualizer + """ def test_decision_bounardies(self): - """Assert no errors occur during KnnDecisionBoundariesVisualizer integration + """ + Assert no errors during kNN DecisionBoundariesVisualizer integration """ model = neighbors.KNeighborsClassifier(3) viz = DecisionViz(model) viz.fit_draw_poof(X_two_cols, y=y) + def test_deprecated(self): + with pytest.deprecated_call(): + model = neighbors.KNeighborsClassifier(3) + DecisionViz(model) + + @pytest.mark.skipif(six.PY2, reason="deprecation warnings filtered in PY2") + def test_deprecated_message(self): + with pytest.warns(DeprecationWarning, match='Will be moved to yellowbrick.contrib in v0.7'): + model = neighbors.KNeighborsClassifier(3) + DecisionViz(model) + def test_init(self): """ - Testing the init method + Test correct initialization of the internal state """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer(model) @@ -109,8 +127,10 @@ def test_scatter_xy_and_features_raise_error(self): model = neighbors.KNeighborsClassifier(3) features = ["temperature", "relative_humidity", "light"] - with self.assertRaises(YellowbrickValueError) as context: - visualizer = DecisionBoundariesVisualizer(model, features=features, x='one', y='two') + with self.assertRaises(YellowbrickValueError): + DecisionBoundariesVisualizer( + model, features=features, x='one', y='two' + ) def test_scatter_xy_changes_to_features(self): """ @@ -123,7 +143,7 @@ def test_scatter_xy_changes_to_features(self): def test_fit(self): """ - Testing the fit method + Testing the fit method works as expected """ model = neighbors.KNeighborsClassifier(3) model.fit = mock.MagicMock() @@ -146,6 +166,9 @@ def test_fit(self): self.assertIsNotNone(fitted_viz.Z_shape) def test_fit_class_labels(self): + """ + Test fit with class labels specified + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer( model, classes=['one', 'two', 'three', 'four']) @@ -157,13 +180,18 @@ def test_fit_class_labels(self): 'one': '0'}) def test_fit_class_labels_class_names_edge_case(self): - """ Edge case that more class labels are defined than in datatset""" + """ + Edge case that more class labels are defined than in datatset + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer( model, classes=['one', 'two', 'three', 'four', 'five']) self.assertRaises(YellowbrickTypeError, viz.fit, X_two_cols, y=y) def test_fit_features_assignment_None(self): + """ + Test fit when features is None + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer(model) self.assertIsNone(viz.features_) @@ -171,6 +199,9 @@ def test_fit_features_assignment_None(self): self.assertEquals(fitted_viz.features_, ['Feature One', 'Feature Two']) def test_fit_features_assignment(self): + """ + Test fit when features are specified + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) fitted_viz = viz.fit(X_two_cols, y=y) @@ -178,6 +209,9 @@ def test_fit_features_assignment(self): @mock.patch("yellowbrick.classifier.boundaries.OrderedDict") def test_draw_ordereddict_calls(self, mock_odict): + """ + Test draw with calls to ordered dict + """ mock_odict.return_value = {} model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) @@ -186,6 +220,9 @@ def test_draw_ordereddict_calls(self, mock_odict): @mock.patch("yellowbrick.classifier.boundaries.resolve_colors") def test_draw_ordereddict_calls_one(self, mock_resolve_colors): + """ + Test ordered dict calls resolve colors + """ mock_resolve_colors.return_value = [] model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) @@ -193,7 +230,9 @@ def test_draw_ordereddict_calls_one(self, mock_resolve_colors): self.assertEquals(len(mock_resolve_colors.mock_calls), 1) def test_draw_ax_show_scatter_true(self): - """Test that the matplotlib functions are being called """ + """ + Test that the matplotlib functions are being called + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer(model, features=['one', 'two']) fitted_viz = viz.fit(X_two_cols, y=y) @@ -208,8 +247,8 @@ def test_draw_ax_show_scatter_true(self): self.assertEquals(len(fitted_viz.ax.legend.mock_calls), 0) def test_draw_ax_show_scatter_False(self): - """Test that the matplotlib functions are being called when the - scatter plot isn't drawn + """ + Test that the matplotlib called when the scatter plot isn't drawn """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer( @@ -226,6 +265,9 @@ def test_draw_ax_show_scatter_False(self): self.assertEquals(len(fitted_viz.ax.legend.mock_calls), 1) def test_finalize(self): + """ + Test the finalize method works as expected + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer( model, features=['one', 'two'], show_scatter=False) @@ -244,6 +286,9 @@ def test_finalize(self): fitted_viz.ax.set_ylabel.assert_called_once_with('two') def test_fit_draw(self): + """ + Test fit draw shortcut + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer( model, features=['one', 'two'], show_scatter=False) @@ -257,6 +302,9 @@ def test_fit_draw(self): viz.draw.assert_called_once_with(X_two_cols, y) def test_fit_draw_poof(self): + """ + Test fit draw poof shortcut + """ model = neighbors.KNeighborsClassifier(3) viz = DecisionBoundariesVisualizer( model, features=['one', 'two'], show_scatter=False) @@ -273,6 +321,9 @@ def test_fit_draw_poof(self): def test_integrated_plot_numpy_named_arrays(self): + """ + Test integration of visualizer with numpy named arrays + """ model = naive_bayes.MultinomialNB() X = np.array([ @@ -297,14 +348,20 @@ def test_integrated_plot_numpy_named_arrays(self): self.assert_images_similar(visualizer) def test_integrated_scatter_numpy_arrays_no_names(self): + """ + Test integration of visualizer with numpy arrays + """ model = neighbors.KNeighborsClassifier(3) visualizer = DecisionBoundariesVisualizer(model, features=[1, 2]) visualizer.fit_draw_poof(X, y) self.assertEquals(visualizer.features_, [1, 2]) - @unittest.skipUnless(pd is not None, "Pandas is not installed, could not run test.") + @pytest.mark.skipif(pd is None, reason="test requires pandas") def test_real_data_set_viz(self): + """ + Test integration of visualizer with pandas dataset + """ model = naive_bayes.MultinomialNB() data = datasets.load_iris() @@ -317,8 +374,11 @@ def test_real_data_set_viz(self): visualizer.fit_draw_poof(X, y) self.assert_images_similar(visualizer) - @unittest.skipUnless(pd is not None, "Pandas is not installed, could not run test.") + @pytest.mark.skipif(pd is None, reason="test requires pandas") def test_quick_method(self): + """ + Test quick function shortcut of visualizer + """ model = naive_bayes.MultinomialNB() data = datasets.load_iris() @@ -327,4 +387,4 @@ def test_quick_method(self): X = df[['sepal_length_(cm)', 'sepal_width_(cm)']].as_matrix() y = data.target - visualizer = decisionviz(model, X, y) + decisionviz(model, X, y) diff --git a/tests/test_classifier/test_class_prediction_error.py b/tests/test_classifier/test_class_prediction_error.py new file mode 100644 index 000000000..298bec09f --- /dev/null +++ b/tests/test_classifier/test_class_prediction_error.py @@ -0,0 +1,117 @@ +# tests.test_classifier.test_class_prediction_error +# Testing for the ClassPredictionError visualizer +# +# Author: Benjamin Bengfort +# Author: Rebecca Bilbro +# Author: Larry Gray +# Created: Tue May 23 13:41:55 2017 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_rocauc.py [] benjamin@bengfort.com $ + +""" +Testing for the ClassPredictionError visualizer +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +import numpy as np +import matplotlib.pyplot as plt + +from yellowbrick.classifier.class_balance import * +from yellowbrick.exceptions import ModelError + +from sklearn.svm import LinearSVC +from sklearn.ensemble import RandomForestClassifier +from sklearn.datasets import make_multilabel_classification + +from tests.base import VisualTestCase + +########################################################################## +## Data +########################################################################## + +X = np.array( + [[2.318, 2.727, 4.260, 7.212, 4.792], + [2.315, 2.726, 4.295, 7.140, 4.783], + [2.315, 2.724, 4.260, 7.135, 4.779], + [2.110, 3.609, 4.330, 7.985, 5.595], + [2.110, 3.626, 4.330, 8.203, 5.621], + [2.110, 3.620, 4.470, 8.210, 5.612]] +) + +y = np.array([1, 1, 0, 1, 0, 0]) + +########################################################################## +## Tests +########################################################################## + + +class ClassPredictionErrorTests(VisualTestCase): + + def test_integration_class_prediction_error_(self): + """ + Assert no errors occur during class prediction error integration + """ + model = LinearSVC() + model.fit(X, y) + visualizer = ClassPredictionError(model, classes=["A", "B"]) + visualizer.score(X, y) + self.assert_images_similar(visualizer) + + def test_class_prediction_error_quickmethod(self): + """ + Test the ClassPreditionError quickmethod + """ + fig = plt.figure() + ax = fig.add_subplot() + + clf = LinearSVC(random_state=42) + g = class_prediction_error(clf, X, y, ax) + + self.assert_images_similar(ax=g) + + def test_classes_greater_than_indices(self): + """ + Assert error when y and y_pred contain zero values for + one of the specified classess + """ + model = LinearSVC() + model.fit(X, y) + with self.assertRaises(ModelError): + visualizer = ClassPredictionError(model, + classes=["A", "B", "C"]) + visualizer.score(X, y) + + def test_classes_less_than_indices(self): + """ + Assert error when there is an attempt to filter classes + """ + model = LinearSVC() + model.fit(X, y) + with self.assertRaises(NotImplementedError): + visualizer = ClassPredictionError(model, classes=["A"]) + visualizer.score(X, y) + + @pytest.mark.skip(reason="not implemented yet") + def test_no_classes_provided(self): + """ + Assert no errors when no classes are provided + """ + pass + + def test_class_type(self): + """ + Test class must be either binary or multiclass type + """ + X, y = make_multilabel_classification() + model = RandomForestClassifier() + model.fit(X, y) + with self.assertRaises(YellowbrickValueError): + visualizer = ClassPredictionError(model) + visualizer.score(X, y) diff --git a/tests/test_classifier/test_classification_report.py b/tests/test_classifier/test_classification_report.py index c93994bec..5177355bd 100644 --- a/tests/test_classifier/test_classification_report.py +++ b/tests/test_classifier/test_classification_report.py @@ -1,36 +1,212 @@ +# tests.test_classifier.test_classification_report +# Tests for the classification report visualizer +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Created: Sun Mar 18 16:57:27 2018 -0400 +# +# ID: test_classification_report.py [] benjamin@bengfort.com $ + +""" +Tests for the classification report visualizer +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +import yellowbrick as yb +import matplotlib.pyplot as plt + +from collections import namedtuple from yellowbrick.classifier.classification_report import * + from tests.base import VisualTestCase +from tests.dataset import DatasetMixin from sklearn.svm import LinearSVC +from sklearn.naive_bayes import GaussianNB +from sklearn.tree import DecisionTreeClassifier +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split as tts +from sklearn.linear_model import LassoCV, LogisticRegression + +try: + import pandas as pd +except ImportError: + pd = None + ########################################################################## -## Data +## Fixtures ########################################################################## -X = np.array( - [[ 2.318, 2.727, 4.260, 7.212, 4.792], - [ 2.315, 2.726, 4.295, 7.140, 4.783,], - [ 2.315, 2.724, 4.260, 7.135, 4.779,], - [ 2.110, 3.609, 4.330, 7.985, 5.595,], - [ 2.110, 3.626, 4.330, 8.203, 5.621,], - [ 2.110, 3.620, 4.470, 8.210, 5.612,]] +# Helpers for fixtures +Dataset = namedtuple('Dataset', 'X,y') +Split = namedtuple('Split', 'train,test') + + +@pytest.fixture(scope='class') +def binary(request): + """ + Creates a random binary classification dataset fixture + """ + X, y = make_classification( + n_samples=500, n_features=20, n_informative=8, n_redundant=2, + n_classes=2, n_clusters_per_class=3, random_state=87 + ) + + X_train, X_test, y_train, y_test = tts( + X, y, test_size=0.2, random_state=93 ) -y = np.array([1, 1, 0, 1, 0, 0]) + dataset = Dataset(Split(X_train, X_test), Split(y_train, y_test)) + request.cls.binary = dataset + + +@pytest.fixture(scope='class') +def multiclass(request): + """ + Creates a random multiclass classification dataset fixture + """ + X, y = make_classification( + n_samples=500, n_features=20, n_informative=8, n_redundant=2, + n_classes=6, n_clusters_per_class=3, random_state=87 + ) + + X_train, X_test, y_train, y_test = tts( + X, y, test_size=0.2, random_state=93 + ) + + dataset = Dataset(Split(X_train, X_test), Split(y_train, y_test)) + request.cls.multiclass = dataset + + ########################################################################## ## Test for Classification Report ########################################################################## -class ClassificationReportTests(VisualTestCase): +@pytest.mark.usefixtures("binary", "multiclass") +class ClassificationReportTests(VisualTestCase, DatasetMixin): + """ + ClassificationReport visualizer tests + """ + + def test_binary_class_report(self): + """ + Correctly generates a report for binary classification with LinearSVC + """ + _, ax = plt.subplots() + + viz = ClassificationReport(LinearSVC(), ax=ax) + viz.fit(self.binary.X.train, self.binary.y.train) + viz.score(self.binary.X.test, self.binary.y.test) - def test_class_report(self): + self.assert_images_similar(viz) + + assert viz.scores_ == { + 'precision': {0: 0.7446808510638298, 1: 0.8490566037735849}, + 'recall': {0: 0.813953488372093, 1: 0.7894736842105263}, + 'f1': {0: 0.7777777777777778, 1: 0.8181818181818182} + } + + def test_multiclass_class_report(self): """ - Assert no errors occur during classification report integration + Correctly generates report for multi-class with LogisticRegression + """ + _, ax = plt.subplots() + + viz = ClassificationReport(LogisticRegression(random_state=12), ax=ax) + viz.fit(self.multiclass.X.train, self.multiclass.y.train) + viz.score(self.multiclass.X.test, self.multiclass.y.test) + + self.assert_images_similar(viz) + + assert viz.scores_ == { + 'precision': { + 0: 0.5333333333333333, 1: 0.5, 2: 0.45, + 3: 0.4, 4: 0.4, 5: 0.5882352941176471 + }, 'recall': { + 0: 0.42105263157894735, 1: 0.5625, 2: 0.6428571428571429, + 3: 0.3157894736842105, 4: 0.375, 5: 0.625 + }, 'f1': { + 0: 0.47058823529411764, 1: 0.5294117647058824, + 2: 0.5294117647058824, 3: 0.35294117647058826, + 4: 0.38709677419354843, 5: 0.6060606060606061 + }} + + @pytest.mark.skipif(pd is None, reason="test requires pandas") + def test_pandas_integration(self): """ - model = LinearSVC() - model.fit(X,y) - visualizer = ClassificationReport(model, classes=["A", "B"]) - visualizer.score(X,y) - self.assert_images_similar(visualizer) + Test with Pandas DataFrame and Series input + """ + _, ax = plt.subplots() + + # Load the occupancy dataset from fixtures + data = self.load_data('occupancy') + target = 'occupancy' + features = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + # Create instances and target + X = pd.DataFrame(data[features]) + y = pd.Series(data[target].astype(int)) + + # Create train/test splits + splits = tts(X, y, test_size=0.2, random_state=4512) + X_train, X_test, y_train, y_test = splits + + classes = ['unoccupied', 'occupied'] + + # Create classification report + model = GaussianNB() + viz = ClassificationReport(model, ax=ax, classes=classes) + viz.fit(X_train, y_train) + viz.score(X_test, y_test) + + self.assert_images_similar(viz, tol=0.1) + + # Ensure correct classification scores under the hood + assert viz.scores_ == { + 'precision': { + 'unoccupied': 0.999347471451876, + 'occupied': 0.8825214899713467 + }, 'recall': { + 'unoccupied': 0.9613935969868174, + 'occupied': 0.9978401727861771 + }, 'f1': { + 'unoccupied': 0.9800031994880819, + 'occupied': 0.9366447034972124 + }} + + @pytest.mark.skip(reason="requires random state in quick method") + def test_quick_method(self): + """ + Test the quick method with a random dataset + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=27 + ) + + _, ax = plt.subplots() + classification_report(DecisionTreeClassifier(), X, y, ax=ax) + + self.assert_images_similar(ax=ax) + + def test_isclassifier(self): + """ + Assert that only classifiers can be used with the visualizer. + """ + + message = ( + 'This estimator is not a classifier; ' + 'try a regression or clustering score visualizer instead!' + ) + + with pytest.raises(yb.exceptions.YellowbrickError, match=message): + ClassificationReport(LassoCV()) diff --git a/tests/test_classifier/test_confusion_matrix.py b/tests/test_classifier/test_confusion_matrix.py index 75e5c3aea..5f88bad23 100644 --- a/tests/test_classifier/test_confusion_matrix.py +++ b/tests/test_classifier/test_confusion_matrix.py @@ -1,89 +1,348 @@ -import yellowbrick +# tests.test_classifier.test_confusion_matrix +# Tests for the confusion matrix visualizer +# +# Aithor: Neal Humphrey +# Author: Benjamin Bengfort +# Created: Tue May 03 11:05:11 2017 -0700 +# +# ID: test_confusion_matrix.py [] benjamin@bengfort.com $ + +""" +Tests for the confusion matrix visualizer +""" + +########################################################################## +## Imports +########################################################################## + +import six +import pytest +import yellowbrick as yb +import numpy.testing as npt +import matplotlib.pyplot as plt + +from collections import namedtuple + from yellowbrick.classifier.confusion_matrix import * + from tests.base import VisualTestCase -from sklearn.preprocessing import LabelEncoder +from tests.dataset import DatasetMixin +from sklearn.svm import SVC from sklearn.datasets import load_digits -from sklearn.model_selection import train_test_split +from sklearn.naive_bayes import GaussianNB +from sklearn.preprocessing import LabelEncoder +from sklearn.tree import DecisionTreeClassifier +from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.linear_model import PassiveAggressiveRegressor +from sklearn.model_selection import train_test_split as tts +try: + import pandas as pd +except ImportError: + pd = None -class ConfusionMatrixTests(VisualTestCase): - def __init__(self, *args, **kwargs): - super(ConfusionMatrixTests, self).__init__(*args, **kwargs) - #Use the same data for all the tests - self.digits = load_digits() +########################################################################## +## Fixtures +########################################################################## - X = self.digits.data - y = self.digits.target - - X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11) - self.X_train = X_train - self.X_test = X_test - self.y_train = y_train - self.y_test = y_test +# Helpers for fixtures +Dataset = namedtuple('Dataset', 'X,y') +Split = namedtuple('Split', 'train,test') + + +@pytest.fixture(scope='class') +def digits(request): + """ + Creates a fixture of train and test splits for the sklearn digits dataset + For ease of use returns a Dataset named tuple composed of two Split tuples. + """ + data = load_digits() + X_train, X_test, y_train, y_test = tts( + data.data, data.target, test_size=0.2, random_state=11 + ) + + # Set a class attribute for digits + request.cls.digits = Dataset( + Split(X_train, X_test), Split(y_train, y_test) + ) + + +########################################################################## +## Test Cases +########################################################################## + +@pytest.mark.usefixtures("digits") +class ConfusionMatrixTests(VisualTestCase, DatasetMixin): + """ + ConfusionMatrix visualizer tests + """ def test_confusion_matrix(self): - model = LogisticRegression() - cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9]) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) + """ + Integration test on digits dataset with LogisticRegression + """ + _, ax = plt.subplots() + + model = LogisticRegression(random_state=93) + cm = ConfusionMatrix(model, ax=ax, classes=[0,1,2,3,4,5,6,7,8,9]) + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + self.assert_images_similar(cm, tol=10) + + # Ensure correct confusion matrix under the hood + npt.assert_array_equal(cm.confusion_matrix_, np.array([ + [38, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [ 0, 35, 0, 0, 0, 0, 0, 0, 2, 0], + [ 0, 0, 39, 0, 0, 0, 0, 0, 0, 0], + [ 0, 0, 0, 38, 0, 1, 0, 0, 2, 0], + [ 0, 0, 0, 0, 40, 0, 0, 1, 0, 0], + [ 0, 0, 0, 0, 0, 27, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 1, 29, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 35, 0, 1], + [ 0, 2, 0, 0, 0, 0, 0, 0, 32, 0], + [ 0, 0, 0, 0, 0, 0, 0, 1, 1, 35]])) def test_no_classes_provided(self): - model = LogisticRegression() - cm = ConfusionMatrix(model) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) - - def test_raw_count_mode(self): - model = LogisticRegression() - cm = ConfusionMatrix(model) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test, percent=False) - - def test_zoomed_in(self): - model = LogisticRegression() - cm = ConfusionMatrix(model, classes=[0,1,2]) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) + """ + Integration test on digits dataset with GaussianNB, no classes + """ + _, ax = plt.subplots() + + model = GaussianNB() + cm = ConfusionMatrix(model, ax=ax, classes=None) + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + self.assert_images_similar(cm, tol=10) + + # Ensure correct confusion matrix under the hood + npt.assert_array_equal(cm.confusion_matrix_, np.array([ + [36, 0, 0, 0, 1, 0, 0, 1, 0, 0], + [ 0, 31, 0, 0, 0, 0, 0, 1, 3, 2], + [ 0, 1, 34, 0, 0, 0, 0, 0, 4, 0], + [ 0, 1, 0, 33, 0, 2, 0, 2, 3, 0], + [ 0, 0, 0, 0, 36, 0, 0, 5, 0, 0], + [ 0, 0, 0, 0, 0, 27, 0, 0, 0, 0], + [ 0, 0, 1, 0, 1, 0, 28, 0, 0, 0], + [ 0, 0, 0, 0, 0, 0, 0, 36, 0, 0], + [ 0, 3, 0, 1, 0, 1, 0, 4, 25, 0], + [ 1, 2, 0, 0, 1, 0, 0, 8, 3, 22]])) + + def test_fontsize(self): + """ + Test confusion matrix with smaller fontsize on digits dataset with SVC + """ + _, ax = plt.subplots() + + model = SVC(random_state=93) + cm = ConfusionMatrix(model, ax=ax, fontsize=8) + + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + self.assert_images_similar(cm, tol=10) + + def test_percent_mode(self): + """ + Test confusion matrix in percent mode on digits dataset with SVC + """ + _, ax = plt.subplots() + + model = SVC(random_state=93) + cm = ConfusionMatrix(model, ax=ax, percent=True) + + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + self.assert_images_similar(cm, tol=10) + + # Ensure correct confusion matrix under the hood + npt.assert_array_equal(cm.confusion_matrix_, np.array([ + [16, 0, 0, 0, 0, 22, 0, 0, 0, 0], + [ 0, 11, 0, 0, 0, 26, 0, 0, 0, 0], + [ 0, 0, 10, 0, 0, 29, 0, 0, 0, 0], + [ 0, 0, 0, 6, 0, 35, 0, 0, 0, 0], + [ 0, 0, 0, 0, 11, 30, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 27, 0, 0, 0, 0], + [ 0, 0, 0, 0, 0, 9, 21, 0, 0, 0], + [ 0, 0, 0, 0, 0, 29, 0, 7, 0, 0], + [ 0, 0, 0, 0, 0, 32, 0, 0, 2, 0], + [ 0, 0, 0, 0, 0, 34, 0, 0, 0, 3]])) + + def test_deprecated_fit_kwargs(self): + """ + Test that passing percent or sample_weight is deprecated + """ + if yb.__version_info__['minor'] >= 9: + pytest.fail("deprecation warnings should be removed after 0.9") + + args = (self.digits.X.test, self.digits.y.test) + cm = ConfusionMatrix(LogisticRegression()) + cm.fit(self.digits.X.train, self.digits.y.train) + + # Deprecated percent in score + pytest.deprecated_call(cm.score, *args, percent=True) + + # Deprecated sample_weight in score + pytest.deprecated_call(cm.score, *args, sample_weight=np.arange(360)) + + def test_class_filter_eg_zoom_in(self): + """ + Test filtering classes zooms in on the confusion matrix. + """ + _, ax = plt.subplots() + + model = LogisticRegression(random_state=93) + cm = ConfusionMatrix(model, ax=ax, classes=[0,1,2]) + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + self.assert_images_similar(cm, tol=10) + + # Ensure correct confusion matrix under the hood + npt.assert_array_equal(cm.confusion_matrix_, np.array([ + [38, 0, 0], + [ 0, 35, 0], + [ 0, 0, 39]])) def test_extra_classes(self): - model = LogisticRegression() - cm = ConfusionMatrix(model, classes=[0,1,2,11]) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) - self.assertTrue(cm.selected_class_counts[3]==0) + """ + Assert that any extra classes are simply ignored + """ + # TODO: raise exception instead + _, ax = plt.subplots() + + model = LogisticRegression(random_state=93) + cm = ConfusionMatrix(model, ax=ax, classes=[0,1,2,11]) + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + npt.assert_array_equal(cm.class_counts_, [38, 37, 39, 0]) + + # Ensure correct confusion matrix under the hood + npt.assert_array_equal(cm.confusion_matrix_, np.array([ + [38, 0, 0, 0], + [ 0, 35, 0, 0], + [ 0, 0, 39, 0], + [ 0, 0, 0, 0]])) + + self.assert_images_similar(cm, tol=10) def test_one_class(self): - model = LogisticRegression() - cm = ConfusionMatrix(model, classes=[0]) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) + """ + Test single class confusion matrix with LogisticRegression + """ + _, ax = plt.subplots() + + model = LogisticRegression(random_state=93) + cm = ConfusionMatrix(model, ax=ax, classes=[0]) + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + self.assert_images_similar(cm, tol=10) def test_defined_mapping(self): - model = LogisticRegression() + """ + Test mapping as label encoder to define tick labels + """ + _, ax = plt.subplots() + + model = LogisticRegression(random_state=93) classes = ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'] mapping = {0: 'zero', 1: 'one', 2: 'two', 3: 'three', 4: 'four', 5: 'five', 6: 'six', 7: 'seven', 8: 'eight', 9: 'nine'} - cm = ConfusionMatrix(model, classes=classes, label_encoder = mapping) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) + cm = ConfusionMatrix(model, ax=ax, classes=classes, label_encoder=mapping) + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + assert [l.get_text() for l in ax.get_xticklabels()] == classes + ylabels = [l.get_text() for l in ax.get_yticklabels()] + ylabels.reverse() def test_inverse_mapping(self): - model = LogisticRegression() + """ + Test LabelEncoder as label encoder to define tick labels + """ + _, ax = plt.subplots() + + model = LogisticRegression(random_state=93) le = LabelEncoder() classes = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'] le.fit(['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']) - cm = ConfusionMatrix(model, classes=classes, label_encoder=le) - cm.fit(self.X_train, self.y_train) - cm.score(self.X_test, self.y_test) + + cm = ConfusionMatrix(model, ax=ax, classes=classes, label_encoder=le) + cm.fit(self.digits.X.train, self.digits.y.train) + cm.score(self.digits.X.test, self.digits.y.test) + + assert [l.get_text() for l in ax.get_xticklabels()] == classes + ylabels = [l.get_text() for l in ax.get_yticklabels()] + ylabels.reverse() + assert ylabels == classes + + @pytest.mark.skipif(pd is None, reason="test requires pandas") + def test_pandas_integration(self): + """ + Test with Pandas DataFrame and Series input + """ + _, ax = plt.subplots() + + # Load the occupancy dataset from fixtures + data = self.load_data('occupancy') + target = 'occupancy' + features = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + # Create instances and target + X = pd.DataFrame(data[features]) + y = pd.Series(data[target].astype(int)) + + # Create train/test splits + splits = tts(X, y, test_size=0.2, random_state=8873) + X_train, X_test, y_train, y_test = splits + + # Create confusion matrix + model = GaussianNB() + cm = ConfusionMatrix(model, ax=ax, classes=None) + cm.fit(X_train, y_train) + cm.score(X_test, y_test) + + tol = 0.1 if six.PY3 else 40 + self.assert_images_similar(cm, tol=tol) + + # Ensure correct confusion matrix under the hood + npt.assert_array_equal(cm.confusion_matrix_, np.array([ + [3012, 114], + [ 1, 985] + ])) + + @pytest.mark.skip(reason="requires random state in quick method") + def test_quick_method(self): + """ + Test the quick method with a random dataset + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=27 + ) + + _, ax = plt.subplots() + confusion_matrix(DecisionTreeClassifier(), X, y, ax=ax) + + self.assert_images_similar(ax=ax) def test_isclassifier(self): + """ + Assert that only classifiers can be used with the visualizer. + """ model = PassiveAggressiveRegressor() - message = 'This estimator is not a classifier; try a regression or clustering score visualizer instead!' - classes = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'] - - with self.assertRaisesRegexp(yellowbrick.exceptions.YellowbrickError, message): - ConfusionMatrix(model, classes=classes) + message = ( + 'This estimator is not a classifier; ' + 'try a regression or clustering score visualizer instead!' + ) + with pytest.raises(yb.exceptions.YellowbrickError, match=message): + ConfusionMatrix(model) diff --git a/tests/test_classifier/test_learning_curve.py b/tests/test_classifier/test_learning_curve.py index 091f20f6c..6491153e1 100644 --- a/tests/test_classifier/test_learning_curve.py +++ b/tests/test_classifier/test_learning_curve.py @@ -23,7 +23,6 @@ from ..base import VisualTestCase from sklearn.svm import LinearSVC -from sklearn.datasets import load_digits from sklearn.model_selection import ShuffleSplit from yellowbrick.classifier.learning_curve import LearningCurveVisualizer from yellowbrick.classifier.learning_curve import learning_curve_plot @@ -54,8 +53,8 @@ def test_learning_curve_comprehensive(self): """ try: - visualizer = LearningCurveVisualizer(LinearSVC(random_state=0), train_sizes=np.linspace(.1, 1.0, 5), - cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0), + visualizer = LearningCurveVisualizer(LinearSVC(random_state=0), train_sizes=np.linspace(.1, 1.0, 5), + cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0), n_jobs=4) visualizer.fit(X, y) visualizer.poof() @@ -66,7 +65,7 @@ def test_learning_curve_comprehensive(self): def test_learning_curve_model_only(self): """ - Test learning curve with inputting model only. + Test learning curve with inputting model only. """ try: @@ -95,7 +94,7 @@ def test_learning_curve_model_trainsize_cv_only(self): """ try: - visualizer = LearningCurveVisualizer(LinearSVC(), + visualizer = LearningCurveVisualizer(LinearSVC(), train_sizes=np.linspace(.1, 1.0, 5), cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)) visualizer.fit(X, y) @@ -109,9 +108,26 @@ def test_learning_curve_bad_trainsize(self): """ with self.assertRaises(YellowbrickError): - visualizer = LearningCurveVisualizer(LinearSVC(), + visualizer = LearningCurveVisualizer(LinearSVC(), train_sizes=10000, cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)) visualizer.fit(X, y) visualizer.poof() - \ No newline at end of file + + def test_learning_curve_quick_method(self): + """ + Test the learning curve quick method acts as expected + """ + try: + learning_curve_plot( + X, y, + LinearSVC(random_state=0), + train_sizes=np.linspace(.1, 1.0, 5), + cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0), + n_jobs=4 + ) + except Exception as e: + self.fail("error during learning curve: {}".format(e)) + + # TODO: assert images are similar + # self.assert_images_similar(visualizer) diff --git a/tests/test_classifier/test_rocauc.py b/tests/test_classifier/test_rocauc.py index 37ede3248..d420414a5 100644 --- a/tests/test_classifier/test_rocauc.py +++ b/tests/test_classifier/test_rocauc.py @@ -18,7 +18,7 @@ ## Imports ########################################################################## -import unittest +import pytest import numpy as np import numpy.testing as npt @@ -27,7 +27,7 @@ from yellowbrick.classifier.rocauc import * from yellowbrick.exceptions import ModelError -from sklearn.svm import LinearSVC, SVC +from sklearn.svm import LinearSVC from sklearn.naive_bayes import MultinomialNB from sklearn.tree import DecisionTreeClassifier from sklearn.datasets import load_breast_cancer @@ -84,7 +84,7 @@ def load_binary_data(self): y = data['occupancy'].astype(int) # Convert X to an ndarray - X = np.array(X.tolist()) + X = X.copy().view((float, len(X.dtype.names))) # Return train/test splits return tts(X, y, test_size=0.2, random_state=42) @@ -95,7 +95,7 @@ def load_multiclass_data(self): """ raise NotImplementedError("Need to add multiclass data soon!") - @unittest.skip("binary classifiers don't currently work as expected") + @pytest.mark.skip(reason="binary classifiers don't currently work as expected") def test_binary_rocauc(self): """ Test ROCAUC with a binary classifier @@ -127,6 +127,7 @@ def test_binary_rocauc(self): visualizer.poof() self.assert_images_similar(visualizer) + @pytest.mark.xfail(reason="see issue #315") def test_multiclass_rocauc(self): """ Test ROCAUC with a multiclass classifier @@ -158,7 +159,7 @@ def test_multiclass_rocauc(self): # Compare the images visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=0.071) def test_rocauc_quickmethod(self): """ @@ -168,8 +169,9 @@ def test_rocauc_quickmethod(self): model = DecisionTreeClassifier() # TODO: impage comparison of the quick method - ax = roc_auc(model, data.data, data.target) + roc_auc(model, data.data, data.target) + @pytest.mark.xfail(reason="see issue #315") def test_rocauc_no_micro(self): """ Test ROCAUC without a micro average @@ -194,6 +196,7 @@ def test_rocauc_no_micro(self): visualizer.poof() self.assert_images_similar(visualizer) + @pytest.mark.xfail(reason="see issue #315") def test_rocauc_no_macro(self): """ Test ROCAUC without a macro average @@ -247,6 +250,7 @@ def test_rocauc_no_macro_no_micro(self): visualizer.poof() self.assert_images_similar(visualizer) + @pytest.mark.xfail(reason="see issue #315") def test_rocauc_no_classes(self): """ Test ROCAUC without per-class curves @@ -291,21 +295,21 @@ def test_rocauc_no_curves(self): visualizer.poof() self.assert_images_similar(visualizer) - @unittest.skip("Not implemented yet") + @pytest.mark.skip(reason="not implemented yet") def test_rocauc_label_encoded(self): """ Test ROCAUC with label encoding before scoring """ pass - @unittest.skip("Not implemented yet") + @pytest.mark.skip(reason="not implemented yet") def test_rocauc_not_label_encoded(self): """ Test ROCAUC without label encoding before scoring """ pass - @unittest.skip("Not working with expected precision") + @pytest.mark.xfail(reason="not working with expected precision") def test_decision_function_rocauc(self): """ Test ROCAUC with classifiers that have a decision function diff --git a/tests/test_classifier/test_threshold.py b/tests/test_classifier/test_threshold.py new file mode 100644 index 000000000..44d531998 --- /dev/null +++ b/tests/test_classifier/test_threshold.py @@ -0,0 +1,123 @@ +# tests.test_classifier.test_threshold +# Ensure that the threshold visualizations work. +# +# Author: Nathan Danielsen +# Created: Wed April 26 20:17:29 2017 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_threshold.py [] nathan.danielsen@gmail.com $ +""" +Ensure that the Threshold visualizations work. +""" + +########################################################################## +## Imports +########################################################################## +import unittest +import numpy as np +try: + import pandas as pd +except ImportError: + pd = None + +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin +from yellowbrick.classifier import * + +from sklearn.naive_bayes import BernoulliNB + +########################################################################## +## Data +########################################################################## + +# yapf: disable +X = np.array([ + [2.318, 2.727, 4.260, 7.212, 4.792], + [2.315, 2.726, 4.295, 7.140, 4.783, ], + [2.315, 2.724, 4.260, 7.135, 4.779, ], + [2.110, 3.609, 4.330, 7.985, 5.595, ], + [2.110, 3.626, 4.330, 8.203, 5.621, ], + [2.110, 3.620, 4.470, 8.210, 5.612, ] + ]) +# yapf: enable +y = np.array([1, 0, 1, 0, 1, 0]) + +########################################################################## +## Threshold visualizer test case +########################################################################## + + +class ThresholdVisualizerTest(VisualTestCase, DatasetMixin): + def setUp(self): + self.occupancy = self.load_data('occupancy') + super(ThresholdVisualizerTest, self).setUp() + + def tearDown(self): + self.occupancy = None + super(ThresholdVisualizerTest, self).tearDown() + + def test_threshold_vi__init__(self): + """ + Test that init params are in order + """ + model = BernoulliNB(3) + viz = ThresholdVisualizer(model) + self.assertIs(viz.estimator, model) + self.assertIsNone(viz.color) + self.assertIsNone(viz.title) + self.assertIsNone(viz.plot_data) + self.assertEqual(viz.n_trials, 50) + self.assertEqual(viz.test_size_percent, 0.1) + self.assertEqual(viz.quantiles, (0.1, 0.5, 0.9)) + + + def test_threshold_viz(self): + """ + Integration test of threshold visualizers + """ + model = BernoulliNB(3) + visualizer = ThresholdVisualizer(model, random_state=0) + + # assert that fit method returns ax + self.assertIs(visualizer.ax, visualizer.fit(X, y=y)) + + visualizer.poof() + + self.assert_images_similar(visualizer) + + @unittest.skipUnless(pd is not None, "Pandas is not installed, could not run test.") + def test_threshold_viz_read_data(self): + """ + Test ThresholdVisualizer on the real, occupancy data set with pandas + """ + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = self.occupancy['occupancy'].astype(int) + + # Convert X to a pandas dataframe + X = pd.DataFrame(X) + X.columns = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + model = BernoulliNB(3) + visualizer = ThresholdVisualizer(model, random_state=0) + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y) + visualizer.draw() + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_threshold_viz_quick_method_read_data(self): + """ + Test for thresholdviz quick method with visual unit test + """ + model = BernoulliNB(3) + + visualizer = type('Visualizer', (object, ), + {'ax': thresholdviz(model, X, y)}) + self.assert_images_similar(visualizer) diff --git a/tests/test_cluster/test_base.py b/tests/test_cluster/test_base.py index 32734db5f..66d91ada8 100644 --- a/tests/test_cluster/test_base.py +++ b/tests/test_cluster/test_base.py @@ -44,7 +44,7 @@ def test_clusterer_enforcement(self): for nomodel in nomodels: with self.assertRaises(YellowbrickTypeError): - visualizer = ClusteringScoreVisualizer(nomodel()) + ClusteringScoreVisualizer(nomodel()) models = [ KMeans, MiniBatchKMeans, AffinityPropagation, MeanShift, DBSCAN, Birch @@ -52,6 +52,6 @@ def test_clusterer_enforcement(self): for model in models: try: - visualizer = ClusteringScoreVisualizer(model()) + ClusteringScoreVisualizer(model()) except YellowbrickTypeError: self.fail("could not pass clustering estimator to visualizer") diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index 10206d718..2eae2fc06 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -17,8 +17,10 @@ ## Imports ########################################################################## +import pytest import unittest import numpy as np +import matplotlib.pyplot as plt from ..base import VisualTestCase from ..dataset import DatasetMixin @@ -28,6 +30,7 @@ from yellowbrick.cluster.elbow import distortion_score from yellowbrick.cluster.elbow import KElbowVisualizer from yellowbrick.exceptions import YellowbrickValueError +from numpy.testing.utils import assert_array_almost_equal ########################################################################## @@ -59,6 +62,9 @@ class KElbowHelperTests(unittest.TestCase): + """ + Helper functions for K-Elbow Visualizer + """ def test_distortion_score(self): """ @@ -73,42 +79,55 @@ def test_distortion_score(self): ########################################################################## class KElbowVisualizerTests(VisualTestCase, DatasetMixin): + """ + K-Elbow Visualizer Tests + """ + @pytest.mark.skip("images not close due to timing lines") def test_integrated_kmeans_elbow(self): """ Test no exceptions for kmeans k-elbow visualizer on blobs dataset - - See #182: cannot use occupancy dataset because of memory usage """ + # NOTE #182: cannot use occupancy dataset because of memory usage # Generate a blobs data set X,y = make_blobs( - n_samples=1000, n_features=12, centers=6, shuffle=True + n_samples=1000, n_features=12, centers=6, shuffle=True, random_state=42 ) try: - visualizer = KElbowVisualizer(KMeans(), k=4) + fig = plt.figure() + ax = fig.add_subplot() + + visualizer = KElbowVisualizer(KMeans(random_state=42), k=4, ax=ax) visualizer.fit(X) visualizer.poof() + + self.assert_images_similar(visualizer) except Exception as e: self.fail("error during k-elbow: {}".format(e)) + @pytest.mark.skip("images not close due to timing lines") def test_integrated_mini_batch_kmeans_elbow(self): """ Test no exceptions for mini-batch kmeans k-elbow visualizer - - See #182: cannot use occupancy dataset because of memory usage """ + # NOTE #182: cannot use occupancy dataset because of memory usage # Generate a blobs data set X,y = make_blobs( - n_samples=1000, n_features=12, centers=6, shuffle=True + n_samples=1000, n_features=12, centers=6, shuffle=True, random_state=42 ) try: - visualizer = KElbowVisualizer(MiniBatchKMeans(), k=4) + fig = plt.figure() + ax = fig.add_subplot() + + visualizer = KElbowVisualizer(MiniBatchKMeans(random_state=42), k=4, ax=ax) visualizer.fit(X) visualizer.poof() + + self.assert_images_similar(visualizer) except Exception as e: self.fail("error during k-elbow: {}".format(e)) @@ -118,68 +137,66 @@ def test_invalid_k(self): """ with self.assertRaises(YellowbrickValueError): - model = KElbowVisualizer(KMeans(), k=(1,2,3,4,5)) + KElbowVisualizer(KMeans(), k=(1,2,3,4,5)) with self.assertRaises(YellowbrickValueError): - model = KElbowVisualizer(KMeans(), k="foo") + KElbowVisualizer(KMeans(), k="foo") def test_distortion_metric(self): """ Test the distortion metric of the k-elbow visualizer """ - visualizer = KElbowVisualizer(KMeans(), k=5, metric="distortion") + visualizer = KElbowVisualizer(KMeans(random_state=0), k=5, metric="distortion", timings=False) visualizer.fit(X) - expected = [ - 7.6777850157143783, 8.3643185158057669, - 9.5203330222217666, 8.9777589843618912 - ] + expected = np.array([ 7.677785, 8.364319, 8.893634, 8.013021]) self.assertEqual(len(visualizer.k_scores_), 4) - # kMeans is stochastic so numbers will change - # self.assertEqual(visualizer.k_scores_, expected) + visualizer.poof() + self.assert_images_similar(visualizer) + assert_array_almost_equal(visualizer.k_scores_, expected) def test_silhouette_metric(self): """ Test the silhouette metric of the k-elbow visualizer """ - visualizer = KElbowVisualizer(KMeans(), k=5, metric="silhouette") + visualizer = KElbowVisualizer(KMeans(random_state=0), k=5, metric="silhouette", timings=False) visualizer.fit(X) - expected = [ - 0.69163638040000031, 0.4534779796676191, - 0.24802958481973392, 0.21792458448172247 - ] + expected = np.array([ 0.691636, 0.456646, 0.255174, 0.239842]) self.assertEqual(len(visualizer.k_scores_), 4) - # kMeans is stochastic so numbers will change - # self.assertEqual(visualizer.k_scores_, expected) + visualizer.poof() + self.assert_images_similar(visualizer) + assert_array_almost_equal(visualizer.k_scores_, expected) def test_calinski_harabaz_metric(self): """ Test the calinski-harabaz metric of the k-elbow visualizer """ - visualizer = KElbowVisualizer(KMeans(), k=5, metric="calinski_harabaz") + visualizer = KElbowVisualizer(KMeans(random_state=0), k=5, metric="calinski_harabaz", timings=False) visualizer.fit(X) - expected = [ + expected = np.array([ 81.662726256035683, 50.992378259195554, - 40.952179227847012, 37.068658049555459 - ] + 40.952179227847012, 35.939494 + ]) + self.assertEqual(len(visualizer.k_scores_), 4) - # kMeans is stochastic so numbers will change - # self.assertEqual(visualizer.k_scores_, expected) + visualizer.poof() + self.assert_images_similar(visualizer) + assert_array_almost_equal(visualizer.k_scores_, expected) def test_bad_metric(self): """ Assert KElbow raises an exception when a bad metric is supplied """ with self.assertRaises(YellowbrickValueError): - visualizer = KElbowVisualizer(KMeans(), k=5, metric="foo") + KElbowVisualizer(KMeans(), k=5, metric="foo") def test_timings(self): """ Test the twinx double axes with k-elbow timings """ - visualizer = KElbowVisualizer(KMeans(), k=5, timings=True) + visualizer = KElbowVisualizer(KMeans(random_state=0), k=5, timings=True) visualizer.fit(X) # Check that we kept track of time @@ -189,3 +206,15 @@ def test_timings(self): # Check that we plotted time on a twinx self.assertTrue(hasattr(visualizer, "axes")) self.assertEqual(len(visualizer.axes), 2) + + # delete the timings axes and + # overwrite k_timers_, k_values_ for image similarity Tests + visualizer.axes[1].remove() + visualizer.k_timers_ = [0.01084589958190918, 0.011144161224365234, 0.017028093338012695, 0.010634183883666992] + visualizer.k_values_ = [2, 3, 4, 5] + + # call draw again which is normally called in fit + visualizer.draw() + visualizer.poof() + + self.assert_images_similar(visualizer) diff --git a/tests/test_cluster/test_silhouette.py b/tests/test_cluster/test_silhouette.py index 5dcaac792..7e7bc6325 100644 --- a/tests/test_cluster/test_silhouette.py +++ b/tests/test_cluster/test_silhouette.py @@ -17,12 +17,13 @@ ## Imports ########################################################################## +import matplotlib.pyplot as plt + from ..base import VisualTestCase -from sklearn.cluster import KMeans, MiniBatchKMeans from sklearn.datasets import make_blobs +from sklearn.cluster import KMeans, MiniBatchKMeans -from yellowbrick.exceptions import YellowbrickValueError from yellowbrick.cluster.silhouette import SilhouetteVisualizer @@ -31,41 +32,52 @@ ########################################################################## class SilhouetteVisualizerTests(VisualTestCase): + """ + Silhouette Visualizer + """ def test_integrated_kmeans_silhouette(self): """ Test no exceptions for kmeans silhouette visualizer on blobs dataset - - See #182: cannot use occupancy dataset because of memory usage """ + # NOTE see #182: cannot use occupancy dataset because of memory usage # Generate a blobs data set X, y = make_blobs( - n_samples=1000, n_features=12, centers=8, shuffle=True, + n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0 ) try: - visualizer = SilhouetteVisualizer(KMeans()) + fig = plt.figure() + ax = fig.add_subplot() + + visualizer = SilhouetteVisualizer(KMeans(random_state=0), ax=ax) visualizer.fit(X) visualizer.poof() + + self.assert_images_similar(visualizer) except Exception as e: self.fail("error during silhouette: {}".format(e)) def test_integrated_mini_batch_kmeans_silhouette(self): """ Test no exceptions for mini-batch kmeans silhouette visualizer - - See #182: cannot use occupancy dataset because of memory usage """ + # NOTE see #182: cannot use occupancy dataset because of memory usage # Generate a blobs data set X, y = make_blobs( - n_samples=1000, n_features=12, centers=8, shuffle=True, + n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0 ) try: - visualizer = SilhouetteVisualizer(MiniBatchKMeans()) + fig = plt.figure() + ax = fig.add_subplot() + + visualizer = SilhouetteVisualizer(MiniBatchKMeans(random_state=0), ax=ax) visualizer.fit(X) visualizer.poof() + + self.assert_images_similar(visualizer) except Exception as e: self.fail("error during silhouette: {}".format(e)) diff --git a/tests/test_features/test_importances.py b/tests/test_features/test_importances.py new file mode 100644 index 000000000..16a411e38 --- /dev/null +++ b/tests/test_features/test_importances.py @@ -0,0 +1,391 @@ +# tests.test_features.test_importances +# Test the feature importance visualizers +# +# Author: Benjamin Bengfort +# Created: Fri Mar 02 15:23:22 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_importances.py [] benjamin@bengfort.com $ + +""" +Test the feature importance visualizers +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +import numpy as np +import numpy.testing as npt +import matplotlib.pyplot as plt + +from yellowbrick.exceptions import NotFitted +from yellowbrick.features.importances import * + +from sklearn.base import BaseEstimator +from sklearn.linear_model import Lasso +from sklearn.ensemble import RandomForestClassifier +from sklearn.ensemble import GradientBoostingClassifier + +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin + +try: + from unittest import mock +except ImportError: + import mock + +try: + import pandas as pd +except ImportError: + pd = None + + +########################################################################## +## Feature Importances Tests +########################################################################## + +class TestFeatureImportancesVisualizer(VisualTestCase, DatasetMixin): + """ + FeatureImportances visualizer + """ + + def test_integration_feature_importances(self): + """ + Integration test of visualizer with feature importances param + """ + + occupancy = self.load_data('occupancy') + features = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + # Extract X and y as numpy arrays + X = occupancy[features].copy() + X = X.view((float, len(X.dtype.names))) + y = occupancy['occupancy'].astype(int) + + fig = plt.figure() + ax = fig.add_subplot() + + clf = GradientBoostingClassifier(random_state=42) + viz = FeatureImportances(clf, ax=ax) + viz.fit(X, y) + viz.poof() + + self.assert_images_similar(viz) + + def test_integration_coef(self): + """ + Integration test of visualizer with coef param + """ + + concrete = self.load_data('concrete') + feats = ['cement','slag','ash','water','splast','coarse','fine','age'] + + # Create X and y datasets as numpy arrays + X = concrete[feats].copy() + X = X.view((float, len(X.dtype.names))) + y = concrete['strength'] + + fig = plt.figure() + ax = fig.add_subplot() + + reg = Lasso(random_state=42) + feats = list(map(lambda s: s.title(), feats)) + viz = FeatureImportances(reg, ax=ax, labels=feats, relative=False) + viz.fit(X, y) + viz.poof() + + self.assert_images_similar(viz) + + def test_integration_quick_method(self): + """ + Integration test of quick method + """ + + occupancy = self.load_data('occupancy') + features = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + # Create X and y datasets as numpy arrays + X = occupancy[features].copy() + X = X.view((float, len(X.dtype.names))) + y = occupancy['occupancy'].astype(int) + + fig = plt.figure() + ax = fig.add_subplot() + + clf = RandomForestClassifier(random_state=42) + g = feature_importances(clf, X, y, ax) + + self.assert_images_similar(ax=g) + + def test_fit_no_importances_model(self): + """ + Fitting a model without feature importances raises an exception + """ + X = np.random.rand(100, 42) + y = np.random.rand(100) + + visualizer = FeatureImportances(MockEstimator()) + expected_error = "could not find feature importances param on MockEstimator" + + with pytest.raises(YellowbrickTypeError, match=expected_error): + visualizer.fit(X, y) + + def test_fit_sorted_params(self): + """ + On fit, sorted features_ and feature_importances_ params are created + """ + coefs = np.array([0.4, 0.2, 0.08, 0.07, 0.16, .23, 0.38, 0.1, 0.05]) + names = np.array(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']) + + model = MockEstimator() + model.make_importance_param(value=coefs) + + visualizer = FeatureImportances(model, labels=names) + visualizer.fit(np.random.rand(100, len(names)), np.random.rand(100)) + + assert hasattr(visualizer, 'features_') + assert hasattr(visualizer, 'feature_importances_') + + # get the expected sort index + sort_idx = np.argsort(coefs) + + # assert sorted + npt.assert_array_equal(names[sort_idx], visualizer.features_) + npt.assert_array_equal(coefs[sort_idx], visualizer.feature_importances_) + + def test_fit_relative(self): + """ + Test fit computes relative importances + """ + coefs = np.array([0.4, 0.2, 0.08, 0.07, 0.16, .23, 0.38, 0.1, 0.05]) + + model = MockEstimator() + model.make_importance_param(value=coefs) + + visualizer = FeatureImportances(model, relative=True) + visualizer.fit(np.random.rand(100, len(coefs)), np.random.rand(100)) + + expected = 100.0 * coefs / coefs.max() + expected.sort() + npt.assert_array_equal(visualizer.feature_importances_, expected) + + def test_fit_not_relative(self): + """ + Test fit stores unmodified importances + """ + coefs = np.array([0.4, 0.2, 0.08, 0.07, 0.16, .23, 0.38, 0.1, 0.05]) + + model = MockEstimator() + model.make_importance_param(value=coefs) + + visualizer = FeatureImportances(model, relative=False) + visualizer.fit(np.random.rand(100, len(coefs)), np.random.rand(100)) + + coefs.sort() + npt.assert_array_equal(visualizer.feature_importances_, coefs) + + def test_fit_absolute(self): + """ + Test fit with absolute values + """ + coefs = np.array([0.4, 0.2, -0.08, 0.07, 0.16, .23, -0.38, 0.1, -0.05]) + + model = MockEstimator() + model.make_importance_param(value=coefs) + + # Test absolute value + visualizer = FeatureImportances(model, absolute=True, relative=False) + visualizer.fit(np.random.rand(100, len(coefs)), np.random.rand(100)) + + expected = np.array([0.05, 0.07, 0.08, 0.1, 0.16, 0.2, .23, 0.38, 0.4]) + npt.assert_array_equal(visualizer.feature_importances_, expected) + + # Test no absolute value + visualizer = FeatureImportances(model, absolute=False, relative=False) + visualizer.fit(np.random.rand(100, len(coefs)), np.random.rand(100)) + + expected = np.array([-0.38, -0.08, -0.05, 0.07, 0.1, 0.16, 0.2, .23, 0.4]) + npt.assert_array_equal(visualizer.feature_importances_, expected) + + + @pytest.mark.skipif(pd is None, reason="pandas is required for this test") + def test_fit_dataframe(self): + """ + Ensure feature names are extracted from DataFrame columns + """ + labels = ['a', 'b', 'c', 'd', 'e', 'f'] + df = pd.DataFrame(np.random.rand(100, 6), columns=labels) + s = pd.Series(np.random.rand(100), name='target') + + assert df.shape == (100, 6) + + model = MockEstimator() + model.make_importance_param(value=np.linspace(0, 1, 6)) + + visualizer = FeatureImportances(model) + visualizer.fit(df, s) + + assert hasattr(visualizer, 'features_') + npt.assert_array_equal(visualizer.features_, np.array(df.columns)) + + def test_fit_makes_labels(self): + """ + Assert that the fit process makes label indices + """ + model = MockEstimator() + model.make_importance_param(value=np.linspace(0, 1, 10)) + + visualizer = FeatureImportances(model) + visualizer.fit(np.random.rand(100, 10), np.random.rand(100)) + + # Don't have to worry about label space since importances are linspace + assert hasattr(visualizer, 'features_') + npt.assert_array_equal(np.arange(10), visualizer.features_) + + def test_fit_calls_draw(self): + """ + Assert that fit calls draw + """ + model = MockEstimator() + model.make_importance_param('coef_') + + visualizer = FeatureImportances(model) + + with mock.patch.object(visualizer, 'draw') as mdraw: + visualizer.fit(np.random.rand(100,42), np.random.rand(100)) + mdraw.assert_called_once() + + def test_draw_raises_unfitted(self): + """ + Assert draw raises exception when not fitted + """ + visualizer = FeatureImportances(Lasso()) + with pytest.raises(NotFitted): + visualizer.draw() + + def test_find_importances_param(self): + """ + Test the expected parameters can be found + """ + params = ('feature_importances_', 'coef_') + + for param in params: + model = MockEstimator() + model.make_importance_param(param, 'foo') + visualizer = FeatureImportances(model) + + assert hasattr(model, param), "expected '{}' missing".format(param) + for oparam in params: + if oparam == param: continue + assert not hasattr(model, oparam), "unexpected '{}'".format(oparam) + + importances = visualizer._find_importances_param() + assert importances == 'foo' + + def test_find_importances_param_priority(self): + """ + With both feature_importances_ and coef_, one has priority + """ + model = MockEstimator() + model.make_importance_param('feature_importances_', 'foo') + model.make_importance_param('coef_', 'bar') + visualizer = FeatureImportances(model) + + assert hasattr(model, 'feature_importances_') + assert hasattr(model, 'coef_') + + importances = visualizer._find_importances_param() + assert importances == 'foo' + + def test_find_importances_param_not_found(self): + """ + Raises an exception when importances param not found + """ + model = MockEstimator() + visualizer = FeatureImportances(model) + + assert not hasattr(model, 'feature_importances_') + assert not hasattr(model, 'coef_') + + with pytest.raises(YellowbrickTypeError): + visualizer._find_importances_param() + + def test_xlabel(self): + """ + Check the various xlabels are sensical + """ + model = MockEstimator() + model.make_importance_param('feature_importances_') + visualizer = FeatureImportances(model, xlabel="foo", relative=True) + + # Assert the visualizer uses the user supplied xlabel + assert visualizer._get_xlabel() == "foo", "could not set user xlabel" + + # Check the visualizer default relative xlabel + visualizer.set_params(xlabel=None) + assert "relative" in visualizer._get_xlabel() + + # Check value xlabel with default + visualizer.set_params(relative=False) + assert "relative" not in visualizer._get_xlabel() + + # Check coeficients + model = MockEstimator() + model.make_importance_param('coef_') + visualizer = FeatureImportances(model, xlabel="baz", relative=True) + + # Assert the visualizer uses the user supplied xlabel + assert visualizer._get_xlabel() == "baz", "could not set user xlabel" + + # Check the visualizer default relative xlabel + visualizer.set_params(xlabel=None) + assert "coefficient" in visualizer._get_xlabel() + assert "relative" in visualizer._get_xlabel() + + # Check value xlabel with default + visualizer.set_params(relative=False) + assert "coefficient" in visualizer._get_xlabel() + assert "relative" not in visualizer._get_xlabel() + + + def test_is_fitted(self): + """ + Test identification if is fitted + """ + visualizer = FeatureImportances(Lasso()) + assert not visualizer._is_fitted() + + visualizer.features_ = "foo" + assert not visualizer._is_fitted() + + visualizer.feature_importances_ = "bar" + assert visualizer._is_fitted() + + del visualizer.features_ + assert not visualizer._is_fitted() + + +########################################################################## +## Mock Estimator +########################################################################## + +class MockEstimator(BaseEstimator): + """ + Creates params when fit is called on demand. + """ + + def make_importance_param(self, name='feature_importances_', value=None): + if value is None: + value = np.random.rand(42) + setattr(self, name, value) + + def fit(self, X, y=None, **kwargs): + return self diff --git a/tests/test_features/test_jointplot.py b/tests/test_features/test_jointplot.py index 05ba757d4..d6c2b98e0 100644 --- a/tests/test_features/test_jointplot.py +++ b/tests/test_features/test_jointplot.py @@ -25,9 +25,10 @@ import unittest import numpy as np import matplotlib as mpl -import numpy.testing as npt +import matplotlib.pyplot as plt from tests.dataset import DatasetMixin +from tests.base import VisualTestCase from yellowbrick.features.jointplot import * ########################################################################## @@ -38,7 +39,7 @@ MPL_VERS_MAJ = int(mpl.__version__.split(".")[0]) -class JointPlotTests(unittest.TestCase, DatasetMixin): +class JointPlotTests(VisualTestCase, DatasetMixin): X = np.array([1, 2, 3, 5, 8, 10]) @@ -56,13 +57,13 @@ def test_warning(self): Ensure that the jointplot warns if mpl version is < 2.0.0 """ # Note Python 3.2+ has a self.assertWarns ... but we need to be - # Python 2.7 compatible, so we're going to do this. + # Python 2.7 compatible, so we're going to do this. with warnings.catch_warnings(record=True) as w: # Cause all warnings to always be triggered. warnings.simplefilter("always") # Trigger a warning. - visualizer = JointPlotVisualizer() + JointPlotVisualizer() # Ensure that a warning occurred self.assertEqual(len(w), 1) @@ -78,11 +79,15 @@ def test_jointplot_has_no_errors(self): """ Assert no errors occur during jointplot visualizer integration """ + fig = plt.figure() + ax = fig.add_subplot() - visualizer = JointPlotVisualizer() + visualizer = JointPlotVisualizer(ax=ax) visualizer.fit(self.X, self.y) visualizer.poof() + self.assert_images_similar(visualizer) + @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") def test_jointplot_integrated_has_no_errors(self): @@ -90,6 +95,9 @@ def test_jointplot_integrated_has_no_errors(self): Test jointplot on the concrete data set """ + fig = plt.figure() + ax = fig.add_subplot() + # Load the data from the fixture X = self.concrete['cement'] y = self.concrete['strength'] @@ -97,16 +105,18 @@ def test_jointplot_integrated_has_no_errors(self): target = 'strength' # Test the visualizer - visualizer = JointPlotVisualizer(feature=feature, target=target, joint_plot="hex") - visualizer.fit(X, y) # Fit the data to the visualizer - g = visualizer.poof() + visualizer = JointPlotVisualizer( + feature=feature, target=target, joint_plot="hex", ax=ax) + visualizer.fit(X, y) + visualizer.poof() + + self.assert_images_similar(visualizer) @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") def test_jointplot_no_matplotlib2_warning(self): """ Assert no UserWarning occurs if matplotlib major version >= 2 - (and not exactly 2.0.0). """ with warnings.catch_warnings(record=True) as ws: # Filter on UserWarnings @@ -123,4 +133,3 @@ def test_jointplot_no_matplotlib2_warning(self): mpl_ver_cnt += 1 self.assertEqual(0, mpl_ver_cnt, ws[-1].message \ if ws else "No error") - diff --git a/tests/test_features/test_pca.py b/tests/test_features/test_pca.py index 7b67fab82..955e185b7 100644 --- a/tests/test_features/test_pca.py +++ b/tests/test_features/test_pca.py @@ -17,13 +17,14 @@ ## Imports ########################################################################## -import unittest -import yellowbrick +import pytest import numpy as np import numpy.testing as npt from tests.base import VisualTestCase from yellowbrick.features.pca import * +from yellowbrick.exceptions import YellowbrickError + ########################################################################## ##PCA Tests @@ -85,8 +86,9 @@ def test_scale_true_2d(self): visualizer.fit(X) pca_array = visualizer.transform(X) visualizer.poof() - npt.assert_array_almost_equal(pca_array, X_pca_decomp) + self.assert_images_similar(visualizer) + def test_scale_false_2d(self): """ @@ -198,28 +200,14 @@ def test_scale_false_3d(self): visualizer = PCADecomposition(**params) visualizer.fit(X) pca_array = visualizer.transform(X) - visualizer.poof() - - npt.assert_array_almost_equal(pca_array, X_pca_decomp) def test_scale_true_4d_execption(self): """ Test the PCADecomposition visualizer 4 dimensions scaled (catch YellowbrickError). """ - X = np.array( - [[2.318, 2.727, 4.260, 7.212, 4.792], - [2.315, 2.726, 4.295, 7.140, 4.783, ], - [2.315, 2.724, 4.260, 7.135, 4.779, ], - [2.110, 3.609, 4.330, 7.985, 5.595, ], - [2.110, 3.626, 4.330, 8.203, 5.621, ], - [2.110, 3.620, 4.470, 8.210, 5.612, ]] - ) - - y = np.array([1, 1, 0, 1, 0, 0]) - - params = {'scale': True, 'center': False, 'proj_dim': 4, 'col': y} - with self.assertRaisesRegexp(yellowbrick.exceptions.YellowbrickError, "proj_dim object is not 2 or 3"): + params = {'scale': True, 'center': False, 'proj_dim': 4} + with pytest.raises(YellowbrickError, match="proj_dim object is not 2 or 3"): PCADecomposition(**params) def test_scale_true_3d_execption(self): @@ -240,6 +228,6 @@ def test_scale_true_3d_execption(self): params = {'scale': True, 'center': False, 'proj_dim': 3, 'col': y} - with self.assertRaisesRegexp(ValueError, "n_components=3 must be between 0 and n_features"): + with pytest.raises(ValueError, match="n_components=3 must be between 0 and n_features"): pca = PCADecomposition(**params) pca.fit(X) diff --git a/tests/test_features/test_pcoords.py b/tests/test_features/test_pcoords.py index a4dbe1036..cd0f59ddf 100644 --- a/tests/test_features/test_pcoords.py +++ b/tests/test_features/test_pcoords.py @@ -17,9 +17,9 @@ ## Imports ########################################################################## -import unittest import numpy as np +from tests.base import VisualTestCase from yellowbrick.features.pcoords import * from tests.dataset import DatasetMixin @@ -28,7 +28,7 @@ ########################################################################## -class ParallelCoordinatesTests(unittest.TestCase, DatasetMixin): +class ParallelCoordinatesTests(VisualTestCase, DatasetMixin): X = np.array( [[ 2.318, 2.727, 4.260, 7.212, 4.792], @@ -47,6 +47,9 @@ def test_parallel_coords(self): """ visualizer = ParallelCoordinates() visualizer.fit_transform(self.X, self.y) + visualizer.poof() + self.assert_images_similar(visualizer) + def test_normalized_pcoords(self): """ @@ -54,6 +57,8 @@ def test_normalized_pcoords(self): """ visualizer = ParallelCoordinates(normalize='l2') visualizer.fit_transform(self.X, self.y) + visualizer.poof() + self.assert_images_similar(visualizer) def test_normalized_pcoords_invalid_arg(self): """ @@ -101,8 +106,7 @@ def test_pcoords_sample_invalid_type(self): def test_integrated_pcoords(self): """ - Test parallel coordinates on a real, occupancy data set (downsampled - for speed) + Test parallel coordinates on a real data set (downsampled for speed) """ occupancy = self.load_data('occupancy') @@ -113,8 +117,10 @@ def test_integrated_pcoords(self): y = occupancy['occupancy'].astype(int) # Convert X to an ndarray - X = np.array(X.tolist()) + X = X.copy().view((float, len(X.dtype.names))) # Test the visualizer visualizer = ParallelCoordinates(sample=200) visualizer.fit_transform(X, y) + visualizer.poof() + self.assert_images_similar(visualizer) diff --git a/tests/test_features/test_radviz.py b/tests/test_features/test_radviz.py index 0770d4ac9..abdd806c0 100644 --- a/tests/test_features/test_radviz.py +++ b/tests/test_features/test_radviz.py @@ -16,22 +16,24 @@ ########################################################################## ## Imports ########################################################################## - import unittest -import numpy as np import numpy.testing as npt from tests.base import VisualTestCase from tests.dataset import DatasetMixin from yellowbrick.features.radviz import * +try: + import pandas +except ImportError: + pandas = None ########################################################################## ## RadViz Base Tests ########################################################################## -class RadVizTests(VisualTestCase, DatasetMixin): +class RadVizTests(VisualTestCase, DatasetMixin): X = np.array( [[ 2.318, 2.727, 4.260, 7.212, 4.792], [ 2.315, 2.726, 4.295, 7.140, 4.783,], @@ -45,9 +47,11 @@ class RadVizTests(VisualTestCase, DatasetMixin): def setUp(self): self.occupancy = self.load_data('occupancy') + super(RadVizTests, self).setUp() def tearDown(self): self.occupancy = None + super(RadVizTests, self).tearDown() def test_normalize_x(self): """ @@ -71,6 +75,8 @@ def test_radviz(self): """ visualizer = RadViz() visualizer.fit_transform(self.X, self.y) + visualizer.poof() + self.assert_images_similar(visualizer) def test_integrated_radviz(self): """ @@ -81,11 +87,62 @@ def test_integrated_radviz(self): X = self.occupancy[[ "temperature", "relative_humidity", "light", "C02", "humidity" ]] + X = X.copy().view((float, len(X.dtype.names))) y = self.occupancy['occupancy'].astype(int) - # Convert X to an ndarray - X = X.view((float, len(X.dtype.names))) - # Test the visualizer visualizer = RadViz() visualizer.fit_transform(X, y) + visualizer.poof() + self.assert_images_similar(visualizer) + + @unittest.skipUnless(pandas is not None, + "Pandas is not installed, could not run test.") + def test_integrated_radiz_with_pandas(self): + """ + Test scatterviz on the real, occupancy data set with pandas + """ + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = self.occupancy['occupancy'].astype(int) + + # Convert X to a pandas dataframe + X = pandas.DataFrame(X) + X.columns = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + # Test the visualizer + features = ["temperature", "relative_humidity"] + visualizer = RadViz(features=features) + visualizer.fit_transform_poof(X, y) + self.assert_images_similar(visualizer) + + + @unittest.skipUnless(pandas is not None, + "Pandas is not installed, could not run test.") + def test_integrated_radiz_with_pandas_with_classes(self): + """ + Test scatterviz on the real, occupancy data set with pandas with classes + """ + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + classes = ['unoccupied', 'occupied'] + + y = self.occupancy['occupancy'].astype(int) + + # Convert X to a pandas dataframe + X = pandas.DataFrame(X) + X.columns = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + # Test the visualizer + features = ["temperature", "relative_humidity"] + visualizer = RadViz(features=features, classes=classes) + visualizer.fit_transform_poof(X, y) + self.assert_images_similar(visualizer) diff --git a/tests/test_features/test_rankd.py b/tests/test_features/test_rankd.py new file mode 100644 index 000000000..c73eacedd --- /dev/null +++ b/tests/test_features/test_rankd.py @@ -0,0 +1,132 @@ +# tests.test_features.test_rankd +# Test the rankd feature analysis visualizers +# +# Author: Benjamin Bengfort +# Created: Fri Oct 07 12:19:19 2016 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_rankd.py [01d5996] benjamin@bengfort.com $ + +""" +Test the Rankd feature analysis visualizers +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +import numpy as np + +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin +from yellowbrick.features.rankd import * + +########################################################################## +## Rank1D Base Tests +########################################################################## + + +class Rank1DTests(VisualTestCase, DatasetMixin): + X = np.array( + [[ 2.318, 2.727, 4.260, 7.212, 4.792], + [ 2.315, 2.726, 4.295, 7.140, 4.783,], + [ 2.315, 2.724, 4.260, 7.135, 4.779,], + [ 2.110, 3.609, 4.330, 7.985, 5.595,], + [ 2.110, 3.626, 4.330, 8.203, 5.621,], + [ 2.110, 3.620, 4.470, 8.210, 5.612,]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + + def setUp(self): + super(Rank1DTests, self).setUp() + self.occupancy = self.load_data('occupancy') + + def tearDown(self): + super(Rank1DTests, self).tearDown() + self.occupancy = None + + + def test_rankd1(self): + """ + Assert no errors occur during rand1 visualizer integration + """ + visualizer = Rank1D() + visualizer.fit_transform(self.X, self.y) + visualizer.poof() + self.assert_images_similar(visualizer) + + def test_integrated_rankd1(self): + """ + Test rand1 on the real, occupancy data set + """ + + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + X = X.copy().view((float, len(X.dtype.names))) + y = self.occupancy['occupancy'].astype(int) + + # Test the visualizer + visualizer = Rank1D() + visualizer.fit_transform(X, y) + visualizer.poof() + self.assert_images_similar(visualizer) + + +########################################################################## +## Rank2D Base Tests +########################################################################## + +class Rank2DTests(VisualTestCase, DatasetMixin): + X = np.array( + [[ 2.318, 2.727, 4.260, 7.212, 4.792], + [ 2.315, 2.726, 4.295, 7.140, 4.783,], + [ 2.315, 2.724, 4.260, 7.135, 4.779,], + [ 2.110, 3.609, 4.330, 7.985, 5.595,], + [ 2.110, 3.626, 4.330, 8.203, 5.621,], + [ 2.110, 3.620, 4.470, 8.210, 5.612,]] + ) + + y = np.array([1, 1, 0, 1, 0, 0]) + + def setUp(self): + super(Rank2DTests, self).setUp() + self.occupancy = self.load_data('occupancy') + + def tearDown(self): + super(Rank2DTests, self).tearDown() + self.occupancy = None + + def test_rankd2(self): + """ + Assert no errors occur during rand2 visualizer integration + """ + visualizer = Rank2D() + visualizer.fit_transform(self.X, self.y) + visualizer.poof() + + + @pytest.mark.xfail + def test_integrated_rankd2(self): + """ + Test rand2 on the real, occupancy data set + """ + + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + X = X.copy().view((float, len(X.dtype.names))) + y = self.occupancy['occupancy'].astype(int) + + # Test the visualizer + visualizer = Rank2D() + visualizer.fit_transform(X, y) + visualizer.poof() + self.assert_images_similar(visualizer) +# diff --git a/tests/test_features/test_scatter.py b/tests/test_features/test_scatter.py index cfcfc5436..d91c8826e 100644 --- a/tests/test_features/test_scatter.py +++ b/tests/test_features/test_scatter.py @@ -16,9 +16,10 @@ # Imports ########################################################################## +import six +import pytest import unittest import numpy as np -import numpy.testing as npt import matplotlib as mptl from yellowbrick.features.scatter import * @@ -38,7 +39,7 @@ # ScatterViz Base Tests ########################################################################## - +@pytest.mark.filterwarnings('ignore') class ScatterVizTests(VisualTestCase, DatasetMixin): # yapf: disable @@ -66,6 +67,17 @@ def test_init_alias(self): visualizer = ScatterVisualizer(features=features, markers=['*']) self.assertIsNotNone(visualizer.markers) + def test_deprecated(self): + with pytest.deprecated_call(): + features = ["temperature", "relative_humidity"] + ScatterViz(features=features) + + @pytest.mark.skipif(six.PY2, reason="deprecation warnings filtered in PY2") + def test_deprecated_message(self): + with pytest.warns(DeprecationWarning, match='Will be moved to yellowbrick.contrib in v0.7'): + features = ["temperature", "relative_humidity"] + ScatterViz(features=features) + def test_scatter(self): """ Assert no errors occur during scatter visualizer integration @@ -100,9 +112,8 @@ def test_scatter_only_two_features_allowed_init(self): """ features = ["temperature", "relative_humidity", "light"] - with self.assertRaises(YellowbrickValueError) as context: - visualizer = ScatterViz(features=features) - + with self.assertRaises(YellowbrickValueError): + ScatterViz(features=features) def test_scatter_xy_and_features_raise_error(self): """ @@ -110,8 +121,8 @@ def test_scatter_xy_and_features_raise_error(self): """ features = ["temperature", "relative_humidity", "light"] - with self.assertRaises(YellowbrickValueError) as context: - visualizer = ScatterViz(features=features, x='one', y='two') + with self.assertRaises(YellowbrickValueError): + ScatterViz(features=features, x='one', y='two') def test_scatter_xy_changes_to_features(self): """ @@ -138,10 +149,10 @@ def test_integrated_scatter(self): X = self.occupancy[[ "temperature", "relative_humidity", "light", "C02", "humidity" ]] - y = self.occupancy['occupancy'].astype(int) - # Convert X to an ndarray - X = X.view((float, len(X.dtype.names))) + # Convert to numpy arrays + X = X.copy().view((float, len(X.dtype.names))) + y = self.occupancy['occupancy'].astype(int) # Test the visualizer features = ["temperature", "relative_humidity"] @@ -156,17 +167,17 @@ def test_scatter_quick_method(self): X = self.occupancy[[ "temperature", "relative_humidity", "light", "C02", "humidity" ]] - y = self.occupancy['occupancy'].astype(int) - # Convert X to an ndarray - X = X.view((float, len(X.dtype.names))) + # Convert to numpy arrays + X = X.copy().view((float, len(X.dtype.names))) + y = self.occupancy['occupancy'].astype(int) # Test the visualizer features = ["temperature", "relative_humidity"] ax = scatterviz(X[:, :2], y=y, ax=None, features=features) # test that is returns a matplotlib obj with axes - self.assertIn('Axes', str(ax.properties()['axes'])) + self.assertIsInstance(ax, mptl.axes.Axes) @unittest.skipUnless(pandas is not None, "Pandas is not installed, could not run test.") @@ -237,7 +248,7 @@ def test_scatter_image(self): def test_scatter_image_fail(self): """ - Assert bad image similarity on scatterviz errors + Assert bad image similarity on scatterviz errors """ X_two_cols = self.X[:, :2] diff --git a/tests/test_features/tests/actual_images/test_features/test_rankd/test_integrated_rankd1.png 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('bad', Thing()), ('model', MockEstimator()), @@ -107,21 +107,21 @@ def test_validate_steps(self): # validate a bad final estimator on the Pipeline with self.assertRaises(TypeError): - pipeline = Pipeline([ + Pipeline([ ('real', MockTransformer()), ('bad', Thing()), ]) # validate a bad final estimator on the VisualPipeline with self.assertRaises(TypeError): - pipeline = VisualPipeline([ + VisualPipeline([ ('real', MockTransformer()), ('bad', Thing()), ]) # validate visual transformers on a Pipeline try: - pipeline = Pipeline([ + Pipeline([ ('real', MockTransformer()), ('visual', MockVisualTransformer()), ('model', MockEstimator()), @@ -131,7 +131,7 @@ def test_validate_steps(self): # validate visual transformers on a VisualPipeline try: - pipeline = VisualPipeline([ + VisualPipeline([ ('real', MockTransformer()), ('visual', MockVisualTransformer()), ('model', MockEstimator()), diff --git a/tests/test_regressor/test_alphas.py b/tests/test_regressor/test_alphas.py index af0844465..5af600993 100644 --- a/tests/test_regressor/test_alphas.py +++ b/tests/test_regressor/test_alphas.py @@ -17,7 +17,6 @@ ## Imports ########################################################################## -import unittest import numpy as np from tests.base import VisualTestCase @@ -46,11 +45,11 @@ def test_regressor_cv(self): for model in (SVR, Ridge, Lasso, LassoLars, ElasticNet): with self.assertRaises(YellowbrickTypeError): - alphas = AlphaSelection(model()) + AlphaSelection(model()) for model in (RidgeCV, LassoCV, LassoLarsCV, ElasticNetCV): try: - alphas = AlphaSelection(model()) + AlphaSelection(model()) except YellowbrickTypeError: self.fail("could not instantiate RegressorCV on alpha selection") @@ -59,7 +58,7 @@ def test_only_regressors(self): Assert AlphaSelection only works with regressors """ with self.assertRaises(YellowbrickTypeError): - model = AlphaSelection(SVC()) + AlphaSelection(SVC()) def test_store_cv_values(self): """ @@ -120,3 +119,17 @@ def test_get_errors_param(self): self.assertTrue(len(errors) > 0) except YellowbrickValueError: self.fail("could not find errors on {}".format(model.name)) + + + def test_similar_image(self): + """ + Test similar plot drawn + """ + + visualizer = AlphaSelection(LassoCV(random_state=0)) + + X, y = make_regression(random_state=0) + visualizer.fit(X, y) + visualizer.poof() + + self.assert_images_similar(visualizer) diff --git a/tests/test_regressor/test_residuals.py b/tests/test_regressor/test_residuals.py index 0de6e6ccd..1b60365f8 100644 --- a/tests/test_regressor/test_residuals.py +++ b/tests/test_regressor/test_residuals.py @@ -24,7 +24,6 @@ from yellowbrick.regressor.residuals import * from sklearn.svm import SVR -from sklearn import cross_validation as cv from sklearn.model_selection import train_test_split as tts ########################################################################## @@ -68,6 +67,20 @@ def test_pred_error(self): visualizer.ax.grid(False) self.assert_images_similar(visualizer) + @unittest.skip("not implemented yet") + def test_peplot_shared_limits(self): + """ + Test shared limits on the peplot + """ + raise NotImplementedError("not yet implemented") + + @unittest.skip("not implemented yet") + def test_peplot_draw_bounds(self): + """ + Test the peplot +/- one bounding in draw + """ + raise NotImplementedError("not yet implemented") + ########################################################################## ## Residuals Plots test case ########################################################################## diff --git a/tests/test_style/test_colors.py b/tests/test_style/test_colors.py index 01121ac74..36894571e 100644 --- a/tests/test_style/test_colors.py +++ b/tests/test_style/test_colors.py @@ -10,18 +10,21 @@ # ID: test_colors.py [c6aff34] benjamin@bengfort.com $ """ -Tests for the color utilities and helpers module +Tests for the color utilities and helper functions """ ########################################################################## ## Imports ########################################################################## -import warnings -import unittest +import pytest + +from matplotlib import cm +from cycler import Cycler -from yellowbrick.style import * from yellowbrick.style.colors import * +from yellowbrick.style.palettes import ColorPalette, PALETTES + from tests.base import VisualTestCase @@ -29,15 +32,200 @@ ## Color Tests ########################################################################## -class ColorUtilitiesTests(VisualTestCase): +class TestGetColorCycle(VisualTestCase): + """ + Test get_color_cycle helper function + """ - def test_get_color_cycle(self): + def test_cycle_depends_on_palette(self): """ - Test the retreival of the current color cycle + Ensure the color cycle depends on the palette """ c = get_color_cycle() - self.assertEqual(len(c), 6) + assert len(c) == 6 + + with ColorPalette('paired'): + c = get_color_cycle() + assert len(c) == 12 - set_palette('paired') c = get_color_cycle() - self.assertEqual(len(c), 12) + assert len(c) == 6 + + @pytest.mark.filterwarnings() + @pytest.mark.skipif(not mpl_ge_150, reason="requires matplotlib 1.5 or later") + def test_mpl_ge_150(self): + """ + Test get color cycle with matplotlib 1.5 or later + """ + colors = get_color_cycle() + cycle = mpl.rcParams['axes.prop_cycle'] + + # Ensure the cycle is in fact a cycle + assert isinstance(cycle, Cycler) + + # Ensure that colors is actually a list (might change in the future) + assert isinstance(colors, list) + + # Ensure the cycler and the colors have the same length + cycle = list(cycle) + assert len(colors) == len(cycle) + + # Ensure the colors and cycle match + for color, cycle_color in zip(colors, cycle): + assert color == cycle_color['color'] + + + @pytest.mark.filterwarnings() + @pytest.mark.skipif(mpl_ge_150, reason="requires matplotlib ealier than 1.5") + def test_mpl_lt_150(self): + """ + Test get color cycle with matplotlib earlier than 1.5 + """ + assert get_color_cycle() == mpl.rcParams['axes.color_cycle'] + + +class TestResolveColors(VisualTestCase): + """ + Test resolve_colors helper function + """ + + def test_resolve_colors_default(self): + """ + Provides reasonable defaults provided no arguments + """ + colors = resolve_colors() + assert colors == get_color_cycle() + + def test_resolve_colors_default_truncate(self): + """ + Truncates default colors when n_colors is smaller than palette + """ + assert len(get_color_cycle()) > 3 + assert len(resolve_colors(3)) == 3 + + def test_resolve_colors_default_multiply(self): + """ + Multiplies default colors when n_colors is larger than palette + """ + assert len(get_color_cycle()) < 18 + assert len(resolve_colors(18)) == 18 + + def test_warning_on_colormap_and_colors_args(self): + """ + Warns when both colormap and colors is used, colors is default + """ + with pytest.warns(Warning, match="both colormap and colors specified"): + colors = resolve_colors(colormap='RdBu', colors=['r', 'g', 'b']) + assert colors == ['r', 'g', 'b'] + + def test_colormap_invalid(self): + """ + Exception raised when invalid colormap is supplied + """ + with pytest.raises(YellowbrickValueError): + resolve_colors(12, colormap='foo') + + def test_colormap_string(self): + """ + Check resolve colors works when a colormap string is passed + """ + cases = ( + ( + {'n_colors': 6, 'colormap': 'RdBu'}, + [ + (0.403921568627451, 0.0, 0.12156862745098039, 1.0), + (0.8392156862745098, 0.3764705882352941, 0.30196078431372547, 1.0), + (0.9921568627450981, 0.8588235294117647, 0.7803921568627451, 1.0), + (0.8196078431372551, 0.8980392156862746, 0.9411764705882353, 1.0), + (0.2627450980392157, 0.5764705882352941, 0.7647058823529411, 1.0), + (0.0196078431372549, 0.18823529411764706, 0.3803921568627451, 1.0) + ], + ), + ( + {'n_colors': 18, 'colormap': 'viridis'}, + [ + (0.267004, 0.004874, 0.329415, 1.0), + (0.281924, 0.089666, 0.412415, 1.0), + (0.280255, 0.165693, 0.476498, 1.0), + (0.263663, 0.237631, 0.518762, 1.0), + (0.237441, 0.305202, 0.541921, 1.0), + (0.208623, 0.367752, 0.552675, 1.0), + (0.182256, 0.426184, 0.55712, 1.0), + (0.159194, 0.482237, 0.558073, 1.0), + (0.13777, 0.537492, 0.554906, 1.0), + (0.121148, 0.592739, 0.544641, 1.0), + (0.128087, 0.647749, 0.523491, 1.0), + (0.180653, 0.701402, 0.488189, 1.0), + (0.274149, 0.751988, 0.436601, 1.0), + (0.395174, 0.797475, 0.367757, 1.0), + (0.535621, 0.835785, 0.281908, 1.0), + (0.688944, 0.865448, 0.182725, 1.0), + (0.845561, 0.887322, 0.099702, 1.0), + (0.993248, 0.906157, 0.143936, 1.0) + ], + ), + ( + {'n_colors': 9, 'colormap': 'Set1'}, + [ + (0.8941176470588236, 0.10196078431372549, 0.10980392156862745, 1.0), + (0.21568627450980393, 0.49411764705882355, 0.7215686274509804, 1.0), + (0.30196078431372547, 0.6862745098039216, 0.2901960784313726, 1.0), + (0.596078431372549, 0.3058823529411765, 0.6392156862745098, 1.0), + (1.0, 0.4980392156862745, 0.0, 1.0), + (1.0, 1.0, 0.2, 1.0), + (0.6509803921568628, 0.33725490196078434, 0.1568627450980392, 1.0), + (0.9686274509803922, 0.5058823529411764, 0.7490196078431373, 1.0), + (0.6, 0.6, 0.6, 1.0) + ], + ), + ) + + for kwds, expected in cases: + colors = resolve_colors(**kwds) + assert isinstance(colors, list) + assert colors == expected + + def test_colormap_string_default_length(self): + """ + Check colormap when n_colors is not specified + """ + n_colors = len(get_color_cycle()) + assert len(resolve_colors(colormap='autumn')) == n_colors + + def test_colormap_cmap(self): + """ + Assert that supplying a maptlotlib.cm as colormap works + """ + cmap = cm.get_cmap('nipy_spectral') + colors = resolve_colors(4, colormap=cmap) + assert colors == [ + (0.0, 0.0, 0.0, 1.0), + (0.0, 0.6444666666666666, 0.7333666666666667, 1.0), + (0.7999666666666666, 0.9777666666666667, 0.0, 1.0), + (0.8, 0.8, 0.8, 1.0) + ] + + def test_colors(self): + """ + Test passing in a list of colors + """ + c = PALETTES['flatui'] + assert resolve_colors(colors=c) == c + + def test_colors_truncate(self): + """ + Test passing in a list of colors with n_colors truncate + """ + c = PALETTES['flatui'] + + assert len(c) > 3 + assert len(resolve_colors(n_colors=3, colors=c)) == 3 + + def test_colors_multiply(self): + """ + Test passing in a list of colors with n_colors multiply + """ + c = PALETTES['flatui'] + + assert len(c) < 12 + assert len(resolve_colors(n_colors=12, colors=c)) == 12 diff --git a/tests/test_style/test_palettes.py b/tests/test_style/test_palettes.py index 69d8e8e5e..6ff78e71f 100644 --- a/tests/test_style/test_palettes.py +++ b/tests/test_style/test_palettes.py @@ -17,7 +17,6 @@ ## Imports ########################################################################## -import warnings import unittest import numpy as np import matplotlib as mpl @@ -31,6 +30,7 @@ from tests.base import VisualTestCase + ########################################################################## ## Color Palette Tests ########################################################################## @@ -271,16 +271,6 @@ def test_as_hex(self): for rgb_e, rgb_v in zip(pal, pal.as_hex().as_rgb()): self.assertEqual(rgb_e, rgb_v) - def test_get_color_cycle(self): - """ - Test getting the default color cycle - """ - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - result = get_color_cycle() - expected = mpl.rcParams['axes.color_cycle'] - self.assertEqual(result, expected) - def test_preserved_palette_length(self): """ Test palette length is preserved when modified @@ -312,17 +302,17 @@ def test_color_sequence_unrecocognized(self): Test value errors for unrecognized sequences """ with self.assertRaises(YellowbrickValueError): - cmap = color_sequence('PepperBucks', 3) + color_sequence('PepperBucks', 3) def test_color_sequence_bounds(self): """ Test color sequence out of bounds value error """ with self.assertRaises(YellowbrickValueError): - cmap = color_sequence('RdBu', 18) + color_sequence('RdBu', 18) with self.assertRaises(YellowbrickValueError): - cmap = color_sequence('RdBu', 2) + color_sequence('RdBu', 2) if __name__ == "__main__": unittest.main() diff --git a/tests/test_text/test_freqdist.py b/tests/test_text/test_freqdist.py index 793928bca..00a327869 100644 --- a/tests/test_text/test_freqdist.py +++ b/tests/test_text/test_freqdist.py @@ -17,10 +17,9 @@ ## Imports ########################################################################## -import unittest - from yellowbrick.text.freqdist import * from tests.dataset import DatasetMixin +from tests.base import VisualTestCase from sklearn.feature_extraction.text import CountVectorizer @@ -28,7 +27,7 @@ ## FreqDist Tests ########################################################################## -class FreqDistTests(unittest.TestCase, DatasetMixin): +class FreqDistTests(VisualTestCase, DatasetMixin): def test_integrated_freqdist(self): @@ -43,3 +42,6 @@ def test_integrated_freqdist(self): visualizer = FreqDistVisualizer(features) visualizer.fit(docs) + + visualizer.poof() + self.assert_images_similar(visualizer) diff --git a/tests/test_text/test_postag.py b/tests/test_text/test_postag.py index 15f22f01d..31e8bb260 100644 --- a/tests/test_text/test_postag.py +++ b/tests/test_text/test_postag.py @@ -18,17 +18,18 @@ ## Imports ########################################################################## -import unittest +import pytest from yellowbrick.text.postag import * + try: import nltk - from nltk.corpus import wordnet as wn from nltk import pos_tag, word_tokenize except ImportError: nltk = None + ########################################################################## ## Data ########################################################################## @@ -55,9 +56,12 @@ ## PosTag Tests ########################################################################## -class PosTagTests(unittest.TestCase): +class TestPosTag(object): + """ + PosTag (Part of Speech Tagging Visualizer) Tests + """ - @unittest.skipUnless(nltk is not None, "NLTK is not installed, could not run test.") + @pytest.mark.skipif(nltk is None, reason="test requires nltk") def test_integrated_postag(self): """ Assert no errors occur during postag integration diff --git a/tests/test_text/test_tsne.py b/tests/test_text/test_tsne.py index 8d9044e91..ce533743b 100644 --- a/tests/test_text/test_tsne.py +++ b/tests/test_text/test_tsne.py @@ -17,28 +17,38 @@ ## Imports ########################################################################## - -import unittest -import numpy as np +import six +import pytest from yellowbrick.text.tsne import * +from tests.base import VisualTestCase from tests.dataset import DatasetMixin from yellowbrick.exceptions import YellowbrickValueError + +from sklearn.datasets import make_classification from sklearn.feature_extraction.text import TfidfVectorizer +try: + import pandas +except ImportError: + pandas = None + ########################################################################## ## TSNE Tests ########################################################################## -class TSNETests(unittest.TestCase, DatasetMixin): +class TestTSNE(VisualTestCase, DatasetMixin): + """ + TSNEVisualizer tests + """ def test_bad_decomposition(self): """ Ensure an error is raised when a bad decompose argument is specified """ - with self.assertRaises(YellowbrickValueError): - tsne = TSNEVisualizer(decompose='bob') + with pytest.raises(YellowbrickValueError): + TSNEVisualizer(decompose='bob') def test_make_pipeline(self): """ @@ -46,26 +56,117 @@ def test_make_pipeline(self): """ tsne = TSNEVisualizer() # Should not cause an exception. - self.assertIsNotNone(tsne.transformer_) + assert tsne.transformer_ is not None svdp = tsne.make_transformer('svd', 90) - self.assertEqual(len(svdp.steps), 2) + assert len(svdp.steps) == 2 pcap = tsne.make_transformer('pca') - self.assertEqual(len(pcap.steps), 2) + assert len(pcap.steps) == 2 none = tsne.make_transformer(None) - self.assertEqual(len(none.steps), 1) + assert len(none.steps) == 1 def test_integrated_tsne(self): """ - Assert no errors occur during tsne integration + Check tSNE integrated visualization on the hobbies corpus """ corpus = self.load_data('hobbies') tfidf = TfidfVectorizer() docs = tfidf.fit_transform(corpus.data) - labels = corpus.target + labels = corpus.target - tsne = TSNEVisualizer() + tsne = TSNEVisualizer(random_state=8392, colormap='Set1') tsne.fit_transform(docs, labels) + + tol = 40 if six.PY3 else 55 + self.assert_images_similar(tsne, tol=tol) + + def test_make_classification_tsne(self): + """ + Test tSNE integrated visualization on a sklearn classifier dataset + """ + + ## produce random data + X, y = make_classification(n_samples=200, n_features=100, + n_informative=20, n_redundant=10, + n_classes=3, random_state=42) + + ## visualize data with t-SNE + tsne = TSNEVisualizer(random_state=87) + tsne.fit(X, y) + + tol = 0.1 if six.PY3 else 40 + self.assert_images_similar(tsne, tol=tol) + + def test_make_classification_tsne_class_labels(self): + """ + Test tSNE integrated visualization with class labels specified + """ + + ## produce random data + X, y = make_classification(n_samples=200, n_features=100, + n_informative=20, n_redundant=10, + n_classes=3, random_state=42) + + ## visualize data with t-SNE + tsne = TSNEVisualizer(random_state=87, labels=['a', 'b', 'c']) + tsne.fit(X, y) + + tol = 0.1 if six.PY3 else 40 + self.assert_images_similar(tsne, tol=tol) + + def test_tsne_mismtached_labels(self): + """ + Assert exception is raised when number of labels doesn't match + """ + ## produce random data + X, y = make_classification(n_samples=200, n_features=100, + n_informative=20, n_redundant=10, + n_classes=3, random_state=42) + + ## fewer labels than classes + tsne = TSNEVisualizer(random_state=87, labels=['a', 'b']) + with pytest.raises(YellowbrickValueError): + tsne.fit(X,y) + + ## more labels than classes + tsne = TSNEVisualizer(random_state=87, labels=['a', 'b', 'c', 'd']) + with pytest.raises(YellowbrickValueError): + tsne.fit(X,y) + + + def test_no_target_tsne(self): + """ + Test tSNE when no target or classes are specified + """ + ## produce random data + X, y = make_classification(n_samples=200, n_features=100, + n_informative=20, n_redundant=10, + n_classes=3, random_state=6897) + + ## visualize data with t-SNE + tsne = TSNEVisualizer(random_state=64) + tsne.fit(X) + + self.assert_images_similar(tsne, tol=0.1) + + @pytest.mark.skipif(pandas is None, reason="test requires pandas") + def test_visualizer_with_pandas(self): + """ + Test tSNE when passed a pandas DataFrame and series + """ + X, y = make_classification( + n_samples=200, n_features=100, n_informative=20, n_redundant=10, + n_classes=3, random_state=3020 + ) + + X = pandas.DataFrame(X) + y = pandas.Series(y) + + tsne = TSNEVisualizer(random_state=64) + tsne.fit(X, y) + + tol = 0.1 if six.PY3 else 40 + self.assert_images_similar(tsne, tol=tol) diff --git a/tests/test_utils/test_helpers.py b/tests/test_utils/test_helpers.py index 5768c754f..fb2b82e7a 100644 --- a/tests/test_utils/test_helpers.py +++ b/tests/test_utils/test_helpers.py @@ -18,64 +18,64 @@ ## Imports ########################################################################## -import unittest +import pytest +import numpy as np +import numpy.testing as npt from yellowbrick.utils.helpers import * from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA -from sklearn.neighbors import LSHForest -from sklearn.linear_model import RidgeCV, LassoCV +from sklearn.neighbors import KNeighborsClassifier +from sklearn.linear_model import LassoCV from sklearn.linear_model import LinearRegression -from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier -from sklearn.cluster import KMeans, MiniBatchKMeans -from sklearn.cluster import AffinityPropagation, Birch - +from sklearn.cluster import KMeans ########################################################################## ## Helper Function Tests ########################################################################## -class HelpersTests(unittest.TestCase): - - ##//////////////////////////////////////////////////////////////////// - ## get_model_name testing - ##//////////////////////////////////////////////////////////////////// +class TestHelpers(object): + """ + Helper functions and utilities + """ def test_real_model(self): """ - Test that model name works for sklearn estimators + Test getting model name for sklearn estimators """ model1 = LassoCV() - model2 = LSHForest() + model2 = KNeighborsClassifier() model3 = KMeans() model4 = RandomForestClassifier() - self.assertEqual(get_model_name(model1), 'LassoCV') - self.assertEqual(get_model_name(model2), 'LSHForest') - self.assertEqual(get_model_name(model3), 'KMeans') - self.assertEqual(get_model_name(model4), 'RandomForestClassifier') + assert get_model_name(model1) == 'LassoCV' + assert get_model_name(model2) == 'KNeighborsClassifier' + assert get_model_name(model3) == 'KMeans' + assert get_model_name(model4) == 'RandomForestClassifier' def test_pipeline(self): """ - Test that model name works for sklearn pipelines + Test getting model name for sklearn pipelines """ pipeline = Pipeline([('reduce_dim', PCA()), ('linreg', LinearRegression())]) - self.assertEqual(get_model_name(pipeline), 'LinearRegression') + assert get_model_name(pipeline) == 'LinearRegression' def test_int_input(self): """ - Assert a type error is raised when an int is passed to model name. + Assert a type error is raised when an int is passed to model name """ - self.assertRaises(TypeError, get_model_name, 1) + with pytest.raises(TypeError): + get_model_name(1) def test_str_input(self): """ - Assert a type error is raised when a str is passed to model name. + Assert a type error is raised when a str is passed to model name """ - self.assertRaises(TypeError, get_model_name, 'helloworld') + with pytest.raises(TypeError): + get_model_name('helloworld') ########################################################################## @@ -83,64 +83,103 @@ def test_str_input(self): ########################################################################## -class DivSafeTests(unittest.TestCase): +class TestNumericFunctions(object): + """ + Numeric helper functions + """ def test_div_1d_by_scalar(self): + """ + Test divide 1D vector by scalar + """ result = div_safe( [-1, 0, 1], 0 ) - self.assertTrue(result.all() == 0) + assert result.all() == 0 def test_div_1d_by_1d(self): - result =div_safe( [-1, 0 , 1], [0,0,0]) - self.assertTrue(result.all() == 0) + """ + Test divide 1D vector by another 1D vector with same length + """ + result = div_safe( [-1, 0 , 1], [0,0,0]) + assert result.all() == 0 def test_div_2d_by_1d(self): + """ + Test divide 2D vector by 1D vector with similar shape component + """ numerator = np.array([[-1,0,1,2],[1,-1,0,3]]) denominator = [0,0,0,0] - result = div_safe(numerator, denominator) + npt.assert_array_equal( + div_safe(numerator, denominator), + np.array([[0,0,0,0], [0,0,0,0]]) + ) def test_invalid_dimensions(self): - numerator = np.array([[-1,0,1,2],[1,-1,0,3]]) - denominator = [0,0] - with self.assertRaises(ValueError): - result = div_safe(numerator, denominator) + """ + Assert an error is raised on division with invalid dimensions + """ + numerator = np.array([[-1,0,1,2],[1,-1,0,3]]) + denominator = [0,0] + with pytest.raises(ValueError): + div_safe(numerator, denominator) def test_div_scalar_by_scalar(self): - with self.assertRaises(ValueError): - result = div_safe(5, 0) + """ + Assert a value error is raised when trying to divide two scalars + """ + with pytest.raises(ValueError): + div_safe(5, 0) ########################################################################## ## Features/Array Tests ########################################################################## -class NarrayIntColumnsTests(unittest.TestCase): +class TestNarrayIntColumns(object): + """ + Features and array helper tests + """ def test_has_ndarray_int_columns_true_int_features(self): + """ + Ensure ndarray with int features has int columns + """ x = np.random.rand(3,5) features = [0, 1] - self.assertTrue(has_ndarray_int_columns(features, x)) + assert has_ndarray_int_columns(features, x) def test_has_ndarray_int_columns_true_int_strings(self): + """ + Ensure ndarray with str(int) features has int columns + """ x = np.random.rand(3,5) features = ['0', '1'] - self.assertTrue(has_ndarray_int_columns(features, x)) + assert has_ndarray_int_columns(features, x) def test_has_ndarray_int_columns_false_not_numeric(self): + """ + Ensure ndarray with str features does not have int columns + """ x = np.random.rand(3,5) features = ['a', '1'] - self.assertFalse(has_ndarray_int_columns(features, x)) + assert not has_ndarray_int_columns(features, x) def test_has_ndarray_int_columns_false_outside_column_range(self): + """ + Ensure ndarray with str(int) outside range does not have int columns + """ x = np.random.rand(3,5) features = ['0', '10'] - self.assertFalse(has_ndarray_int_columns(features, x)) + assert not has_ndarray_int_columns(features, x) ########################################################################## ## String Helpers Tests ########################################################################## -class StringHelpersTests(unittest.TestCase): +class TestStringHelpers(object): + """ + String helper functions + """ def test_slugifiy(self): """ @@ -153,12 +192,4 @@ def test_slugifiy(self): ) for case, expected in cases: - self.assertEqual(expected, slugify(case)) - - -########################################################################## -## Execute Tests -########################################################################## - -if __name__ == "__main__": - unittest.main() + assert expected == slugify(case) diff --git a/tests/test_utils/test_nan_warnings.py b/tests/test_utils/test_nan_warnings.py new file mode 100644 index 000000000..e47e89dd3 --- /dev/null +++ b/tests/test_utils/test_nan_warnings.py @@ -0,0 +1,145 @@ +"""Test the RadViz feature analysis visualizers.""" + +import numpy as np +import pytest + +from yellowbrick.exceptions import DataWarning +from yellowbrick.utils.nan_warnings import count_nan_elements, \ + count_rows_with_nans, warn_if_nans_exist, filter_missing + + +def test_raise_warning_if_nans_exist(): + """Test that a warning is raised if any nans are in the data.""" + data = np.array([ + [1, 2, 3], + [1, 2, np.nan], + ]) + + with pytest.warns(DataWarning): + warn_if_nans_exist(data) + + +def test_count_rows_in_2d_arrays_with_nans(): + """Test that nan-containinr rows in 2d arrays are counted correctly.""" + data_1_row = np.array([ + [1, 2, 3], + ]) + + data_2_rows = np.array([ + [1, 2, 3], + [1, 2, 3], + [np.nan, 2, 3], + [1, np.nan, 3], + ]) + + data_3_rows = np.array([ + [1, 2, 3], + [np.nan, 2, 3], + [1, np.nan, 3], + [np.nan, np.nan, np.nan], + ]) + + assert count_rows_with_nans(data_1_row) == 0 + assert count_rows_with_nans(data_2_rows) == 2 + assert count_rows_with_nans(data_3_rows) == 3 + + +def test_count_nan_elements(): + """Test that nan elements in 1d arrays are counted correctly.""" + data0 = np.array([1, 2, 3]) + data1 = np.array([1, np.nan, 3]) + data3 = np.array([np.nan, np.nan, np.nan]) + + assert count_nan_elements(data0) == 0 + assert count_nan_elements(data1) == 1 + assert count_nan_elements(data3) == 3 + + +def test_clean_data_X_only_no_nans(): + """Test that an array with no nulls is returned intact.""" + X = np.array([ + [1, 2, 3], + [4, 5, 6], + [7, 8, 9], + ]) + + observed = filter_missing(X) + np.testing.assert_array_equal(X, observed) + + +def test_clean_data_X_only(): + """Test that nan-containing X rows are removed without y.""" + X = np.array([ + [1, 2, np.nan], + [4, 5, 6], + [np.nan, np.nan, np.nan], + ]) + + expected = np.array([ + [4, 5, 6] + ]) + observed = filter_missing(X) + + np.testing.assert_array_equal(expected, observed) + + +def test_clean_data_dirty_X_dirty_y(): + """Test that nan-containing X, y rows are removed when both contain nans.""" + X = np.array([ + [1, 2, 3], + [4, 5, 6], + [7, 8, np.nan], + [np.nan, np.nan, np.nan], + ]) + y = np.array([33, np.nan, 44, np.nan]) + + expected_X = np.array([ + [1, 2, 3], + ]) + expected_y = np.array([33]) + observed_X, observed_y = filter_missing(X, y) + + np.testing.assert_array_equal(expected_X, observed_X) + np.testing.assert_array_equal(expected_y, observed_y) + + +def test_clean_data_dirty_X_clean_y(): + """Test that nan-containing X, y rows are removed when X contains nans.""" + X = np.array([ + [1, 2, 3], + [4, 5, 6], + [7, 8, np.nan], + [np.nan, np.nan, np.nan], + ]) + y = np.array([33, 44, 55, 66]) + + expected_X = np.array([ + [1, 2, 3], + [4, 5, 6], + ]) + expected_y = np.array([33, 44]) + observed_X, observed_y = filter_missing(X, y) + + np.testing.assert_array_equal(expected_X, observed_X) + np.testing.assert_array_equal(expected_y, observed_y) + + +def test_clean_data_clean_X_dirty_y(): + """Test that nan-containing X, y rows are removed when y contains nans.""" + X = np.array([ + [1, 2, 3], + [4, 5, 6], + [7, 8, 9], + [10, 11, 12] + ]) + y = np.array([np.nan, 44, np.nan, 66]) + + expected_X = np.array([ + [4, 5, 6], + [10, 11, 12] + ]) + expected_y = np.array([44, 66]) + observed_X, observed_y = filter_missing(X, y) + + np.testing.assert_array_equal(expected_X, observed_X) + np.testing.assert_array_equal(expected_y, observed_y) diff --git a/tests/test_utils/test_types.py b/tests/test_utils/test_types.py index de4b10045..2a964945b 100644 --- a/tests/test_utils/test_types.py +++ b/tests/test_utils/test_types.py @@ -22,7 +22,7 @@ from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA -from sklearn.neighbors import LSHForest +from sklearn.neighbors import NearestNeighbors from sklearn.linear_model import RidgeCV, LassoCV from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression @@ -61,7 +61,7 @@ def test_estimator_instance(self): LinearRegression(), LogisticRegression(), KMeans(), - LSHForest(), + NearestNeighbors(), PCA(), RidgeCV(), LassoCV(), @@ -90,7 +90,7 @@ def test_estimator_class(self): LinearRegression, LogisticRegression, KMeans, - LSHForest, + NearestNeighbors, PCA, RidgeCV, LassoCV, @@ -153,7 +153,7 @@ def test_regressor_instance(self): notregressors = ( KMeans, PCA, - LSHForest, + NearestNeighbors, LogisticRegression, RandomForestClassifier, ) @@ -182,7 +182,7 @@ def test_regressor_class(self): notregressors = ( KMeans, PCA, - LSHForest, + NearestNeighbors, LogisticRegression, RandomForestClassifier, ) @@ -239,7 +239,7 @@ def test_classifier_instance(self): notclassifiers = ( KMeans, PCA, - LSHForest, + NearestNeighbors, LinearRegression, RidgeCV, LassoCV, @@ -268,7 +268,7 @@ def test_classifier_class(self): notclassifiers = ( KMeans, PCA, - LSHForest, + NearestNeighbors, RidgeCV, LassoCV, LinearRegression, @@ -330,7 +330,7 @@ def test_clusterer_instance(self): LassoCV, LinearRegression, PCA, - LSHForest, + NearestNeighbors, LogisticRegression, RandomForestClassifier, ) @@ -362,7 +362,7 @@ def test_clusterer_class(self): LassoCV, LinearRegression, PCA, - LSHForest, + NearestNeighbors, LogisticRegression, RandomForestClassifier, ) diff --git a/tests/test_utils/test_wrapper.py b/tests/test_utils/test_wrapper.py index 35e70ab3d..f35056d3c 100644 --- a/tests/test_utils/test_wrapper.py +++ b/tests/test_utils/test_wrapper.py @@ -18,7 +18,6 @@ ########################################################################## import unittest -import numpy as np from yellowbrick.base import Visualizer from yellowbrick.utils.wrapper import * diff --git a/yellowbrick/__init__.py b/yellowbrick/__init__.py index e6ffdb43e..081e8f675 100644 --- a/yellowbrick/__init__.py +++ b/yellowbrick/__init__.py @@ -23,23 +23,27 @@ _orig_rc_params = mpl.rcParams.copy() # Import the version number at the top level -from .version import get_version +from .version import get_version, __version_info__ # Import the style management functions -from .style.rcmod import * -from .style.palettes import * +from .style.rcmod import reset_defaults, reset_orig +from .style.rcmod import set_aesthetic, set_style, set_palette +from .style.palettes import color_palette, set_color_codes # Import yellowbrick functionality to the top level +# TODO: review top-level functionality from .anscombe import anscombe from .classifier import ROCAUC, ClassBalance, ClassificationScoreVisualizer # from .classifier import crplot, rocplot # from .regressor import peplot, residuals_plot + ########################################################################## ## Set default aesthetics ########################################################################## -set_aesthetic() # modifies mpl.rcParams +set_aesthetic() # NOTE: modifies mpl.rcParams + ########################################################################## ## Package Version diff --git a/yellowbrick/base.py b/yellowbrick/base.py index d0fd7b0c9..ae2c7568d 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -18,7 +18,6 @@ from .utils.wrapper import Wrapper from sklearn.base import BaseEstimator -from .exceptions import YellowbrickTypeError from .utils import get_model_name, isestimator from sklearn.model_selection import cross_val_predict as cvp @@ -51,7 +50,7 @@ class Visualizer(BaseEstimator): ============= ======================================================= Property Description ------------- ------------------------------------------------------- - size specify a size for the figure (currently unimplemented) + size specify a size for the figure color specify a color, colormap, or palette for the figure title specify the title of the figure ============= ======================================================= @@ -89,6 +88,27 @@ def ax(self): def ax(self, ax): self._ax = ax + @property + def size(self): + """ + Returns the actual size in pixels as set by matplotlib, or + the user provided size if available. + """ + if not hasattr(self, "_size") or self._size is None: + fig = plt.gcf() + self._size = fig.get_size_inches()*fig.dpi + return self._size + + @size.setter + def size(self, size): + self._size = size + if self._size is not None: + fig = plt.gcf() + width, height = size + width_in_inches = width / fig.get_dpi() + height_in_inches = height / fig.get_dpi() + fig.set_size_inches(width_in_inches, height_in_inches) + ##//////////////////////////////////////////////////////////////////// ## Estimator interface ##//////////////////////////////////////////////////////////////////// @@ -233,7 +253,7 @@ class ModelVisualizer(Visualizer, Wrapper): kwargs : dict Keyword arguments that are passed to the base class and may influence - the visualization as defined in other Visualizersself. + the visualization as defined by other Visualizers. Notes ----- diff --git a/yellowbrick/bestfit.py b/yellowbrick/bestfit.py index af7c24edd..32b401fa8 100644 --- a/yellowbrick/bestfit.py +++ b/yellowbrick/bestfit.py @@ -131,7 +131,7 @@ def draw_best_fit(X, y, ax, estimator='linear', **kwargs): if X.ndim > 2: raise YellowbrickValueError( - "X must be a (1,) or (n,1) dimensional array not {}".format(x.shape) + "X must be a (1,) or (n,1) dimensional array not {}".format(X.shape) ) # Verify that y is a (n,) dimensional array @@ -284,7 +284,6 @@ def callback(ax): if __name__ == '__main__': import os import pandas as pd - import matplotlib.pyplot as plt path = os.path.join(os.path.dirname(__file__), "..", "examples", "data", "concrete.xls") if not os.path.exists(path): diff --git a/yellowbrick/classifier/__init__.py b/yellowbrick/classifier/__init__.py index 1dac5b54d..786dc7e88 100644 --- a/yellowbrick/classifier/__init__.py +++ b/yellowbrick/classifier/__init__.py @@ -23,9 +23,10 @@ ## Hoist visualizers into the classifier namespace from ..base import ScoreVisualizer from .base import ClassificationScoreVisualizer -from .class_balance import ClassBalance +from .class_balance import ClassBalance, ClassPredictionError from .classification_report import ClassificationReport, classification_report from .confusion_matrix import ConfusionMatrix from .learning_curve import LearningCurveVisualizer, learning_curve_plot from .rocauc import ROCAUC, roc_auc from .boundaries import decisionviz, DecisionBoundariesVisualizer, DecisionViz +from .threshold import ThreshViz, ThresholdVisualizer, thresholdviz diff --git a/yellowbrick/classifier/base.py b/yellowbrick/classifier/base.py index 584be83ec..5755593f6 100644 --- a/yellowbrick/classifier/base.py +++ b/yellowbrick/classifier/base.py @@ -56,7 +56,12 @@ def __init__(self, model, ax=None, classes=None, **kwargs): classes = np.array(classes) # Set up classifier score visualization properties - self.colors = color_palette(kwargs.pop('colors', None)) + if classes is not None: + n_colors = len(classes) + else: + n_colors = None + + self.colors = color_palette(kwargs.pop('colors', None), n_colors) self.classes_ = classes @property diff --git a/yellowbrick/classifier/boundaries.py b/yellowbrick/classifier/boundaries.py index 8c7219427..425115a0f 100644 --- a/yellowbrick/classifier/boundaries.py +++ b/yellowbrick/classifier/boundaries.py @@ -6,28 +6,29 @@ # # Copyright (C) 2017 District Data Labs # For license information, see LICENSE.txt -from collections import OrderedDict + import itertools import numpy as np -import matplotlib.pyplot as plt -from matplotlib.colors import ListedColormap +from collections import OrderedDict + +from sklearn.utils.deprecation import deprecated + from matplotlib.patches import Patch +from matplotlib.colors import ListedColormap from yellowbrick.exceptions import YellowbrickTypeError from yellowbrick.exceptions import YellowbrickValueError from yellowbrick.classifier.base import ClassificationScoreVisualizer -from yellowbrick.utils import get_model_name from yellowbrick.style.colors import resolve_colors -from yellowbrick.utils import is_dataframe, is_structured_array, has_ndarray_int_columns -from yellowbrick.style.palettes import PALETTES - +from yellowbrick.utils import is_dataframe, is_structured_array +from yellowbrick.utils import has_ndarray_int_columns ########################################################################## # Quick Methods ########################################################################## - +@deprecated("Will be moved to yellowbrick.contrib in v0.7") def decisionviz(model, X, y, @@ -122,7 +123,7 @@ def decisionviz(model, ########################################################################## # Static ScatterVisualizer Visualizer ########################################################################## - +@deprecated("Will be moved to yellowbrick.contrib in v0.7") class DecisionBoundariesVisualizer(ClassificationScoreVisualizer): """ DecisionBoundariesVisualizer is a bivariate data visualization algorithm @@ -234,7 +235,7 @@ def _select_feature_columns(self, X): """ """ if len(X.shape) == 1: - X_flat = X.view(np.float64).reshape(len(X), -1) + X_flat = X.copy().view(np.float64).reshape(len(X), -1) else: X_flat = X @@ -252,7 +253,7 @@ def _select_feature_columns(self, X): # handle numpy named/ structured array elif self.features_ is not None and is_structured_array(X): X_selected = X[self.features_] - X_two_cols = X_selected.view(np.float64).reshape(len(X_selected), -1) + X_two_cols = X_selected.copy().view(np.float64).reshape(len(X_selected), -1) # handle features that are numeric columns in ndarray matrix elif self.features_ is not None and has_ndarray_int_columns(self.features_, X): @@ -344,7 +345,7 @@ def draw(self, X, y=None, **kwargs): X = self._select_feature_columns(X) color_cycle = iter( - resolve_colors(color=self.colors, num_colors=len(self.classes_))) + resolve_colors(colors=self.colors, n_colors=len(self.classes_))) colors = OrderedDict([(c, next(color_cycle)) for c in self.classes_.keys()]) diff --git a/yellowbrick/classifier/class_balance.py b/yellowbrick/classifier/class_balance.py index 6ce67ba6d..1e13ebac0 100644 --- a/yellowbrick/classifier/class_balance.py +++ b/yellowbrick/classifier/class_balance.py @@ -4,6 +4,7 @@ # Author: Rebecca Bilbro # Author: Benjamin Bengfort # Author: Neal Humphrey +# Author: Larry Gray # Created: Wed May 18 12:39:40 2016 -0400 # # Copyright (C) 2017 District Data Labs @@ -19,12 +20,18 @@ ## Imports ########################################################################## +import matplotlib.pyplot as plt import numpy as np from .base import ClassificationScoreVisualizer from sklearn.model_selection import train_test_split from sklearn.metrics import precision_recall_fscore_support +from sklearn.utils.multiclass import unique_labels +from sklearn.metrics.classification import _check_targets + +from ..exceptions import ModelError, YellowbrickValueError +from ..style.colors import resolve_colors ########################################################################## @@ -122,7 +129,7 @@ def finalize(self, **kwargs): self.ax.set_xticklabels(self.support.keys()) # Compute the ceiling for the y limit - cmax, cmin = max(self.support.values()), min(self.support.values()) + cmax = max(self.support.values()) self.ax.set_ylim(0, cmax + cmax* 0.1) @@ -168,3 +175,179 @@ def class_balance(model, X, y=None, ax=None, classes=None, **kwargs): # Return the axes object on the visualizer return visualizer.ax + + +########################################################################## +## Class Prediction Error Chart +########################################################################## + +class ClassPredictionError(ClassificationScoreVisualizer): + """ + Class Prediction Error chart that shows the support for each class in the + fitted classification model displayed as a stacked bar. Each bar is + segmented to show the distribution of predicted classes for each + class. It is initialized with a fitted model and generates a + class prediction error chart on draw. + + Parameters + ---------- + ax: axes + the axis to plot the figure on. + model: estimator + Scikit-Learn estimator object. Should be an instance of a classifier, + else ``__init__()`` will raise an exception. + classes: list + A list of class names for the legend. If classes is None and a y value + is passed to fit then the classes are selected from the target vector. + kwargs: dict + Keyword arguments passed to the super class. Here, used + to colorize the bars in the histogram. + Notes + ----- + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + + def score(self, X, y, **kwargs): + """ + Generates a 2D array where each row is the count of the + predicted classes and each column is the true class + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + y : ndarray or Series of length n + An array or series of target or class values + + Returns + ------- + + ax : the axis with the plotted figure + """ + + # We're replying on predict to raise NotFitted + y_pred = self.predict(X) + + y_type, y_true, y_pred = _check_targets(y, y_pred) + + if y_type not in ("binary", "multiclass"): + raise YellowbrickValueError("%s is not supported" % y_type) + + indices = unique_labels(y_true, y_pred) + + if len(self.classes_) > len(indices): + raise ModelError("y and y_pred contain zero values " + "for one of the specified classes") + elif len(self.classes_) < len(indices): + raise NotImplementedError("filtering classes is " + "currently not supported") + + # Create a table of scores whose rows are the true classes + # and whose columns are the predicted classes; each element + # is the count of predictions for that class that match the true + # value of that class. + self.scores_ = np.array([ + [ + (y_pred[y == label_t] == label_p).sum() + for label_p in indices + ] + for label_t in indices + ]) + + return self.draw() + + def draw(self): + """ + Renders the class prediction error across the axis. + Returns + ------- + ax : the axis with the plotted figure + """ + + indices = np.arange(len(self.classes_)) + prev = np.zeros(len(self.classes_)) + + colors = resolve_colors( + colors=self.colors, + n_colors=len(self.classes_)) + + for idx, row in enumerate(self.scores_): + self.ax.bar(indices, row, label=self.classes_[idx], + bottom=prev, color=colors[idx]) + prev += row + + return self.ax + + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + Parameters + ---------- + kwargs: generic keyword arguments. + + """ + + indices = np.arange(len(self.classes_)) + + # Set the title + self.set_title("Class Prediction Error for {}".format(self.name)) + + # Set the x ticks with the class names + self.ax.set_xticks(indices) + self.ax.set_xticklabels(self.classes_) + + # Set the axes labels + self.ax.set_xlabel("actual class") + self.ax.set_ylabel("number of predicted class") + + # Compute the ceiling for the y limit + cmax = max([sum(scores) for scores in self.scores_]) + self.ax.set_ylim(0, cmax + cmax * 0.1) + + # Put the legend outside of the graph + plt.legend(bbox_to_anchor=(1.04, 0.5), loc="center left") + plt.tight_layout(rect=[0, 0, 0.85, 1]) + + +def class_prediction_error(model, X, y=None, ax=None, classes=None, + test_size=0.2, **kwargs): + """Quick method: + Displays the support for each class in the + fitted classification model displayed as a stacked bar plot. + Each bar is segmented to show the distribution of predicted + classes for each class. + + This helper function is a quick wrapper to utilize the ClassPredictionError + ScoreVisualizer for one-off analysis. + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features. + y : ndarray or Series of length n + An array or series of target or class values. + ax : matplotlib axes + The axes to plot the figure on. + model : the Scikit-Learn estimator (should be a classifier) + classes : list of strings + The names of the classes in the target + Returns + ------- + ax : matplotlib axes + Returns the axes that the class prediction error plot was drawn on. + """ + # Instantiate the visualizer + visualizer = ClassPredictionError(model, ax, classes, **kwargs) + + # Create the train and test splits + X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size=test_size, + random_state=42) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X_train, y_train, **kwargs) + visualizer.score(X_test, y_test) + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/classifier/classification_report.py b/yellowbrick/classifier/classification_report.py index bdaec3431..b0ca92bab 100644 --- a/yellowbrick/classifier/classification_report.py +++ b/yellowbrick/classifier/classification_report.py @@ -34,6 +34,11 @@ ## Classification Report ########################################################################## +CMAP_UNDERCOLOR = 'w' +CMAP_OVERCOLOR = '#2a7d4f' +SCORES_KEYS = ('precision', 'recall', 'f1') + + class ClassificationReport(ClassificationScoreVisualizer): """ Classification report that shows the precision, recall, and F1 scores @@ -41,7 +46,6 @@ class ClassificationReport(ClassificationScoreVisualizer): Parameters ---------- - ax : The axis to plot the figure on. model : the Scikit-Learn estimator @@ -52,14 +56,14 @@ class ClassificationReport(ClassificationScoreVisualizer): If classes is None and a y value is passed to fit then the classes are selected from the target vector. - colormap : optional string or matplotlib cmap to colorize lines - Use sequential heatmap. + cmap : string, default: ``'YlOrRd'`` + Specify a colormap to define the heatmap of the predicted class + against the actual class in the confusion matrix. kwargs : keyword arguments passed to the super class. Examples -------- - >>> from yellowbrick.classifier import ClassificationReport >>> from sklearn.linear_model import LogisticRegression >>> viz = ClassificationReport(LogisticRegression()) @@ -67,69 +71,91 @@ class ClassificationReport(ClassificationScoreVisualizer): >>> viz.score(X_test, y_test) >>> viz.poof() + Attributes + ---------- + scores_ : dict of dicts + Outer dictionary composed of precision, recall, and f1 scores with + inner dictionaries specifiying the values for each class listed. """ - def __init__(self, model, ax=None, classes=None, **kwargs): + def __init__(self, model, ax=None, classes=None, cmap='YlOrRd', **kwargs): super(ClassificationReport, self).__init__( model, ax=ax, classes=classes, **kwargs ) - self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) + self.cmap = color_sequence(cmap) + self.cmap.set_under(color=CMAP_UNDERCOLOR) + self.cmap.set_over(color=CMAP_OVERCOLOR) def score(self, X, y=None, **kwargs): """ - Generates the Scikit-Learn classification_report + Generates the Scikit-Learn classification report. Parameters ---------- - X : ndarray or DataFrame of shape n x m A matrix of n instances with m features y : ndarray or Series of length n An array or series of target or class values - """ y_pred = self.predict(X) - keys = ('precision', 'recall', 'f1') - self.scores = precision_recall_fscore_support(y, y_pred) - self.scores = map(lambda s: dict(zip(self.classes_, s)), self.scores[0:3]) - self.scores = dict(zip(keys, self.scores)) - return self.draw(y, y_pred) + scores = precision_recall_fscore_support(y, y_pred) + scores = map(lambda s: dict(zip(self.classes_, s)), scores[0:3]) + self.scores_ = dict(zip(SCORES_KEYS, scores)) + + return self.draw() - def draw(self, y, y_pred): + def draw(self): """ Renders the classification report across each axis. - - Parameters - ---------- - - y : ndarray or Series of length n - An array or series of target or class values - - y_pred : ndarray or Series of length n - An array or series of predicted target values """ - self.matrix = [] - for cls in self.classes_: - self.matrix.append([self.scores['precision'][cls],self.scores['recall'][cls],self.scores['f1'][cls]]) - - for column in range(0,3): #3 columns - prec,rec,f1 - for row in range(len(self.classes_)): - current_score = self.matrix[row][column] - base_color = self.cmap(current_score) - text_color= find_text_color(base_color) - - # Limit the current score to a precision of 3 - current_score = "{:0.3f}".format(current_score) - - self.ax.text(column,row,current_score,va='center',ha='center', color=text_color) - - fig = plt.imshow(self.matrix, interpolation='nearest', cmap=self.cmap, vmin=0, vmax=1, aspect='auto') + # Create display grid + cr_display = np.zeros((len(self.classes_), 3)) + + # For each class row, append columns for precision, recall, and f1 + for idx, cls in enumerate(self.classes_): + for jdx, metric in enumerate(('precision', 'recall', 'f1')): + cr_display[idx, jdx] = self.scores_[metric][cls] + + # Set up the dimensions of the pcolormesh + # NOTE: pcolormesh accepts grids that are (N+1,M+1) + X, Y = np.arange(len(self.classes_)+1), np.arange(4) + self.ax.set_ylim(bottom=0, top=cr_display.shape[0]) + self.ax.set_xlim(left=0, right=cr_display.shape[1]) + + # Set data labels in the grid, enumerating over class, metric pairs + # NOTE: X and Y are one element longer than the classification report + # so skip the last element to label the grid correctly. + for x in X[:-1]: + for y in Y[:-1]: + + # Extract the value and the text label + value = cr_display[x,y] + svalue = "{:0.3f}".format(value) + + # Determine the grid and text colors + base_color = self.cmap(value) + text_color = find_text_color(base_color) + + # Add the label to the middle of the grid + cx, cy = x+0.5, y+0.5 + self.ax.text( + cy, cx, svalue, va='center', ha='center', color=text_color + ) + + + # Draw the heatmap with colors bounded by the min and max of the grid + # NOTE: I do not understand why this is Y, X instead of X, Y it works + # in this order but raises an exception with the other order. + g = self.ax.pcolormesh( + Y, X, cr_display, vmin=0, vmax=1, cmap=self.cmap, edgecolor='w', + ) # Add the color bar - plt.colorbar() + plt.colorbar(g, ax=self.ax) + # Return the axes being drawn on return self.ax def finalize(self, **kwargs): @@ -145,20 +171,14 @@ def finalize(self, **kwargs): # Set the title of the classifiation report self.set_title('{} Classification Report'.format(self.name)) - # Compute the tick marks for both x and y - x_tick_marks = np.arange(len(self.classes_)+1) - y_tick_marks = np.arange(len(self.classes_)) - # Set the tick marks appropriately - self.ax.set_xticks(x_tick_marks) - self.ax.set_yticks(y_tick_marks) + self.ax.set_xticks(np.arange(3)+0.5) + self.ax.set_yticks(np.arange(len(self.classes_))+0.5) self.ax.set_xticklabels(['precision', 'recall', 'f1-score'], rotation=45) self.ax.set_yticklabels(self.classes_) - # Set the labels for the two axes - self.ax.set_ylabel('Classes') - self.ax.set_xlabel('Measures') + plt.tight_layout() def classification_report(model, X, y=None, ax=None, classes=None, **kwargs): diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py index b2fef338b..0fc3c661c 100644 --- a/yellowbrick/classifier/confusion_matrix.py +++ b/yellowbrick/classifier/confusion_matrix.py @@ -17,58 +17,93 @@ ## Imports ########################################################################## +import warnings import numpy as np -import matplotlib.pyplot as plt - -from sklearn.metrics import confusion_matrix from ..utils import div_safe from ..style import find_text_color from ..style.palettes import color_sequence from .base import ClassificationScoreVisualizer +from sklearn.model_selection import train_test_split +from sklearn.metrics import confusion_matrix as confusion_matrix_metric + ########################################################################## ## ConfusionMatrix ########################################################################## +CMAP_UNDERCOLOR = 'w' CMAP_OVERCOLOR = '#2a7d4f' +CMAP_MUTEDCOLOR = '0.75' class ConfusionMatrix(ClassificationScoreVisualizer): """ - Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). A confusion - matrix shows each combination of the true and predicted classes for a test data set. + Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). + A confusion matrix shows each combination of the true and predicted + classes for a test data set. - The default color map uses a yellow/orange/red color scale. The user can choose between - displaying values as the percent of true (cell value divided by sum of row) or as direct - counts. If percent of true mode is selected, 100% accurate predictions are highlighted in green. + The default color map uses a yellow/orange/red color scale. The user can + choose between displaying values as the percent of true (cell value + divided by sum of row) or as direct counts. If percent of true mode is + selected, 100% accurate predictions are highlighted in green. - Requires a classification model + Requires a classification model. Parameters ---------- - model : the Scikit-Learn estimator - Should be an instance of a classifier or __init__ will return an error. - - ax : the matplotlib axis to plot the figure on (if None, a new axis will be created) - - classes : list, default: None - a list of class names to use in the confusion_matrix. This is passed to the 'labels' - parameter of sklearn.metrics.confusion_matrix(), and follows the behaviour - indicated by that function. It may be used to reorder or select a subset of labels. - If None, values that appear at least once in y_true or y_pred are used in sorted order. - - label_encoder : dict or LabelEncoder, default: None - When specifying the ``classes`` argument, the input to ``fit()`` and ``score()`` must match the - expected labels. If the ``X`` and ``y`` datasets have been encoded prior to training and the - labels must be preserved for the visualization, use this argument to provide a mapping from the - encoded class to the correct label. Because typically a Scikit-Learn ``LabelEncoder`` is used to - perform this operation, you may provide it directly to the class to utilize its fitted encoding. + model : estimator + Must be a classifier, otherwise raises YellowbrickTypeError + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + sample_weight: array-like of shape = [n_samples], optional + Passed to ``confusion_matrix`` to weight the samples. + + percent: bool, default: False + Determines whether or not the confusion_matrix is displayed as counts + or as a percent of true predictions. Note, if specifying a subset of + classes, percent should be set to False or inaccurate figures will be + displayed. + + classes : list, default: None + a list of class names to use in the confusion_matrix. + This is passed to the ``labels`` parameter of + ``sklearn.metrics.confusion_matrix()``, and follows the behaviour + indicated by that function. It may be used to reorder or select a + subset of labels. If None, classes that appear at least once in + ``y_true`` or ``y_pred`` are used in sorted order. + + label_encoder : dict or LabelEncoder, default: None + When specifying the ``classes`` argument, the input to ``fit()`` + and ``score()`` must match the expected labels. If the ``X`` and ``y`` + datasets have been encoded prior to training and the labels must be + preserved for the visualization, use this argument to provide a + mapping from the encoded class to the correct label. Because typically + a Scikit-Learn ``LabelEncoder`` is used to perform this operation, you + may provide it directly to the class to utilize its fitted encoding. + + cmap : string, default: ``'YlOrRd'`` + Specify a colormap to define the heatmap of the predicted class + against the actual class in the confusion matrix. + + fontsize : int, default: None + Specify the fontsize of the text in the grid and labels to make the + matrix a bit easier to read. Uses rcParams font size by default. + + Attributes + ---------- + confusion_matrix_ : array, shape = [n_classes, n_classes] + The numeric scores of the confusion matrix + + class_counts_ : array, shape = [n_classes,] + The total number of each class supporting the confusion matrix Examples -------- - >>> from yellowbrick.classifier import ConfusionMatrix >>> from sklearn.linear_model import LogisticRegression >>> viz = ConfusionMatrix(LogisticRegression()) @@ -78,178 +113,254 @@ class ConfusionMatrix(ClassificationScoreVisualizer): """ - def __init__(self, model, ax=None, classes=None, label_encoder=None, **kwargs): + def __init__(self, model, ax=None, classes=None, sample_weight=None, + percent=False, label_encoder=None, cmap='YlOrRd', + fontsize=None, **kwargs): super(ConfusionMatrix, self).__init__( model, ax=ax, classes=classes, **kwargs ) - #Initialize all the other attributes we'll use (for coder clarity) - self.confusion_matrix = None - - self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) - self.cmap.set_under(color = 'w') + # Visual parameters + self.cmap = color_sequence(cmap) + self.cmap.set_under(color=CMAP_UNDERCOLOR) self.cmap.set_over(color=CMAP_OVERCOLOR) - self.edgecolors = [] #used to draw diagonal line for predicted class = true class + self.fontsize = fontsize + + # Estimator parameters self.label_encoder = label_encoder + self.sample_weight = sample_weight + self.percent = percent - def score(self, X, y, sample_weight=None, percent=True): + # Used to draw diagonal line for predicted class = true class + self._edgecolors = [] + + def score(self, X, y, **kwargs): """ - Generates the Scikit-Learn confusion_matrix and applies this to the appropriate axis + Draws a confusion matrix based on the test data supplied by comparing + predictions on instances X with the true values specified by the + target vector y. Parameters ---------- - X : ndarray or DataFrame of shape n x m A matrix of n instances with m features y : ndarray or Series of length n An array or series of target or class values - - sample_weight: optional, passed to the confusion_matrix - - percent: optional, Boolean. Determines whether or not the confusion_matrix - should be displayed as raw numbers or as a percent of the true - predictions. Note, if using a subset of classes in __init__, percent should - be set to False or inaccurate percents will be displayed. """ + # Perform deprecation warnings for attributes to score + # TODO: remove this in v0.9 + for param in ("percent", "sample_weight"): + if param in kwargs: + warnings.warn(PendingDeprecationWarning(( + "specifying '{}' in score is no longer supported, " + "pass to constructor of the visualizer instead." + ).format(param))) + + setattr(self, param, kwargs[param]) + + # Create predictions from X (will raise not fitted error) y_pred = self.predict(X) - + # Encode the target with the supplied label encoder if self.label_encoder: try : y = self.label_encoder.inverse_transform(y) y_pred = self.label_encoder.inverse_transform(y_pred) except AttributeError: # if a mapping is passed to class apply it here. - y = [self.label_encoder[x] for x in y] - y_pred = [self.label_encoder[x] for x in y_pred] + y = np.array([self.label_encoder[x] for x in y]) + y_pred = np.array([self.label_encoder[x] for x in y_pred]) - self.confusion_matrix = confusion_matrix( - y, y_pred, labels=self.classes_, sample_weight=sample_weight + # Compute the confusion matrix and class counts + self.confusion_matrix_ = confusion_matrix_metric( + y, y_pred, labels=self.classes_, sample_weight=self.sample_weight ) - self._class_counts = self.class_counts(y) + self.class_counts_ = self.class_counts(y) - #Make array of only the classes actually being used. - #Needed because sklearn confusion_matrix only returns counts for selected classes - #but percent should be calculated based on all classes + # Make array of only the classes actually being used. + # Needed because sklearn confusion_matrix only returns counts for + # selected classes but percent should be calculated on all classes selected_class_counts = [] for c in self.classes_: try: - selected_class_counts.append(self._class_counts[c]) + selected_class_counts.append(self.class_counts_[c]) except KeyError: selected_class_counts.append(0) - self.selected_class_counts = np.array(selected_class_counts) + self.class_counts_ = np.array(selected_class_counts) - return self.draw(percent) + return self.draw() - def draw(self, percent=True): + def draw(self): + """ + Renders the classification report; must be called after score. """ - Renders the classification report - Should only be called internally, as it uses values calculated in Score - and score calls this method. - - Parameters - ---------- - percent: Boolean - Whether the heatmap should represent "% of True" or raw counts + # Perform display related manipulations on the confusion matrix data + cm_display = self.confusion_matrix_ + + # Convert confusion matrix to percent of each row, i.e. the + # predicted as a percent of true in each class. + if self.percent == True: + # Note: div_safe function returns 0 instead of NAN. + cm_display = div_safe(self.confusion_matrix_, self.class_counts_) + cm_display = np.round(cm_display* 100, decimals=0) + + # Y axis should be sorted top to bottom in pcolormesh + cm_display = cm_display[::-1,::] + + # Set up the dimensions of the pcolormesh + n_classes = len(self.classes_) + X, Y = np.arange(n_classes+1), np.arange(n_classes+1) + self.ax.set_ylim(bottom=0, top=cm_display.shape[0]) + self.ax.set_xlim(left=0, right=cm_display.shape[1]) + + # Fetch the grid labels from the classes in correct order; set ticks. + xticklabels = self.classes_ + yticklabels = self.classes_[::-1] + ticks = np.arange(n_classes) + 0.5 + + self.ax.set(xticks=ticks, yticks=ticks) + self.ax.set_xticklabels(xticklabels, rotation="vertical", fontsize=self.fontsize) + self.ax.set_yticklabels(yticklabels, fontsize=self.fontsize) + + # Set data labels in the grid enumerating over all x,y class pairs. + # NOTE: X and Y are one element longer than the confusion matrix, so + # skip the last element in the enumeration to label grids. + for x in X[:-1]: + for y in Y[:-1]: + + # Extract the value and the text label + value = cm_display[x,y] + svalue = "{:0.0f}".format(value) + if self.percent: + svalue += "%" + + # Determine the grid and text colors + base_color = self.cmap(value / cm_display.max()) + text_color = find_text_color(base_color) + + # Make zero values more subtle + if cm_display[x,y] == 0: + text_color = CMAP_MUTEDCOLOR + + # Add the label to the middle of the grid + cx, cy = x+0.5, y+0.5 + self.ax.text( + cy, cx, svalue, va='center', ha='center', + color=text_color, fontsize=self.fontsize, + ) + + # Add a dark line on the grid with the diagonal. Note that the + # tick labels have already been reversed. + lc = 'k' if xticklabels[x] == yticklabels[y] else 'w' + self._edgecolors.append(lc) + + + # Draw the heatmap with colors bounded by vmin,vmax + vmin = 0.00001 + vmax = 99.999 if self.percent == True else cm_display.max() + self.ax.pcolormesh( + X, Y, cm_display, vmin=vmin, vmax=vmax, + edgecolor=self._edgecolors, cmap=self.cmap, linewidth='0.01' + ) - """ - if percent == True: - #Convert confusion matrix to percent of each row, i.e. the predicted as a percent of true in each class - #div_safe function returns 0 instead of NAN. - self._confusion_matrix_display = div_safe( - self.confusion_matrix, - self.selected_class_counts - ) - self._confusion_matrix_display =np.round(self._confusion_matrix_display* 100, decimals=0) - else: - self._confusion_matrix_display = self.confusion_matrix - - #Y axis should be sorted top to bottom in pcolormesh - self._confusion_matrix_plottable = self._confusion_matrix_display[::-1,::] - - self.max = self._confusion_matrix_plottable.max() - - #Set up the dimensions of the pcolormesh - X = np.linspace(start=0, stop=len(self.classes_), num=len(self.classes_)+1) - Y = np.linspace(start=0, stop=len(self.classes_), num=len(self.classes_)+1) - self.ax.set_ylim(bottom=0, top=self._confusion_matrix_plottable.shape[0]) - self.ax.set_xlim(left=0, right=self._confusion_matrix_plottable.shape[1]) - - #Put in custom axis labels - self.xticklabels = self.classes_ - self.yticklabels = self.classes_[::-1] - self.xticks = np.arange(0, len(self.classes_), 1) + .5 - self.yticks = np.arange(0, len(self.classes_), 1) + .5 - self.ax.set(xticks=self.xticks, yticks=self.yticks) - self.ax.set_xticklabels(self.xticklabels, rotation="vertical", fontsize=8) - self.ax.set_yticklabels(self.yticklabels, fontsize=8) - - ###################### - # Add the data labels to each square - ###################### - for x_index, x in np.ndenumerate(X): - #np.ndenumerate returns a tuple for the index, must access first element using [0] - x_index = x_index[0] - for y_index, y in np.ndenumerate(Y): - #Clean up our iterators - #numpy doesn't like non integers as indexes; also np.ndenumerate returns tuple - x_int = int(x) - y_int = int(y) - y_index = y_index[0] - - #X and Y are one element longer than the confusion_matrix. Don't want to add text for the last X or Y - if x_index == X[-1] or y_index == Y[-1]: - break - - #center the text in the middle of the block - text_x = x + 0.5 - text_y = y + 0.5 - - #extract the value - grid_val = self._confusion_matrix_plottable[x_int,y_int] - - #Determine text color - scaled_grid_val = grid_val / self.max - base_color = self.cmap(scaled_grid_val) - text_color= find_text_color(base_color) - - #make zero values more subtle - if self._confusion_matrix_plottable[x_int,y_int] == 0: - text_color = "0.75" - - #Put the data labels in the middle of the heatmap square - self.ax.text(text_y, - text_x, - "{:.0f}{}".format(grid_val,"%" if percent==True else ""), - va='center', - ha='center', - fontsize=8, - color=text_color) - - #If the prediction is correct, put a bounding box around that square to better highlight it to the user - #This will be used in ax.pcolormesh, setting now since we're iterating over the matrix - #ticklabels are conveniently already reversed properly to match the _confusion_matrix_plottalbe order - if self.xticklabels[x_int] == self.yticklabels[y_int]: - self.edgecolors.append('black') - else: - self.edgecolors.append('w') - - # Draw the heatmap. vmin and vmax operate in tandem with the cmap.set_under and cmap.set_over to alter the color of 0 and 100 - highest_count = self._confusion_matrix_plottable.max() - vmax = 99.999 if percent == True else highest_count - mesh = self.ax.pcolormesh(X, - Y, - self._confusion_matrix_plottable, - vmin=0.00001, - vmax=vmax, - edgecolor=self.edgecolors, - cmap=self.cmap, - linewidth='0.01') #edgecolor='0.75', linewidth='0.01' + # Return the axes being drawn on return self.ax def finalize(self, **kwargs): self.set_title('{} Confusion Matrix'.format(self.name)) self.ax.set_ylabel('True Class') self.ax.set_xlabel('Predicted Class') + + +########################################################################## +## Quick Method +########################################################################## + + +def confusion_matrix(model, X, y, ax=None, classes=None, sample_weight=None, + percent=False, label_encoder=None, cmap='YlOrRd', + fontsize=None, **kwargs): + """Quick method: + + Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). + A confusion matrix shows each combination of the true and predicted + classes for a test data set. + + The default color map uses a yellow/orange/red color scale. The user can + choose between displaying values as the percent of true (cell value + divided by sum of row) or as direct counts. If percent of true mode is + selected, 100% accurate predictions are highlighted in green. + + Requires a classification model. + + Parameters + ---------- + model : estimator + Must be a classifier, otherwise raises YellowbrickTypeError + + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features. + + y : ndarray or Series of length n + An array or series of target or class values. + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + sample_weight: array-like of shape = [n_samples], optional + Passed to ``confusion_matrix`` to weight the samples. + + percent: bool, default: False + Determines whether or not the confusion_matrix is displayed as counts + or as a percent of true predictions. Note, if specifying a subset of + classes, percent should be set to False or inaccurate figures will be + displayed. + + classes : list, default: None + a list of class names to use in the confusion_matrix. + This is passed to the ``labels`` parameter of + ``sklearn.metrics.confusion_matrix()``, and follows the behaviour + indicated by that function. It may be used to reorder or select a + subset of labels. If None, classes that appear at least once in + ``y_true`` or ``y_pred`` are used in sorted order. + + label_encoder : dict or LabelEncoder, default: None + When specifying the ``classes`` argument, the input to ``fit()`` + and ``score()`` must match the expected labels. If the ``X`` and ``y`` + datasets have been encoded prior to training and the labels must be + preserved for the visualization, use this argument to provide a + mapping from the encoded class to the correct label. Because typically + a Scikit-Learn ``LabelEncoder`` is used to perform this operation, you + may provide it directly to the class to utilize its fitted encoding. + + cmap : string, default: ``'YlOrRd'`` + Specify a colormap to define the heatmap of the predicted class + against the actual class in the confusion matrix. + + fontsize : int, default: None + Specify the fontsize of the text in the grid and labels to make the + matrix a bit easier to read. Uses rcParams font size by default. + + Returns + ------- + ax : matplotlib axes + Returns the axes that the classification report was drawn on. + """ + # Instantiate the visualizer + visualizer = ConfusionMatrix( + model, ax, classes, sample_weight, percent, + label_encoder, cmap, fontsize, **kwargs + ) + + # Create the train and test splits + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X_train, y_train, **kwargs) + visualizer.score(X_test, y_test) + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/classifier/learning_curve.py b/yellowbrick/classifier/learning_curve.py index 9371909f0..84f0ce5b0 100644 --- a/yellowbrick/classifier/learning_curve.py +++ b/yellowbrick/classifier/learning_curve.py @@ -10,16 +10,16 @@ # ID: learning_curve.py [] jason.s.keung@gmail.com $ """ -Implementations a learning curve visualizer for classification. +Implementations a learning curve visualizer for classification. """ ########################################################################## ## Imports ########################################################################## import numpy as np -import matplotlib.pyplot as plt + from sklearn.model_selection import learning_curve -from sklearn.model_selection import ShuffleSplit + from yellowbrick.base import ModelVisualizer from yellowbrick.exceptions import YellowbrickError @@ -29,18 +29,18 @@ class LearningCurveVisualizer(ModelVisualizer): """ - Generate a simple plot of the test and training learning curve. + Generate a simple plot of the test and training learning curve. Learning curves demonstrate is a plot of proxy measures for implied - learning with experience. + learning with experience. - * The X axis represents experience, or the number of training samples. + * The X axis represents experience, or the number of training samples. * The Y axis represents learning, or the train and cross validation scores. Parameters ---------- model : a Scikit-Learn estimator - + train_sizes: ndarray or Series, default: np.linspace(.1, 1.0, 5) An array that represents the proportion of data for the learning curve @@ -53,9 +53,9 @@ class LearningCurveVisualizer(ModelVisualizer): - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. - see scikit-learn `cross-validation guide `_ + see scikit-learn `cross-validation guide `_ for more information - + n_jobs : integer, optional Number of jobs to run in parallel (default 1). @@ -73,10 +73,10 @@ class LearningCurveVisualizer(ModelVisualizer): >>> model.poof() """ def __init__(self, model, train_sizes=None, cv=None, n_jobs=1, **kwargs): - + # Call super to initialize the class super(LearningCurveVisualizer, self).__init__(model, **kwargs) - + # Set parameters self.cv = cv self.n_jobs = n_jobs @@ -92,7 +92,7 @@ def __init__(self, model, train_sizes=None, cv=None, n_jobs=1, **kwargs): self.train_scores_std = None self.test_scores_mean = None self.test_scores_std = None - + def fit(self, X, y, **kwargs): """ The fit method is the primary drawing input for the learning curve @@ -122,11 +122,11 @@ def fit(self, X, y, **kwargs): self.train_scores_std = np.std(self.train_scores, axis=1) self.test_scores_mean = np.mean(self.test_scores, axis=1) self.test_scores_std = np.std(self.test_scores, axis=1) - + self.draw(**kwargs) return self - + def draw(self, **kwargs): """ Renders the learning curve across each axis. @@ -147,9 +147,9 @@ def draw(self, **kwargs): label="Training Score") self.ax.plot(self.train_sizes, self.test_scores_mean, 'o-', color='g', - label="Cross-validation Score") + label="Cross-validation Score") - return self.ax + return self.ax def finalize(self, **kwargs): """ @@ -173,8 +173,8 @@ def finalize(self, **kwargs): def learning_curve_plot(X, y, model, ax=None, train_sizes=None, cv=None, n_jobs=1, **kwargs): """ - Displays a learning curve based on number of samples vs training and - cross validation scores. The learning curve aims to show how a model + Displays a learning curve based on number of samples vs training and + cross validation scores. The learning curve aims to show how a model learns and improves with experience. This helper function is a quick wrapper to utilize the LearningCurveVisualizer @@ -193,7 +193,7 @@ def learning_curve_plot(X, y, model, ax=None, train_sizes=None, The axes to plot the figure on. model : a Scikit-Learn estimator - + train_sizes: ndarray or Series, default: np.linspace(.1, 1.0, 5) An array that represents the proportion of data for the learning curve @@ -210,9 +210,6 @@ def learning_curve_plot(X, y, model, ax=None, train_sizes=None, :class:`StratifiedKFold` used. If the estimator is not a classifier or if ``y`` is neither binary nor multiclass, :class:`KFold` is used. - Refer :ref:`User Guide ` for the various - cross-validators that can be used here. - n_jobs : integer, optional Number of jobs to run in parallel (default 1). @@ -225,7 +222,7 @@ def learning_curve_plot(X, y, model, ax=None, train_sizes=None, ax : matplotlib axes Returns the axes that the learning curve were drawn on. """ - + # Instantiate the visualizer visualizer = LearningCurveVisualizer(model, train_sizes, cv, n_jobs, **kwargs) diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py index 7b71c2712..794e86d46 100644 --- a/yellowbrick/classifier/rocauc.py +++ b/yellowbrick/classifier/rocauc.py @@ -60,11 +60,12 @@ class ROCAUC(ClassificationScoreVisualizer): Parameters ---------- - ax : the axis to plot the figure on. + model : estimator + Must be a classifier, otherwise raises YellowbrickTypeError - model : the Scikit-Learn estimator - Should be an instance of a classifier, else the __init__ will - return an error. + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). classes : list A list of class names for the legend. If classes is None and a y value @@ -219,7 +220,7 @@ def draw(self): if self.micro: self.ax.plot( self.fpr[MICRO], self.tpr[MICRO], linestyle="--", - color= self.colors[len(self.classes_)], + color= self.colors[len(self.classes_)-1], label='micro-average ROC curve, AUC = {:0.2f}'.format( self.roc_auc["micro"], ) @@ -229,7 +230,7 @@ def draw(self): if self.macro: self.ax.plot( self.fpr[MACRO], self.tpr[MACRO], linestyle="--", - color= self.colors[len(self.classes_)+1], + color= self.colors[len(self.classes_)-1], label='macro-average ROC curve, AUC = {:0.2f}'.format( self.roc_auc["macro"], ) diff --git a/yellowbrick/classifier/threshold.py b/yellowbrick/classifier/threshold.py new file mode 100644 index 000000000..26065a891 --- /dev/null +++ b/yellowbrick/classifier/threshold.py @@ -0,0 +1,310 @@ +# yellowbrick.classifier.threshold +# Threshold classifier visualizer for Yellowbrick. +# +# Author: Nathan Danielsen +# Created: Wed April 26 20:17:29 2017 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +import bisect + +import numpy as np +from scipy.stats import mstats + +from sklearn.model_selection import train_test_split +from sklearn.metrics import precision_recall_curve + +from yellowbrick.exceptions import YellowbrickTypeError +from yellowbrick.style.colors import resolve_colors +from yellowbrick.base import ModelVisualizer +from yellowbrick.utils import isclassifier + + +########################################################################## +# Quick Methods +########################################################################## + + +def thresholdviz(model, + X, + y, + color=None, + n_trials=50, + test_size_percent=0.1, + quantiles=(0.1, 0.5, 0.9), + random_state=0, + **kwargs): + """Quick method for ThresholdVisualizer. + Visualizes the bounds of precision, recall and queue rate at different + thresholds for binary targets after a given number of trials. + + The visualization shows the threshold precentage on the x-axis which can be + compared against the queue rate, precision, and recall as percentages on + the y-axis. The default that each of the medium curves is set at the 90%% + central interval, but can be adjusted. + + This visualization will help the user determine given their tolerances for + precision, queue and recall the appropriate threshold to set in their + application. + + See also:: + ``http://blog.insightdatalabs.com/visualizing-classifier-thresholds/`` + + Parameters + ---------- + + model : a Scikit-Learn classifier, required + Should be an instance of a classifier otherwise a will raise a + YellowbrickTypeError exception on instantiation. + + color : string, default: None + Optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis + + n_trials : integer, default: 50 + Number of trials to conduct via train_test_split + + quantiles : sequence, default: (0.1, 0.5, .9) + Setting the quantiles for visualizing model variability using + scipy.stats.mstats.mquantiles + + random_state : integer, default: None + Random state integer for sampling in train_test_split + + kwargs : keyword arguments passed to the super class. + + Returns + ------- + ax : matplotlib axes + Returns the axes that the parallel coordinates were drawn on. + """ + # Instantiate the visualizer + visualizer = ThresholdVisualizer( + model, + color=color, + n_trials=n_trials, + test_size_percent=test_size_percent, + quantiles=quantiles, + random_state=random_state, + **kwargs) + + # Fit and transform the visualizer (calls draw) + visualizer.fit_poof(X, y) + + # Return the axes object on the visualizer + return visualizer.ax + + +########################################################################## +# Static ThresholdVisualizer Visualizer +########################################################################## + + +class ThresholdVisualizer(ModelVisualizer): + """Visualizes the bounds of precision, recall and queue rate at different + thresholds for binary targets after a given number of trials. + + The visualization shows the threshold precentage on the x-axis which can be + compared against the queue rate, precision, and recall as percentages on + the y-axis. The default that each of the medium curves is set at the 90%% + central interval, but can be adjusted. + + This visualization will help the user determine given their tolerances for + precision, queue and recall the appropriate threshold to set in their + application. + + See also:: + ``http://blog.insightdatalabs.com/visualizing-classifier-thresholds/`` + + Parameters + ---------- + + model : a Scikit-Learn classifier, required + Should be an instance of a classifier otherwise a will raise a + YellowbrickTypeError exception on instantiation. + + color : string, default: None + Optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis + + n_trials : integer, default: 50 + Number of trials to conduct via train_test_split + + quantiles : sequence, default: (0.1, 0.5, .9) + Setting the quantiles for visualizing model variability using + scipy.stats.mstats.mquantiles + + random_state : integer, default: None + Random state integer for sampling in train_test_split + + kwargs : keyword arguments passed to the super class. + """ + + def __init__(self, + model, + n_trials=50, + test_size_percent=0.1, + quantiles=(0.1, 0.5, 0.9), + random_state=None, + **kwargs): + # Check to see if model is an instance of a classifier. + # Should return an error if it isn't. + if not isclassifier(model): + raise YellowbrickTypeError( + "This estimator is not a classifier; try a regression or clustering score visualizer instead!" + ) + super(ThresholdVisualizer, self).__init__(model, **kwargs) + + self.estimator = model + self.n_trials = n_trials + self.test_size_percent = test_size_percent + self.quantiles = quantiles + self.random_state = random_state + + # to be set later + self.plot_data = None + + def fit(self, X, y=None, **kwargs): + """ + Parameters + ---------- + + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs: dict + keyword arguments passed to Scikit-Learn API. + + Returns + ------- + self : instance + Returns the instance of the visualizer + """ + self.plot_data = [] + + for _ in range(self.n_trials): + train_X, test_X, train_y, test_y = train_test_split( + X, + y, + test_size=self.test_size_percent, + random_state=self.random_state # defaults to None + ) + self.estimator.fit(train_X, train_y) + # get prediction probabilities for each + predictions = self.estimator.predict_proba(test_X)[:, 1] + + precision, recall, thresholds = precision_recall_curve( + test_y, predictions) + # add one to each so that thresh ends at 1 + thresholds = np.append(thresholds, 1) + queue_rate = [] + for threshold in thresholds: + queue_rate.append((predictions >= threshold).mean()) + + trial_data = { + 'thresholds': thresholds, + 'precision': precision, + 'recall': recall, + 'queue_rate': queue_rate + } + self.plot_data.append(trial_data) + + return self.draw() + + def draw(self, *kwargs): + """ + Renders the visualization + + Parameters + ---------- + kwargs: dict + keyword arguments passed to Scikit-Learn API. + + Returns + ------- + self.ax : AxesSubplot of the visualizer + Returns the AxesSubplot instance of the visualizer + """ + # Set the colors from the supplied values or reasonable defaults + color_values = resolve_colors(n_colors=3, colors=self.color) + + uniform_thresholds = np.linspace(0, 1, num=101) + uniform_precision_plots = [] + uniform_recall_plots = [] + uniform_queue_rate_plots = [] + + for data in self.plot_data: + uniform_precision = [] + uniform_recall = [] + uniform_queue_rate = [] + for ut in uniform_thresholds: + index = bisect.bisect_left(data['thresholds'], ut) + uniform_precision.append(data['precision'][index]) + uniform_recall.append(data['recall'][index]) + uniform_queue_rate.append(data['queue_rate'][index]) + + uniform_precision_plots.append(uniform_precision) + uniform_recall_plots.append(uniform_recall) + uniform_queue_rate_plots.append(uniform_queue_rate) + + uplots = (uniform_precision_plots, uniform_recall_plots, uniform_queue_rate_plots) + + for uniform_plot, color in zip(uplots, color_values): + # Compute the lower, median, and upper plots + lower, median, upper = mstats.mquantiles(uniform_plot, prob=self.quantiles, axis=0) + + # Draw the median line + self.ax.plot(uniform_thresholds, median, color=color) + + # Draw the fill between the lower and upper bounds + self.ax.fill_between(uniform_thresholds, upper, lower, alpha=0.5, linewidth=0, color=color) + + return self.ax + + def finalize(self, **kwargs): + """Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + """ + super(ThresholdVisualizer, self).finalize(**kwargs) + + # Set the title + if self.title is None: + self.set_title("Threshold Plot of Binary Classifier") + + self.ax.legend( + ('precision', 'recall', 'queue_rate'), frameon=True, loc='best') + self.ax.set_xlabel('threshold') + self.ax.set_ylabel('percent') + + def fit_poof(self, X, y=None, **kwargs): + """Convience method to fit, draw and poof / finalize the visualizer in + one step after instantiation. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs: dict + keyword arguments passed to Scikit-Learn API. + + Returns + ------- + self : instance + Returns the instance of the visualizer + """ + self.fit(X, y) + self.poof() + return self + +ThreshViz = ThresholdVisualizer diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py index eb3769fa3..c719641f4 100644 --- a/yellowbrick/cluster/elbow.py +++ b/yellowbrick/cluster/elbow.py @@ -19,7 +19,6 @@ ########################################################################## import time -import numpy as np from .base import ClusteringScoreVisualizer from ..exceptions import YellowbrickValueError @@ -247,7 +246,7 @@ def fit(self, X, y=None, **kwargs): ) self.draw() - + return self def draw(self): diff --git a/yellowbrick/cluster/silhouette.py b/yellowbrick/cluster/silhouette.py index 97ba87561..f247670d1 100644 --- a/yellowbrick/cluster/silhouette.py +++ b/yellowbrick/cluster/silhouette.py @@ -19,12 +19,12 @@ import numpy as np +from ..style import color_palette from .base import ClusteringScoreVisualizer -from ..exceptions import YellowbrickValueError -from ..style import resolve_colors, color_palette from sklearn.metrics import silhouette_score, silhouette_samples + ## Packages for export __all__ = [ "SilhouetteVisualizer" diff --git a/yellowbrick/exceptions.py b/yellowbrick/exceptions.py index 726daa6b9..9f4870ec6 100644 --- a/yellowbrick/exceptions.py +++ b/yellowbrick/exceptions.py @@ -10,13 +10,14 @@ # ID: exceptions.py [cb75e0e] benjamin@bengfort.com $ """ -Exceptions hierarchy for the yellowbrick library +Exceptions and warnings hierarchy for the yellowbrick library """ ########################################################################## ## Exceptions Hierarchy ########################################################################## + class YellowbrickError(Exception): """ The root exception for all yellowbrick related errors. @@ -38,6 +39,13 @@ class ModelError(YellowbrickError): pass +class NotFitted(ModelError): + """ + An action was called that requires a fitted model. + """ + pass + + class YellowbrickTypeError(YellowbrickError, TypeError): """ There was an unexpected type or none for a property or input. @@ -50,3 +58,24 @@ class YellowbrickValueError(YellowbrickError, ValueError): A bad value was passed into a function. """ pass + + +class YellowbrickKeyError(YellowbrickError, KeyError): + """ + An invalid key was used in a hash (dict or set). + """ + pass + + +class YellowbrickWarning(UserWarning): + """ + Warning class used to notify users of Yellowbrick-specific issues. + """ + pass + + +class DataWarning(YellowbrickWarning): + """ + The supplied data has an issue that may produce unexpected visualizations. + """ + pass diff --git a/yellowbrick/features/__init__.py b/yellowbrick/features/__init__.py index 0b0661a2b..624f0f83b 100644 --- a/yellowbrick/features/__init__.py +++ b/yellowbrick/features/__init__.py @@ -23,3 +23,5 @@ from .rankd import Rank1D, rank1d, Rank2D, rank2d from .scatter import ScatterViz, ScatterVisualizer, scatterviz from .jointplot import JointPlotVisualizer +from .pca import PCADecomposition, pca_decomposition +from .importances import FeatureImportances, feature_importances diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index 9add7ca58..50078994b 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -18,6 +18,8 @@ ## Imports ########################################################################## +import numpy as np + from yellowbrick.base import Visualizer from yellowbrick.utils import is_dataframe from sklearn.base import TransformerMixin @@ -27,6 +29,7 @@ ## Feature Visualizers ########################################################################## + class FeatureVisualizer(Visualizer, TransformerMixin): """ Base class for feature visualization to investigate features @@ -41,16 +44,6 @@ class FeatureVisualizer(Visualizer, TransformerMixin): def __init__(self, ax=None, **kwargs): super(FeatureVisualizer, self).__init__(ax=ax, **kwargs) - def fit(self, X, y=None, **fit_params): - """ - This method performs preliminary computations in order to set up the - figure or perform other analyses. It can also call drawing methods in - order to set up various non-instance related figure elements. - - This method must return self. - """ - return self - def transform(self, X): """ Primarily a pass-through to ensure that the feature visualizer will @@ -74,13 +67,66 @@ def fit_transform_poof(self, X, y=None, **kwargs): return Xp +class MultiFeatureVisualizer(FeatureVisualizer): + """ + MultiFeatureVisualiers are a subclass of FeatureVisualizer that visualize + several features at once. This class provides base functionality for + getting the names of features for use in plot annotation. + + Parameters + ---------- + + ax: matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + features: list, default: None + a list of feature names to use + If a DataFrame is passed to fit and features is None, feature + names are selected as the columns of the DataFrame. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + """ + + def __init__(self, ax=None, features=None, **kwargs): + super(MultiFeatureVisualizer, self).__init__(ax=ax, **kwargs) + + # Data Parameters + self.features_ = features + + def fit(self, X, y=None, **fit_params): + """ + This method performs preliminary computations in order to set up the + figure or perform other analyses. It can also call drawing methods in + order to set up various non-instance related figure elements. + + This method must return self. + """ + + # Handle the feature names if they're None. + if self.features_ is None: + + # If X is a data frame, get the columns off it. + if is_dataframe(X): + self.features_ = np.array(X.columns) + + # Otherwise create numeric labels for each column. + else: + _, ncols = X.shape + self.features_ = np.arange(0, ncols) + + return self + ########################################################################## ## Data Visualizers ########################################################################## -class DataVisualizer(FeatureVisualizer): +class DataVisualizer(MultiFeatureVisualizer): """ - Data Visualizers are a subclass of Feature Visualiers which plot the + Data Visualizers are a subclass of Feature Visualizers which plot the instances in feature space (also called data space, hence the name of the visualizer). Feature space is a multi-dimensional space defined by the columns of the instance dependent vector input, X which is passed to @@ -136,10 +182,9 @@ def __init__(self, ax=None, features=None, classes=None, color=None, Initialize the data visualization with many of the options required in order to make most visualizations work. """ - super(DataVisualizer, self).__init__(ax=ax, **kwargs) + super(DataVisualizer, self).__init__(ax=ax, features=features, **kwargs) # Data Parameters - self.features_ = features self.classes_ = classes # Visual Parameters @@ -148,7 +193,7 @@ def __init__(self, ax=None, features=None, classes=None, color=None, def fit(self, X, y=None, **kwargs): """ - The fit method is the primary drawing input for the parallel coords + The fit method is the primary drawing input for the visualization since it has both the X and y data required for the viz and the transform method does not. @@ -168,27 +213,13 @@ def fit(self, X, y=None, **kwargs): self : instance Returns the instance of the transformer/visualizer """ - # Get the shape of the data - nrows, ncols = X.shape + super(DataVisualizer, self).fit(X, y, **kwargs) # Store the classes for the legend if they're None. if self.classes_ is None: # TODO: Is this the most efficient method? self.classes_ = [str(label) for label in set(y)] - # Handle the feature names if they're None. - if self.features_ is None: - - # If X is a data frame, get the columns off it. - if is_dataframe(X): - self.features_ = X.columns - - # Otherwise create numeric labels for each column. - else: - self.features_ = [ - str(cdx) for cdx in range(ncols) - ] - # Draw the instances self.draw(X, y, **kwargs) diff --git a/yellowbrick/features/decomposition.py b/yellowbrick/features/decomposition.py new file mode 100644 index 000000000..220ee4c08 --- /dev/null +++ b/yellowbrick/features/decomposition.py @@ -0,0 +1,130 @@ +########################################################################## +## Imports +########################################################################## + +from .base import FeatureVisualizer +from yellowbrick.style import palettes + +from sklearn.pipeline import Pipeline +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler + +########################################################################## +## Quick Methods +########################################################################## + +def explained_variance_visualizer(X, y=None, ax=None, scale=True, + center=True, colormap=palettes.DEFAULT_SEQUENCE, + **kwargs): + """Produce a plot of the explained variance produced by a dimensionality + reduction algorithm using n=1 to n=n_components dimensions. This is a single + plot to help identify the best trade off between number of dimensions + and amount of information retained within the data. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n rows with m features + + y : ndarray or Series of length n + An array or Series of target or class values + + ax : matplotlib Axes, default: None + The aces to plot the figure on + + scale : bool, default: True + Boolean that indicates if the values of X should be scaled. + + colormap : string or cmap, default: None + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + >>> from sklearn import datasets + >>> bc = datasets.load_breast_cancer() + >>> X = bc = bc.data + >>> explained_variance_visualizer(X, scale=True, center=True, colormap='RdBu_r') + + """ + + # Instantiate the visualizer + visualizer = ExplainedVariance(X=X) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y, **kwargs) + visualizer.transform(X) + + # Return the axes object on the visualizer + return visualizer.poof() + + +########################################################################## +## Explained Variance Feature Visualizer +########################################################################## + +class ExplainedVariance(FeatureVisualizer): + """ + + Parameters + ---------- + + + + Examples + -------- + + >>> visualizer = ExplainedVariance() + >>> visualizer.fit(X) + >>> visualizer.transform(X) + >>> visualizer.poof() + + Notes + ----- + + """ + + def __init__(self, n_components=None, ax=None, scale=True, center=True, + colormap=palettes.DEFAULT_SEQUENCE, **kwargs): + + super(ExplainedVariance, self).__init__(ax=ax, **kwargs) + + self.colormap = colormap + self.n_components = n_components + self.center = center + self.scale = scale + self.pipeline = Pipeline([('scale', StandardScaler(with_mean=self.center, + with_std=self.scale)), + ('pca', PCA(n_components=self.n_components))]) + self.pca_features = None + + @property + def explained_variance_(self): + return self.pipeline.steps[-1][1].explained_variance_ + + def fit(self, X, y=None): + self.pipeline.fit(X) + self.draw() + return self + + def transform(self, X): + self.pca_features = self.pipeline.transform(X) + return self.pca_features + + def draw(self): + X = self.explained_variance_ + self.ax.plot(X) + return self.ax + + def finalize(self, **kwargs): + # Set the title + self.set_title('Explained Variance Plot') + + # Set the axes labels + self.ax.set_ylabel('Explained Variance') + self.ax.set_xlabel('Number of Components') diff --git a/yellowbrick/features/importances.py b/yellowbrick/features/importances.py new file mode 100644 index 000000000..8ccae0ad0 --- /dev/null +++ b/yellowbrick/features/importances.py @@ -0,0 +1,301 @@ +# yellowbrick.features.importances +# Feature importance visualizer +# +# Author: Benjamin Bengfort +# Created: Fri Mar 02 15:21:36 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: importances.py [] benjamin@bengfort.com $ + +""" +Implementation of a feature importances visualizer. This visualizer sits in +kind of a weird place since it is technically a model scoring visualizer, but +is generally used for feature engineering. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np +import matplotlib.pyplot as plt + +from yellowbrick.utils import is_dataframe +from yellowbrick.base import ModelVisualizer +from yellowbrick.exceptions import YellowbrickTypeError, NotFitted + + +########################################################################## +## Feature Visualizer +########################################################################## + +class FeatureImportances(ModelVisualizer): + """ + Displays the most informative features in a model by showing a bar chart + of features ranked by their importances. Although primarily a feature + engineering mechanism, this visualizer requires a model that has either a + ``coef_`` or ``feature_importances_`` parameter after fit. + + Parameters + ---------- + model : Estimator + A Scikit-Learn estimator that learns feature importances. Must support + either ``coef_`` or ``feature_importances_`` parameters. + + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + labels : list, default: None + A list of feature names to use. If a DataFrame is passed to fit and + features is None, feature names are selected as the column names. + + relative : bool, default: True + If true, the features are described by their relative importance as a + percentage of the strongest feature component; otherwise the raw + numeric description of the feature importance is shown. + + absolute : bool, default: False + Make all coeficients absolute to more easily compare negative + coeficients with positive ones. + + xlabel : str, default: None + The label for the X-axis. If None is automatically determined by the + underlying model and options provided. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + features_ : np.array + The feature labels ranked according to their importance + + feature_importances_ : np.array + The numeric value of the feature importance computed by the model + + Examples + -------- + + >>> from sklearn.ensemble import GradientBoostingClassifier + >>> visualizer = FeatureImportances(GradientBoostingClassifier()) + >>> visualizer.fit(X, y) + >>> visualizer.poof() + """ + + def __init__(self, model, ax=None, labels=None, relative=True, + absolute=False, xlabel=None, **kwargs): + super(FeatureImportances, self).__init__(model, ax, **kwargs) + + # Data Parameters + self.set_params( + labels=labels, relative=relative, absolute=absolute, + xlabel=xlabel, + ) + + def fit(self, X, y=None, **kwargs): + """ + Fits the estimator to discover the feature importances described by + the data, then draws those importances as a bar plot. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs : dict + Keyword arguments passed to the fit method of the estimator. + + Returns + ------- + self : visualizer + The fit method must always return self to support pipelines. + """ + super(FeatureImportances, self).fit(X, y, **kwargs) + + # Get the feature importances from the model + self.feature_importances_ = self._find_importances_param() + + # Apply absolute value filter before normalization + if self.absolute: + self.feature_importances_ = np.abs(self.feature_importances_) + + # Normalize features relative to the maximum + if self.relative: + maxv = self.feature_importances_.max() + self.feature_importances_ /= maxv + self.feature_importances_ *= 100.0 + + # Create labels for the feature importances + # NOTE: this code is duplicated from MultiFeatureVisualizer + if self.labels is None: + # Use column names if a dataframe + if is_dataframe(X): + self.features_ = np.array(X.columns) + + # Otherwise use the column index as the labels + else: + _, ncols = X.shape + self.features_ = np.arange(0, ncols) + else: + self.features_ = np.array(self.labels) + + # Sort the features and their importances + sort_idx = np.argsort(self.feature_importances_) + self.features_ = self.features_[sort_idx] + self.feature_importances_ = self.feature_importances_[sort_idx] + + # Draw the feature importances + self.draw() + return self + + def draw(self, **kwargs): + """ + Draws the feature importances as a bar chart; called from fit. + """ + # Quick validation + for param in ('feature_importances_', 'features_'): + if not hasattr(self, param): + raise NotFitted("missing required param '{}'".format(param)) + + # Find the positions for each bar + pos = np.arange(self.features_.shape[0]) + 0.5 + + # Plot the bar chart + self.ax.barh(pos, self.feature_importances_, align='center') + + # Set the labels for the bars + self.ax.set_yticks(pos) + self.ax.set_yticklabels(self.features_) + + return self.ax + + def finalize(self, **kwargs): + """ + Finalize the drawing setting labels and title. + """ + # Set the title + self.set_title('Feature Importances of {} Features using {}'.format( + len(self.features_), self.name)) + + # Set the xlabel + self.ax.set_xlabel(self._get_xlabel()) + + # Remove the ygrid + self.ax.grid(False, axis='y') + + # Ensure we have a tight fit + plt.tight_layout() + + def _find_importances_param(self): + """ + Searches the wrapped model for the feature importances parameter. + """ + for attr in ("feature_importances_", "coef_"): + try: + return getattr(self.estimator, attr) + except AttributeError: + continue + + raise YellowbrickTypeError( + "could not find feature importances param on {}".format( + self.estimator.__class__.__name__ + ) + ) + + def _get_xlabel(self): + """ + Determines the xlabel based on the underlying data structure + """ + # Return user-specified label + if self.xlabel: + return self.xlabel + + # Label for coefficients + if hasattr(self.estimator, "coef_"): + if self.relative: + return "relative coefficient magnitude" + return "coefficient value" + + # Default label for feature_importances_ + if self.relative: + return "relative importance" + return "feature importance" + + def _is_fitted(self): + """ + Returns true if the visualizer has been fit. + """ + return hasattr(self, 'feature_importances_') and hasattr(self, 'features_') + + +########################################################################## +## Quick Method +########################################################################## + +def feature_importances(model, X, y=None, ax=None, labels=None, + relative=True, absolute=False, xlabel=None, **kwargs): + """ + Displays the most informative features in a model by showing a bar chart + of features ranked by their importances. Although primarily a feature + engineering mechanism, this visualizer requires a model that has either a + ``coef_`` or ``feature_importances_`` parameter after fit. + + Parameters + ---------- + model : Estimator + A Scikit-Learn estimator that learns feature importances. Must support + either ``coef_`` or ``feature_importances_`` parameters. + + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n, optional + An array or series of target or class values + + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + labels : list, default: None + A list of feature names to use. If a DataFrame is passed to fit and + features is None, feature names are selected as the column names. + + relative : bool, default: True + If true, the features are described by their relative importance as a + percentage of the strongest feature component; otherwise the raw + numeric description of the feature importance is shown. + + absolute : bool, default: False + Make all coeficients absolute to more easily compare negative + coeficients with positive ones. + + xlabel : str, default: None + The label for the X-axis. If None is automatically determined by the + underlying model and options provided. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Returns + ------- + ax : matplotlib axes + Returns the axes that the parallel coordinates were drawn on. + """ + # Instantiate the visualizer + visualizer = FeatureImportances( + model, ax, labels, relative, absolute, xlabel, **kwargs) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y) + visualizer.finalize() + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/features/jointplot.py b/yellowbrick/features/jointplot.py index d2946e070..fc8443c30 100644 --- a/yellowbrick/features/jointplot.py +++ b/yellowbrick/features/jointplot.py @@ -98,8 +98,8 @@ class JointPlotVisualizer(FeatureVisualizer): histcolor_y used to set the color for the histogram on the y axis ============== ===================================================== - size: float, default: 6 - Size of each side of the figure in inches + size: float, default: 600 + Size of each side of the figure in pixels ratio: float, default: 5 Ratio of joint axis size to the x and y axes height @@ -127,7 +127,7 @@ class JointPlotVisualizer(FeatureVisualizer): def __init__(self, ax=None, feature=None, target=None, joint_plot='scatter', joint_args=None, xy_plot='hist', xy_args=None, - size=6, ratio=5, space=.2, **kwargs): + size=600, ratio=5, space=.2, **kwargs): # Check matplotlib version - needs to be version 2.0.0 or greater. mpl_vers_maj = int(mpl.__version__.split(".")[0]) @@ -145,7 +145,7 @@ def __init__(self, ax=None, feature=None, target=None, self.joint_args = joint_args self.xy_plot = xy_plot self.xy_args = xy_args - self.size = size + self.size = (size, size) self.ratio = ratio self.space = space @@ -210,8 +210,7 @@ def draw(self, X, y, **kwargs): Sets up the layout for the joint plot draw calls ``draw_joint`` and ``draw_xy`` to render the visualizations. """ - - fig = plt.figure(figsize=(self.size, self.size)) + fig = plt.gcf() gs = plt.GridSpec(self.ratio + 1, self.ratio + 1) #Set up the 3 axes objects diff --git a/yellowbrick/features/pca.py b/yellowbrick/features/pca.py index e255e79df..4ef07c782 100644 --- a/yellowbrick/features/pca.py +++ b/yellowbrick/features/pca.py @@ -17,10 +17,11 @@ ## Imports ########################################################################## +# NOTE: must import mplot3d to load the 3D projection +import mpl_toolkits.mplot3d # noqa import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from yellowbrick.features.base import DataVisualizer +from yellowbrick.features.base import FeatureVisualizer from yellowbrick.style import palettes from yellowbrick.exceptions import YellowbrickValueError @@ -37,9 +38,9 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, colormap=palettes.DEFAULT_SEQUENCE, color=None, **kwargs): """Produce a two or three dimensional principal component plot of the data array ``X`` projected onto it's largest sequential principal components. It is common practice to scale the - data array ``X`` before applying a PC decomposition. Variable scaling can be controlled using + data array ``X`` before applying a PC decomposition. Variable scaling can be controlled using the ``scale`` argument. - + Parameters ---------- X : ndarray or DataFrame of shape n x m @@ -88,14 +89,16 @@ def pca_decomposition(X, y=None, ax=None, scale=True, proj_dim=2, # Return the axes object on the visualizer return visualizer.poof() + ########################################################################## ##2D and #3D PCA Visualizer ########################################################################## -class PCADecomposition(DataVisualizer): + +class PCADecomposition(FeatureVisualizer): """ Produce a two or three dimensional principal component plot of the data array ``X`` projected onto it's largest sequential principal components. It is common practice to scale the - data array ``X`` before applying a PC decomposition. Variable scaling can be controlled using + data array ``X`` before applying a PC decomposition. Variable scaling can be controlled using the ``scale`` argument. Parameters @@ -134,22 +137,22 @@ class PCADecomposition(DataVisualizer): >>> iris = datasets.load_iris() >>> X = iris.data >>> y = iris.target - >>> params = {'scale': True, 'center': False, 'col': y} + >>> params = {'scale': True, 'center': False, 'color': y} >>> visualizer = PCADecomposition(**params) >>> visualizer.fit(X) >>> visualizer.transform(X) >>> visualizer.poof() """ - def __init__(self, X=None, y=None, ax=None, scale=True, color=None, proj_dim=2, + def __init__(self, ax=None, scale=True, color=None, proj_dim=2, colormap=palettes.DEFAULT_SEQUENCE, **kwargs): super(PCADecomposition, self).__init__(ax=ax, **kwargs) + # Data Parameters if proj_dim not in (2, 3): raise YellowbrickValueError("proj_dim object is not 2 or 3.") self.color = color - self.pca_features_ = None self.scale = scale self.proj_dim = proj_dim self.pca_transformer = Pipeline([('scale', StandardScaler(with_std=self.scale)), diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index ba14ea238..6693057e9 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -19,15 +19,14 @@ ## Imports ########################################################################## -import numpy as np -import matplotlib.pyplot as plt -from sklearn.preprocessing import (MinMaxScaler, MaxAbsScaler, Normalizer, - StandardScaler) +from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler +from sklearn.preprocessing import Normalizer, StandardScaler from yellowbrick.utils import is_dataframe from yellowbrick.features.base import DataVisualizer from yellowbrick.exceptions import YellowbrickTypeError, YellowbrickValueError -from yellowbrick.style.colors import resolve_colors, get_color_cycle +from yellowbrick.style.colors import resolve_colors + ########################################################################## ## Quick Methods @@ -264,10 +263,9 @@ def draw(self, X, y, **kwargs): # Create the colors # TODO: Allow both colormap, listed colors, and palette definition # TODO: Make this an independent function or property for override! - # color_values = resolve_colors( - # num_colors=len(self.classes_), colormap=self.colormap, color=self.color - # ) - color_values = get_color_cycle() + color_values = resolve_colors( + n_colors=len(self.classes_), colormap=self.colormap, colors=self.color + ) colors = dict(zip(self.classes_, color_values)) # Track which labels are already in the legend diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 41e80c802..964a75c4d 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -18,12 +18,12 @@ ########################################################################## import numpy as np -import matplotlib.pyplot as plt import matplotlib.patches as patches +from yellowbrick.utils import is_dataframe from yellowbrick.features.base import DataVisualizer -from yellowbrick.exceptions import YellowbrickTypeError -from yellowbrick.style.colors import resolve_colors, get_color_cycle +import yellowbrick.utils.nan_warnings as nan_warnings +from yellowbrick.style.colors import resolve_colors ########################################################################## @@ -158,6 +158,13 @@ def draw(self, X, y, **kwargs): draws each instance as a class or target colored point, whose location is determined by the feature data set. """ + # Convert from dataframe + if is_dataframe(X): + X = X.as_matrix() + + # Clean out nans and warn that the user they aren't plotted + nan_warnings.warn_if_nans_exist(X) + X, y = nan_warnings.filter_missing(X, y) # Get the shape of the data nrows, ncols = X.shape @@ -169,10 +176,9 @@ def draw(self, X, y, **kwargs): # Create the colors # TODO: Allow both colormap, listed colors, and palette definition # TODO: Make this an independent function or property for override! - # color_values = resolve_colors( - # num_colors=len(self.classes_), colormap=self.colormap, color=self.color - # ) - color_values = get_color_cycle() + color_values = resolve_colors( + n_colors=len(self.classes_), colormap=self.colormap, colors=self.color + ) colors = dict(zip(self.classes_, color_values)) # Create a data structure to hold scatter plot representations @@ -208,7 +214,7 @@ def draw(self, X, y, **kwargs): # Add the circular axis path # TODO: Make this a seperate function (along with labeling) - self.ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none')) + self.ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none', edgecolor='grey', linewidth=.5 )) # Add the feature names for xy, name in zip(s, self.features_): diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 0a8ab19e9..2e3029bd9 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -18,13 +18,11 @@ ########################################################################## import numpy as np -import matplotlib.pyplot as plt from scipy.stats import shapiro from yellowbrick.utils import is_dataframe -from yellowbrick.features.base import FeatureVisualizer +from yellowbrick.features.base import MultiFeatureVisualizer from yellowbrick.exceptions import YellowbrickValueError -from yellowbrick.style.colors import resolve_colors, get_color_cycle __all__ = ["rank1d", "rank2d", "Rank1D", "Rank2D"] @@ -142,7 +140,7 @@ def rank2d(X, y=None, ax=None, algorithm='pearson', features=None, ## Base Feature Visualizer ########################################################################## -class RankDBase(FeatureVisualizer): +class RankDBase(MultiFeatureVisualizer): """ Base visualizer for Rank1D and Rank2D @@ -197,53 +195,14 @@ def __init__(self, ax=None, algorithm=None, features=None, Initialize the class with the options required to rank and order features as well as visualize the result. """ - super(RankDBase, self).__init__(ax=ax, **kwargs) + super(RankDBase, self).__init__(ax=ax, features=features, **kwargs) # Data Parameters self.ranking_ = algorithm - self.features_ = features + # Display parameters self.show_feature_names_ = show_feature_names - def fit(self, X, y=None, **kwargs): - """ - The fit method gathers information about the state of the visualizer. - - Parameters - ---------- - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features - - y : ndarray or Series of length n - An array or series of target or class values - - kwargs : dict - Pass generic arguments to the drawing method - - Returns - ------- - self : instance - Returns the instance of the transformer/visualizer - """ - # Get the shape of the data - nrows, ncols = X.shape - - # Handle the feature names if they're None. - if self.features_ is None: - - # If X is a data frame, get the columns off it. - if is_dataframe(X): - self.features_ = X.columns - - # Otherwise create numeric labels for each column. - else: - self.features_ = [ - str(cdx) for cdx in range(ncols) - ] - - # Fit always returns self. - return self - def transform(self, X, **kwargs): """ The transform method is the primary drawing hook for ranking classes. @@ -359,13 +318,12 @@ class Rank1D(RankDBase): Attributes ---------- - ``ranks_`` : ndarray + ranks_ : ndarray An array of rank scores with shape (n,), where n is the number of features. It is computed during `fit`. Examples -------- - >>> visualizer = Rank1D() >>> visualizer.fit(X, y) >>> visualizer.transform(X) @@ -383,7 +341,7 @@ def __init__(self, ax=None, algorithm='shapiro', features=None, order features as well as visualize the result. """ super(Rank1D, self).__init__( - ax=None, algorithm=algorithm, features=features, + ax=ax, algorithm=algorithm, features=features, show_feature_names=show_feature_names, **kwargs ) self.orientation_ = orient @@ -468,9 +426,9 @@ class Rank2D(RankDBase): Attributes ---------- - ``ranks_`` : ndarray + ranks_ : ndarray An array of rank scores with shape (n,n), where n is the - number of features. It is computed during ``fit``. + number of features. It is computed during `fit`. Examples -------- @@ -498,7 +456,7 @@ def __init__(self, ax=None, algorithm='pearson', features=None, order features as well as visualize the result. """ super(Rank2D, self).__init__( - ax=None, algorithm=algorithm, features=features, + ax=ax, algorithm=algorithm, features=features, show_feature_names=show_feature_names, **kwargs ) self.colormap=colormap diff --git a/yellowbrick/features/scatter.py b/yellowbrick/features/scatter.py index 2076b1f6d..004c3bba3 100644 --- a/yellowbrick/features/scatter.py +++ b/yellowbrick/features/scatter.py @@ -14,22 +14,23 @@ ########################################################################## # Imports ########################################################################## -import itertools +import itertools +from sklearn.utils.deprecation import deprecated import numpy as np -import matplotlib.pyplot as plt -import matplotlib.patches as patches from yellowbrick.features.base import DataVisualizer -from yellowbrick.utils import is_dataframe, is_structured_array, has_ndarray_int_columns +from yellowbrick.utils import is_dataframe, is_structured_array +from yellowbrick.utils import has_ndarray_int_columns from yellowbrick.exceptions import YellowbrickValueError -from yellowbrick.style.colors import resolve_colors, get_color_cycle +from yellowbrick.style.colors import resolve_colors + ########################################################################## # Quick Methods ########################################################################## - +@deprecated("Will be moved to yellowbrick.contrib in v0.7") def scatterviz(X, y=None, ax=None, @@ -92,8 +93,7 @@ def scatterviz(X, ########################################################################## # Static ScatterVisualizer Visualizer ########################################################################## - - +@deprecated("Will be moved to yellowbrick.contrib in v0.7") class ScatterVisualizer(DataVisualizer): """ ScatterVisualizer is a bivariate feature data visualization algorithm that @@ -115,7 +115,7 @@ class ScatterVisualizer(DataVisualizer): features : a list of two feature names to use, default: None List of two features that correspond to the columns in the array. - The order of the two features correspond to X and Y axises on the + The order of the two features correspond to X and Y axes on the graph. More than two feature names or columns will raise an error. If a DataFrame is passed to fit and features is None, feature names are selected that are the columns of the DataFrame. @@ -163,7 +163,8 @@ def __init__(self, self.markers = itertools.cycle( kwargs.pop('markers', (',', '+', 'o', '*', 'v', 'h', 'd'))) - + self.color = color + self.colormap = colormap if self.x is not None and self.y is not None and self.features_ is not None: raise YellowbrickValueError( @@ -214,7 +215,7 @@ def fit(self, X, y=None, **kwargs): # handle numpy named/ structured array elif self.features_ is not None and is_structured_array(X): X_selected = X[self.features_] - X_two_cols = X_selected.view((np.float64, len(X_selected.dtype.names))) + X_two_cols = X_selected.copy().view((np.float64, len(X_selected.dtype.names))) # handle features that are numeric columns in ndarray matrix elif self.features_ is not None and has_ndarray_int_columns(self.features_, X): @@ -248,13 +249,11 @@ def draw(self, X, y, **kwargs): self.ax.set_ylim([-1,1]) # set the colors - if self.colormap is not None or self.color is not None: - color_values = resolve_colors( - num_colors=len(self.classes_), - colormap=self.colormap, - color=self.color) - else: - color_values = get_color_cycle() + color_values = resolve_colors( + n_colors=len(self.classes_), + colormap=self.colormap, + colors=self.color + ) colors = dict(zip(self.classes_, color_values)) diff --git a/yellowbrick/gridsearch/__init__.py b/yellowbrick/gridsearch/__init__.py new file mode 100644 index 000000000..76b9e73cc --- /dev/null +++ b/yellowbrick/gridsearch/__init__.py @@ -0,0 +1,10 @@ +""" +Visualizers for the results of GridSearchCV. +""" + +########################################################################## +## Imports +########################################################################## + +## Hoist visualizers into the gridsearch namespace +from .pcolor import * diff --git a/yellowbrick/gridsearch/base.py b/yellowbrick/gridsearch/base.py new file mode 100644 index 000000000..7514b6ea5 --- /dev/null +++ b/yellowbrick/gridsearch/base.py @@ -0,0 +1,189 @@ +""" +Base class for grid search visualizers +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np +from ..utils import is_gridsearch +from ..base import ModelVisualizer +from ..exceptions import (YellowbrickTypeError, + YellowbrickKeyError, + YellowbrickValueError) + + +########################################################################## +## Dimension reduction utility +########################################################################## + +def param_projection(cv_results, x_param, y_param, metric='mean_test_score'): + """ + Projects the grid search results onto 2 dimensions. + + The display value is taken as the max over the non-displayed dimensions. + + Parameters + ---------- + cv_results : dict + A dictionary of results from the `GridSearchCV` object's `cv_results_` + attribute. + + x_param : string + The name of the parameter to be visualized on the horizontal axis. + + y_param : string + The name of the parameter to be visualized on the vertical axis. + + metric : string (default 'mean_test_score') + The field from the grid search's `cv_results` that we want to display. + + Returns + ------- + unique_x_vals : list + The parameter values that will be used to label the x axis. + + unique_y_vals: list + The parameter values that will be used to label the y axis. + + best_scores: 2D numpy array (n_y by n_x) + Array of scores to be displayed for each parameter value pair. + """ + # Extract the parameter values and score corresponding to each gridsearch + # trial. + # These are masked arrays where the cases where each parameter is + # non-applicable are masked. + try: + x_vals = cv_results['param_' + x_param] + except KeyError: + raise YellowbrickKeyError("Parameter '{}' does not exist in the grid " + "search results".format(x_param)) + try: + y_vals = cv_results['param_' + y_param] + except KeyError: + raise YellowbrickKeyError("Parameter '{}' does not exist in the grid " + "search results".format(y_param)) + + if metric not in cv_results: + raise YellowbrickKeyError("Metric '{}' does not exist in the grid " + "search results".format(metric)) + + # Get unique, unmasked values of the two display parameters + unique_x_vals = sorted(list(set(x_vals.compressed()))) + unique_y_vals = sorted(list(set(y_vals.compressed()))) + n_x = len(unique_x_vals) + n_y = len(unique_y_vals) + + # Get mapping of each parameter value -> an integer index + int_mapping_1 = {value: idx for idx, value in enumerate(unique_x_vals)} + int_mapping_2 = {value: idx for idx, value in enumerate(unique_y_vals)} + + # Translate each gridsearch result to indices on the grid + idx_x = [int_mapping_1[value] if value else None for value in x_vals] + idx_y = [int_mapping_2[value] if value else None for value in y_vals] + + # Create an array of all scores for each value of the display parameters. + # This is a n_x by n_y array of lists with `None` in place of empties + # (my kingdom for a dataframe...) + all_scores = [[None for _ in range(n_x)] for _ in range(n_y)] + for x, y, score in zip(idx_x, idx_y, cv_results[metric]): + if x is not None and y is not None: + if all_scores[y][x] is None: + all_scores[y][x] = [] + all_scores[y][x].append(score) + + # Get a numpy array consisting of the best scores for each parameter pair + best_scores = np.empty((n_y, n_x)) + for x in range(n_x): + for y in range(n_y): + if all_scores[y][x] is None: + best_scores[y, x] = np.nan + else: + try: + best_scores[y, x] = max(all_scores[y][x]) + except ValueError: + raise YellowbrickValueError( + "Cannot display grid search results for metric '{}': " + "result values may not all be numeric".format(metric) + ) + + return unique_x_vals, unique_y_vals, best_scores + + +########################################################################## +## Base Grid Search Visualizer +########################################################################## + +class GridSearchVisualizer(ModelVisualizer): + + def __init__(self, model, ax=None, **kwargs): + """ + Check to see if model is an instance of GridSearchCV. + Should return an error if it isn't. + """ + # A bit of type checking + if not is_gridsearch(model): + raise YellowbrickTypeError( + "This estimator is not a GridSearchCV instance" + ) + + # Initialize the super method. + super(GridSearchVisualizer, self).__init__(model, ax=ax, **kwargs) + + def param_projection(self, x_param, y_param, metric): + """ + Projects the grid search results onto 2 dimensions. + + The wrapped GridSearch object is assumed to be fit already. + The display value is taken as the max over the non-displayed dimensions. + + Parameters + ---------- + x_param : string + The name of the parameter to be visualized on the horizontal axis. + + y_param : string + The name of the parameter to be visualized on the vertical axis. + + metric : string (default 'mean_test_score') + The field from the grid search's `cv_results` that we want to display. + + Returns + ------- + unique_x_vals : list + The parameter values that will be used to label the x axis. + + unique_y_vals: list + The parameter values that will be used to label the y axis. + + best_scores: 2D numpy array (n_y by n_x) + Array of scores to be displayed for each parameter value pair. + """ + return param_projection(self.estimator.cv_results_, x_param, y_param, metric) + + def fit(self, X, y=None, **kwargs): + """ + Fits the wrapped grid search and calls draw(). + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs: dict + Keyword arguments passed to the drawing functionality or to the + Scikit-Learn API. See visualizer specific details for how to use + the kwargs to modify the visualization or fitting process. + + Returns + ------- + self : visualizer + The fit method must always return self to support pipelines. + """ + self.estimator.fit(X, y) + self.draw() + return self diff --git a/yellowbrick/gridsearch/pcolor.py b/yellowbrick/gridsearch/pcolor.py new file mode 100644 index 000000000..6585403d1 --- /dev/null +++ b/yellowbrick/gridsearch/pcolor.py @@ -0,0 +1,160 @@ +""" +Colorplot visualizer for gridsearch results. +""" + +import numpy as np + +from .base import GridSearchVisualizer + + +## Packages for export +__all__ = [ + "GridSearchColorPlot", + "gridsearch_color_plot" +] + + +########################################################################## +## Quick method +########################################################################## + +def gridsearch_color_plot(model, x_param, y_param, X=None, y=None, ax=None, + **kwargs): + """Quick method: + Create a color plot showing the best grid search scores across two + parameters. + + This helper function is a quick wrapper to utilize GridSearchColorPlot + for one-off analysis. + + If no `X` data is passed, the model is assumed to be fit already. This + allows quick exploration without waiting for the grid search to re-run. + + Parameters + ---------- + model : Scikit-Learn grid search object + Should be an instance of GridSearchCV. If not, an exception is raised. + The model may be fit or unfit. + + x_param : string + The name of the parameter to be visualized on the horizontal axis. + + y_param : string + The name of the parameter to be visualized on the vertical axis. + + metric : string (default 'mean_test_score') + The field from the grid search's `cv_results` that we want to display. + + X : ndarray or DataFrame of shape n x m or None (default None) + A matrix of n instances with m features. If not None, forces the + GridSearchCV object to be fit. + + y : ndarray or Series of length n or None (default None) + An array or series of target or class values. + + ax : matplotlib axes + The axes to plot the figure on. + + classes : list of strings + The names of the classes in the target + + Returns + ------- + ax : matplotlib axes + Returns the axes that the classification report was drawn on. + """ + # Instantiate the visualizer + visualizer = GridSearchColorPlot(model, x_param, y_param, ax=ax, **kwargs) + + # Fit if necessary + if X is not None: + visualizer.fit(X, y) + else: + visualizer.draw() + + # Return the axes object on the visualizer + return visualizer.ax + + +class GridSearchColorPlot(GridSearchVisualizer): + """ + Create a color plot showing the best grid search scores across two + parameters. + + Parameters + ---------- + model : Scikit-Learn grid search object + Should be an instance of GridSearchCV. If not, an exception is raised. + + x_param : string + The name of the parameter to be visualized on the horizontal axis. + + y_param : string + The name of the parameter to be visualized on the vertical axis. + + metric : string (default 'mean_test_score') + The field from the grid search's `cv_results` that we want to display. + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + colormap : string or cmap, default: 'RdBu_r' + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + >>> from yellowbrick.gridsearch import GridSearchColorPlot + >>> from sklearn.model_selection import GridSearchCV + >>> from sklearn.svm import SVC + >>> gridsearch = GridSearchCV(SVC(), + {'kernel': ['rbf', 'linear'], 'C': [1, 10]}) + >>> model = GridSearchColorPlot(gridsearch, x_param='kernel', y_param='C') + >>> model.fit(X) + >>> model.poof() + """ + + def __init__(self, model, x_param, y_param, metric='mean_test_score', + colormap='RdBu_r', ax=None, **kwargs): + super(GridSearchColorPlot, self).__init__(model, ax=ax, **kwargs) + self.x_param = x_param + self.y_param = y_param + self.metric = metric + self.colormap = colormap + + def draw(self): + # Project the grid search results to 2 dimensions + x_vals, y_vals, best_scores = self.param_projection( + self.x_param, self.y_param, metric=self.metric + ) + + # Mask nans so that they can be filled with a hatch + data = np.ma.masked_invalid(best_scores) + + # Plot and fill in hatch for nans + mesh = self.ax.pcolor(data, cmap=self.colormap, + vmin=np.nanmin(data), vmax=np.nanmax(data)) + self.ax.patch.set(hatch='x', edgecolor='black') + + # Ticks and tick labels + self.ax.set_xticks(np.arange(len(x_vals)) + 0.5) + self.ax.set_yticks(np.arange(len(y_vals)) + 0.5) + self.ax.set_xticklabels(x_vals, rotation=45) + self.ax.set_yticklabels(y_vals, rotation=45) + + # Add the colorbar + cb = self.ax.figure.colorbar(mesh, None, self.ax) + cb.outline.set_linewidth(0) + + self.ax.set_aspect("equal") + + def finalize(self): + self.set_title("Grid Search Scores") + self.ax.set_xlabel(self.x_param) + self.ax.set_ylabel(self.y_param) diff --git a/yellowbrick/regressor/alphas.py b/yellowbrick/regressor/alphas.py index 11fba7c94..0069c5c92 100644 --- a/yellowbrick/regressor/alphas.py +++ b/yellowbrick/regressor/alphas.py @@ -18,7 +18,6 @@ ########################################################################## import numpy as np -import matplotlib.pyplot as plt from functools import partial @@ -201,9 +200,8 @@ def _find_errors_param(self): """ # NOTE: The order of the search is very important! - for attr in ('cv_mse_path_', 'mse_path_'): - if hasattr(self.estimator, attr): - return getattr(self.estimator, attr).mean(1) + if hasattr(self.estimator, 'mse_path_'): + return self.estimator.mse_path_.mean(1) if hasattr(self.estimator, 'cv_values_'): return self.estimator.cv_values_.mean(0) diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index 86cedb7fb..815024f61 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -18,13 +18,10 @@ ## Imports ########################################################################## -import matplotlib.pyplot as plt - from sklearn.model_selection import train_test_split from ..style.palettes import LINE_COLOR from .base import RegressionScoreVisualizer -from ..exceptions import YellowbrickTypeError from ..bestfit import draw_best_fit, draw_identity_line diff --git a/yellowbrick/style/colors.py b/yellowbrick/style/colors.py index 12eca0f04..d495947fe 100644 --- a/yellowbrick/style/colors.py +++ b/yellowbrick/style/colors.py @@ -28,6 +28,7 @@ from six import string_types from yellowbrick.exceptions import YellowbrickValueError + # Check to see if matplotlib is at least sorta up to date from distutils.version import LooseVersion mpl_ge_150 = LooseVersion(mpl.__version__) >= "1.5.0" @@ -45,7 +46,7 @@ def get_color_cycle(): cyl = mpl.rcParams['axes.prop_cycle'] # matplotlib 1.5 verifies that axes.prop_cycle *is* a cycler # but no garuantee that there's a `color` key. - # so users could have a custom rcParmas w/ no color... + # so users could have a custom rcParams w/ no color... try: return [x['color'] for x in cyl] except KeyError: @@ -53,58 +54,71 @@ def get_color_cycle(): return mpl.rcParams['axes.color_cycle'] -def resolve_colors(num_colors=None, colormap=None, color=None): +def resolve_colors(n_colors=None, colormap=None, colors=None): """ - Resolves the colormap or the color list with the number of colors. - See: https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L163 + Generates a list of colors based on common color arguments, for example + the name of a colormap or palette or another iterable of colors. The list + is then truncated (or multiplied) to the specific number of requested + colors. Parameters ---------- - num_colors : int or None - the number of colors in the cycle or colormap - - colormap : str or None - the colormap used to create the sequence of colors - - color : list or None - the list of colors to specifically use with the plot - + n_colors : int, default: None + Specify the length of the list of returned colors, which will either + truncate or multiple the colors available. If None the length of the + colors will not be modified. + + colormap : str, default: None + The name of the matplotlib color map with which to generate colors. + + colors : iterable, default: None + A collection of colors to use specifically with the plot. + + Returns + ------- + colors : list + A list of colors that can be used in matplotlib plots. + + Notes + ----- + This function was originally based on a similar function in the pandas + plotting library that has been removed in the new version of the library. """ - # Work with the colormap - if color is None and colormap is None: - if isinstance(colormap, str): - cmap = colormap - colormap = cm.get_cmap(colormap) + # Work with the colormap if specified and colors is not + if colormap is not None and colors is None: + if isinstance(colormap, string_types): + try: + colormap = cm.get_cmap(colormap) + except ValueError as e: + raise YellowbrickValueError(e) - if colormap is None: - raise YellowbrickValueError( - "Colormap {0} is not a valid matploblib cmap".format(cmap) - ) - colors = list(map(colormap, np.linspace(0, 1, num=num_colors))) + n_colors = n_colors or len(get_color_cycle()) + _colors = list(map(colormap, np.linspace(0, 1, num=n_colors))) # Work with the color list - elif color is not None: + elif colors is not None: + # Warn if both colormap and colors is specified. if colormap is not None: warnings.warn( - "'color' and 'colormap' cannot be used simultaneously! Using 'color'." + "both colormap and colors specified; using colors" ) - colors = list(color) # Ensure colors is a list + _colors = list(colors) # Ensure colors is a list # Get the default colors else: - colors = get_color_cycle() + _colors = get_color_cycle() - if len(colors) != num_colors: - multiple = num_colors // len(colors) - 1 - mod = num_colors % len(colors) - colors += multiple * colors - colors += colors[:mod] + # Truncate or multiple the color list according to the number of colors + if n_colors is not None and len(_colors) != n_colors: + _colors = [ + _colors[idx % len(_colors)] for idx in np.arange(n_colors) + ] - return colors + return _colors class ColorMap(object): @@ -133,6 +147,9 @@ def colors(self, value): Converts color strings into a color listing. """ if isinstance(value, string_types): + # Must import here to avoid recursive import + from .palettes import PALETTES + if value not in PALETTES: raise YellowbrickValueError( "'{}' is not a registered color palette".format(value) diff --git a/yellowbrick/style/palettes.py b/yellowbrick/style/palettes.py index 3d8ec521d..d4b4c11a9 100644 --- a/yellowbrick/style/palettes.py +++ b/yellowbrick/style/palettes.py @@ -1,14 +1,33 @@ -# yellowbrick.style.palettes # Implements the variety of colors that yellowbrick allows access to by nam # -# Author: Patrick O'Melveny # Copyright (C) 2016 District Data Lab# For license information, see LICENSE.txt -# D: palettes.py [] pvomelveny@gmail.com "" Implements the variety of colors that yellowbrick allows access to by name This code was originally based on Seaborn's rcmody.py but has since beecleaned up to be Yellowbrick-specific and to dereference tools we don't use. Note that these functions alter the matplotlib rc dictionary on the fly. ######################################################################### ## Import ######################################################################### +# yellowbrick.style.palettes +# Implements the variety of colors that yellowbrick allows access to by name. +# +# Author: Patrick O'Melveny +# +# Copyright (C) 2016 District Data Lab +# For license information, see LICENSE.txt +# +# ID: palettes.py [] pvomelveny@gmail.com + +""" +Implements the variety of colors that yellowbrick allows access to by name. +This code was originally based on Seaborn's rcmody.py but has since been +cleaned up to be Yellowbrick-specific and to dereference tools we don't use. +Note that these functions alter the matplotlib rc dictionary on the fly. +""" + +######################################################################### +## Imports +######################################################################### + from __future__ import division -from itertools import cycle import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.colors as mplcol +from itertools import cycle from six import string_types from six.moves import range diff --git a/yellowbrick/style/rcmod.py b/yellowbrick/style/rcmod.py index b59b13be9..051e561e1 100644 --- a/yellowbrick/style/rcmod.py +++ b/yellowbrick/style/rcmod.py @@ -25,8 +25,6 @@ import numpy as np import matplotlib as mpl -from six import string_types - # Check to see if we have a slightly modern version of mpl from distutils.version import LooseVersion mpl_ge_150 = LooseVersion(mpl.__version__) >= '1.5.0' diff --git a/yellowbrick/style/utils.py b/yellowbrick/style/utils.py index e1f12753e..96c7fd82b 100644 --- a/yellowbrick/style/utils.py +++ b/yellowbrick/style/utils.py @@ -6,44 +6,42 @@ ## Imports ########################################################################## -import math import numpy as np -from yellowbrick.exceptions import YellowbrickTypeError def find_text_color(base_color, dark_color="black", light_color="white", coef_choice=0): """ Takes a background color and returns the appropriate light or dark text color. Users can specify the dark and light text color, or accept the defaults of 'black' and 'white' - base_color: The color of the background. This must be - specified in RGBA with values between 0 and 1 (note, this is the default - return value format of a call to base_color = cmap(number) to get the + base_color: The color of the background. This must be + specified in RGBA with values between 0 and 1 (note, this is the default + return value format of a call to base_color = cmap(number) to get the color corresponding to a desired number). Note, the value of `A` in RGBA is not considered in determining light/dark. - dark_color: Any valid matplotlib color value. + dark_color: Any valid matplotlib color value. Function will return this value if the text should be colored dark - light_color: Any valid matplotlib color value. - Function will return this value if thet text should be colored light. + light_color: Any valid matplotlib color value. + Function will return this value if thet text should be colored light. coef_choice: slightly different approaches to calculating brightness. Currently two options in - a list, user can enter 0 or 1 as list index. 0 is default. + a list, user can enter 0 or 1 as list index. 0 is default. """ - #Coefficients: + #Coefficients: # option 0: http://www.nbdtech.com/Blog/archive/2008/04/27/Calculating-the-Perceived-Brightness-of-a-Color.aspx # option 1: http://stackoverflow.com/questions/596216/formula-to-determine-brightness-of-rgb-color coef_options = [np.array((.241, .691, .068, 0)), np.array((.299, .587, .114, 0)) ] - + coefs= coef_options[coef_choice] - rgb = np.array(base_color) * 255 + rgb = np.array(base_color) * 255 brightness = np.sqrt(np.dot(coefs, rgb**2)) - #Threshold from option 0 link; determined by trial and error. + #Threshold from option 0 link; determined by trial and error. #base is light if brightness > 130: return dark_color diff --git a/yellowbrick/text/base.py b/yellowbrick/text/base.py index 688eda292..7a82c01d5 100644 --- a/yellowbrick/text/base.py +++ b/yellowbrick/text/base.py @@ -18,9 +18,9 @@ ########################################################################## from yellowbrick.base import Visualizer -from yellowbrick.utils import is_dataframe from sklearn.base import TransformerMixin + ########################################################################## ## Text Visualizers ########################################################################## diff --git a/yellowbrick/text/freqdist.py b/yellowbrick/text/freqdist.py index 6745aa2be..d1aa633da 100644 --- a/yellowbrick/text/freqdist.py +++ b/yellowbrick/text/freqdist.py @@ -18,14 +18,11 @@ ########################################################################## import numpy as np -import matplotlib.pyplot as plt -import matplotlib.patches as mpatches from operator import itemgetter from yellowbrick.text.base import TextVisualizer -from yellowbrick.exceptions import YellowbrickTypeError -from yellowbrick.style.colors import resolve_colors, get_color_cycle +from yellowbrick.exceptions import YellowbrickValueError ########################################################################## diff --git a/yellowbrick/text/tsne.py b/yellowbrick/text/tsne.py index 63570c0cd..fbdefc397 100644 --- a/yellowbrick/text/tsne.py +++ b/yellowbrick/text/tsne.py @@ -18,13 +18,12 @@ ########################################################################## import numpy as np -import matplotlib.pyplot as plt from collections import defaultdict from yellowbrick.text.base import TextVisualizer +from yellowbrick.style.colors import resolve_colors from yellowbrick.exceptions import YellowbrickValueError -from yellowbrick.style.colors import resolve_colors, get_color_cycle from sklearn.manifold import TSNE from sklearn.pipeline import Pipeline @@ -133,17 +132,18 @@ class TSNEVisualizer(TextVisualizer): ax : matplotlib axes The axes to plot the figure on. - decompose : string or None + decompose : string or None, default: ``'svd'`` A preliminary decomposition is often used prior to TSNE to make the - projection faster. Specify `"svd"` for sparse data or `"pca"` for - dense data. If decompose is None, the original data set will be used. + projection faster. Specify ``"svd"`` for sparse data or ``"pca"`` for + dense data. If None, the original data set will be used. - decompose_by : int + decompose_by : int, default: 50 Specify the number of components for preliminary decomposition, by default this is 50; the more components, the slower TSNE will be. - classes : list of strings + labels : list of strings The names of the classes in the target, used to create a legend. + Labels must match names of classes in sorted order. colors : list or tuple of colors Specify the colors for each individual class @@ -151,25 +151,32 @@ class TSNEVisualizer(TextVisualizer): colormap : string or matplotlib cmap Sequential colormap for continuous target + random_state : int, RandomState instance or None, optional, default: None + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by np.random. The random state is applied to the preliminary + decomposition as well as tSNE. + kwargs : dict Pass any additional keyword arguments to the TSNE transformer. """ - def __init__(self, ax=None, decompose='svd', decompose_by=50, classes=None, - colors=None, colormap=None, **kwargs): + # NOTE: cannot be np.nan + NULL_CLASS = None + + def __init__(self, ax=None, decompose='svd', decompose_by=50, labels=None, + classes=None, colors=None, colormap=None, random_state=None, **kwargs): """ Initialize the TSNE visualizer with visual hyperparameters. """ super(TSNEVisualizer, self).__init__(ax=ax, **kwargs) - # Visualizer parameters - self.classes_ = classes - self.n_instances_ = 0 - # Visual Parameters - # TODO: Only colors currently works to select the colors of classes. + self.labels = labels self.colors = colors self.colormap = colormap + self.random_state = random_state # TSNE Parameters self.transformer_ = self.make_transformer(decompose, decompose_by, kwargs) @@ -184,12 +191,13 @@ def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}): Parameters ---------- - decompose : string or None - A preliminary decomposition is often used prior to TSNE to make the - projection faster. Specify `"svd"` for sparse data or `"pca"` for - dense data. If decompose is None, the original data set will be used. + decompose : string or None, default: ``'svd'`` + A preliminary decomposition is often used prior to TSNE to make + the projection faster. Specify ``"svd"`` for sparse data or ``"pca"`` + for dense data. If decompose is None, the original data set will + be used. - decompose_by : int + decompose_by : int, default: 50 Specify the number of components for preliminary decomposition, by default this is 50; the more components, the slower TSNE will be. @@ -200,6 +208,8 @@ def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}): Pipelined transformer for TSNE projections """ + # TODO: detect decompose by inferring from sparse matrix or dense or + # If number of features > 50 etc. decompositions = { 'svd': TruncatedSVD, 'pca': PCA, @@ -218,10 +228,12 @@ def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}): # Add the pre-decomposition if decompose: klass = decompositions[decompose] - steps.append((decompose, klass(n_components=decompose_by))) + steps.append((decompose, klass( + n_components=decompose_by, random_state=self.random_state))) # Add the TSNE manifold - steps.append(('tsne', TSNE(n_components=2, **tsne_kwargs))) + steps.append(('tsne', TSNE( + n_components=2, random_state=self.random_state, **tsne_kwargs))) # return the pipeline return Pipeline(steps) @@ -255,13 +267,17 @@ def fit(self, X, y=None, **kwargs): Returns the instance of the transformer/visualizer """ - # If we don't have classes already stored, store them. - if y and self.classes_ is None: - self.classes_ = [str(label) for label in set(y)] + # Store the classes we observed in y + if y is not None: + self.classes_ = np.unique(y) + elif y is None and self.labels is not None: + self.classes_ = np.array([self.labels[0]]) + else: + self.classes_ = np.array([self.NULL_CLASS]) # Fit our internal transformer and transform the data. vecs = self.transformer_.fit_transform(X) - self.n_instances_ += vecs.shape[0] + self.n_instances_ = vecs.shape[0] # Draw the vectors self.draw(vecs, y, **kwargs) @@ -277,34 +293,45 @@ def draw(self, points, target=None, **kwargs): of each of the points. If the target is not specified, then the points are plotted as a single cloud to show similar documents. """ - # Create the color mapping for the classes. - # TODO: Allow both colormap, listed colors, and palette definition - # See the FeatureVisualizer for more on this. - color_values = get_color_cycle() - classes = self.classes_ or [None] - colors = dict(zip(classes, color_values)) + # Resolve the labels with the classes + labels = self.labels if self.labels is not None else self.classes_ + if len(labels) != len(self.classes_): + raise YellowbrickValueError(( + "number of supplied labels ({}) does not " + "match the number of classes ({})" + ).format(len(labels), len(self.classes_))) + + + # Create the color mapping for the labels. + color_values = resolve_colors( + n_colors=len(labels), colormap=self.colormap, colors=self.color) + colors = dict(zip(labels, color_values)) + + # Transform labels into a map of class to label + labels = dict(zip(self.classes_, labels)) # Expand the points into vectors of x and y for scatter plotting, # assigning them to their label if the label has been passed in. # Additionally, filter classes not specified directly by the user. series = defaultdict(lambda: {'x':[], 'y':[]}) - if self.classes_: classes = frozenset(self.classes_) - - if target: - for label, point in zip(target, points): - if self.classes_ and label not in classes: - continue + if target is not None: + for t, point in zip(target, points): + label = labels[t] series[label]['x'].append(point[0]) series[label]['y'].append(point[1]) else: + label = self.classes_[0] for x,y in points: - series[None]['x'].append(x) - series[None]['y'].append(y) + series[label]['x'].append(x) + series[label]['y'].append(y) # Plot the points for label, points in series.items(): - self.ax.scatter(points['x'], points['y'], c=colors[label], alpha=0.7, label=label) + self.ax.scatter( + points['x'], points['y'], c=colors[label], + alpha=0.7, label=label + ) def finalize(self, **kwargs): """ @@ -322,7 +349,7 @@ def finalize(self, **kwargs): self.ax.set_xticks([]) # Add the legend outside of the figure box. - if self.classes_: + if not all(self.classes_ == np.array([self.NULL_CLASS])): box = self.ax.get_position() self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) self.ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) diff --git a/yellowbrick/utils/helpers.py b/yellowbrick/utils/helpers.py index 1b5a9cc9b..799e43d0c 100644 --- a/yellowbrick/utils/helpers.py +++ b/yellowbrick/utils/helpers.py @@ -18,7 +18,6 @@ ########################################################################## import re -import unicodedata import numpy as np from sklearn.pipeline import Pipeline diff --git a/yellowbrick/utils/nan_warnings.py b/yellowbrick/utils/nan_warnings.py new file mode 100644 index 000000000..70a0cada1 --- /dev/null +++ b/yellowbrick/utils/nan_warnings.py @@ -0,0 +1,78 @@ +""" +Small helpers that help find and filter missing data. +""" + +import numpy as np +import warnings +from yellowbrick.exceptions import DataWarning + + +def filter_missing(X, y=None): + """ + Removes rows that contain np.nan values in data. If y is given, + X and y will be filtered together so that their shape remains identical. + For example, rows in X with nans will also remove rows in y, or rows in y + with np.nans will also remove corresponding rows in X. + + Parameters + ------------ + X : array-like + Data in shape (m, n) that possibly contains np.nan values + + y : array-like, optional + Data in shape (m, 1) that possibly contains np.nan values + + Returns + -------- + X' : np.array + Possibly transformed X with any row containing np.nan removed + + y' : np.array + If y is given, will also return possibly transformed y to match the + shape of X'. + + Notes + ------ + This function will return either a np.array if only X is passed or a tuple + if both X and y is passed. Because all return values are indexable, it is + important to recognize what is being passed to the function to determine + its output. + """ + if y is not None: + return filter_missing_X_and_y(X, y) + else: + return X[~np.isnan(X).any(axis=1)] + + +def filter_missing_X_and_y(X, y): + """Remove rows from X and y where either contains nans.""" + y_nans = np.isnan(y) + x_nans = np.isnan(X).any(axis=1) + unioned_nans = np.logical_or(x_nans, y_nans) + + return X[~unioned_nans], y[~unioned_nans] + + +def warn_if_nans_exist(X): + """Warn if nans exist in a numpy array.""" + null_count = count_rows_with_nans(X) + total = len(X) + percent = 100 * null_count / total + + if null_count > 0: + warning_message = \ + 'Warning! Found {} rows of {} ({:0.2f}%) with nan values. Only ' \ + 'complete rows will be plotted.'.format(null_count, total, percent) + warnings.warn(warning_message, DataWarning) + + +def count_rows_with_nans(X): + """Count the number of rows in 2D arrays that contain any nan values.""" + if X.ndim == 2: + return np.where(np.isnan(X).sum(axis=1) != 0, 1, 0).sum() + + +def count_nan_elements(data): + """Count the number of elements in 1D arrays that are nan values.""" + if data.ndim == 1: + return np.isnan(data).sum() diff --git a/yellowbrick/utils/types.py b/yellowbrick/utils/types.py index 51e9a2d21..a0ba10d72 100644 --- a/yellowbrick/utils/types.py +++ b/yellowbrick/utils/types.py @@ -21,7 +21,6 @@ import numpy as np from sklearn.base import BaseEstimator -from yellowbrick.exceptions import YellowbrickTypeError ########################################################################## @@ -129,6 +128,32 @@ def is_clusterer(estimator): isclusterer = is_clusterer +def is_gridsearch(estimator): + """ + Returns True if the given estimator is a clusterer. + + Parameters + ---------- + estimator : class or instance + The object to test if it is a Scikit-Learn clusterer, especially a + Scikit-Learn estimator or Yellowbrick visualizer + """ + # TODO: once we make ScoreVisualizer and ModelVisualizer pass through + # wrappers as in Issue #90, these three lines become unnecessary. + # NOTE: This must be imported here to avoid recursive import. + from yellowbrick.base import Visualizer + if isinstance(estimator, Visualizer): + return is_gridsearch(estimator.estimator) + + # Estimator type for a GridSearchCV object is the type of the model it + # searches over; we need a direct check. + from sklearn.model_selection import GridSearchCV + return isinstance(estimator, GridSearchCV) + +# Alias for closer name to isinstance and issubclass +isgridsearch = is_gridsearch + + def is_dataframe(obj): """ Returns True if the given object is a Pandas Data Frame. diff --git a/yellowbrick/version.py b/yellowbrick/version.py index 231f5ec2b..aac19862b 100644 --- a/yellowbrick/version.py +++ b/yellowbrick/version.py @@ -19,7 +19,7 @@ __version_info__ = { 'major': 0, - 'minor': 5, + 'minor': 6, 'micro': 0, 'releaselevel': 'final', 'serial': 10,

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