From 1a4a1c0df45cf4124d513ea8f8039895a1fde60d Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Thu, 11 Apr 2024 17:59:39 +0200 Subject: [PATCH 01/25] adding an author to test docs --- AUTHORS.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/AUTHORS.rst b/AUTHORS.rst index 79cf92d..6e7ed4f 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -10,4 +10,4 @@ Development Lead Contributors ------------ -None yet. Why not be the first? +* Teo Bucci From a68b1446b759a5bf310264fae8ec9e388d635546 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Fri, 12 Apr 2024 12:15:13 +0200 Subject: [PATCH 02/25] fixing no_lag bug --- hawk/analysis/main.py | 3 ++- hawk/processes/wps_causal.py | 7 +++++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/hawk/analysis/main.py b/hawk/analysis/main.py index ac423ae..85a777d 100644 --- a/hawk/analysis/main.py +++ b/hawk/analysis/main.py @@ -63,7 +63,8 @@ def __init__( self.tefs_features_lags = [] if self.tefs_use_contemporary_features: self.tefs_features_lags.append(0) - self.tefs_features_lags.extend(list(range(1, self.tefs_max_lag_features + 1))) + if self.tefs_max_lag_features > 0: + self.tefs_features_lags.extend(list(range(1, self.tefs_max_lag_features + 1))) self.tefs_target_lags = list(range(1, self.tefs_max_lag_target + 1)) diff --git a/hawk/processes/wps_causal.py b/hawk/processes/wps_causal.py index c411275..e4a88e5 100644 --- a/hawk/processes/wps_causal.py +++ b/hawk/processes/wps_causal.py @@ -221,12 +221,15 @@ def _handler(self, request, response): tefs_direction = request.inputs["tefs_direction"][0].data tefs_use_contemporary_features = request.inputs["tefs_use_contemporary_features"][0].data - tefs_max_lag_features = int(request.inputs["tefs_max_lag_features"][0].data) + if str(request.inputs["tefs_max_lag_features"][0].data) == "no_lag": + tefs_max_lag_features = 0 + else: + tefs_max_lag_features = int(request.inputs["tefs_max_lag_features"][0].data) tefs_max_lag_target = int(request.inputs["tefs_max_lag_target"][0].data) workdir = Path(self.workdir) - if not tefs_use_contemporary_features and tefs_max_lag_features == "no_lag": + if not tefs_use_contemporary_features and tefs_max_lag_features == 0: raise ValueError("You cannot use no lag features and not use contemporary features in TEFS.") causal_analysis = CausalAnalysis( From a9282b452e1d531e1a0bd06781f5f43f53a749aa Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 14:42:14 +0200 Subject: [PATCH 03/25] Remove encoding in pkl format --- hawk/processes/wps_causal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hawk/processes/wps_causal.py b/hawk/processes/wps_causal.py index c411275..35f5cc5 100644 --- a/hawk/processes/wps_causal.py +++ b/hawk/processes/wps_causal.py @@ -10,7 +10,7 @@ FORMAT_PNG = Format("image/png", extension=".png", encoding="base64") FORMAT_PDF = Format("application/pdf", extension=".pdf", encoding="utf-8") -FORMAT_PICKLE = Format("application/octet-stream", extension=".pkl", encoding="utf-8") +FORMAT_PICKLE = Format("application/octet-stream", extension=".pkl") class Causal(Process): From ee6305e6d9ec5da3a529029b16a0113f60b397ec Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 14:43:40 +0200 Subject: [PATCH 04/25] Avoid duplicate file name --- hawk/analysis/postprocessing.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/hawk/analysis/postprocessing.py b/hawk/analysis/postprocessing.py index 8928932..985038a 100644 --- a/hawk/analysis/postprocessing.py +++ b/hawk/analysis/postprocessing.py @@ -257,7 +257,7 @@ def run_postprocessing_pcmci( target_file_plots = {} for image_format in image_formats: target_file_plot = os.path.join( - destination_path, "algorithm_results", "pcmci", f"feature_presence.{image_format}" + destination_path, "algorithm_results", "pcmci", f"feature_presence_pcmci.{image_format}" ) os.makedirs(os.path.dirname(target_file_plot), exist_ok=True) plt.savefig(target_file_plot, bbox_inches="tight") @@ -384,7 +384,9 @@ def run_postprocessing_tefs( ) target_file_plots = {} for image_format in image_formats: - target_file_plot = os.path.join(destination_path, "algorithm_results", "te", f"feature_presence.{image_format}") + target_file_plot = os.path.join( + destination_path, "algorithm_results", "te", f"feature_presence_tefs.{image_format}" + ) os.makedirs(os.path.dirname(target_file_plot), exist_ok=True) plt.savefig(target_file_plot, bbox_inches="tight") target_file_plots[image_format] = target_file_plot From 678f83532d92cf30c49c2925f2f6f992b333b936 Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 14:47:52 +0200 Subject: [PATCH 05/25] Use library's metrics --- hawk/analysis/main.py | 2 +- hawk/analysis/metrics.py | 124 -------------------------------- hawk/analysis/postprocessing.py | 2 +- 3 files changed, 2 insertions(+), 126 deletions(-) delete mode 100644 hawk/analysis/metrics.py diff --git a/hawk/analysis/main.py b/hawk/analysis/main.py index ac423ae..2d51add 100644 --- a/hawk/analysis/main.py +++ b/hawk/analysis/main.py @@ -2,11 +2,11 @@ import os import pandas as pd +from tefs.metrics import regression_analysis from tigramite.independence_tests.cmiknn import CMIknn from tigramite.independence_tests.parcorr import ParCorr from .file_management import save_to_pkl_file -from .metrics import regression_analysis from .pcmci_tools import initialize_tigramite_df from .postprocessing import ( run_postprocessing_pcmci, diff --git a/hawk/analysis/metrics.py b/hawk/analysis/metrics.py deleted file mode 100644 index 066f0c6..0000000 --- a/hawk/analysis/metrics.py +++ /dev/null @@ -1,124 +0,0 @@ -from typing import Any, Dict, Optional, Tuple - -import pandas as pd -from sklearn.linear_model import LinearRegression -from sklearn.metrics import r2_score -from sklearn.model_selection import BaseCrossValidator, cross_val_score - -inputs_names_lags_doc = """ -:param inputs_names_lags: A dictionary mapping input feature names to their corresponding list of lags. - For example, {'feature1': [1, 2], 'feature2': [1]} indicates 'feature1' should be lagged by 1 and 2 periods, - and 'feature2' by 1 period. -""" - -target_name_doc = """ -:param target_name: The name of the target variable in the DataFrame. -""" - - -def prepare_data_with_lags( - df: pd.DataFrame, - inputs_names_lags: Dict[str, list[int]], - target_name: str, -) -> Tuple[pd.DataFrame, pd.Series]: - f""" - Prepares data for regression by generating lagged features for specified variables and targets. - - :param df: The pandas DataFrame containing the time series data. - {inputs_names_lags_doc} - {target_name_doc} - :return: A tuple containing the lagged features DataFrame and the target variable Series. - """ - - required_columns = set([*inputs_names_lags.keys(), target_name]) - if not required_columns.issubset(set(df.columns)): - raise ValueError( - "DataFrame 'df' must contain all the columns specified in 'features_names' and 'targets_names'." - ) - - for lags in inputs_names_lags.values(): - if lags and min(lags) < 0: - raise ValueError("Lag for independent variables must be a non-negative integer.") - - # Initialize a list to hold all DataFrame chunks - lagged_chunks = [] - - # Generate lagged inputs for the independent variables - for input, lags in inputs_names_lags.items(): - for lag in lags: - lagged_chunk = df[input].shift(lag).to_frame(f"{input}_t-{lag}") - lagged_chunks.append(lagged_chunk) - - # Adding target column - lagged_chunks.append(df[target_name].to_frame(target_name)) - - # Concatenate chunks - df_lagged = pd.concat(lagged_chunks, axis=1) - - # Dropping rows with NaN values caused by shifting - df_lagged = df_lagged.dropna() - - return df_lagged.drop(columns=target_name), df_lagged[target_name] - - -def regression_analysis( - inputs_names_lags: Dict[str, list[int]], - target_name: str, - df: Optional[pd.DataFrame] = None, - cv_scheme: Optional[BaseCrossValidator] = None, - df_train: Optional[pd.DataFrame] = None, - df_test: Optional[pd.DataFrame] = None, -) -> Any: - f""" - Performs regression analysis with support for either cross-validation or a train-test split, - based on the arguments provided. - - {inputs_names_lags_doc} - {target_name_doc} - :param df: DataFrame for cross-validation mode. If specified, cv_scheme must also be provided. - :param cv_scheme: Cross-validator object for cross-validation mode. If specified, df must also be provided. - :param df_train: Training DataFrame for train-test split mode. Required if df_test is provided. - :param df_test: Testing DataFrame for train-test split mode. Requires df_train to be specified. - :return: Cross-validated scores or R-squared scores from train-test evaluation. - """ - - # Check that exactly one mode is specified - cross_val_mode = bool(df is not None and cv_scheme is not None) - train_test_mode = bool(df_train is not None and df_test is not None) - if not (cross_val_mode ^ train_test_mode): - raise ValueError( - "Specify either a 'cv_scheme' and 'df', or a train-test split with 'df_train' and 'df_test', not both." - ) - - if cross_val_mode: - if df is None or cv_scheme is None: - raise ValueError("Both 'df' and 'cv_scheme' must be specified for cross-validation mode.") - - X, y = prepare_data_with_lags( - df, - inputs_names_lags, - target_name, - ) - - model = LinearRegression() - return cross_val_score(model, X, y, cv=cv_scheme) - - elif train_test_mode: - if df_train is None or df_test is None: - raise ValueError("Both 'df_train' and 'df_test' must be specified for train-test split mode.") - - X_train, y_train = prepare_data_with_lags( - df_train, - inputs_names_lags, - target_name, - ) - - X_test, y_test = prepare_data_with_lags( - df_test, - inputs_names_lags, - target_name, - ) - - model = LinearRegression().fit(X_train, y_train) - y_pred = model.predict(X_test) - return r2_score(y_test, y_pred) diff --git a/hawk/analysis/postprocessing.py b/hawk/analysis/postprocessing.py index 985038a..bff5452 100644 --- a/hawk/analysis/postprocessing.py +++ b/hawk/analysis/postprocessing.py @@ -6,10 +6,10 @@ import numpy as np import pandas as pd import seaborn as sns +from tefs.metrics import regression_analysis # from tigramite import plotting as tp from .file_management import save_to_pkl_file -from .metrics import regression_analysis from .pcmci_tools import get_connected_variables From 8dd1f08fb836490632c492cbecded4724fda013c Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 15:04:34 +0200 Subject: [PATCH 06/25] Change interactive defaults --- hawk/processes/simulation_interactive.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/hawk/processes/simulation_interactive.py b/hawk/processes/simulation_interactive.py index 2d3bd68..e833b84 100644 --- a/hawk/processes/simulation_interactive.py +++ b/hawk/processes/simulation_interactive.py @@ -1,9 +1,5 @@ from birdy import WPSClient -train_file_path = "Emiliani1_train.csv" -test_file_path = "Emiliani1_test.csv" -target_column_name = "cyclostationary_mean_rr_4w_1" - # ----------------- WPS ----------------- wps = WPSClient("http://localhost:5002/wps", verify=False) @@ -11,14 +7,14 @@ # Input some data for the causal process resp = wps.causal( - dataset_train=open(train_file_path), - dataset_test=open(test_file_path), - target_column_name=target_column_name, + dataset_train="https://raw.githubusercontent.com/climateintelligence/hawk/main/hawk/demo/Ticino_train.csv", + dataset_test="https://raw.githubusercontent.com/climateintelligence/hawk/main/hawk/demo/Ticino_train.csv", + target_column_name="target", pcmci_test_choice="ParCorr", - pcmci_max_lag="0", - tefs_direction="both", - tefs_use_contemporary_features="Yes", - tefs_max_lag_features="1", + pcmci_max_lag="1", + tefs_direction="forward", + tefs_use_contemporary_features=True, + tefs_max_lag_features="2", tefs_max_lag_target="1", ) From d715d137293747da5e0d33afa858bac7b5e0088d Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 15:05:05 +0200 Subject: [PATCH 07/25] Change test defaults --- tests/test_causal_analysis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_causal_analysis.py b/tests/test_causal_analysis.py index a9a0ae2..64948a4 100644 --- a/tests/test_causal_analysis.py +++ b/tests/test_causal_analysis.py @@ -34,8 +34,8 @@ def test_causal_analysis(): df_test = pd.read_csv("hawk/demo/Ticino_test.csv", header=0) target_column_name = "target" pcmci_test_choice = "ParCorr" - pcmci_max_lag = 0 - tefs_direction = "forward" + pcmci_max_lag = 2 + tefs_direction = "both" tefs_use_contemporary_features = True tefs_max_lag_features = 1 tefs_max_lag_target = 1 From 207e586840296eda79ae971cff11a95dea2ddd02 Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 15:05:52 +0200 Subject: [PATCH 08/25] Fix typo and default --- hawk/processes/wps_causal.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/hawk/processes/wps_causal.py b/hawk/processes/wps_causal.py index 35f5cc5..70b733d 100644 --- a/hawk/processes/wps_causal.py +++ b/hawk/processes/wps_causal.py @@ -79,8 +79,8 @@ def __init__(self): "tefs_use_contemporary_features", "TEFS Use Contemporary Features", data_type="boolean", - abstract="Choose whether to use comtemporary features in the TEFS algorithm.", - default="Yes", + abstract="Choose whether to use contemporary features in the TEFS algorithm.", + default=True, ), LiteralInput( "tefs_max_lag_features", From 9930bbec99ea71b62767d5be864ff87f4ee67f16 Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 15:06:04 +0200 Subject: [PATCH 09/25] Add defaults --- hawk/processes/wps_causal.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/hawk/processes/wps_causal.py b/hawk/processes/wps_causal.py index 70b733d..4f549ae 100644 --- a/hawk/processes/wps_causal.py +++ b/hawk/processes/wps_causal.py @@ -39,12 +39,14 @@ def __init__(self): "Target Column Name", data_type="string", abstract="Please enter the case-specific name of the target variable in the dataframe.", + default="target", ), LiteralInput( "pcmci_test_choice", "PCMCI Test Choice", data_type="string", abstract="Choose the independence test to be used in PCMCI.", + default="ParCorr", allowed_values=[ "ParCorr", "CMIknn", @@ -55,6 +57,7 @@ def __init__(self): "PCMCI Max Lag", data_type="string", abstract="Choose the maximum lag to test used in PCMCI.", + default="1", allowed_values=[ "0", "1", @@ -69,6 +72,7 @@ def __init__(self): "TEFS Direction", data_type="string", abstract="Choose the direction of the TEFS algorithm.", + default="both", allowed_values=[ "forward", "backward", @@ -87,6 +91,7 @@ def __init__(self): "TEFS Max Lag Features", data_type="string", abstract="Choose the maximum lag of the features in the TEFS algorithm.", + default="1", allowed_values=[ "no_lag", "1", @@ -101,6 +106,7 @@ def __init__(self): "TEFS Max Lag Target", data_type="string", abstract="Choose the maximum lag of the target in the TEFS algorithm.", + default="1", allowed_values=[ "1", "2", From 81599c3d38369d2905d694bf94ad574c24591477 Mon Sep 17 00:00:00 2001 From: Teo Bucci Date: Sat, 13 Apr 2024 15:08:17 +0200 Subject: [PATCH 10/25] Specify tefs version to 0.3.1 --- environment.yml | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index d34d498..a78fab3 100644 --- a/environment.yml +++ b/environment.yml @@ -11,7 +11,7 @@ dependencies: - psutil - birdy #- tigramite -#- tefs +#- tefs==0.3.1 - pandas - scikit-learn - numpy diff --git a/requirements.txt b/requirements.txt index f89c934..2bcfac0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ psutil pywps>=4.5.1,<4.6 birdhouse-birdy tigramite>=5.2.5.1 -tefs +tefs==0.3.1 pandas scikit-learn numpy \ No newline at end of file From 90472f28ca17ec26c4e0394b10758168592aa7ee Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Mon, 15 Apr 2024 18:18:58 +0200 Subject: [PATCH 11/25] Update README.rst --- README.rst | 34 ++++++++++++++++++++-------------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/README.rst b/README.rst index e6b22be..ece244f 100644 --- a/README.rst +++ b/README.rst @@ -22,38 +22,44 @@ Hawk :alt: Join the chat at https://gitter.im/bird-house/birdhouse Hawk (the bird) - *Hawk is a bird designed to perform causal analysis for climate data or, in general, for spatio-temporal data.* + *Hawk is a bird designed to perform causal analysis for climate data or, in general, for time-series.* -Hawk is designed to take as input a dataset, divided into train and test set, producing a causal analysis as output, which depends on the additional parameters set by the users. +The hawk is a raptor with high visual acuity. This prototype takes the name of this bird since the causal analysis that is performed tries to provide an insight on the causal relationship between the variables of a given dataset. -Hawk does not address a specific causal analysis on a specific dataset, but it can be applied to any supervised learning task based on time-series. +Hawk is designed to take as input a dataset, divided into train and test set, provided by the user. This prototype then produces a causal analysis as output, which depends on the additional parameters set by the user, analysing the relationship between the features and the target variable of the dataset provided. -The work described in https://www.politesi.polimi.it/handle/10589/219074 contains additional details and examples of the causal methods contained in Hawk, showing causal analyses on some relevant sub-basins of the Po River. +Hawk does not address a specific causal analysis on a specific dataset, but it can be applied to any supervised learning task based on time-series. The current implementation is based on the four classical assumptions of causal discovery: acyclicity, causal sufficiency, faithfulness and Markov assumption. + +The Hawk prototype provides a framework to identify causal relationships in supervised learning settings, which is a generalization of the methodology that has firstly been explored in https://www.politesi.polimi.it/handle/10589/219074. Specifically, that thesis contains additional details on the methodology followed by this prototype and applicative examples, showing causal analyses on some relevant sub-basins of the Po River. Hawk is based on two main algorithms: PCMCI and TEFS. -The PCMCI algorithm (J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019).) and its variants perform causal inference for time series data. +The PCMCI algorithm (J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019)) and its variants perform causal inference for time series data. -Specifically, this methodology is designed to estimate causal graphs from observational time series, without the possibility of intervention (observational causal discovery). +Specifically, the PCMCI methodology is designed to estimate causal graphs from observational time series, without the possibility of intervention (observational causal discovery). -The main parameter of this approach is the choice of the (conditional) independence tests to perform, which is exploited by the algorithm to remove as many spurious causal links among variables as possible. -The PCMCI approach can be applied in combination with linear or nonlinear independence tests. The other main parameter of this method is the time lag which the user considers to be relevant for the current values of the variables. If contemporaneous causal effects are allowed, the methods is called PCMCI+. +The main parameter of this approach is the choice of the (conditional) independence tests to perform, which is exploited by the algorithm to remove as many spurious causal links among variables as possible.The PCMCI approach can be applied in combination with linear or nonlinear independence tests. +The other main parameter of this method is the time lag which the user considers to be relevant for the current values of the variables. If contemporaneous causal effects are allowed, the methods is called PCMCI+ (and is applied in Hawk). -The main guarantee of this method resides in the identification of a partially directed graph, which removes spurious correlations with high probability and that asymptotically converges to the correct causal graph (if the underlying assumptions hold). +The main guarantee of the PCMCI method resides in the identification of a partially directed graph, which removes spurious correlations with high probability and that asymptotically converges to the correct causal graph (if the underlying assumptions hold). On the other hand, its main weakness in a supervised learning setting resides in being agnostic w.r.t. the concept of target, without providing any guarantee on the regression error. -In the adaptation of the original PCMCI implementation within the Hawk bird, the target variable has been considered, together with the features, as an individual variable. Therefore, Hawk considers the autoregressive component of the target variable, and the incoming or undirected causal links related to it, to identify the subset of relevant features and the importance of the autoregressive component. +In the adaptation of the original PCMCI implementation within the Hawk prototype, the target variable and each individual feature are considered as input variables. Then, the Hawk runs the PCMCI+ algorithm, obtaining an estimated causal graph. From this output, it considers the autoregressive component of the target variable (if it is identified as significant by the PCMCI+), and the incoming or undirected causal links related to the target variable itself, as the importance of the autoregressive component and the subset of relevant features, respectively. The second approach implemented in Hawk is the Transfer Entropy Feature selection algorithm (TEFS, "Bonetti, P., Metelli, A. M., & Restelli, M. (2023, October 17). Causal Feature Selection via Transfer Entropy (https://arxiv.org/abs/2310.11059)."). Differently from the PCMCI, the main characteristic of this approach is to identify a set of relevant features, focusing on providing general guarantees on the regression performance. -Specifically, this approach iteratively identifies the most relevant features, filtering the autoregressive component of the target variable to identify the acyclic flow of information. Therefore, the selected causal features are the variables, ranked by importance, that provide additional information on the evolution of the target w.r.t. its previous observed values. +Specifically, this approach iteratively identifies the most relevant features, filtering the autoregressive component of the target variable to identify the acyclic flow of information. Therefore, the selected causal features are the variables, ranked by importance, that provide additional information on the evolution of the target w.r.t. its previous observed values and the set of already selected relevant features. + +As described in the original paper, assuming an exact estimation of the transfer entropy, it is possible to control the amount of information loss that follows from the reduction of the original features, and discarding features that have zero transfer entropy with the target guarantee no loss of information. However, given the difficulty to correctly extimate the transfer entropy in practice, the Hawk provides a "conservative" output, in the sense that it selects all the features that iteratively provide a positive amount of estimated transfer entropy as causally relevant. On the other hand, this methodology does not provide asymptotic guarantees on the identification of the correct underlying causal graph, but it only addresses causality in the sense of the classical Granger definition, i.e., a feature is causally relevant for a target variable if it improves the regression performance w.r.t. its autoregressive component and the other features. -More specifically, the TEFS methodology can be applied in a forward and in a backward manner, with the first that may be preferred for computational and efficiency reasons. Additionally, as for the PCMCI, the choice of the time lag to consider both for the features and the target have an impact on the final output, since they balance the amount of past timestaps that the user considers to be relevant for the actual target. +More specifically, in line with classical feature selection approaches, the TEFS methodology can be applied in a forward and in a backward manner, with the first that may be preferred for computational and efficiency reasons, which follow from empirical considerations and that have also a theoretical confirmation. Additionally, as for the PCMCI, the choice of the time lag to consider both for the features and the target have an impact on the final output, since they balance the amount of past timesteps that the user considers to be relevant for the actual target. In the Hawk prototype it is therefore possible to select the maximum time lag to consider for features, target, and to include contemporaneous causal effects. + +Finally, a wrapper analysis based on transfer entropy and linear regression is additionally available in the Hawk outputs. Again, this analysis iteratively identifies the most important features in a forward manner, focusing on transfer entropy. However, rather than automatically stop when no information is added by the further selection of a feature, this analysis continues the selection, up to selecting the full set of initial features. Then, the output plot provides the score, in terms of validation coefficient of determination of linear regression. This way, the user can ponder between focusing on transfer entropy for the selection, which is a filter method independent from the subsequent model, and the interest of achieving the best predictive performance. Documentation ------------- @@ -74,7 +80,7 @@ License ------- * Free software: GNU General Public License v3 -* Documentation: https://hawk.readthedocs.io. +* Documentation: https://clint-hawk.readthedocs.io. Credits @@ -89,4 +95,4 @@ The two Python libraries TEFS_ and tigramite_ have been exploited to perform the .. _`Developer Guide`: https://hawk.readthedocs.io/en/latest/dev_guide.html .. _bump2version: https://hawk.readthedocs.io/en/latest/dev_guide.html#bump-a-new-version .. _tigramite: https://github.com/jakobrunge/tigramite -.. _TEFS: https://github.com/teobucci/tefs \ No newline at end of file +.. _TEFS: https://github.com/teobucci/tefs From de95743737fd0ba853b94e72ef4051e52fea7c18 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Tue, 16 Apr 2024 15:44:08 +0200 Subject: [PATCH 12/25] Adding a test for "no_lag" option --- tests/test_causal_analysis_noLag.py | 60 +++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 tests/test_causal_analysis_noLag.py diff --git a/tests/test_causal_analysis_noLag.py b/tests/test_causal_analysis_noLag.py new file mode 100644 index 0000000..11133f7 --- /dev/null +++ b/tests/test_causal_analysis_noLag.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python + +"""Tests for `hawk` package.""" + +import os + +import pandas as pd +import pytest +from click.testing import CliRunner # noqa: F401 + +import hawk # noqa: F401 +from hawk import cli # noqa: F401 +from hawk.analysis import CausalAnalysis + + +@pytest.fixture +def response(): + """Sample pytest fixture. + + See more at: http://doc.pytest.org/en/latest/fixture.html + """ + # import requests + # return requests.get('https://github.com/audreyr/cookiecutter-pypackage') + + +def test_content(response): + """Sample pytest test function with the pytest fixture as an argument.""" + # from bs4 import BeautifulSoup + # assert 'GitHub' in BeautifulSoup(response.content).title.string + + +def test_causal_analysis(): + df_train = pd.read_csv("hawk/demo/Ticino_train.csv", header=0) + df_test = pd.read_csv("hawk/demo/Ticino_test.csv", header=0) + target_column_name = "target" + pcmci_test_choice = "ParCorr" + pcmci_max_lag = 2 + tefs_direction = "both" + tefs_use_contemporary_features = True + tefs_max_lag_features = "no_lag" + tefs_max_lag_target = 1 + workdir = "tests/output" + + causal_analysis = CausalAnalysis( + df_train, + df_test, + target_column_name, + pcmci_test_choice, + pcmci_max_lag, + tefs_direction, + tefs_use_contemporary_features, + tefs_max_lag_features, + tefs_max_lag_target, + workdir, + response=None, + ) + + causal_analysis.run() + + os.system("rm -r tests/output") \ No newline at end of file From feee7fc06b1b6515ae1c3697edd90935a1893266 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Tue, 16 Apr 2024 17:25:51 +0200 Subject: [PATCH 13/25] fixing no_lag test --- tests/test_causal_analysis_noLag.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tests/test_causal_analysis_noLag.py b/tests/test_causal_analysis_noLag.py index 11133f7..4cac0b8 100644 --- a/tests/test_causal_analysis_noLag.py +++ b/tests/test_causal_analysis_noLag.py @@ -29,7 +29,7 @@ def test_content(response): # assert 'GitHub' in BeautifulSoup(response.content).title.string -def test_causal_analysis(): +def test_causal_analysis_noLag(): df_train = pd.read_csv("hawk/demo/Ticino_train.csv", header=0) df_test = pd.read_csv("hawk/demo/Ticino_test.csv", header=0) target_column_name = "target" @@ -41,6 +41,14 @@ def test_causal_analysis(): tefs_max_lag_target = 1 workdir = "tests/output" + if str(tefs_max_lag_features) == "no_lag": + tefs_max_lag_features = 0 + else: + tefs_max_lag_features = int(tefs_max_lag_features) + + if not tefs_use_contemporary_features and tefs_max_lag_features == 0: + raise ValueError("You cannot use no lag features and not use contemporary features in TEFS.") + causal_analysis = CausalAnalysis( df_train, df_test, From e0f430bfb4c8e7ca1adb18f4f4e139d423098750 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Tue, 16 Apr 2024 17:29:31 +0200 Subject: [PATCH 14/25] adding new line at end of test file --- tests/test_causal_analysis_noLag.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_causal_analysis_noLag.py b/tests/test_causal_analysis_noLag.py index 4cac0b8..b0bf588 100644 --- a/tests/test_causal_analysis_noLag.py +++ b/tests/test_causal_analysis_noLag.py @@ -65,4 +65,5 @@ def test_causal_analysis_noLag(): causal_analysis.run() - os.system("rm -r tests/output") \ No newline at end of file + os.system("rm -r tests/output") + \ No newline at end of file From fd4d89362d92318fe7e4468bf06414c320be6d91 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Tue, 16 Apr 2024 17:32:37 +0200 Subject: [PATCH 15/25] removing whitespace --- tests/test_causal_analysis_noLag.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_causal_analysis_noLag.py b/tests/test_causal_analysis_noLag.py index b0bf588..dfc88d7 100644 --- a/tests/test_causal_analysis_noLag.py +++ b/tests/test_causal_analysis_noLag.py @@ -66,4 +66,3 @@ def test_causal_analysis_noLag(): causal_analysis.run() os.system("rm -r tests/output") - \ No newline at end of file From b4ff42da34b28103b4dcd573002f2fab7753211f Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Tue, 23 Apr 2024 14:47:23 +0200 Subject: [PATCH 16/25] adding some fixes on baselines and runs --- hawk/analysis/checks.ipynb | 755 ++++++++++++++++++ hawk/analysis/main.py | 98 ++- hawk/analysis/postprocessing.py | 5 +- hawk/demo/Ticino_test.csv | 458 +++++------ hawk/demo/Ticino_train.csv | 1280 +++++++++++++++---------------- 5 files changed, 1691 insertions(+), 905 deletions(-) create mode 100644 hawk/analysis/checks.ipynb diff --git a/hawk/analysis/checks.ipynb b/hawk/analysis/checks.ipynb new file mode 100644 index 0000000..796c4f0 --- /dev/null +++ b/hawk/analysis/checks.ipynb @@ -0,0 +1,755 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "57588eeb-9985-4622-aa24-1b41616b343d", + "metadata": {}, + "outputs": [], + "source": [ + "from birdy import WPSClient\n", + "wps = WPSClient(\"http://localhost:5002/wps\", verify=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ede8e3cb-8a45-4a88-b4c9-e6e1661993be", + "metadata": {}, + "outputs": [], + "source": [ + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "### check different tefs_max_lag_target\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"3\",\n", + ")\n", + "\n", + "### check different tefs_max_lag_features\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"no_lag\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"3\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=False,\n", + " tefs_max_lag_features=\"3\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=False,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=False,\n", + " tefs_max_lag_features=\"no_lag\",\n", + " tefs_max_lag_target=\"1\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50f5bcb5-07e7-4a98-bd82-582d399083d6", + "metadata": {}, + "outputs": [], + "source": [ + "### PCMCI\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"2\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"0\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"CMIknn\",\n", + " pcmci_max_lag=\"0\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"CMIknn\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")\n", + "\n", + "# problema con TEFS, CMIknn, lag 0, in cui sceglie HS e rr invece di tg_0" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "eb6b7828-3c92-4883-b3b1-44803dc236ae", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['graph', 'p_matrix', 'val_matrix', 'sepsets', 'ambiguous_triples', 'conf_matrix'])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "results = pd.read_pickle(\"/work/bk1318/b382633/pcmci_results.pkl\")\n", + "results[0] # dictionary of 4 elements, keys: ['results', 'params', 'dataset_name', 'execution_time']\n", + "results[0][\"dataset_name\"] # is just \"normal\"\n", + "results[0][\"execution_time\"] # is just the execution time in seconds\n", + "results[0][\"params\"] # reports the lag, the independence test and the algorithm\n", + "results[0][\"results\"] # dictionary of 6 elements, keys: ['graph', 'p_matrix', 'val_matrix', 'sepsets', 'ambiguous_triples', 'conf_matrix']\n", + "results[0][\"results\"].keys() " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "01b6c32d-a0ce-46ef-8142-630c82d8f070", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[''],\n", + " [''],\n", + " [''],\n", + " [''],\n", + " [''],\n", + " ['o-o'],\n", + " ['']],\n", + "\n", + " [[''],\n", + " [''],\n", + " ['o-o'],\n", + " ['o-o'],\n", + " ['o-o'],\n", + " [''],\n", + " ['o-o']],\n", + "\n", + " [[''],\n", + " ['o-o'],\n", + " [''],\n", + " [''],\n", + " ['o-o'],\n", + " [''],\n", + " ['o-o']],\n", + "\n", + " [[''],\n", + " ['o-o'],\n", + " [''],\n", + " [''],\n", + " [''],\n", + " [''],\n", + " ['']],\n", + 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cyclostationary_mean_tg_0cyclostationary_mean_HS_0cyclostationary_mean_rr_4w_0cyclostat_level_Maggiore_16wcyclostat_level_Luganocyclostationary_mean_tg_2target
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4-0.6897091.9001560.6472030.8038430.0738880.0129200.324134
........................
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639 rows × 7 columns

\n", + "" + ], + "text/plain": [ + " cyclostationary_mean_tg_0 cyclostationary_mean_HS_0 \\\n", + "0 -0.410998 2.523716 \n", + "1 0.803930 2.134136 \n", + "2 -0.911268 1.914711 \n", + "3 0.538884 1.959702 \n", + "4 -0.689709 1.900156 \n", + ".. ... ... \n", + "634 0.789685 0.949804 \n", + "635 2.141001 0.223155 \n", + "636 1.106946 -0.302170 \n", + "637 1.171064 -0.562534 \n", + "638 1.583469 -0.459527 \n", + "\n", + " cyclostationary_mean_rr_4w_0 cyclostat_level_Maggiore_16w \\\n", + "0 0.611605 0.542394 \n", + "1 1.691336 0.593148 \n", + "2 0.832271 0.674726 \n", + "3 0.859041 0.749432 \n", + "4 0.647203 0.803843 \n", + ".. ... ... \n", + "634 2.835166 0.557533 \n", + "635 2.575132 0.567490 \n", + "636 2.832534 0.560946 \n", + "637 1.469015 0.520204 \n", + "638 -0.690965 0.492773 \n", + "\n", + " cyclostat_level_Lugano cyclostationary_mean_tg_2 target \n", + "0 0.082042 -0.447071 0.264043 \n", + "1 0.162350 0.647375 0.354618 \n", + "2 0.085054 -0.540914 0.427990 \n", + "3 0.062613 0.453677 0.339495 \n", + "4 0.073888 0.012920 0.324134 \n", + ".. ... ... ... \n", + "634 0.927179 1.167781 0.459076 \n", + "635 0.190525 2.330281 0.413500 \n", + "636 0.080250 2.359644 0.395620 \n", + "637 0.040875 1.598315 0.427921 \n", + "638 0.040813 0.994373 0.373354 \n", + "\n", + "[639 rows x 7 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_csv(\"/work/bk1318/b382633/hawk/hawk/demo/Ticino_train.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a15adee-268a-45ad-bf05-924740cc977d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf430535-0114-4c31-8864-0b68ae2caee6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7807fbff-aaf6-4280-abc6-ef582e10b1c1", + "metadata": {}, + "outputs": [], + "source": [ + "def run_postprocessing_pcmci(\n", + " results_pcmci,\n", + " target_column_name,\n", + " datasets,\n", + " destination_path,\n", + " image_formats=[\"pdf\", \"png\"],\n", + "):\n", + " all_basin_variables = set()\n", + " results_table_pcmci = []\n", + " for simulation in results_pcmci:\n", + " dataframe = datasets[simulation[\"dataset_name\"]]\n", + " var_names = dataframe[\"var_names\"]\n", + " all_basin_variables.update(var_names)\n", + "\n", + " results = simulation[\"results\"]\n", + "\n", + " # Plot only the connections to any of the target variables\n", + " temp_graph = results[\"graph\"].copy()\n", + "\n", + " # Show only the connections to the target variables\n", + " # Identify the indexes of the target variables\n", + " # target_vars = np.where([\"target\" in var for var in var_names.values])[0]\n", + " # for i in range(temp_graph.shape[0]):\n", + " # for j in range(temp_graph.shape[1]):\n", + " # # if the edge is not connected to the target variables\n", + " # if i not in target_vars and j not in target_vars:\n", + " # # remove the edge\n", + " # temp_graph[i, j, :] = ''\n", + " # temp_graph[j, i, :] = ''\n", + "\n", + " # Base arguments for tp.plot_graph\n", + " plot_args = {\n", + " \"val_matrix\": results[\"val_matrix\"],\n", + " \"graph\": temp_graph,\n", + " \"var_names\": var_names,\n", + " \"link_colorbar_label\": \"cross-MCI\",\n", + " \"node_colorbar_label\": \"auto-MCI\",\n", + " \"show_autodependency_lags\": False,\n", + " }\n", + "\n", + " # Additional arguments to include if the independence_test is CMIknn\n", + " if simulation[\"params\"][\"independencetest\"] == \"cmiknn\":\n", + " plot_args.update(\n", + " {\n", + " \"vmin_edges\": 0.0,\n", + " \"vmax_edges\": 0.1,\n", + " \"edge_ticks\": 0.05,\n", + " \"cmap_edges\": \"OrRd\",\n", + " \"vmin_nodes\": 0,\n", + " \"vmax_nodes\": 0.1,\n", + " \"node_ticks\": 0.1,\n", + " \"cmap_nodes\": \"OrRd\",\n", + " }\n", + " )\n", + "\n", + " # Plot causal graph\n", + " # target_file = os.path.join(constants.path_figures, \"algorithm_results\", basin_name, \"pcmci\", key + \".pdf\")\n", + " # if not os.path.exists(target_file):\n", + " # fig, ax = plt.subplots()\n", + " # tp.plot_graph(**plot_args, fig_ax=(fig, ax))\n", + " # os.makedirs(os.path.dirname(target_file), exist_ok=True)\n", + " # plt.savefig(target_file, bbox_inches=\"tight\")\n", + " # plt.close(fig)\n", + "\n", + " # # Plot time series graph if lag > 0\n", + " # if simulation[\"params\"][\"lag\"] > 0:\n", + " # target_file = os.path.join(\n", + " # constants.path_figures, \"algorithm_results\", basin_name, \"pcmci\", key + \"_timeseries.pdf\"\n", + " # )\n", + " # if not os.path.exists(target_file):\n", + " # fig, ax = plt.subplots()\n", + " # tp.plot_time_series_graph(\n", + " # figsize=(6, 4),\n", + " # fig_ax=(fig, ax),\n", + " # val_matrix=results[\"val_matrix\"],\n", + " # graph=results[\"graph\"],\n", + " # var_names=var_names,\n", + " # link_colorbar_label=\"MCI\",\n", + " # )\n", + " # os.makedirs(os.path.dirname(target_file), exist_ok=True)\n", + " # plt.savefig(target_file, bbox_inches=\"tight\")\n", + " # plt.close(fig)\n", + "\n", + " # Extract the selected features\n", + " selected_features = get_connected_variables(results[\"graph\"], var_names)\n", + "\n", + " # Compute the R2 scores\n", + " inputs_names_lags = {feature: [0] for feature in selected_features}\n", + " score_r2 = (\n", + " regression_analysis(\n", + " inputs_names_lags=inputs_names_lags,\n", + " target_name=target_column_name,\n", + " df_train=dataframe[\"train\"],\n", + " df_test=dataframe[\"test\"],\n", + " )\n", + " if len(selected_features) > 0\n", + " else np.nan\n", + " )\n", + "\n", + " inputs_names_lags = {feature: list(range(0, simulation[\"params\"][\"lag\"] + 1)) for feature in selected_features}\n", + " score_r2_lag = (\n", + " regression_analysis(\n", + " inputs_names_lags=inputs_names_lags,\n", + " target_name=target_column_name,\n", + " df_train=dataframe[\"train\"],\n", + " df_test=dataframe[\"test\"],\n", + " )\n", + " if len(selected_features) > 0\n", + " else np.nan\n", + " )\n", + "\n", + " inputs_names_lags = {feature: list(range(0, simulation[\"params\"][\"lag\"] + 1)) for feature in selected_features}\n", + " inputs_names_lags[target_column_name] = list(range(1, simulation[\"params\"][\"lag\"] + 1))\n", + " score_r2_lag_ar = regression_analysis(\n", + " inputs_names_lags=inputs_names_lags,\n", + " target_name=target_column_name,\n", + " df_train=dataframe[\"train\"],\n", + " df_test=dataframe[\"test\"],\n", + " )\n", + "\n", + " # Table of results\n", + " results_table_pcmci.append(\n", + " {\n", + " \"selected_features\": \" \".join(selected_features),\n", + " \"score_r2\": score_r2,\n", + " \"score_r2_lag\": score_r2_lag,\n", + " \"score_r2_lag_ar\": score_r2_lag_ar,\n", + " \"dataset_name\": simulation[\"dataset_name\"],\n", + " \"algorithm\": simulation[\"params\"][\"algorithm\"],\n", + " \"independencetest\": simulation[\"params\"][\"independencetest\"],\n", + " \"lag\": simulation[\"params\"][\"lag\"],\n", + " \"execution_time\": simulation[\"execution_time\"],\n", + " }\n", + " )\n", + "\n", + " # Export the file to pkl\n", + " target_file_results_details = os.path.join(destination_path, \"results_details_pcmci.pkl\")\n", + " save_to_pkl_file(target_file_results_details, results_table_pcmci)\n", + "\n", + " # Feature presences heatmap\n", + " if target_column_name in all_basin_variables:\n", + " all_basin_variables.remove(target_column_name)\n", + " all_basin_variables = sorted(list(all_basin_variables))\n", + " df_presence = pd.DataFrame(index=all_basin_variables, columns=range(len(results_pcmci)))\n", + " scores = []\n", + " scores_lag = []\n", + " scores_lag_ar = []\n", + "\n", + " for index, result in enumerate(results_table_pcmci):\n", + " scores.append(result[\"score_r2\"])\n", + " scores_lag.append(result[\"score_r2_lag\"])\n", + " scores_lag_ar.append(result[\"score_r2_lag_ar\"])\n", + "\n", + " # loop through the rows of the df, if the feature is in the list of selected features, put a 1\n", + " for feature in df_presence.index:\n", + " if feature in result[\"selected_features\"]:\n", + " df_presence.loc[feature, index] = 1\n", + " else:\n", + " df_presence.loc[feature, index] = 0\n", + " if feature not in datasets[result[\"dataset_name\"]][\"var_names\"]:\n", + " df_presence.loc[feature, index] = 2\n", + "\n", + " df_presence = df_presence.astype(float)\n", + " scores = np.array(scores)\n", + " scores_lag = np.array(scores_lag)\n", + " scores_lag_ar = np.array(scores_lag_ar)\n", + "\n", + " fig, ax = plot_feature_presence_and_r2(\n", + " df_presence=df_presence,\n", + " scores_values=[scores, scores_lag, scores_lag_ar],\n", + " scores_labels=[r\"$R^2$\", r\"$R^2$ (lag)\", r\"$R^2$ (lag + AR)\"],\n", + " )\n", + "\n", + " target_file_plots = {}\n", + " for image_format in image_formats:\n", + " target_file_plot = os.path.join(\n", + " destination_path, \"algorithm_results\", \"pcmci\", f\"feature_presence_pcmci.{image_format}\"\n", + " )\n", + " os.makedirs(os.path.dirname(target_file_plot), exist_ok=True)\n", + " plt.savefig(target_file_plot, bbox_inches=\"tight\")\n", + " target_file_plots[image_format] = target_file_plot\n", + " plt.close(fig)\n", + " \n", + " print(f\"PCMCI results details: {results_table_pcmci}\")\n", + "\n", + " return target_file_plots, target_file_results_details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e717aa75-bfcb-4f34-8440-a1896e6c6573", + "metadata": {}, + "outputs": [], + "source": [ + "### TEFS\n", + "\n", + "resp = wps.causal(\n", + " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", + " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", + " target_column_name=\"target\",\n", + " pcmci_test_choice=\"ParCorr\",\n", + " pcmci_max_lag=\"1\",\n", + " tefs_direction=\"forward\",\n", + " tefs_use_contemporary_features=True,\n", + " tefs_max_lag_features=\"1\",\n", + " tefs_max_lag_target=\"1\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "400aabea-f8b3-4a07-ad9c-e5a5f339d560", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (tensorflow_env)", + "language": "python", + "name": "tensorflow_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hawk/analysis/main.py b/hawk/analysis/main.py index e56aae8..154c259 100644 --- a/hawk/analysis/main.py +++ b/hawk/analysis/main.py @@ -82,20 +82,36 @@ def run_baseline_analysis(self): baseline = {} features_names = self.datasets["normal"]["var_names"] + + print(f"features_names: {features_names}") configs = [] - # Autoregressive baselines - for i in range(1, self.tefs_max_lag_target): + # Only autoregressive baselines from 1 to the maximum target lag + for i in range(1, self.tefs_max_lag_target+1): configs.append((f"AR({i})", {self.target_column_name: list(range(1, i + 1))})) + + print(f"AR configs: {configs}") - # With all features + # All features without AR configs.append( ( "All features", - {feature: self.tefs_features_lags for feature in features_names}, + {feature: self.tefs_features_lags for feature in features_names if feature != self.target_column_name}, ) ) + + print(f"Full configs: {configs}") + + # All features with AR + configs.append( + ( + "All features and AR", + {feature: self.tefs_features_lags if feature != self.target_column_name else list(range(1, self.tefs_max_lag_target+1)) for feature in features_names}, + ) + ) + + print(f"Full configs: {configs}") for label, inputs_names_lags in configs: baseline[label] = { @@ -107,6 +123,8 @@ def run_baseline_analysis(self): df_test=self.datasets["normal"]["test"], ), } + + print(f"Baselines: {baseline}") target_file = os.path.join(self.workdir, "baseline.pkl") save_to_pkl_file(target_file, baseline) @@ -152,6 +170,8 @@ def run_tefs_analysis( "dataset_name": dataset_name, } configurations.append(configuration) + + print(f"TEFS configurations: {configurations}") # Run the analysis results = [] @@ -163,23 +183,26 @@ def run_tefs_analysis( config=config, ) ) + + print(f"TEFS results: {results}") return results def run_pcmci_analysis( self, ): - lag_options = self.pcmci_features_lags # max lag + lag_options = self.pcmci_features_lags # list from 0 to max_lag # Define the tests parcorr = ParCorr(significance="analytic") - cmiknn = CMIknn( - significance="shuffle_test", - knn=0.1, - shuffle_neighbors=5, - transform="ranks", - sig_samples=200, - ) + #cmiknn = CMIknn( + # significance="shuffle_test", + # knn=0.1, + # shuffle_neighbors=5, + # transform="ranks", + # sig_samples=200, + #) + cmiknn = CMIknn() # Create the dictionary of tests independence_tests = { @@ -215,6 +238,8 @@ def run_pcmci_analysis( "dataset_name": dataset_name, } configurations.append(configuration) + + print(f'PCMCI configurations: {configurations}') # Run the analysis results = [] @@ -226,7 +251,10 @@ def run_pcmci_analysis( independence_tests=independence_tests, ) ) - + print(f"PCMCI+ results: {results}") + + save_to_pkl_file("/work/bk1318/b382633/pcmci_results.pkl", results) + return results def run(self): @@ -236,19 +264,19 @@ def run(self): if self.response: self.response.update_status("Performing TEFS analysis", 33) tefs_results = self.run_tefs_analysis() - if self.response: - self.response.update_status("Performing PCMCI analysis", 66) - pcmci_results = self.run_pcmci_analysis() - - if self.response: - self.response.update_status("Postprocessing PCMCI", 80) - self.plot_pcmci, self.details_pcmci = run_postprocessing_pcmci( - results_pcmci=pcmci_results, - target_column_name=self.target_column_name, - datasets=self.datasets, - destination_path=self.workdir, - image_formats=["pdf", "png"], - ) + #if self.response: + # self.response.update_status("Performing PCMCI analysis", 66) + #pcmci_results = self.run_pcmci_analysis() + + #if self.response: + # self.response.update_status("Postprocessing PCMCI", 80) + #self.plot_pcmci, self.details_pcmci = run_postprocessing_pcmci( + # results_pcmci=pcmci_results, + # target_column_name=self.target_column_name, + # datasets=self.datasets, + # destination_path=self.workdir, + # image_formats=["pdf", "png"], + #) if self.response: self.response.update_status("Postprocessing TEFS", 90) self.plot_tefs, self.details_tefs = run_postprocessing_tefs( @@ -258,12 +286,12 @@ def run(self): destination_path=self.workdir, image_formats=["pdf", "png"], ) - if self.response: - self.response.update_status("Postprocessing TEFS Wrapper", 95) - self.plot_tefs_wrapper, self.details_tefs_wrapper = run_postprocessing_tefs_wrapper( - results_tefs=tefs_results, - target_column_name=self.target_column_name, - datasets=self.datasets, - destination_path=self.workdir, - image_formats=["pdf", "png"], - ) + #if self.response: + # self.response.update_status("Postprocessing TEFS Wrapper", 95) + #self.plot_tefs_wrapper, self.details_tefs_wrapper = run_postprocessing_tefs_wrapper( + # results_tefs=tefs_results, + # target_column_name=self.target_column_name, + # datasets=self.datasets, + # destination_path=self.workdir, + # image_formats=["pdf", "png"], + #) diff --git a/hawk/analysis/postprocessing.py b/hawk/analysis/postprocessing.py index bff5452..4f822c9 100644 --- a/hawk/analysis/postprocessing.py +++ b/hawk/analysis/postprocessing.py @@ -12,7 +12,6 @@ from .file_management import save_to_pkl_file from .pcmci_tools import get_connected_variables - # Adjusted custom sort key function to handle lag sequences and replace them with the last lag def general_custom_sort_key(s): # Find all sequences like [0,1], [1,2,3,5,6], etc. @@ -263,6 +262,8 @@ def run_postprocessing_pcmci( plt.savefig(target_file_plot, bbox_inches="tight") target_file_plots[image_format] = target_file_plot plt.close(fig) + + print(f"PCMCI results details: {results_table_pcmci}") return target_file_plots, target_file_results_details @@ -348,6 +349,8 @@ def run_postprocessing_tefs( # Export the file to pkl target_file_results_details = os.path.join(destination_path, "results_details_te.pkl") save_to_pkl_file(target_file_results_details, results_table_te) + + print(f"results_table_te: {results_table_te}") # Feature presences heatmap if target_column_name in all_basin_variables: diff --git a/hawk/demo/Ticino_test.csv b/hawk/demo/Ticino_test.csv index 188632c..5df3a86 100644 --- a/hawk/demo/Ticino_test.csv +++ b/hawk/demo/Ticino_test.csv @@ -1,229 +1,229 @@ -target,cyclostationary_mean_tg_0,cyclostationary_mean_HS_0,cyclostationary_mean_rr_4w_0,cyclostat_level_Maggiore_16w,cyclostat_level_Lugano 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z^lk8VwH{W>^{`~5Xu6=Df0)pcZ#Vk|_g1T)|GtxNMWP7?f`MQl7zhUT#sJT3$@IuE z+F&3U2nN0x(EgCA!qTuh+Sb8F*Jq7Sgc91iBnxxH(y%&mgpxd!=&7kaF_Nd#pIcmM zSRFkb$!2DqHuKlxCE4usXA4Ivj?o4K!N8b-L+{RX{$KK!=|1wuDbWiCf`Na=K$ Date: Thu, 25 Apr 2024 10:44:02 +0200 Subject: [PATCH 21/25] Delete hawk/analysis/checks.ipynb --- hawk/analysis/checks.ipynb | 755 ------------------------------------- 1 file changed, 755 deletions(-) delete mode 100644 hawk/analysis/checks.ipynb diff --git a/hawk/analysis/checks.ipynb b/hawk/analysis/checks.ipynb deleted file mode 100644 index 796c4f0..0000000 --- a/hawk/analysis/checks.ipynb +++ /dev/null @@ -1,755 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "57588eeb-9985-4622-aa24-1b41616b343d", - "metadata": {}, - "outputs": [], - "source": [ - "from birdy import WPSClient\n", - "wps = WPSClient(\"http://localhost:5002/wps\", verify=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ede8e3cb-8a45-4a88-b4c9-e6e1661993be", - "metadata": {}, - "outputs": [], - "source": [ - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "### check different tefs_max_lag_target\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"3\",\n", - ")\n", - "\n", - "### check different tefs_max_lag_features\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"no_lag\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"3\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=False,\n", - " tefs_max_lag_features=\"3\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=False,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=False,\n", - " tefs_max_lag_features=\"no_lag\",\n", - " tefs_max_lag_target=\"1\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50f5bcb5-07e7-4a98-bd82-582d399083d6", - "metadata": {}, - "outputs": [], - "source": [ - "### PCMCI\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"2\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"0\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"CMIknn\",\n", - " pcmci_max_lag=\"0\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"CMIknn\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")\n", - "\n", - "# problema con TEFS, CMIknn, lag 0, in cui sceglie HS e rr invece di tg_0" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "eb6b7828-3c92-4883-b3b1-44803dc236ae", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['graph', 'p_matrix', 'val_matrix', 'sepsets', 'ambiguous_triples', 'conf_matrix'])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "results = pd.read_pickle(\"/work/bk1318/b382633/pcmci_results.pkl\")\n", - "results[0] # dictionary of 4 elements, keys: ['results', 'params', 'dataset_name', 'execution_time']\n", - "results[0][\"dataset_name\"] # is just \"normal\"\n", - "results[0][\"execution_time\"] # is just the execution time in seconds\n", - "results[0][\"params\"] # reports the lag, the independence test and the algorithm\n", - "results[0][\"results\"] # dictionary of 6 elements, keys: ['graph', 'p_matrix', 'val_matrix', 'sepsets', 'ambiguous_triples', 'conf_matrix']\n", - "results[0][\"results\"].keys() " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "01b6c32d-a0ce-46ef-8142-630c82d8f070", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[''],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " ['o-o'],\n", - " ['']],\n", - "\n", - " [[''],\n", - " [''],\n", - " ['o-o'],\n", - " ['o-o'],\n", - " ['o-o'],\n", - " [''],\n", - " ['o-o']],\n", - "\n", - " [[''],\n", - " ['o-o'],\n", - " [''],\n", - " [''],\n", - " ['o-o'],\n", - " [''],\n", - " ['o-o']],\n", - "\n", - " [[''],\n", - " ['o-o'],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " ['']],\n", - "\n", - " [[''],\n", - " ['o-o'],\n", - " ['o-o'],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " ['']],\n", - "\n", - " [['o-o'],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " ['']],\n", - "\n", - " [[''],\n", - " ['o-o'],\n", - " ['o-o'],\n", - " [''],\n", - " [''],\n", - " [''],\n", - " ['']]], dtype='\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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cyclostationary_mean_tg_0cyclostationary_mean_HS_0cyclostationary_mean_rr_4w_0cyclostat_level_Maggiore_16wcyclostat_level_Luganocyclostationary_mean_tg_2target
0-0.4109982.5237160.6116050.5423940.082042-0.4470710.264043
10.8039302.1341361.6913360.5931480.1623500.6473750.354618
2-0.9112681.9147110.8322710.6747260.085054-0.5409140.427990
30.5388841.9597020.8590410.7494320.0626130.4536770.339495
4-0.6897091.9001560.6472030.8038430.0738880.0129200.324134
........................
6340.7896850.9498042.8351660.5575330.9271791.1677810.459076
6352.1410010.2231552.5751320.5674900.1905252.3302810.413500
6361.106946-0.3021702.8325340.5609460.0802502.3596440.395620
6371.171064-0.5625341.4690150.5202040.0408751.5983150.427921
6381.583469-0.459527-0.6909650.4927730.0408130.9943730.373354
\n", - "

639 rows × 7 columns

\n", - "" - ], - "text/plain": [ - " cyclostationary_mean_tg_0 cyclostationary_mean_HS_0 \\\n", - "0 -0.410998 2.523716 \n", - "1 0.803930 2.134136 \n", - "2 -0.911268 1.914711 \n", - "3 0.538884 1.959702 \n", - "4 -0.689709 1.900156 \n", - ".. ... ... \n", - "634 0.789685 0.949804 \n", - "635 2.141001 0.223155 \n", - "636 1.106946 -0.302170 \n", - "637 1.171064 -0.562534 \n", - "638 1.583469 -0.459527 \n", - "\n", - " cyclostationary_mean_rr_4w_0 cyclostat_level_Maggiore_16w \\\n", - "0 0.611605 0.542394 \n", - "1 1.691336 0.593148 \n", - "2 0.832271 0.674726 \n", - "3 0.859041 0.749432 \n", - "4 0.647203 0.803843 \n", - ".. ... ... \n", - "634 2.835166 0.557533 \n", - "635 2.575132 0.567490 \n", - "636 2.832534 0.560946 \n", - "637 1.469015 0.520204 \n", - "638 -0.690965 0.492773 \n", - "\n", - " cyclostat_level_Lugano cyclostationary_mean_tg_2 target \n", - "0 0.082042 -0.447071 0.264043 \n", - "1 0.162350 0.647375 0.354618 \n", - "2 0.085054 -0.540914 0.427990 \n", - "3 0.062613 0.453677 0.339495 \n", - "4 0.073888 0.012920 0.324134 \n", - ".. ... ... ... \n", - "634 0.927179 1.167781 0.459076 \n", - "635 0.190525 2.330281 0.413500 \n", - "636 0.080250 2.359644 0.395620 \n", - "637 0.040875 1.598315 0.427921 \n", - "638 0.040813 0.994373 0.373354 \n", - "\n", - "[639 rows x 7 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.read_csv(\"/work/bk1318/b382633/hawk/hawk/demo/Ticino_train.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a15adee-268a-45ad-bf05-924740cc977d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf430535-0114-4c31-8864-0b68ae2caee6", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7807fbff-aaf6-4280-abc6-ef582e10b1c1", - "metadata": {}, - "outputs": [], - "source": [ - "def run_postprocessing_pcmci(\n", - " results_pcmci,\n", - " target_column_name,\n", - " datasets,\n", - " destination_path,\n", - " image_formats=[\"pdf\", \"png\"],\n", - "):\n", - " all_basin_variables = set()\n", - " results_table_pcmci = []\n", - " for simulation in results_pcmci:\n", - " dataframe = datasets[simulation[\"dataset_name\"]]\n", - " var_names = dataframe[\"var_names\"]\n", - " all_basin_variables.update(var_names)\n", - "\n", - " results = simulation[\"results\"]\n", - "\n", - " # Plot only the connections to any of the target variables\n", - " temp_graph = results[\"graph\"].copy()\n", - "\n", - " # Show only the connections to the target variables\n", - " # Identify the indexes of the target variables\n", - " # target_vars = np.where([\"target\" in var for var in var_names.values])[0]\n", - " # for i in range(temp_graph.shape[0]):\n", - " # for j in range(temp_graph.shape[1]):\n", - " # # if the edge is not connected to the target variables\n", - " # if i not in target_vars and j not in target_vars:\n", - " # # remove the edge\n", - " # temp_graph[i, j, :] = ''\n", - " # temp_graph[j, i, :] = ''\n", - "\n", - " # Base arguments for tp.plot_graph\n", - " plot_args = {\n", - " \"val_matrix\": results[\"val_matrix\"],\n", - " \"graph\": temp_graph,\n", - " \"var_names\": var_names,\n", - " \"link_colorbar_label\": \"cross-MCI\",\n", - " \"node_colorbar_label\": \"auto-MCI\",\n", - " \"show_autodependency_lags\": False,\n", - " }\n", - "\n", - " # Additional arguments to include if the independence_test is CMIknn\n", - " if simulation[\"params\"][\"independencetest\"] == \"cmiknn\":\n", - " plot_args.update(\n", - " {\n", - " \"vmin_edges\": 0.0,\n", - " \"vmax_edges\": 0.1,\n", - " \"edge_ticks\": 0.05,\n", - " \"cmap_edges\": \"OrRd\",\n", - " \"vmin_nodes\": 0,\n", - " \"vmax_nodes\": 0.1,\n", - " \"node_ticks\": 0.1,\n", - " \"cmap_nodes\": \"OrRd\",\n", - " }\n", - " )\n", - "\n", - " # Plot causal graph\n", - " # target_file = os.path.join(constants.path_figures, \"algorithm_results\", basin_name, \"pcmci\", key + \".pdf\")\n", - " # if not os.path.exists(target_file):\n", - " # fig, ax = plt.subplots()\n", - " # tp.plot_graph(**plot_args, fig_ax=(fig, ax))\n", - " # os.makedirs(os.path.dirname(target_file), exist_ok=True)\n", - " # plt.savefig(target_file, bbox_inches=\"tight\")\n", - " # plt.close(fig)\n", - "\n", - " # # Plot time series graph if lag > 0\n", - " # if simulation[\"params\"][\"lag\"] > 0:\n", - " # target_file = os.path.join(\n", - " # constants.path_figures, \"algorithm_results\", basin_name, \"pcmci\", key + \"_timeseries.pdf\"\n", - " # )\n", - " # if not os.path.exists(target_file):\n", - " # fig, ax = plt.subplots()\n", - " # tp.plot_time_series_graph(\n", - " # figsize=(6, 4),\n", - " # fig_ax=(fig, ax),\n", - " # val_matrix=results[\"val_matrix\"],\n", - " # graph=results[\"graph\"],\n", - " # var_names=var_names,\n", - " # link_colorbar_label=\"MCI\",\n", - " # )\n", - " # os.makedirs(os.path.dirname(target_file), exist_ok=True)\n", - " # plt.savefig(target_file, bbox_inches=\"tight\")\n", - " # plt.close(fig)\n", - "\n", - " # Extract the selected features\n", - " selected_features = get_connected_variables(results[\"graph\"], var_names)\n", - "\n", - " # Compute the R2 scores\n", - " inputs_names_lags = {feature: [0] for feature in selected_features}\n", - " score_r2 = (\n", - " regression_analysis(\n", - " inputs_names_lags=inputs_names_lags,\n", - " target_name=target_column_name,\n", - " df_train=dataframe[\"train\"],\n", - " df_test=dataframe[\"test\"],\n", - " )\n", - " if len(selected_features) > 0\n", - " else np.nan\n", - " )\n", - "\n", - " inputs_names_lags = {feature: list(range(0, simulation[\"params\"][\"lag\"] + 1)) for feature in selected_features}\n", - " score_r2_lag = (\n", - " regression_analysis(\n", - " inputs_names_lags=inputs_names_lags,\n", - " target_name=target_column_name,\n", - " df_train=dataframe[\"train\"],\n", - " df_test=dataframe[\"test\"],\n", - " )\n", - " if len(selected_features) > 0\n", - " else np.nan\n", - " )\n", - "\n", - " inputs_names_lags = {feature: list(range(0, simulation[\"params\"][\"lag\"] + 1)) for feature in selected_features}\n", - " inputs_names_lags[target_column_name] = list(range(1, simulation[\"params\"][\"lag\"] + 1))\n", - " score_r2_lag_ar = regression_analysis(\n", - " inputs_names_lags=inputs_names_lags,\n", - " target_name=target_column_name,\n", - " df_train=dataframe[\"train\"],\n", - " df_test=dataframe[\"test\"],\n", - " )\n", - "\n", - " # Table of results\n", - " results_table_pcmci.append(\n", - " {\n", - " \"selected_features\": \" \".join(selected_features),\n", - " \"score_r2\": score_r2,\n", - " \"score_r2_lag\": score_r2_lag,\n", - " \"score_r2_lag_ar\": score_r2_lag_ar,\n", - " \"dataset_name\": simulation[\"dataset_name\"],\n", - " \"algorithm\": simulation[\"params\"][\"algorithm\"],\n", - " \"independencetest\": simulation[\"params\"][\"independencetest\"],\n", - " \"lag\": simulation[\"params\"][\"lag\"],\n", - " \"execution_time\": simulation[\"execution_time\"],\n", - " }\n", - " )\n", - "\n", - " # Export the file to pkl\n", - " target_file_results_details = os.path.join(destination_path, \"results_details_pcmci.pkl\")\n", - " save_to_pkl_file(target_file_results_details, results_table_pcmci)\n", - "\n", - " # Feature presences heatmap\n", - " if target_column_name in all_basin_variables:\n", - " all_basin_variables.remove(target_column_name)\n", - " all_basin_variables = sorted(list(all_basin_variables))\n", - " df_presence = pd.DataFrame(index=all_basin_variables, columns=range(len(results_pcmci)))\n", - " scores = []\n", - " scores_lag = []\n", - " scores_lag_ar = []\n", - "\n", - " for index, result in enumerate(results_table_pcmci):\n", - " scores.append(result[\"score_r2\"])\n", - " scores_lag.append(result[\"score_r2_lag\"])\n", - " scores_lag_ar.append(result[\"score_r2_lag_ar\"])\n", - "\n", - " # loop through the rows of the df, if the feature is in the list of selected features, put a 1\n", - " for feature in df_presence.index:\n", - " if feature in result[\"selected_features\"]:\n", - " df_presence.loc[feature, index] = 1\n", - " else:\n", - " df_presence.loc[feature, index] = 0\n", - " if feature not in datasets[result[\"dataset_name\"]][\"var_names\"]:\n", - " df_presence.loc[feature, index] = 2\n", - "\n", - " df_presence = df_presence.astype(float)\n", - " scores = np.array(scores)\n", - " scores_lag = np.array(scores_lag)\n", - " scores_lag_ar = np.array(scores_lag_ar)\n", - "\n", - " fig, ax = plot_feature_presence_and_r2(\n", - " df_presence=df_presence,\n", - " scores_values=[scores, scores_lag, scores_lag_ar],\n", - " scores_labels=[r\"$R^2$\", r\"$R^2$ (lag)\", r\"$R^2$ (lag + AR)\"],\n", - " )\n", - "\n", - " target_file_plots = {}\n", - " for image_format in image_formats:\n", - " target_file_plot = os.path.join(\n", - " destination_path, \"algorithm_results\", \"pcmci\", f\"feature_presence_pcmci.{image_format}\"\n", - " )\n", - " os.makedirs(os.path.dirname(target_file_plot), exist_ok=True)\n", - " plt.savefig(target_file_plot, bbox_inches=\"tight\")\n", - " target_file_plots[image_format] = target_file_plot\n", - " plt.close(fig)\n", - " \n", - " print(f\"PCMCI results details: {results_table_pcmci}\")\n", - "\n", - " return target_file_plots, target_file_results_details" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e717aa75-bfcb-4f34-8440-a1896e6c6573", - "metadata": {}, - "outputs": [], - "source": [ - "### TEFS\n", - "\n", - "resp = wps.causal(\n", - " dataset_train=open(\"../hawk/hawk/demo/Ticino_train.csv\"),\n", - " dataset_test=open(\"../hawk/hawk/demo/Ticino_test.csv\"),\n", - " target_column_name=\"target\",\n", - " pcmci_test_choice=\"ParCorr\",\n", - " pcmci_max_lag=\"1\",\n", - " tefs_direction=\"forward\",\n", - " tefs_use_contemporary_features=True,\n", - " tefs_max_lag_features=\"1\",\n", - " tefs_max_lag_target=\"1\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "400aabea-f8b3-4a07-ad9c-e5a5f339d560", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (tensorflow_env)", - "language": "python", - "name": "tensorflow_env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 5ecdb24198d30ecb7294f35bc1b7ff65edb300ce Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Thu, 25 Apr 2024 10:49:37 +0200 Subject: [PATCH 22/25] Update postprocessing.py --- hawk/analysis/postprocessing.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/hawk/analysis/postprocessing.py b/hawk/analysis/postprocessing.py index f17d027..bff5452 100644 --- a/hawk/analysis/postprocessing.py +++ b/hawk/analysis/postprocessing.py @@ -264,8 +264,6 @@ def run_postprocessing_pcmci( target_file_plots[image_format] = target_file_plot plt.close(fig) - print(f"PCMCI results details: {results_table_pcmci}") - return target_file_plots, target_file_results_details @@ -351,8 +349,6 @@ def run_postprocessing_tefs( target_file_results_details = os.path.join(destination_path, "results_details_te.pkl") save_to_pkl_file(target_file_results_details, results_table_te) - print(f"results_table_te: {results_table_te}") - # Feature presences heatmap if target_column_name in all_basin_variables: all_basin_variables.remove(target_column_name) From 99dd774be15994eb421093d36c0730e262316991 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Thu, 25 Apr 2024 10:56:14 +0200 Subject: [PATCH 23/25] Update main.py --- hawk/analysis/main.py | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/hawk/analysis/main.py b/hawk/analysis/main.py index ae888d5..1f5bad9 100644 --- a/hawk/analysis/main.py +++ b/hawk/analysis/main.py @@ -83,16 +83,12 @@ def run_baseline_analysis(self): features_names = self.datasets["normal"]["var_names"] - print(f"features_names: {features_names}") - configs = [] # Only autoregressive baselines from 1 to the maximum target lag for i in range(1, self.tefs_max_lag_target+1): configs.append((f"AR({i})", {self.target_column_name: list(range(1, i + 1))})) - print(f"AR configs: {configs}") - # All features without AR configs.append( ( @@ -101,8 +97,6 @@ def run_baseline_analysis(self): ) ) - print(f"Full configs: {configs}") - # All features with AR configs.append( ( @@ -112,8 +106,6 @@ def run_baseline_analysis(self): ) ) - print(f"Full configs: {configs}") - for label, inputs_names_lags in configs: baseline[label] = { "inputs": inputs_names_lags, @@ -125,8 +117,6 @@ def run_baseline_analysis(self): ), } - print(f"Baselines: {baseline}") - target_file = os.path.join(self.workdir, "baseline.pkl") save_to_pkl_file(target_file, baseline) @@ -172,8 +162,6 @@ def run_tefs_analysis( } configurations.append(configuration) - print(f"TEFS configurations: {configurations}") - # Run the analysis results = [] for config in configurations: @@ -185,8 +173,6 @@ def run_tefs_analysis( ) ) - print(f"TEFS results: {results}") - return results def run_pcmci_analysis( @@ -240,8 +226,6 @@ def run_pcmci_analysis( } configurations.append(configuration) - print(f'PCMCI configurations: {configurations}') - # Run the analysis results = [] for config in configurations: @@ -252,7 +236,6 @@ def run_pcmci_analysis( independence_tests=independence_tests, ) ) - print(f"PCMCI+ results: {results}") return results From f5204acfb8c3fa010ac33b5565b75f8004e4f741 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Thu, 2 May 2024 12:04:39 +0200 Subject: [PATCH 24/25] adding MPLCONFIGDIR path for matplotlib --- hawk/processes/wps_causal.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/hawk/processes/wps_causal.py b/hawk/processes/wps_causal.py index b92d47b..22a335d 100644 --- a/hawk/processes/wps_causal.py +++ b/hawk/processes/wps_causal.py @@ -5,6 +5,8 @@ import logging import pandas as pd from hawk.analysis import CausalAnalysis +from pathlib import Path +import os LOGGER = logging.getLogger("PYWPS") @@ -234,6 +236,7 @@ def _handler(self, request, response): tefs_max_lag_target = int(request.inputs["tefs_max_lag_target"][0].data) workdir = Path(self.workdir) + os.environ['MPLCONFIGDIR'] = os.path.join(workdir, "/matplotlib") if not tefs_use_contemporary_features and tefs_max_lag_features == 0: raise ValueError("You cannot use no lag features and not use contemporary features in TEFS.") From c1c38284bc28b9e217d83ba94887853dd0729a12 Mon Sep 17 00:00:00 2001 From: PaoloBonettiPolimi <94172434+PaoloBonettiPolimi@users.noreply.github.com> Date: Thu, 2 May 2024 12:07:01 +0200 Subject: [PATCH 25/25] delete double import --- hawk/processes/wps_causal.py | 1 - 1 file changed, 1 deletion(-) diff --git a/hawk/processes/wps_causal.py b/hawk/processes/wps_causal.py index 22a335d..feeb5a7 100644 --- a/hawk/processes/wps_causal.py +++ b/hawk/processes/wps_causal.py @@ -5,7 +5,6 @@ import logging import pandas as pd from hawk.analysis import CausalAnalysis -from pathlib import Path import os LOGGER = logging.getLogger("PYWPS")