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Unpleasent tests, part 2 #339

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Unpleasent tests, part 2 #339

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b91b83b
added new unpleasent tests
bobokvsky Aug 31, 2024
706a02b
fix subset_id string type
bobokvsky Aug 31, 2024
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152 changes: 150 additions & 2 deletions tests/test_complex_pipeline.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,17 @@
from typing import cast

import pandas as pd
from sqlalchemy import Column
from sqlalchemy.sql.sqltypes import Integer, String

from datapipe.compute import Catalog, Pipeline, Table, build_compute, run_steps
from datapipe.datatable import DataStore
from datapipe.step.batch_generate import BatchGenerate
from datapipe.step.batch_transform import BatchTransform
from datapipe.store.database import TableStoreDB
from datapipe.types import IndexDF

from .util import assert_datatable_equal
from .util import assert_datatable_equal, assert_df_equal

TEST__ITEM = pd.DataFrame(
{
Expand Down Expand Up @@ -152,7 +155,7 @@ def complex_function(
TEST__PIPELINE,
TEST__PREDICTION,
TEST__KEYPOINT,
idx=pd.DataFrame(columns=["item_id", "pipeline_id"]),
idx=cast(IndexDF, pd.DataFrame(columns=["item_id", "pipeline_id"])),
)
run_steps(ds, steps)
assert_datatable_equal(ds.get_table("output"), TEST_RESULT)
Expand Down Expand Up @@ -287,3 +290,148 @@ def train(
assert len(
ds.get_table("pipeline__is_trained_on__frozen_dataset").get_data()
) == len(TEST__FROZEN_DATASET) * len(TEST__TRAIN_CONFIG)


def complex_transform_with_many_recordings_but_take_only_val_items(dbconn, train_N: int, val_N: int):
ds = DataStore(dbconn, create_meta_table=True)
catalog = Catalog(
{
"tbl_image": Table(
store=TableStoreDB(
dbconn,
"tbl_image",
[
Column("image_id", Integer, primary_key=True),
Column("image__attribute", Integer),
],
True,
)
),
"tbl_subset__has__image": Table(
store=TableStoreDB(
dbconn,
"tbl_subset__has__image",
[
Column("image_id", Integer, primary_key=True),
Column("subset_id", String, primary_key=True),
],
True,
)
),
"tbl_model": Table(
store=TableStoreDB(
dbconn,
"tbl_best_model",
[
Column("model_id", Integer, primary_key=True),
Column("model__attribute", Integer),
],
True,
)
),
"tbl_prediction": Table(
store=TableStoreDB(
dbconn,
"tbl_prediction",
[
Column("image_id", Integer, primary_key=True),
Column("model_id", Integer, primary_key=True),
Column("prediction__attribite", Integer),
],
True,
)
),
}
)

def gen_tbls(df1, df2, df3):
yield df1, df2, df3

test_df__image = pd.DataFrame({
"image_id": range(train_N+val_N),
"image__attribute": range(train_N+val_N)
})
test_df__subset__has__image = pd.DataFrame(
{
"image_id": list(range(train_N)) + list(range(train_N, train_N+val_N)),
"subset_id": ["train"] * train_N + ["val"] * val_N,
}
)
test_df__model = pd.DataFrame(
{
"model_id": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"model__attribute": [100 * model_id for model_id in range(10)]
}
)

def inference_model(
df__image: pd.DataFrame,
df__image__has__subset: pd.DataFrame,
df__model: pd.DataFrame,
):
df__image__has__subset = df__image__has__subset[
df__image__has__subset["subset_id"] == "val" # FIXME: хочется, чтобы это было снаружи функции
]
df__image = pd.merge(df__image, df__image__has__subset)
df__prediction = pd.merge(df__image, df__model, how="cross")
df__prediction["prediction__attribite"] = df__prediction["image__attribute"] + df__prediction["model__attribute"]
return df__prediction[["image_id", "model_id", "prediction__attribite"]]

pipeline = Pipeline(
[
BatchGenerate(
func=gen_tbls,
outputs=[
"tbl_image",
"tbl_subset__has__image",
"tbl_model",
],
kwargs=dict(
df1=test_df__image,
df2=test_df__subset__has__image,
df3=test_df__model,
),
),
BatchTransform(
func=inference_model,
inputs=[
"tbl_image", # ["image_id"]
"tbl_subset__has__image", # ["image_id", "subset_id"]
"tbl_model" # ["model_id"]
],
outputs=["tbl_prediction"], # ["image_id", "model_id", "prediction__attribite"]
transform_keys=["image_id", "model_id"],
chunk_size=100
),
]
)
steps = build_compute(ds, catalog, pipeline)
run_steps(ds, steps)
test__df_prediction = pd.DataFrame(
{
"image_id": list(range(train_N, train_N+val_N)) * 10,
"model_id": [model_id for model_id in range(10) for _ in range(val_N)],
"prediction__attribite": [
(model_id * 100) + x
for model_id in range(10)
for x in range(train_N, train_N+val_N)
]
}
)
assert_df_equal(
ds.get_table("tbl_prediction").get_data(),
test__df_prediction,
index_cols=["image_id", "model_id"],
)


def test_complex_transform_with_many_recordings_but_take_only_val_items_trainN100_valN100(dbconn):
complex_transform_with_many_recordings_but_take_only_val_items(dbconn, train_N=100, val_N=100)


def test_complex_transform_with_many_recordings_but_take_only_val_items_trainN10000_valN1000(dbconn):
complex_transform_with_many_recordings_but_take_only_val_items(dbconn, train_N=10000, val_N=1000)


def test_complex_transform_with_many_recordings_but_take_only_val_items_trainN100000_valN1000(dbconn):
complex_transform_with_many_recordings_but_take_only_val_items(dbconn, train_N=100000, val_N=1000)
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