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test_pd_extractors.py
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test_pd_extractors.py
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from datetime import timedelta
import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from fklearn.data.datasets import make_tutorial_data
from fklearn.metrics.pd_extractors import (combined_evaluator_extractor,
evaluator_extractor, extract,
split_evaluator_extractor,
temporal_split_evaluator_extractor)
from fklearn.training.regression import linear_regression_learner
from fklearn.validation.evaluators import (combined_evaluators, r2_evaluator,
spearman_evaluator, split_evaluator,
temporal_split_evaluator, mse_evaluator)
from fklearn.validation.splitters import (
forward_stability_curve_time_splitter, out_of_time_and_space_splitter,
stability_curve_time_splitter, time_learning_curve_splitter)
from fklearn.validation.validator import validator
def test__split_evaluator_extractor__when_split_value_is_missing():
expected = [{'mse_evaluator__target': np.nan,
'split_evaluator__date': pd.Timestamp('2015-01-06 00:00:00'),
'split_evaluator__feature3': 'a'},
{'mse_evaluator__target': 0.0,
'split_evaluator__date': pd.Timestamp('2015-01-06 00:00:00'),
'split_evaluator__feature3': 'b'},
{'mse_evaluator__target': np.nan,
'split_evaluator__date': pd.Timestamp('2015-01-14 00:00:00'),
'split_evaluator__feature3': 'a'},
{'mse_evaluator__target': 0.0,
'split_evaluator__date': pd.Timestamp('2015-01-14 00:00:00'),
'split_evaluator__feature3': 'b'},
{'mse_evaluator__target': np.nan,
'split_evaluator__date': pd.Timestamp('2015-01-22 00:00:00'),
'split_evaluator__feature3': 'a'},
{'mse_evaluator__target': 0.0,
'split_evaluator__date': pd.Timestamp('2015-01-22 00:00:00'),
'split_evaluator__feature3': 'b'},
{'mse_evaluator__target': 0.0,
'split_evaluator__date': pd.Timestamp('2015-01-30 00:00:00'),
'split_evaluator__feature3': 'a'},
{'mse_evaluator__target': np.nan,
'split_evaluator__date': pd.Timestamp('2015-01-30 00:00:00'),
'split_evaluator__feature3': 'b'},
{'mse_evaluator__target': 0.0,
'split_evaluator__date': pd.Timestamp('2015-03-08 00:00:00'),
'split_evaluator__feature3': 'a'},
{'mse_evaluator__target': np.nan,
'split_evaluator__date': pd.Timestamp('2015-03-08 00:00:00'),
'split_evaluator__feature3': 'b'},
{'mse_evaluator__target': 0.0,
'split_evaluator__date': pd.Timestamp('2015-03-09 00:00:00'),
'split_evaluator__feature3': 'a'},
{'mse_evaluator__target': np.nan,
'split_evaluator__date': pd.Timestamp('2015-03-09 00:00:00'),
'split_evaluator__feature3': 'b'},
{'mse_evaluator__target': np.nan,
'split_evaluator__date': pd.Timestamp('2015-04-04 00:00:00'),
'split_evaluator__feature3': 'a'},
{'mse_evaluator__target': 0.0,
'split_evaluator__date': pd.Timestamp('2015-04-04 00:00:00'),
'split_evaluator__feature3': 'b'}]
expected_df = pd.DataFrame.from_dict(expected)
data = make_tutorial_data(50).dropna(subset=["feature3"]).assign(prediction=lambda d: d.target)
feature3_evaluator = split_evaluator(eval_fn=mse_evaluator, split_col="feature3")
feature3_date_evaluator = split_evaluator(eval_fn=feature3_evaluator, split_col="date")
results = feature3_date_evaluator(data)
date_values = [
np.datetime64("2015-01-06T00:00:00.000000000"),
np.datetime64("2015-01-14T00:00:00.000000000"),
np.datetime64("2015-01-22T00:00:00.000000000"),
np.datetime64("2015-01-30T00:00:00.000000000"),
np.datetime64("2015-03-08T00:00:00.000000000"),
np.datetime64("2015-03-09T00:00:00.000000000"),
np.datetime64("2015-04-04T00:00:00.000000000"),
]
base_evaluator = evaluator_extractor(evaluator_name="mse_evaluator__target")
feature3_extractor = split_evaluator_extractor(
base_extractor=base_evaluator, split_col="feature3", split_values=["a", "b"]
)
feature3_date_extractor = split_evaluator_extractor(
base_extractor=feature3_extractor, split_col="date", split_values=date_values
)
actual_df = feature3_date_extractor(results).reset_index(drop=True)
pd.testing.assert_frame_equal(actual_df, expected_df, check_like=True)
def test_extract():
boston = load_boston()
df = pd.DataFrame(boston['data'], columns=boston['feature_names'])
df['target'] = boston['target']
df['time'] = pd.date_range(start='2015-01-01', periods=len(df))
np.random.seed(42)
df['space'] = np.random.randint(0, 100, size=len(df))
# Define train function
train_fn = linear_regression_learner(features=boston['feature_names'].tolist(), target="target")
# Define evaluator function
base_evaluator = combined_evaluators(evaluators=[
r2_evaluator(target_column='target', prediction_column='prediction'),
spearman_evaluator(target_column='target', prediction_column='prediction')
])
splitter = split_evaluator(eval_fn=base_evaluator, split_col='RAD', split_values=[4.0, 5.0, 24.0])
temporal_week_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y-%W')
temporal_year_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y')
eval_fn = combined_evaluators(evaluators=[base_evaluator, splitter])
temporal_week_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_week_splitter])
temporal_year_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_year_splitter])
# Define splitters
cv_split_fn = out_of_time_and_space_splitter(
n_splits=5, in_time_limit='2016-01-01', time_column='time', space_column='space'
)
tlc_split_fn = time_learning_curve_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)
sc_split_fn = stability_curve_time_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)
fw_sc_split_fn = forward_stability_curve_time_splitter(
training_time_start="2015-01-01",
training_time_end="2016-01-01",
holdout_gap=timedelta(days=30),
holdout_size=timedelta(days=30),
step=timedelta(days=30),
time_column='time'
)
# Validate results
cv_results = validator(df, cv_split_fn, train_fn, eval_fn)['validator_log']
tlc_results = validator(df, tlc_split_fn, train_fn, eval_fn)['validator_log']
sc_results = validator(df, sc_split_fn, train_fn, eval_fn)['validator_log']
fw_sc_results = validator(df, fw_sc_split_fn, train_fn, eval_fn)['validator_log']
# temporal evaluation results
predict_fn, _, _ = train_fn(df)
temporal_week_results = temporal_week_eval_fn(predict_fn(df))
temporal_year_results = temporal_year_eval_fn(predict_fn(df))
# Define extractors
base_extractors = combined_evaluator_extractor(base_extractors=[
evaluator_extractor(evaluator_name="r2_evaluator__target"),
evaluator_extractor(evaluator_name="spearman_evaluator__target")
])
splitter_extractor = split_evaluator_extractor(split_col='RAD', split_values=[4.0, 5.0, 24.0],
base_extractor=base_extractors)
temporal_week_splitter_extractor = temporal_split_evaluator_extractor(
time_col='time', time_format='%Y-%W', base_extractor=base_extractors)
temporal_year_splitter_extractor = temporal_split_evaluator_extractor(
time_col='time', time_format='%Y', base_extractor=base_extractors)
assert extract(cv_results, base_extractors).shape == (5, 9)
assert extract(cv_results, splitter_extractor).shape == (15, 10)
assert extract(tlc_results, base_extractors).shape == (12, 9)
assert extract(tlc_results, splitter_extractor).shape == (36, 10)
assert extract(sc_results, base_extractors).shape == (5, 9)
assert extract(sc_results, splitter_extractor).shape == (15, 10)
assert extract(fw_sc_results, base_extractors).shape == (3, 9)
assert extract(fw_sc_results, splitter_extractor).shape == (9, 10)
n_time_week_folds = len(df['time'].dt.strftime('%Y-%W').unique())
n_time_year_folds = len(df['time'].dt.strftime('%Y').unique())
assert temporal_week_splitter_extractor(temporal_week_results).shape == (n_time_week_folds, 3)
assert temporal_year_splitter_extractor(temporal_year_results).shape == (n_time_year_folds, 3)