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Julien RousselJulien Roussel
Julien Roussel
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Julien Roussel
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‎examples/tutorials/plot_tuto_categorical.ipynb‎

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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a220df49",
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"id": "7bec9ffc",
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 122,
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"id": "3587aa0a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, 5)"
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]
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},
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"execution_count": 122,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.array([[1, 2, 3, np.nan, 5]]).shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "80d3ba10",
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"cell_type": "markdown",
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"id": "d8193a27",
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"# Imputation analysis"
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qolmat/imputations/estimators.py renamed to qolmat/imputations/preprocessing.py

File renamed without changes.

tests/imputations/test_estimators.py renamed to tests/imputations/test_preprocessing.py

Lines changed: 31 additions & 28 deletions
Original file line numberDiff line numberDiff line change
@@ -2,18 +2,15 @@
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import pandas as pd
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import pytest
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from sklearn.compose import make_column_selector as selector
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from sklearn.ensemble import (
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HistGradientBoostingClassifier,
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HistGradientBoostingRegressor,
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)
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from sklearn.pipeline import Pipeline
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from sklearn.base import BaseEstimator
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from sklearn.metrics import mean_squared_error
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from sklearn.utils.estimator_checks import check_estimator
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from sklearn.utils.validation import check_X_y, check_array
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from sklearn.model_selection import train_test_split
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from sklearn.compose import ColumnTransformer
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from qolmat.imputations.estimators import (
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from qolmat.imputations.preprocessing import (
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BinTransformer,
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MixteHGBM,
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make_pipeline_mixte_preprocessing,
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def test_fit_transform(bin_transformer):
74-
X = np.array([1, 2, 3, np.nan, 5])
75-
transformed_X = bin_transformer.fit_transform(X)
76-
assert np.array_equal(transformed_X, np.array([1, 2, 3, np.nan, 5]), equal_nan=True)
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X = np.array([[1, 2, 3, np.nan, 5]]).T
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X_transformed = bin_transformer.fit_transform(X)
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assert np.array_equal(X_transformed, X, equal_nan=True)
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def test_transform(bin_transformer):
8077
bin_transformer.dict_df_bins_ = {
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0: pd.DataFrame({"value": [1, 2, 3, 4, 5], "min": [-np.inf, 1.5, 2.5, 3.5, 4.5]})
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}
83-
X = np.array([4.2, -1, 3.0, 4.5, 12])
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transformed_X = bin_transformer.transform(X)
85-
assert np.array_equal(transformed_X, np.array([4, 1, 3, 5, 5]))
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X = np.array([[4.2, -1, 3.0, 4.5, 12]]).T
81+
X_transformed = bin_transformer.transform(X)
82+
print(X_transformed)
83+
print(X)
84+
assert np.array_equal(X_transformed, np.array([[4, 1, 3, 5, 5]]).T)
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88-
def test_fit_transform_with_series(bin_transformer):
89-
X = pd.Series([1, 2, 3, np.nan, 5])
90-
transformed_X = bin_transformer.fit_transform(X)
91-
pd.testing.assert_series_equal(transformed_X, pd.Series([1, 2, 3, np.nan, 5]))
87+
def test_fit_transform_with_dataframes(bin_transformer):
88+
X = pd.DataFrame({"0": [1, 2, 3, np.nan, 5]})
89+
X_transformed = bin_transformer.fit_transform(X)
90+
print(X_transformed)
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print(X)
92+
pd.testing.assert_frame_equal(X_transformed, X)
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94-
def test_transform_with_series(bin_transformer):
95+
def test_transform_with_dataframes(bin_transformer):
9596
bin_transformer.dict_df_bins_ = {
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}
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X = pd.Series([1, 2, 3, 4, 5])
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transformed_X = bin_transformer.transform(X)
100-
pd.testing.assert_series_equal(transformed_X, pd.Series([1, 2, 3, 4, 5], dtype=float))
99+
X = pd.DataFrame({"0": [1, 2, 3, 4, 5]})
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X_transformed = bin_transformer.transform(X)
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print(X_transformed)
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print(X)
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pd.testing.assert_frame_equal(X_transformed, X)
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# Testing make_pipeline_mixte_preprocessing
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# Test with numerical features
116119
X_num = pd.DataFrame([[1, 2], [3, 4], [5, 6]])
117-
transformed_X = preprocessing_pipeline.fit_transform(X_num)
118-
assert isinstance(transformed_X, pd.DataFrame)
119-
assert transformed_X.shape[1] == X_num.shape[1]
120+
X_transformed = preprocessing_pipeline.fit_transform(X_num)
121+
assert isinstance(X_transformed, pd.DataFrame)
122+
assert X_transformed.shape[1] == X_num.shape[1]
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121124
# Test with categorical features
122125
X_cat = pd.DataFrame([["a", "b"], ["c", "d"], ["e", "f"]])
123-
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124-
assert isinstance(transformed_X, pd.DataFrame)
125-
assert transformed_X.shape[1] > X_cat.shape[1]
126+
X_transformed = preprocessing_pipeline.fit_transform(X_cat)
127+
assert isinstance(X_transformed, pd.DataFrame)
128+
assert X_transformed.shape[1] > X_cat.shape[1]
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127130
# Test with mixed features
128131
X_mixed = pd.DataFrame([[1, "a"], [2, "b"], [3, "c"]])
129-
transformed_X = preprocessing_pipeline.fit_transform(X_mixed)
130-
assert isinstance(transformed_X, pd.DataFrame)
131-
assert transformed_X.shape[1] > X_mixed.shape[1]
132+
X_transformed = preprocessing_pipeline.fit_transform(X_mixed)
133+
assert isinstance(X_transformed, pd.DataFrame)
134+
assert X_transformed.shape[1] > X_mixed.shape[1]
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134137
# Testing make_robust_MixteHGB

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