def test_benchmark_classification_cv(self):
data, label = get_data_label(load_iris())
# Benchmark
score_df_sequential, selected_df_sequential, runtime_df_sequential = benchmark(
self.selectors, data, label, cv=5, output_filename=None)
score_df_p1, selected_df_p1, runtime_df_p1 = benchmark(
self.selectors, data, label, cv=5, output_filename=None, n_jobs=1)
score_df_p2, selected_df_p2, runtime_df_p2 = benchmark(
self.selectors, data, label, cv=5, output_filename=None, n_jobs=2)
# Aggregate scores from different cv-folds
score_df_sequential = score_df_sequential.groupby(
score_df_sequential.index).mean()
score_df_p1 = score_df_p1.groupby(score_df_p1.index).mean()
score_df_p2 = score_df_p2.groupby(score_df_p2.index).mean()
# Scores
self.assertListAlmostEqual([0.223276, 0.035431, 0.262547, 0.506591],
score_df_sequential["linear"].to_list())
self.assertListAlmostEqual([0.280393, 0.948935, 0.662777, 0.476188],
score_df_sequential["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p1["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p2["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p1["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p2["lasso"].to_list())
def test_benchmark_regression(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
# Benchmark
score_df, selected_df, runtime_df = benchmark(self.selectors,
data,
label,
output_filename=None)
_ = calculate_statistics(score_df, selected_df)
self.assertListAlmostEqual([
0.4787777784012165, 0.47170429073431874, 0.5596288196730658,
0.4400410275414326, 0.5674082968785575
], score_df["corr_pearson"].to_list())
self.assertListAlmostEqual([
0.5357134888110283, 0.48128808343101986, 0.5132201793752295,
0.3384081264406572, 0.49448886053070107
], score_df["corr_kendall"].to_list())
self.assertListAlmostEqual([
0.6542231557010167, 0.5538583519391704, 0.6267310661636885,
0.3924548536221991, 0.5984933578623318
], score_df["corr_spearman"].to_list())
self.assertListAlmostEqual([
89.48611475768125, 75.25764229895405, 83.47745921923685,
63.05422911249312, 601.6178711099022
], score_df["univ_anova"].to_list())
self.assertListAlmostEqual([0, 0, 0, 0, 0],
score_df["univ_chi_square"].to_list())
self.assertListAlmostEqual([
0.3421450205863028, 0.1806168920395521, 0.31266011627421086,
0.16107911083428794, 0.666208499757925
], score_df["univ_mutual_info"].to_list())
self.assertListAlmostEqual([
0.06901111285092865, 0.05408618283036938, 0.06145227292569164,
0.006510036424819454, 0.9546615660373198
], score_df["linear"].to_list())
self.assertListAlmostEqual([
0.05682706487290267, 0.051008405488957305, 0.05319245109490162,
0.007176306398647428, 0.9231211889322195
], score_df["lasso"].to_list())
self.assertListAlmostEqual([
0.0690214777400926, 0.054087779998048285, 0.06144441861097637,
0.006510854482697315, 0.95459417786841
], score_df["ridge"].to_list())
self.assertListAlmostEqual([
0.10947144861974874, 0.020211076089938374, 0.08416074180466389,
0.045604950489313435, 0.7405517829963355
], score_df["random_forest"].to_list())
def test_benchmark_classification(self):
data, label = get_data_label(load_iris())
# Benchmark
score_df, selected_df, runtime_df = benchmark(self.selectors,
data,
label,
output_filename=None)
_ = calculate_statistics(score_df, selected_df)
self.assertListAlmostEqual([
0.7018161715727902, 0.47803395524999537, 0.8157648279049796,
0.7867331225527027
], score_df["corr_pearson"].to_list())
self.assertListAlmostEqual([
0.6127053183332257, 0.35502921869499415, 0.6778502590804124,
0.6548312268837866
], score_df["corr_kendall"].to_list())
self.assertListAlmostEqual([
0.7207411401565564, 0.4413611232398492, 0.7823000090067262,
0.7652468370362326
], score_df["corr_spearman"].to_list())
self.assertListAlmostEqual([
119.26450218449871, 49.16004008961098, 1180.1611822529776,
960.0071468018025
], score_df["univ_anova"].to_list())
self.assertListAlmostEqual([
10.81782087849401, 3.7107283035324987, 116.31261309207022,
67.04836020011116
], score_df["univ_chi_square"].to_list())
self.assertListAlmostEqual([
0.4742659474041446, 0.2458627871667194, 0.9899864089960027,
0.9892550496360593
], score_df["univ_mutual_info"].to_list())
self.assertListAlmostEqual([
0.28992981466266715, 0.5607438535573831, 0.2622507287680856,
0.04272068858604694
], score_df["linear"].to_list())
self.assertListAlmostEqual([
0.7644807315853743, 0.594582626209646, 0.3661598482641388,
1.0152555188158772
], score_df["lasso"].to_list())
self.assertListAlmostEqual([
1.646830819860649e-15, 1.572815951552305e-15,
3.2612801348363973e-15, 5.773159728050814e-15
], score_df["ridge"].to_list())
self.assertListAlmostEqual([
0.09210348279677849, 0.03045933928742506, 0.4257647994615192,
0.45167237845427727
], score_df["random_forest"].to_list())
def test_correlation_fit_trans(self):
data, label = get_data_label(load_boston())
method = SelectionMethod.Correlation(0.60)
selector = Selective(method)
subset = selector.fit_transform(data)
self.assertListEqual(list(subset.columns), ['CRIM', 'ZN', 'INDUS', 'CHAS', 'RM', 'PTRATIO', 'B'])
def test_correlation_small(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=["CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"])
method = SelectionMethod.Correlation(0.60)
selector = Selective(method)
selector.fit(data)
subset = selector.transform(data)
self.assertListEqual(list(subset.columns), ['CRIM', 'ZN', 'AGE', 'B'])
def test_variance_drop_all(self):
data, label = get_data_label(load_boston())
method = SelectionMethod.Variance(threshold=100000)
selector = Selective(method)
try:
selector.fit(data)
selector.transform(data)
except ValueError:
pass
def test_tree_invalid_num_features(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
method = SelectionMethod.TreeBased(num_features=100)
selector = Selective(method)
with self.assertRaises(ValueError):
selector.fit(data, label)
def test_chi_regress_top_percentile_invalid(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
method = SelectionMethod.Statistical(num_features=0.6,
method="chi_square")
selector = Selective(method)
with self.assertRaises(TypeError):
selector.fit(data, label)
def test_variance_zero_threshold(self):
data, label = get_data_label(load_boston())
method = SelectionMethod.Variance(threshold=0)
selector = Selective(method)
selector.fit(data)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(data.shape[1], 13)
self.assertEqual(subset.shape[1], 13)
def test_anova_classif_top_percentile(self):
data, label = get_data_label(load_iris())
method = SelectionMethod.Statistical(num_features=0.5)
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 2)
self.assertListEqual(list(subset.columns), ['petal length (cm)', 'petal width (cm)'])
def test_variance_drop_target(self):
data, label = get_data_label(load_boston())
method = SelectionMethod.Variance(threshold=85)
selector = Selective(method)
selector.fit(data)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(data.shape[1], 13)
self.assertEqual(subset.shape[1], 4)
self.assertListEqual(list(subset.columns), ['ZN', 'AGE', 'TAX', 'B'])
def test_tree_classif_top_k(self):
data, label = get_data_label(load_iris())
method = SelectionMethod.TreeBased(num_features=2)
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 2)
self.assertListEqual(list(subset.columns),
['petal length (cm)', 'petal width (cm)'])
def test_tree_estimator_lightgbm_classif_top_k(self):
data, label = get_data_label(load_iris())
method = SelectionMethod.TreeBased(
num_features=2,
estimator=XGBClassifier(random_state=Constants.default_seed))
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 2)
def test_anova_regress_top_percentile_all(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=["CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"])
method = SelectionMethod.Statistical(num_features=1.0)
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(data.shape[1], subset.shape[1])
self.assertListEqual(list(data.columns), list(subset.columns))
def test_linear_regress_top_k_all(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
method = SelectionMethod.Linear(num_features=5)
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertListEqual(list(data.columns), list(subset.columns))
def test_vif_top_k_no_label(self):
data, label = get_data_label(load_iris())
method = SelectionMethod.Statistical(num_features=2,
method="variance_inflation")
selector = Selective(method)
selector.fit(data)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 2)
self.assertListEqual(list(subset.columns),
['sepal width (cm)', 'petal width (cm)'])
def test_benchmark_regression(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
# Benchmark
score_df_sequential, selected_df_sequential, runtime_df_sequential = benchmark(
self.selectors, data, label)
score_df_p1, selected_df_p1, runtime_df_p1 = benchmark(self.selectors,
data,
label,
verbose=True,
n_jobs=1)
score_df_p2, selected_df_p2, runtime_df_p2 = benchmark(self.selectors,
data,
label,
verbose=True,
n_jobs=2)
# Scores
self.assertListAlmostEqual(
[0.069011, 0.054086, 0.061452, 0.006510, 0.954662],
score_df_sequential["linear"].to_list())
self.assertListAlmostEqual(
[0.056827, 0.051008, 0.053192, 0.007176, 0.923121],
score_df_sequential["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p1["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p2["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p1["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p2["lasso"].to_list())
# Selected
self.assertListEqual([1, 0, 1, 0, 1],
selected_df_sequential["linear"].to_list())
self.assertListEqual([1, 0, 1, 0, 1],
selected_df_sequential["lasso"].to_list())
self.assertListEqual(selected_df_sequential["linear"].to_list(),
selected_df_p1["linear"].to_list())
self.assertListEqual(selected_df_sequential["linear"].to_list(),
selected_df_p2["linear"].to_list())
self.assertListEqual(selected_df_sequential["lasso"].to_list(),
selected_df_p1["lasso"].to_list())
self.assertListEqual(selected_df_sequential["lasso"].to_list(),
selected_df_p2["lasso"].to_list())
def test_variance_lt1_fit_trans(self):
data, label = get_data_label(load_boston())
method = SelectionMethod.Variance(threshold=1.0)
selector = Selective(method)
subset = selector.fit_transform(data)
# Reduced columns
self.assertEqual(data.shape[1], 13)
self.assertEqual(subset.shape[1], 10)
self.assertListEqual(list(subset.columns), [
'CRIM', 'ZN', 'INDUS', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B',
'LSTAT'
])
def test_linear_classif_top_percentile_all(self):
data, label = get_data_label(load_iris())
method = SelectionMethod.Linear(num_features=1.0)
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 4)
self.assertListEqual(list(subset.columns), [
'sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',
'petal width (cm)'
])
def test_ridge_regress_top_k(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
method = SelectionMethod.Linear(num_features=3, regularization="ridge")
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 3)
self.assertListEqual(list(subset.columns), ['CRIM', 'AGE', 'LSTAT'])
def test_tree_regress_top_percentile(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
method = SelectionMethod.TreeBased(num_features=0.6)
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 3)
self.assertListEqual(list(subset.columns), ['CRIM', 'AGE', 'LSTAT'])
def test_tree_estimator_adaboost_classif_top_k(self):
data, label = get_data_label(load_iris())
method = SelectionMethod.TreeBased(
num_features=2,
estimator=AdaBoostClassifier(random_state=Constants.default_seed))
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 2)
self.assertListEqual(list(subset.columns),
['petal length (cm)', 'petal width (cm)'])
def test_tree_estimator_xgboost_regress_top_k(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
method = SelectionMethod.TreeBased(
num_features=3,
estimator=XGBRegressor(random_state=Constants.default_seed))
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 3)
def test_vif_top_k_regression(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
method = SelectionMethod.Statistical(num_features=2,
method="variance_inflation")
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 2)
self.assertListEqual(list(subset.columns), ['CRIM', 'ZN'])
def test_mutual_classif_top_k_all(self):
data, label = get_data_label(load_iris())
method = SelectionMethod.Statistical(num_features=4,
method="mutual_info")
selector = Selective(method)
selector.fit(data, label)
subset = selector.transform(data)
# Reduced columns
self.assertEqual(subset.shape[1], 4)
self.assertListEqual(list(subset.columns), [
'sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',
'petal width (cm)'
])
def test_benchmark_classification(self):
data, label = get_data_label(load_iris())
# Benchmark
score_df_sequential, selected_df_sequential, runtime_df_sequential = benchmark(
self.selectors, data, label)
score_df_p1, selected_df_p1, runtime_df_p1 = benchmark(self.selectors,
data,
label,
n_jobs=1)
score_df_p2, selected_df_p2, runtime_df_p2 = benchmark(self.selectors,
data,
label,
n_jobs=2)
# Scores
self.assertListAlmostEqual([0.289930, 0.560744, 0.262251, 0.042721],
score_df_sequential["linear"].to_list())
self.assertListAlmostEqual([0.764816, 0.593482, 0.365352, 1.015095],
score_df_sequential["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p1["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p2["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p1["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p2["lasso"].to_list())
# Selected
self.assertListEqual([1, 1, 1, 0],
selected_df_sequential["linear"].to_list())
self.assertListEqual([1, 1, 0, 1],
selected_df_sequential["lasso"].to_list())
self.assertListEqual(selected_df_sequential["linear"].to_list(),
selected_df_p1["linear"].to_list())
self.assertListEqual(selected_df_sequential["linear"].to_list(),
selected_df_p2["linear"].to_list())
self.assertListEqual(selected_df_sequential["lasso"].to_list(),
selected_df_p1["lasso"].to_list())
self.assertListEqual(selected_df_sequential["lasso"].to_list(),
selected_df_p2["lasso"].to_list())
def test_benchmark_regression_cv(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
# Benchmark
score_df_sequential, selected_df_sequential, runtime_df_sequential = benchmark(
self.selectors, data, label, cv=5, output_filename=None)
score_df_p1, selected_df_p1, runtime_df_p1 = benchmark(
self.selectors, data, label, cv=5, output_filename=None, n_jobs=1)
score_df_p2, selected_df_p2, runtime_df_p2 = benchmark(
self.selectors, data, label, cv=5, output_filename=None, n_jobs=2)
# Aggregate scores from different cv-folds
score_df_sequential = score_df_sequential.groupby(
score_df_sequential.index).mean()
score_df_p1 = score_df_p1.groupby(score_df_p1.index).mean()
score_df_p2 = score_df_p2.groupby(score_df_p2.index).mean()
# Scores
self.assertListAlmostEqual(
[0.061577, 0.006446, 0.066933, 0.957603, 0.053797],
score_df_sequential["linear"].to_list())
self.assertListAlmostEqual(
[0.053294, 0.007117, 0.054563, 0.926039, 0.050716],
score_df_sequential["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p1["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["linear"].to_list(),
score_df_p2["linear"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p1["lasso"].to_list())
self.assertListAlmostEqual(score_df_sequential["lasso"].to_list(),
score_df_p2["lasso"].to_list())
def test_benchmark_classification_cv(self):
data, label = get_data_label(load_iris())
# Benchmark
score_df, selected_df, runtime_df = benchmark(self.selectors,
data,
label,
cv=5,
output_filename=None)
_ = calculate_statistics(score_df, selected_df)
# Aggregate scores from different cv-folds
score_df = score_df.groupby(score_df.index).mean()
self.assertListAlmostEqual([
0.8161221983271784, 0.7871883928143776, 0.7020705184086643,
0.4793198034473529
], score_df["corr_pearson"].to_list())
self.assertListAlmostEqual([
0.6780266710547757, 0.6550828618428932, 0.6125815664695313,
0.35594860548691776
], score_df["corr_kendall"].to_list())
self.assertListAlmostEqual([
0.78225620681015, 0.7652859083343029, 0.7201874607448919,
0.44222588698925963
], score_df["corr_spearman"].to_list())
self.assertListAlmostEqual([
946.9891701851375, 781.7441886012473, 95.65931730842011,
39.49994604080157
], score_df["univ_anova"].to_list())
self.assertListAlmostEqual([
92.9884264821005, 53.62326775665224, 8.659084856298207,
2.9711267637858163
], score_df["univ_chi_square"].to_list())
self.assertListAlmostEqual([
0.994113677302704, 0.9907696444894937, 0.4998955427118911,
0.2298786031192229
], score_df["univ_mutual_info"].to_list())
self.assertListAlmostEqual([
0.22327603204146848, 0.03543066514916661, 0.26254667473769594,
0.506591069316828
], score_df["linear"].to_list())
self.assertListAlmostEqual([
0.280393459805252, 0.9489351779830099, 0.6627768115497065,
0.4761878539373159
], score_df["lasso"].to_list())
self.assertListAlmostEqual([
1.1049393460379105e-15, 2.0872192862952944e-15,
6.504056552595708e-16, 4.218847493575594e-16
], score_df["ridge"].to_list())
self.assertListAlmostEqual([
0.4185294825699565, 0.4472560913161835, 0.10091608418224696,
0.03329834193161316
], score_df["random_forest"].to_list())
def test_benchmark_regression_cv(self):
data, label = get_data_label(load_boston())
data = data.drop(columns=[
"CHAS", "NOX", "RM", "DIS", "RAD", "TAX", "PTRATIO", "INDUS"
])
# Benchmark
score_df, selected_df, runtime_df = benchmark(self.selectors,
data,
label,
cv=5,
output_filename=None)
_ = calculate_statistics(score_df, selected_df)
# Aggregate scores from different cv-folds
score_df = score_df.groupby(score_df.index).mean()
self.assertListAlmostEqual([
0.5598624197527886, 0.43999689309372514, 0.47947203347292133,
0.5677393697964164, 0.4718904343871402
], score_df["corr_pearson"].to_list())
self.assertListAlmostEqual([
0.5133150872001859, 0.33830236220280874, 0.5355471187677026,
0.4944995007684703, 0.4812959438381611
], score_df["corr_kendall"].to_list())
self.assertListAlmostEqual([
0.6266784101694156, 0.3922216387923788, 0.6538541627239243,
0.598348546553966, 0.5537572894805117
], score_df["corr_spearman"].to_list())
self.assertListAlmostEqual([
66.9096213925407, 50.470199216622746, 71.84642313219175,
481.0566386481166, 60.5346993182466
], score_df["univ_anova"].to_list())
self.assertListAlmostEqual([0, 0, 0, 0, 0],
score_df["univ_chi_square"].to_list())
self.assertListAlmostEqual([
0.31315151982855777, 0.16552049446241074, 0.3376809619388398,
0.681986210957143, 0.18450178283973817
], score_df["univ_mutual_info"].to_list())
self.assertListAlmostEqual([
0.06157747888912044, 0.006445566885590223, 0.06693250180688959,
0.9576028432508157, 0.053796504696545476
], score_df["linear"].to_list())
self.assertListAlmostEqual([
0.05329389111187177, 0.007117077997740284, 0.054563375238215125,
0.9260391103473467, 0.05071613235478144
], score_df["lasso"].to_list())
self.assertListAlmostEqual([
0.061567603158881413, 0.006446613222308434, 0.06694625250225411,
0.9575175129470551, 0.05379855880797472
], score_df["ridge"].to_list())
self.assertListAlmostEqual([
0.07819877553940296, 0.04385018441841779, 0.11432712180337742,
0.7401304941703286, 0.023493424068473153
], score_df["random_forest"].to_list())
def test_benchmark_classification(self):
data, label = get_data_label(load_iris())
num_features = 3
corr_threshold = 0.5
alpha = 1000
tree_params = {"random_state": 123, "n_estimators": 100}
selectors = {
"corr_pearson": SelectionMethod.Correlation(corr_threshold, method="pearson"),
"corr_kendall": SelectionMethod.Correlation(corr_threshold, method="kendall"),
"corr_spearman": SelectionMethod.Correlation(corr_threshold, method="spearman"),
"univ_anova": SelectionMethod.Statistical(num_features, method="anova"),
"univ_chi_square": SelectionMethod.Statistical(num_features, method="chi_square"),
"univ_mutual_info": SelectionMethod.Statistical(num_features, method="mutual_info"),
"linear": SelectionMethod.Linear(num_features, regularization="none"),
"lasso": SelectionMethod.Linear(num_features, regularization="lasso", alpha=alpha),
"ridge": SelectionMethod.Linear(num_features, regularization="ridge", alpha=alpha),
"random_forest": SelectionMethod.TreeBased(num_features),
"xgboost_clf": SelectionMethod.TreeBased(num_features, estimator=XGBClassifier(**tree_params)),
"xgboost_reg": SelectionMethod.TreeBased(num_features, estimator=XGBRegressor(**tree_params)),
"extra_clf": SelectionMethod.TreeBased(num_features, estimator=ExtraTreesClassifier(**tree_params)),
"extra_reg": SelectionMethod.TreeBased(num_features, estimator=ExtraTreesRegressor(**tree_params)),
"lgbm_clf": SelectionMethod.TreeBased(num_features, estimator=LGBMClassifier(**tree_params)),
"lgbm_reg": SelectionMethod.TreeBased(num_features, estimator=LGBMRegressor(**tree_params)),
"gradient_clf": SelectionMethod.TreeBased(num_features, estimator=GradientBoostingClassifier(**tree_params)),
"gradient_reg": SelectionMethod.TreeBased(num_features, estimator=GradientBoostingRegressor(**tree_params)),
"adaboost_clf": SelectionMethod.TreeBased(num_features, estimator=AdaBoostClassifier(**tree_params)),
"adaboost_reg": SelectionMethod.TreeBased(num_features, estimator=AdaBoostRegressor(**tree_params)),
"catboost_clf": SelectionMethod.TreeBased(num_features, estimator=CatBoostClassifier(**tree_params, silent=True)),
"catboost_reg": SelectionMethod.TreeBased(num_features, estimator=CatBoostRegressor(**tree_params, silent=True))
}
# Benchmark
score_df, selected_df, runtime_df = benchmark(selectors, data, label, output_filename=None)
_ = calculate_statistics(score_df, selected_df)
self.assertListAlmostEqual([0.7018161715727902, 0.47803395524999537, 0.8157648279049796, 0.7867331225527027],
score_df["corr_pearson"].to_list())
self.assertListAlmostEqual([0.6127053183332257, 0.35502921869499415, 0.6778502590804124, 0.6548312268837866],
score_df["corr_kendall"].to_list())
self.assertListAlmostEqual([0.7207411401565564, 0.4413611232398492, 0.7823000090067262, 0.7652468370362326],
score_df["corr_spearman"].to_list())
self.assertListAlmostEqual([119.26450218449871, 49.16004008961098, 1180.1611822529776, 960.0071468018025],
score_df["univ_anova"].to_list())
self.assertListAlmostEqual([10.81782087849401, 3.7107283035324987, 116.31261309207022, 67.04836020011116],
score_df["univ_chi_square"].to_list())
self.assertListAlmostEqual([0.4742659474041446, 0.2458627871667194, 0.9899864089960027, 0.9892550496360593],
score_df["univ_mutual_info"].to_list())
self.assertListAlmostEqual([0.28992981466266715, 0.5607438535573831, 0.2622507287680856, 0.04272068858604694],
score_df["linear"].to_list())
self.assertListAlmostEqual([0.7644807315853743, 0.594582626209646, 0.3661598482641388, 1.0152555188158772],
score_df["lasso"].to_list())
self.assertListAlmostEqual([1.646830819860649e-15, 1.572815951552305e-15, 3.2612801348363973e-15, 5.773159728050814e-15],
score_df["ridge"].to_list())
self.assertListAlmostEqual([0.09210348279677849, 0.03045933928742506, 0.4257647994615192, 0.45167237845427727],
score_df["random_forest"].to_list())
