def test_linear_regression():
regressor_cls = get_sklearn_learner(
'sklearn.linear_model.LinearRegression')
from warnings import simplefilter
from sklearn.exceptions import ConvergenceWarning
simplefilter("ignore", category=ConvergenceWarning)
fs = RFE(regressor_cls)
dataset = Abalone()
target_column = dataset.y_column
df = dataset.df
# Features generation
features_df = df.kxy.generate_features(entity=None,
max_lag=None,
entity_name='*',
exclude=[target_column])
# Feature selection
x_columns = [_ for _ in features_df.columns if _ != target_column]
x_df = features_df[x_columns]
y_df = features_df[[target_column]]
n_vars = max(x_df.shape[1] - 5, 1)
m = fs.fit(x_df, y_df, n_vars)
# Assertions
assert len(fs.selected_variables) == n_vars
assert fs.selected_variables == ['Height.ABS(* - MEDIAN(*))', 'Height.ABS(* - MEAN(*))', 'Viscera weight.ABS(* - MEDIAN(*))', \
'Shell weight', 'Diameter.ABS(* - MEAN(*))', 'Shell weight.ABS(* - MEDIAN(*))', 'Shucked weight', 'Viscera weight.ABS(* - MEAN(*))', \
'Shell weight.ABS(* - MEAN(*))', 'Diameter.ABS(* - MEDIAN(*))', 'Diameter', 'Height', 'Length.ABS(* - Q25(*))', \
'Shucked weight.ABS(* - Q75(*))', 'Height.ABS(* - Q75(*))', 'Viscera weight', 'Shell weight.ABS(* - Q25(*))', 'Diameter.ABS(* - Q75(*))', \
'Shucked weight.ABS(* - MEDIAN(*))', 'Whole weight.ABS(* - Q25(*))', 'Length', 'Shucked weight.ABS(* - MEAN(*))', 'Viscera weight.ABS(* - Q75(*))', \
'Shucked weight.ABS(* - Q25(*))', 'Whole weight', 'Whole weight.ABS(* - MEAN(*))', 'Viscera weight.ABS(* - Q25(*))', 'Whole weight.ABS(* - Q75(*))', \
'Whole weight.ABS(* - MEDIAN(*))', 'Length.ABS(* - MEDIAN(*))', 'Length.ABS(* - Q75(*))', 'Length.ABS(* - MEAN(*))', 'Shell weight.ABS(* - Q75(*))']
def test_lightgbm_classifier():
# Classification
params = params = {
'objective': 'binary',
'metric': ['auc', 'binary_logloss'],
'boosting_type': 'gbdt',
}
classifier_cls = get_lightgbm_learner_learning_api(params, num_boost_round=10000, \
early_stopping_rounds=5, verbose_eval=50, split_random_seed=42)
fs = RFE(classifier_cls)
dataset = BankNote()
target_column = dataset.y_column
df = dataset.df
# Features generation
features_df = df.kxy.generate_features(entity=None,
max_lag=None,
entity_name='*',
exclude=[target_column])
# Feature selection
x_columns = [_ for _ in features_df.columns if _ != target_column]
x_df = features_df[x_columns]
y_df = features_df[[target_column]]
n_vars = max(x_df.shape[1] - 5, 1)
m = fs.fit(x_df, y_df, n_vars)
# Assertions
assert len(fs.selected_variables) == n_vars
assert fs.selected_variables == ['Variance', 'Kurtosis', 'Skewness.ABS(* - MEAN(*))', 'Skewness', 'Variance.ABS(* - MEAN(*))', \
'Entropy', 'Variance.ABS(* - Q25(*))', 'Kurtosis.ABS(* - MEDIAN(*))', 'Kurtosis.ABS(* - Q75(*))', 'Skewness.ABS(* - MEDIAN(*))', \
'Kurtosis.ABS(* - Q25(*))', 'Kurtosis.ABS(* - MEAN(*))', 'Variance.ABS(* - MEDIAN(*))', 'Entropy.ABS(* - MEDIAN(*))', \
'Entropy.ABS(* - Q25(*))']
def test_xgboost_regression():
regressor_cls = get_xgboost_learner('xgboost.XGBRegressor', random_state=0)
fs = RFE(regressor_cls)
dataset = Abalone()
target_column = dataset.y_column
df = dataset.df
# Features generation
features_df = df.kxy.generate_features(entity=None,
max_lag=None,
entity_name='*',
exclude=[target_column])
# Feature selection
x_columns = [_ for _ in features_df.columns if _ != target_column]
x_df = features_df[x_columns]
y_df = features_df[[target_column]]
n_vars = max(x_df.shape[1] - 5, 1)
m = fs.fit(x_df, y_df, n_vars)
# Assertions
assert len(fs.selected_variables) == n_vars
assert fs.selected_variables == ['Shell weight', 'Sex_I', 'Shucked weight.ABS(* - Q25(*))', 'Shucked weight', 'Shucked weight.ABS(* - MEDIAN(*))', \
'Shucked weight.ABS(* - MEAN(*))', 'Diameter.ABS(* - Q75(*))', 'Height.ABS(* - Q75(*))', 'Diameter.ABS(* - MEAN(*))', 'Diameter.ABS(* - Q25(*))', \
'Whole weight.ABS(* - MEDIAN(*))', 'Viscera weight.ABS(* - Q75(*))', 'Sex_M', 'Height.ABS(* - MEAN(*))', 'Shucked weight.ABS(* - Q75(*))', \
'Viscera weight.ABS(* - MEAN(*))', 'Height.ABS(* - Q25(*))', 'Whole weight.ABS(* - MEAN(*))', 'Shell weight.ABS(* - Q25(*))', 'Whole weight.ABS(* - Q25(*))', \
'Length.ABS(* - MEAN(*))', 'Length.ABS(* - Q75(*))', 'Whole weight.ABS(* - Q75(*))', 'Diameter.ABS(* - MEDIAN(*))', 'Shell weight.ABS(* - Q75(*))', \
'Shell weight.ABS(* - MEAN(*))', 'Shell weight.ABS(* - MEDIAN(*))', 'Length.ABS(* - MEDIAN(*))', 'Sex_F', 'Viscera weight', 'Whole weight', \
'Length.ABS(* - Q25(*))', 'Viscera weight.ABS(* - MEDIAN(*))']
def test_xgboost_classifier():
# Binary classification
classifier_cls = get_xgboost_learner('xgboost.XGBClassifier',
use_label_encoder=False,
eval_metric='logloss',
learning_rate=0.1,
max_depth=10)
fs = RFE(classifier_cls)
dataset = BankNote()
target_column = dataset.y_column
df = dataset.df
# Features generation
features_df = df.kxy.generate_features(entity=None,
max_lag=None,
entity_name='*',
exclude=[target_column])
# Feature selection
x_columns = [_ for _ in features_df.columns if _ != target_column]
x_df = features_df[x_columns]
y_df = features_df[[target_column]]
n_vars = max(x_df.shape[1] - 5, 1)
m = fs.fit(x_df, y_df, n_vars)
# Assertions
assert len(fs.selected_variables) == n_vars
assert fs.selected_variables == ['Variance', 'Skewness', 'Kurtosis', 'Entropy', 'Skewness.ABS(* - MEDIAN(*))', \
'Variance.ABS(* - MEAN(*))', 'Skewness.ABS(* - MEAN(*))', 'Kurtosis.ABS(* - MEDIAN(*))', 'Kurtosis.ABS(* - Q25(*))', \
'Entropy.ABS(* - MEDIAN(*))', 'Skewness.ABS(* - Q25(*))', 'Entropy.ABS(* - MEAN(*))', 'Variance.ABS(* - Q25(*))', \
'Kurtosis.ABS(* - MEAN(*))', 'Kurtosis.ABS(* - Q75(*))']
def test_lightgbm_regression():
lgbm_params = {
'objective': 'rmse',
'boosting_type': 'gbdt',
'n_jobs': -1,
'learning_rate': 0.1,
'verbose': -1,
}
regressor_cls = get_lightgbm_learner_learning_api(lgbm_params, num_boost_round=2000, \
early_stopping_rounds=5, split_random_seed=0)
fs = RFE(regressor_cls)
dataset = Abalone()
target_column = dataset.y_column
df = dataset.df
# Features generation
features_df = df.kxy.generate_features(entity=None,
max_lag=None,
entity_name='*',
exclude=[target_column])
# Feature selection
x_columns = [_ for _ in features_df.columns if _ != target_column]
x_df = features_df[x_columns]
y_df = features_df[[target_column]]
n_vars = max(x_df.shape[1] - 5, 1)
m = fs.fit(x_df, y_df, n_vars)
assert len(fs.selected_variables) == n_vars
assert fs.selected_variables == ['Shell weight', 'Shucked weight', 'Shucked weight.ABS(* - MEAN(*))', 'Whole weight.ABS(* - MEAN(*))', \
'Viscera weight', 'Shucked weight.ABS(* - MEDIAN(*))', 'Shell weight.ABS(* - MEAN(*))', 'Whole weight', 'Shucked weight.ABS(* - Q75(*))', \
'Height', 'Diameter', 'Sex_I', 'Shell weight.ABS(* - Q75(*))', 'Whole weight.ABS(* - Q25(*))', 'Shucked weight.ABS(* - Q25(*))', \
'Length', 'Viscera weight.ABS(* - Q75(*))', 'Viscera weight.ABS(* - MEAN(*))', 'Diameter.ABS(* - MEAN(*))', 'Viscera weight.ABS(* - MEDIAN(*))', \
'Whole weight.ABS(* - MEDIAN(*))', 'Height.ABS(* - Q75(*))', 'Diameter.ABS(* - Q75(*))', 'Sex_F', 'Whole weight.ABS(* - Q75(*))', \
'Viscera weight.ABS(* - Q25(*))', 'Shell weight.ABS(* - Q25(*))', 'Length.ABS(* - MEDIAN(*))', 'Height.ABS(* - MEAN(*))', \
'Shell weight.ABS(* - MEDIAN(*))', 'Diameter.ABS(* - Q25(*))', 'Length.ABS(* - MEAN(*))', 'Height.ABS(* - MEDIAN(*))']
n_vars = 5
m = fs.fit(x_df, y_df, n_vars)
# Assertions
assert len(fs.selected_variables) == n_vars
assert fs.selected_variables == [
'Shell weight', 'Shucked weight', 'Whole weight', 'Viscera weight',
'Shucked weight.ABS(* - MEAN(*))'
]