def clean_data(X):
X.dropna(subset=['target'], inplace=True)
y = X.pop('target')
X.drop(columns='ID', inplace=True)
X['v22'] = X['v22'].apply(az_to_int)
cat_cols = X.select_dtypes(include=['object']).columns.tolist()
con_cols = X.select_dtypes(include=['number']).columns.tolist()
num_missing_imputer = SimpleImputer(strategy='median')
cat_missing_imputer = CategoricalImputer(fill_value='__MISS__')
rare_label_encoder = RareLabelEncoder(tol=0.01, n_categories=10, replace_with='__OTHER__')
cat_freq_encoder = CountFrequencyEncoder(encoding_method="frequency")
X[con_cols] = num_missing_imputer.fit_transform(X[con_cols])
X[cat_cols] = cat_missing_imputer.fit_transform(X[cat_cols])
X[cat_cols] = rare_label_encoder.fit_transform(X[cat_cols])
X[cat_cols] = cat_freq_encoder.fit_transform(X[cat_cols])
# more cleaning
trimmer = Winsorizer(capping_method='quantiles', tail='both', fold=0.005)
X = trimmer.fit_transform(X)
undersampler = RandomUnderSampler(sampling_strategy=0.7, random_state=1234)
X, Y = undersampler.fit_resample(X, y)
quasi_constant = DropConstantFeatures(tol=0.998)
X = quasi_constant.fit_transform(X)
print(f"Quasi Features to drop {quasi_constant.features_to_drop_}")
# Remove duplicated features¶
duplicates = DropDuplicateFeatures()
X = duplicates.fit_transform(X)
print(f"Duplicate feature sets {duplicates.duplicated_feature_sets_}")
print(f"Dropping duplicate features {duplicates.features_to_drop_}")
drop_corr = DropCorrelatedFeatures(method="pearson", threshold=0.95, missing_values="ignore")
X = drop_corr.fit_transform(X)
print(f"Drop correlated feature sets {drop_corr.correlated_feature_sets_}")
print(f"Dropping correlared features {drop_corr.features_to_drop_}")
X['target'] = Y
return X
def test_fit_attributes(df_duplicate_features):
transformer = DropDuplicateFeatures()
transformer.fit(df_duplicate_features)
assert transformer.features_to_drop_ == {"dob", "dob3", "City2", "Age2"}
assert transformer.duplicated_feature_sets_ == [
{"dob", "dob2", "dob3"},
{"City", "City2"},
{"Age", "Age2"},
]
def test_drop_duplicates_features(df_duplicate_features):
transformer = DropDuplicateFeatures()
X = transformer.fit_transform(df_duplicate_features)
# expected result
df = pd.DataFrame({
"Name": ["tom", "nick", "krish", "jack"],
"dob2": pd.date_range("2020-02-24", periods=4, freq="T"),
"City": ["London", "Manchester", "Liverpool", "Bristol"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
})
pd.testing.assert_frame_equal(X, df)
def test_with_df_with_na(df_duplicate_features_with_na):
transformer = DropDuplicateFeatures()
X = transformer.fit_transform(df_duplicate_features_with_na)
# expected result
df = pd.DataFrame({
"Name": ["tom", "nick", "krish", "jack", np.nan],
"dob2":
pd.date_range("2020-02-24", periods=5, freq="T"),
"City": ["London", "Manchester", "Liverpool", "Bristol", np.nan],
"Age": [20, 21, np.nan, 18, 34],
"Marks": [0.9, 0.8, 0.7, 0.6, 0.5],
})
pd.testing.assert_frame_equal(X, df)
assert transformer.duplicated_features_ == {"dob", "dob3", "City2", "Age2"}
assert transformer.duplicated_feature_sets_ == [
{"dob", "dob2", "dob3"},
{"City", "City2"},
{"Age", "Age2"},
]
assert transformer.input_shape_ == (5, 9)
def test_non_fitted_error(df_duplicate_features):
# test case 3: when fit is not called prior to transform
with pytest.raises(NotFittedError):
transformer = DropDuplicateFeatures()
transformer.transform(df_duplicate_features)
def test_error_if_fit_input_not_dataframe():
with pytest.raises(TypeError):
DropDuplicateFeatures().fit({"Name": ["Karthik"]})
def test_variables_assigned_correctly(df_duplicate_features):
transformer = DropDuplicateFeatures()
assert transformer.variables is None
transformer.fit(df_duplicate_features)
assert transformer.variables == (list(df_duplicate_features.columns))
def fake_columns(var_list,df):
dupis = DropDuplicateFeatures()
dupis_train = dupis.fit(df[var_list])
duplicates_train = list(dupis_train.features_to_drop_)
return duplicates_train
DropFeatures,
DropHighPSIFeatures,
RecursiveFeatureAddition,
RecursiveFeatureElimination,
SelectByShuffling,
SelectBySingleFeaturePerformance,
SelectByTargetMeanPerformance,
SmartCorrelatedSelection,
)
_logreg = LogisticRegression(C=0.0001, max_iter=2, random_state=1)
_estimators = [
DropFeatures(features_to_drop=["0"]),
DropConstantFeatures(missing_values="ignore"),
DropDuplicateFeatures(),
DropCorrelatedFeatures(),
DropHighPSIFeatures(bins=5),
SmartCorrelatedSelection(),
SelectByShuffling(estimator=_logreg, scoring="accuracy"),
SelectByTargetMeanPerformance(bins=3, regression=False),
SelectBySingleFeaturePerformance(estimator=_logreg, scoring="accuracy"),
RecursiveFeatureAddition(estimator=_logreg, scoring="accuracy"),
RecursiveFeatureElimination(estimator=_logreg, scoring="accuracy", threshold=-100),
]
_multivariate_estimators = [
DropDuplicateFeatures(),
DropCorrelatedFeatures(),
SmartCorrelatedSelection(),
SelectByShuffling(estimator=_logreg, scoring="accuracy"),
