def test_simple_imputer_mean():
X = pd.DataFrame([[np.nan, 0, 1, np.nan], [1, 2, 3, 2], [1, 2, 3, 0]])
# test impute_strategy
transformer = SimpleImputer(impute_strategy='mean')
X_expected_arr = pd.DataFrame([[1, 0, 1, 1], [1, 2, 3, 2], [1, 2, 3, 0]])
X_t = transformer.fit_transform(X)
assert_frame_equal(X_expected_arr, X_t.to_dataframe(), check_dtype=False)
def test_simple_imputer_median():
X = pd.DataFrame([[np.nan, 0, 1, np.nan], [1, 2, 3, 2], [10, 2, np.nan, 2],
[10, 2, 5, np.nan], [6, 2, 7, 0]])
transformer = SimpleImputer(impute_strategy='median')
X_expected_arr = pd.DataFrame([[8, 0, 1, 2], [1, 2, 3, 2], [10, 2, 4, 2],
[10, 2, 5, 2], [6, 2, 7, 0]])
X_t = transformer.fit_transform(X)
assert_frame_equal(X_expected_arr, X_t, check_dtype=False)
def test_simple_imputer_boolean_dtype(data_type, make_data_type):
X = pd.DataFrame([True, np.nan, False, np.nan, True], dtype='boolean')
y = pd.Series([1, 0, 0, 1, 0])
X_expected_arr = pd.DataFrame([True, True, False, True, True],
dtype='boolean')
X = make_data_type(data_type, X)
imputer = SimpleImputer()
imputer.fit(X, y)
X_t = imputer.transform(X)
assert_frame_equal(X_expected_arr, X_t.to_dataframe())
def test_simple_imputer_all_bool_return_original(data_type, make_data_type):
X = pd.DataFrame([True, True, False, True, True], dtype=bool)
y = pd.Series([1, 0, 0, 1, 0])
X = make_data_type(data_type, X)
y = make_data_type(data_type, y)
X_expected_arr = pd.DataFrame([True, True, False, True, True], dtype=bool)
imputer = SimpleImputer()
imputer.fit(X, y)
X_t = imputer.transform(X)
assert_frame_equal(X_expected_arr, X_t)
def test_simple_imputer_numpy_input():
X = np.array([[np.nan, 0, 1, np.nan], [np.nan, 2, 3, 2], [np.nan, 2, 3,
0]])
transformer = SimpleImputer(impute_strategy='mean')
X_expected_arr = np.array([[0, 1, 1], [2, 3, 2], [2, 3, 0]])
assert np.allclose(X_expected_arr, transformer.fit_transform(X))
np.testing.assert_almost_equal(
X,
np.array([[np.nan, 0, 1, np.nan], [np.nan, 2, 3, 2], [np.nan, 2, 3,
0]]))
def test_simple_imputer_most_frequent():
X = pd.DataFrame([[np.nan, 0, 1, np.nan], ["a", 2, np.nan, 3],
["b", 2, 1, 0]])
transformer = SimpleImputer(impute_strategy='most_frequent')
X_expected_arr = pd.DataFrame([["a", 0, 1, 0], ["a", 2, 1, 3],
["b", 2, 1, 0]])
X_expected_arr = X_expected_arr.astype({0: 'category'})
X_t = transformer.fit_transform(X)
assert_frame_equal(X_expected_arr, X_t.to_dataframe(), check_dtype=False)
def test_simple_imputer_bool_dtype_object(data_type):
X = pd.DataFrame([True, np.nan, False, np.nan, True], dtype=object)
y = pd.Series([1, 0, 0, 1, 0])
X_expected_arr = pd.DataFrame([True, True, False, True, True],
dtype='category')
if data_type == 'ww':
X = ww.DataTable(X)
imputer = SimpleImputer()
imputer.fit(X, y)
X_t = imputer.transform(X)
assert_frame_equal(X_expected_arr, X_t)
def test_simple_imputer_constant():
# test impute strategy is constant and fill value is not specified
X = pd.DataFrame([[np.nan, 0, 1, np.nan], ["a", 2, np.nan, 3],
["b", 2, 3, 0]])
transformer = SimpleImputer(impute_strategy='constant', fill_value=3)
X_expected_arr = pd.DataFrame([[3, 0, 1, 3], ["a", 2, 3, 3],
["b", 2, 3, 0]])
X_expected_arr = X_expected_arr.astype({0: 'category'})
X_t = transformer.fit_transform(X)
assert_frame_equal(X_expected_arr, X_t.to_dataframe(), check_dtype=False)
def test_simple_imputer_fill_value(data_type):
if data_type == "numeric":
X = pd.DataFrame({
"some numeric": [np.nan, 1, 0],
"another numeric": [0, np.nan, 2]
})
fill_value = -1
expected = pd.DataFrame({
"some numeric": [-1, 1, 0],
"another numeric": [0, -1, 2]
})
else:
X = pd.DataFrame({
"categorical with nan":
pd.Series([np.nan, "1", np.nan, "0", "3"], dtype='category'),
"object with nan": ["b", "b", np.nan, "c", np.nan]
})
fill_value = "fill"
expected = pd.DataFrame({
"categorical with nan":
pd.Series(["fill", "1", "fill", "0", "3"], dtype='category'),
"object with nan":
pd.Series(["b", "b", "fill", "c", "fill"], dtype='category'),
})
y = pd.Series([0, 0, 1, 0, 1])
imputer = SimpleImputer(impute_strategy="constant", fill_value=fill_value)
imputer.fit(X, y)
transformed = imputer.transform(X, y)
assert_frame_equal(expected, transformed.to_dataframe(), check_dtype=False)
imputer = SimpleImputer(impute_strategy="constant", fill_value=fill_value)
transformed = imputer.fit_transform(X, y)
assert_frame_equal(expected, transformed.to_dataframe(), check_dtype=False)
def test_simple_imputer_transform_drop_all_nan_columns_empty():
X = pd.DataFrame([[np.nan, np.nan, np.nan]])
transformer = SimpleImputer(impute_strategy='most_frequent')
assert transformer.fit_transform(X).to_dataframe().empty
assert_frame_equal(X, pd.DataFrame([[np.nan, np.nan, np.nan]]))
transformer = SimpleImputer(impute_strategy='most_frequent')
transformer.fit(X)
assert transformer.transform(X).to_dataframe().empty
assert_frame_equal(X, pd.DataFrame([[np.nan, np.nan, np.nan]]))
def test_simple_imputer_does_not_reset_index():
X = pd.DataFrame({'input_val': np.arange(10), 'target': np.arange(10)})
X.loc[5, 'input_val'] = np.nan
assert X.index.tolist() == list(range(10))
X.drop(0, inplace=True)
y = X.pop('target')
pd.testing.assert_frame_equal(
pd.DataFrame({'input_val': [1.0, 2, 3, 4, np.nan, 6, 7, 8, 9]},
dtype=float,
index=list(range(1, 10))), X)
imputer = SimpleImputer(impute_strategy="mean")
imputer.fit(X, y=y)
transformed = imputer.transform(X)
pd.testing.assert_frame_equal(
pd.DataFrame({'input_val': [1, 2, 3, 4, 5, 6, 7, 8, 9]},
dtype=float,
index=list(range(1, 10))), transformed.to_dataframe())
def test_simple_imputer_multitype_with_one_bool(data_type, make_data_type):
X_multi = pd.DataFrame({
"bool with nan":
pd.Series([True, np.nan, False, np.nan, False], dtype='boolean'),
"bool no nan":
pd.Series([False, False, False, False, True], dtype=bool),
})
y = pd.Series([1, 0, 0, 1, 0])
X_multi_expected_arr = pd.DataFrame({
"bool with nan":
pd.Series([True, False, False, False, False], dtype='boolean'),
"bool no nan":
pd.Series([False, False, False, False, True], dtype='boolean'),
})
X_multi = make_data_type(data_type, X_multi)
imputer = SimpleImputer()
imputer.fit(X_multi, y)
X_multi_t = imputer.transform(X_multi)
assert_frame_equal(X_multi_expected_arr, X_multi_t.to_dataframe())
def test_simple_imputer_multitype_with_one_bool(data_type):
X_multi = pd.DataFrame({
"bool with nan":
pd.Series([True, np.nan, False, np.nan, False], dtype=object),
"bool no nan":
pd.Series([False, False, False, False, True], dtype=bool),
})
y = pd.Series([1, 0, 0, 1, 0])
X_multi_expected_arr = pd.DataFrame({
"bool with nan":
pd.Series([True, False, False, False, False], dtype='category'),
"bool no nan":
pd.Series([False, False, False, False, True], dtype=object),
})
if data_type == 'ww':
X_multi = ww.DataTable(X_multi)
imputer = SimpleImputer()
imputer.fit(X_multi, y)
X_multi_t = imputer.transform(X_multi)
assert_frame_equal(X_multi_expected_arr, X_multi_t)
def test_simple_imputer_fit_transform_drop_all_nan_columns():
X = pd.DataFrame({
"all_nan": [np.nan, np.nan, np.nan],
"some_nan": [np.nan, 1, 0],
"another_col": [0, 1, 2]
})
transformer = SimpleImputer(impute_strategy='most_frequent')
X_expected_arr = pd.DataFrame({
"some_nan": [0, 1, 0],
"another_col": [0, 1, 2]
})
X_t = transformer.fit_transform(X)
assert_frame_equal(X_expected_arr, X_t.to_dataframe(), check_dtype=False)
assert_frame_equal(
X,
pd.DataFrame({
"all_nan": [np.nan, np.nan, np.nan],
"some_nan": [np.nan, 1, 0],
"another_col": [0, 1, 2]
}))
def test_simple_imputer_with_none():
X = pd.DataFrame({
"int with None": [1, 0, 5, None],
"float with None": [0.1, 0.0, 0.5, None],
"all None": [None, None, None, None]
})
y = pd.Series([0, 0, 1, 0, 1])
imputer = SimpleImputer(impute_strategy="mean")
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"int with None": [1, 0, 5, 2],
"float with None": [0.1, 0.0, 0.5, 0.2]
})
assert_frame_equal(expected, transformed.to_dataframe(), check_dtype=False)
X = pd.DataFrame({
"category with None":
pd.Series(["b", "a", "a", None], dtype='category'),
"boolean with None":
pd.Series([True, None, False, True], dtype='boolean'),
"object with None": ["b", "a", "a", None],
"all None": [None, None, None, None]
})
y = pd.Series([0, 0, 1, 0, 1])
imputer = SimpleImputer()
imputer.fit(X, y)
transformed = imputer.transform(X, y)
expected = pd.DataFrame({
"category with None":
pd.Series(["b", "a", "a", "a"], dtype='category'),
"boolean with None":
pd.Series([True, True, False, True], dtype='boolean'),
"object with None":
pd.Series(["b", "a", "a", "a"], dtype='category')
})
assert_frame_equal(expected, transformed.to_dataframe(), check_dtype=False)
def test_simple_imputer_woodwork_custom_overrides_returned_by_components(
X_df, has_nan, impute_strategy):
y = pd.Series([1, 2, 1])
if has_nan:
X_df.iloc[len(X_df) - 1, 0] = np.nan
override_types = [Integer, Double, Categorical, NaturalLanguage, Boolean]
for logical_type in override_types:
try:
X = ww.DataTable(X_df, logical_types={0: logical_type})
except TypeError:
continue
impute_strategy_to_use = impute_strategy
if logical_type in [NaturalLanguage, Categorical]:
impute_strategy_to_use = "most_frequent"
imputer = SimpleImputer(impute_strategy=impute_strategy_to_use)
imputer.fit(X, y)
transformed = imputer.transform(X, y)
assert isinstance(transformed, ww.DataTable)
if impute_strategy_to_use == "most_frequent" or not has_nan:
assert transformed.logical_types == {0: logical_type}
else:
assert transformed.logical_types == {0: Double}
def test_describe_component():
enc = OneHotEncoder()
imputer = Imputer()
simple_imputer = SimpleImputer("mean")
column_imputer = PerColumnImputer({"a": "mean", "b": ("constant", 100)})
scaler = StandardScaler()
feature_selection_clf = RFClassifierSelectFromModel(n_estimators=10, number_features=5, percent_features=0.3, threshold=-np.inf)
feature_selection_reg = RFRegressorSelectFromModel(n_estimators=10, number_features=5, percent_features=0.3, threshold=-np.inf)
drop_col_transformer = DropColumns(columns=['col_one', 'col_two'])
drop_null_transformer = DropNullColumns()
datetime = DateTimeFeaturizer()
text_featurizer = TextFeaturizer()
lsa = LSA()
pca = PCA()
lda = LinearDiscriminantAnalysis()
ft = DFSTransformer()
us = Undersampler()
assert enc.describe(return_dict=True) == {'name': 'One Hot Encoder', 'parameters': {'top_n': 10,
'features_to_encode': None,
'categories': None,
'drop': 'if_binary',
'handle_unknown': 'ignore',
'handle_missing': 'error'}}
assert imputer.describe(return_dict=True) == {'name': 'Imputer', 'parameters': {'categorical_impute_strategy': "most_frequent",
'categorical_fill_value': None,
'numeric_impute_strategy': "mean",
'numeric_fill_value': None}}
assert simple_imputer.describe(return_dict=True) == {'name': 'Simple Imputer', 'parameters': {'impute_strategy': 'mean', 'fill_value': None}}
assert column_imputer.describe(return_dict=True) == {'name': 'Per Column Imputer', 'parameters': {'impute_strategies': {'a': 'mean', 'b': ('constant', 100)}, 'default_impute_strategy': 'most_frequent'}}
assert scaler.describe(return_dict=True) == {'name': 'Standard Scaler', 'parameters': {}}
assert feature_selection_clf.describe(return_dict=True) == {'name': 'RF Classifier Select From Model', 'parameters': {'number_features': 5, 'n_estimators': 10, 'max_depth': None, 'percent_features': 0.3, 'threshold': -np.inf, 'n_jobs': -1}}
assert feature_selection_reg.describe(return_dict=True) == {'name': 'RF Regressor Select From Model', 'parameters': {'number_features': 5, 'n_estimators': 10, 'max_depth': None, 'percent_features': 0.3, 'threshold': -np.inf, 'n_jobs': -1}}
assert drop_col_transformer.describe(return_dict=True) == {'name': 'Drop Columns Transformer', 'parameters': {'columns': ['col_one', 'col_two']}}
assert drop_null_transformer.describe(return_dict=True) == {'name': 'Drop Null Columns Transformer', 'parameters': {'pct_null_threshold': 1.0}}
assert datetime.describe(return_dict=True) == {'name': 'DateTime Featurization Component',
'parameters': {'features_to_extract': ['year', 'month', 'day_of_week', 'hour'],
'encode_as_categories': False}}
assert text_featurizer.describe(return_dict=True) == {'name': 'Text Featurization Component', 'parameters': {}}
assert lsa.describe(return_dict=True) == {'name': 'LSA Transformer', 'parameters': {}}
assert pca.describe(return_dict=True) == {'name': 'PCA Transformer', 'parameters': {'n_components': None, 'variance': 0.95}}
assert lda.describe(return_dict=True) == {'name': 'Linear Discriminant Analysis Transformer', 'parameters': {'n_components': None}}
assert ft.describe(return_dict=True) == {'name': 'DFS Transformer', 'parameters': {"index": "index"}}
assert us.describe(return_dict=True) == {'name': 'Undersampler', 'parameters': {"balanced_ratio": 4, "min_samples": 100, "min_percentage": 0.1}}
# testing estimators
base_classifier = BaselineClassifier()
base_regressor = BaselineRegressor()
lr_classifier = LogisticRegressionClassifier()
en_classifier = ElasticNetClassifier()
en_regressor = ElasticNetRegressor()
et_classifier = ExtraTreesClassifier(n_estimators=10, max_features="auto")
et_regressor = ExtraTreesRegressor(n_estimators=10, max_features="auto")
rf_classifier = RandomForestClassifier(n_estimators=10, max_depth=3)
rf_regressor = RandomForestRegressor(n_estimators=10, max_depth=3)
linear_regressor = LinearRegressor()
svm_classifier = SVMClassifier()
svm_regressor = SVMRegressor()
assert base_classifier.describe(return_dict=True) == {'name': 'Baseline Classifier', 'parameters': {'strategy': 'mode'}}
assert base_regressor.describe(return_dict=True) == {'name': 'Baseline Regressor', 'parameters': {'strategy': 'mean'}}
assert lr_classifier.describe(return_dict=True) == {'name': 'Logistic Regression Classifier', 'parameters': {'penalty': 'l2', 'C': 1.0, 'n_jobs': -1, 'multi_class': 'auto', 'solver': 'lbfgs'}}
assert en_classifier.describe(return_dict=True) == {'name': 'Elastic Net Classifier', 'parameters': {'alpha': 0.5, 'l1_ratio': 0.5, 'n_jobs': -1, 'max_iter': 1000, "loss": 'log', 'penalty': 'elasticnet'}}
assert en_regressor.describe(return_dict=True) == {'name': 'Elastic Net Regressor', 'parameters': {'alpha': 0.5, 'l1_ratio': 0.5, 'max_iter': 1000, 'normalize': False}}
assert et_classifier.describe(return_dict=True) == {'name': 'Extra Trees Classifier', 'parameters': {'n_estimators': 10, 'max_features': 'auto', 'max_depth': 6, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_jobs': -1}}
assert et_regressor.describe(return_dict=True) == {'name': 'Extra Trees Regressor', 'parameters': {'n_estimators': 10, 'max_features': 'auto', 'max_depth': 6, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_jobs': -1}}
assert rf_classifier.describe(return_dict=True) == {'name': 'Random Forest Classifier', 'parameters': {'n_estimators': 10, 'max_depth': 3, 'n_jobs': -1}}
assert rf_regressor.describe(return_dict=True) == {'name': 'Random Forest Regressor', 'parameters': {'n_estimators': 10, 'max_depth': 3, 'n_jobs': -1}}
assert linear_regressor.describe(return_dict=True) == {'name': 'Linear Regressor', 'parameters': {'fit_intercept': True, 'normalize': False, 'n_jobs': -1}}
assert svm_classifier.describe(return_dict=True) == {'name': 'SVM Classifier', 'parameters': {'C': 1.0, 'kernel': 'rbf', 'gamma': 'scale', 'probability': True}}
assert svm_regressor.describe(return_dict=True) == {'name': 'SVM Regressor', 'parameters': {'C': 1.0, 'kernel': 'rbf', 'gamma': 'scale'}}
try:
xgb_classifier = XGBoostClassifier(eta=0.1, min_child_weight=1, max_depth=3, n_estimators=75)
xgb_regressor = XGBoostRegressor(eta=0.1, min_child_weight=1, max_depth=3, n_estimators=75)
assert xgb_classifier.describe(return_dict=True) == {'name': 'XGBoost Classifier', 'parameters': {'eta': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 75}}
assert xgb_regressor.describe(return_dict=True) == {'name': 'XGBoost Regressor', 'parameters': {'eta': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 75}}
except ImportError:
pass
try:
cb_classifier = CatBoostClassifier()
cb_regressor = CatBoostRegressor()
assert cb_classifier.describe(return_dict=True) == {'name': 'CatBoost Classifier', 'parameters': {'allow_writing_files': False, 'n_estimators': 10, 'eta': 0.03, 'max_depth': 6, 'bootstrap_type': None, 'silent': True}}
assert cb_regressor.describe(return_dict=True) == {'name': 'CatBoost Regressor', 'parameters': {'allow_writing_files': False, 'n_estimators': 10, 'eta': 0.03, 'max_depth': 6, 'bootstrap_type': None, 'silent': False}}
except ImportError:
pass
try:
lg_classifier = LightGBMClassifier()
lg_regressor = LightGBMRegressor()
assert lg_classifier.describe(return_dict=True) == {'name': 'LightGBM Classifier', 'parameters': {'boosting_type': 'gbdt', 'learning_rate': 0.1, 'n_estimators': 100, 'max_depth': 0, 'num_leaves': 31,
'min_child_samples': 20, 'n_jobs': -1, 'bagging_fraction': 0.9, 'bagging_freq': 0}}
assert lg_regressor.describe(return_dict=True) == {'name': 'LightGBM Regressor', 'parameters': {'boosting_type': 'gbdt', 'learning_rate': 0.1, 'n_estimators': 20, 'max_depth': 0, 'num_leaves': 31,
'min_child_samples': 20, 'n_jobs': -1, 'bagging_fraction': 0.9, 'bagging_freq': 0}}
except ImportError:
pass
def test_simple_imputer_col_with_non_numeric():
# test col with all strings
X = pd.DataFrame([["a", 0, 1, np.nan], ["b", 2, 3, 3], ["a", 2, 3, 1],
[np.nan, 2, 3, 0]])
transformer = SimpleImputer(impute_strategy='mean')
with pytest.raises(ValueError,
match="Cannot use mean strategy with non-numeric data"):
transformer.fit_transform(X)
with pytest.raises(ValueError,
match="Cannot use mean strategy with non-numeric data"):
transformer.fit(X)
transformer = SimpleImputer(impute_strategy='median')
with pytest.raises(
ValueError,
match="Cannot use median strategy with non-numeric data"):
transformer.fit_transform(X)
with pytest.raises(
ValueError,
match="Cannot use median strategy with non-numeric data"):
transformer.fit(X)
transformer = SimpleImputer(impute_strategy='most_frequent')
X_expected_arr = pd.DataFrame([["a", 0, 1, 0], ["b", 2, 3, 3],
["a", 2, 3, 1], ["a", 2, 3, 0]])
X_expected_arr = X_expected_arr.astype({0: 'category'})
X_t = transformer.fit_transform(X)
assert_frame_equal(X_expected_arr, X_t.to_dataframe(), check_dtype=False)
transformer = SimpleImputer(impute_strategy='constant', fill_value=2)
X_expected_arr = pd.DataFrame([["a", 0, 1, 2], ["b", 2, 3, 3],
["a", 2, 3, 1], [2, 2, 3, 0]])
X_expected_arr = X_expected_arr.astype({0: 'category'})
X_t = transformer.fit_transform(X)
assert_frame_equal(X_expected_arr, X_t.to_dataframe(), check_dtype=False)
