def test_lda_numeric(data_type, make_data_type):
X = pd.DataFrame([[3, 0, 1, 6], [1, 2, 1, 6], [10, 2, 1, 6], [10, 2, 2, 5],
[6, 2, 2, 5]])
y = pd.Series([0, 1, 0, 1, 1])
X = make_data_type(data_type, X)
y = make_data_type(data_type, y)
lda = LinearDiscriminantAnalysis()
expected_X_t = pd.DataFrame(
[[-3.7498560857993817], [1.984459921694517], [-3.234411950294312],
[1.3401547523131798], [3.659653362085993]],
columns=["component_0"])
X_t = lda.fit_transform(X, y)
assert_frame_equal(expected_X_t, X_t.to_dataframe())
def test_lda_array():
X = np.array([[3, 0, 1, 6], [1, 2, 1, 6], [10, 2, 1, 6], [10, 2, 2, 5],
[6, 2, 2, 5]])
y = np.array([2, 2, 0, 1, 0])
lda = LinearDiscriminantAnalysis()
expected_X_t = pd.DataFrame([[-0.6412164311777084, 0.5197032695565076],
[0.9499648898073094, -0.6919658287324498],
[0.7364892645407753, 0.884637532109161],
[-0.570057889422197, -0.005831184057363141],
[-0.4751798337481819, -0.7065437888758568]],
columns=[f"component_{i}" for i in range(2)])
lda.fit(X, y)
X_t = lda.transform(X)
assert_frame_equal(expected_X_t, X_t.to_dataframe())
def test_lda_woodwork_custom_overrides_returned_by_components():
y = pd.Series([1, 2, 1])
X_df = pd.DataFrame([[3, 0, 1, 6],
[1, 2, 1, 6],
[10, 2, 1, 6],
[10, 2, 2, 5],
[6, 2, 2, 5]])
y = pd.Series([0, 1, 0, 1, 1])
override_types = [Integer, Double]
for logical_type in override_types:
X = ww.DataTable(X_df, logical_types={0: logical_type, 1: logical_type, 2: logical_type, 3: logical_type})
lda = LinearDiscriminantAnalysis(n_components=1)
lda.fit(X, y)
transformed = lda.transform(X, y)
assert isinstance(transformed, ww.DataTable)
assert transformed.logical_types == {'component_0': ww.logical_types.Double}
def test_invalid_n_components():
X = pd.DataFrame([[3, 0, 1, 6, 5, 10], [1, 3, 1, 3, 11, 4],
[10, 2, 3, 12, 5, 6], [10, 6, 4, 3, 0, 1],
[6, 8, 9, 3, 3, 5], [3, 2, 1, 2, 1, 3],
[12, 11, 1, 1, 3, 3]])
y = [0, 1, 2, 1, 2, 0, 2]
lda_invalid = LinearDiscriminantAnalysis(n_components=4)
with pytest.raises(ValueError, match="is too large"):
lda_invalid.fit(X, y)
X = pd.DataFrame([[3, 0, 1], [1, 3, 1], [10, 2, 3], [10, 6, 4], [6, 8, 9],
[3, 2, 1], [12, 11, 1]])
y = [0, 1, 2, 3, 4, 3, 4, 5]
lda_invalid = LinearDiscriminantAnalysis(n_components=4)
with pytest.raises(ValueError, match="is too large"):
lda_invalid.fit(X, y)
def test_n_components():
X = pd.DataFrame([[3, 0, 1, 6, 5, 10], [1, 3, 1, 3, 11, 4],
[10, 2, 3, 12, 5, 6], [10, 6, 4, 3, 0, 1],
[6, 8, 9, 3, 3, 5], [3, 2, 1, 2, 1, 3],
[12, 11, 1, 1, 3, 3]])
y = [0, 3, 3, 1, 2, 0, 2]
lda = LinearDiscriminantAnalysis(n_components=3)
X_t = lda.fit_transform(X, y)
assert X_t.shape[1] == 3
lda = LinearDiscriminantAnalysis(n_components=1)
X_t = lda.fit_transform(X, y)
assert X_t.shape[1] == 1
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_lda_invalid_init():
with pytest.raises(
ValueError,
match=
"Invalid number of compponents for Linear Discriminant Analysis"):
LinearDiscriminantAnalysis(n_components=-1)
def test_lda_invalid():
X = pd.DataFrame([[3, 0, 1, 6], [1, None, 1, 6], [10, 2, 1, 6],
[10, 2, 2, np.nan], [None, 2, 2, 5]])
y = [2, 0, 1, 1, 0]
lda = LinearDiscriminantAnalysis()
with pytest.raises(ValueError, match="must be all numeric"):
lda.fit(X, y)
X = pd.DataFrame([[3, 0, 1, 6], ['a', 'b', 'a', 'b'], [10, 2, 1, 6],
[10, 2, 2, 23], [0, 2, 2, 5]])
lda = LinearDiscriminantAnalysis()
with pytest.raises(ValueError, match="must be all numeric"):
lda.fit_transform(X, y)
X_ok = pd.DataFrame([[3, 0, 1, 6], [1, 2, 1, 6], [10, 2, 1, 6],
[10, 2, 2, 5], [6, 2, 2, 5]])
lda = LinearDiscriminantAnalysis()
lda.fit(X_ok, y)
with pytest.raises(ValueError, match="must be all numeric"):
lda.transform(X)