def test_inverse_transform(self): from lale.lib.sklearn import OneHotEncoder, OrdinalEncoder fproc_ohe = OneHotEncoder(handle_unknown="ignore") # test_init_fit_transform trained_ohe = fproc_ohe.fit(self.X_train, self.y_train) transformed_X = trained_ohe.transform(self.X_test) orig_X_ohe = trained_ohe._impl._wrapped_model.inverse_transform(transformed_X) fproc_oe = OrdinalEncoder(handle_unknown="ignore") # test_init_fit_transform trained_oe = fproc_oe.fit(self.X_train, self.y_train) transformed_X = trained_oe.transform(self.X_test) orig_X_oe = trained_oe._impl.inverse_transform(transformed_X) self.assertEqual(orig_X_ohe.all(), orig_X_oe.all())
def test_bool_label(self): import pandas as pd data_records = [ {'IS_TENT':False, 'GENDER':'M', 'AGE':20, 'MARITAL_STATUS':'Single', 'PROFESSION':'Sales'}, {'IS_TENT':False, 'GENDER':'M', 'AGE':20, 'MARITAL_STATUS':'Single', 'PROFESSION':'Sales'}, {'IS_TENT':False, 'GENDER':'F', 'AGE':37, 'MARITAL_STATUS':'Single', 'PROFESSION':'Other'}, {'IS_TENT':False, 'GENDER':'M', 'AGE':42, 'MARITAL_STATUS':'Married', 'PROFESSION':'Other'}, {'IS_TENT':True, 'GENDER':'F', 'AGE':24, 'MARITAL_STATUS':'Married', 'PROFESSION':'Retail'}, {'IS_TENT':False, 'GENDER':'F', 'AGE':24, 'MARITAL_STATUS':'Married', 'PROFESSION':'Retail'}, {'IS_TENT':False, 'GENDER':'M', 'AGE':29, 'MARITAL_STATUS':'Single', 'PROFESSION':'Retail'}, {'IS_TENT':False, 'GENDER':'M', 'AGE':29, 'MARITAL_STATUS':'Single', 'PROFESSION':'Retail'}, {'IS_TENT':True, 'GENDER':'M', 'AGE':43, 'MARITAL_STATUS':'Married', 'PROFESSION':'Trades'}, {'IS_TENT':False, 'GENDER':'M', 'AGE':43, 'MARITAL_STATUS':'Married', 'PROFESSION':'Trades'}] df = pd.DataFrame.from_records(data_records) X = df.drop(['IS_TENT'], axis=1).values y = df['IS_TENT'].values from lale.lib.sklearn import OneHotEncoder as Enc from lale.lib.sklearn import GradientBoostingClassifier as Clf trainable = Enc() >> Clf() trained = trainable.fit(X, y)
def test_bool_label(self): import pandas as pd data_records = [ { "IS_TENT": False, "GENDER": "M", "AGE": 20, "MARITAL_STATUS": "Single", "PROFESSION": "Sales", }, { "IS_TENT": False, "GENDER": "M", "AGE": 20, "MARITAL_STATUS": "Single", "PROFESSION": "Sales", }, { "IS_TENT": False, "GENDER": "F", "AGE": 37, "MARITAL_STATUS": "Single", "PROFESSION": "Other", }, { "IS_TENT": False, "GENDER": "M", "AGE": 42, "MARITAL_STATUS": "Married", "PROFESSION": "Other", }, { "IS_TENT": True, "GENDER": "F", "AGE": 24, "MARITAL_STATUS": "Married", "PROFESSION": "Retail", }, { "IS_TENT": False, "GENDER": "F", "AGE": 24, "MARITAL_STATUS": "Married", "PROFESSION": "Retail", }, { "IS_TENT": False, "GENDER": "M", "AGE": 29, "MARITAL_STATUS": "Single", "PROFESSION": "Retail", }, { "IS_TENT": False, "GENDER": "M", "AGE": 29, "MARITAL_STATUS": "Single", "PROFESSION": "Retail", }, { "IS_TENT": True, "GENDER": "M", "AGE": 43, "MARITAL_STATUS": "Married", "PROFESSION": "Trades", }, { "IS_TENT": False, "GENDER": "M", "AGE": 43, "MARITAL_STATUS": "Married", "PROFESSION": "Trades", }, ] df = pd.DataFrame.from_records(data_records) X = df.drop(["IS_TENT"], axis=1).values y = df["IS_TENT"].values from lale.lib.sklearn import GradientBoostingClassifier as Clf from lale.lib.sklearn import OneHotEncoder as Enc trainable = Enc() >> Clf() _ = trainable.fit(X, y)