def fit_transform(self, X, y):
self.fit(X, y)
kf = CustomFolds(validation_scheme=self.fold_scheme, num_folds=self.folds, random_state=self.random_state, shuffle=self.shuffle)
X = self.convert_input(X)
copy_X = deepcopy(X)
for i, (train_index, test_index) in enumerate(kf.split(copy_X)):
encoding_dict, iv = self.create_encoding_dict(X.iloc[train_index], y[train_index])
copy_X.iloc[test_index] = self.apply_encoding(X.iloc[test_index], encoding_dict)
if self.return_df:
return copy_X
else:
return copy_X.values
def fit(self, x, y, use_oof=False, n_jobs=-1):
if not hasattr(self.model, 'fit') :
raise Exception ("Model/algorithm needs to implement fit()")
fitted_models = []
scaler_models= []
if use_oof:
folds = CustomFolds(num_folds=self.n_splits,
random_state=random.randint(0,1000) if self.random_state=='random' else self.random_state,
shuffle=self.shuffle, validation_scheme=self.validation_scheme)
self.indices = folds.split(x, y, group=self.cv_group_col)
for i, (train_index, test_index) in enumerate(self.indices):
model = clone(self.model)
model.n_jobs = n_jobs
if (isinstance(model, LGBMClassifier) and self.early_stopping_rounds is not None):
model.fit(X=x[train_index], y=y[train_index], eval_set=[(x[test_index],y[test_index]),(x[train_index],y[train_index])],
verbose=100, eval_metric='auc', early_stopping_rounds=self.early_stopping_rounds)
elif (isinstance(model, XGBClassifier) and self.early_stopping_rounds is not None):
model.fit(X=x[train_index], y=y[train_index], eval_set=[(x[test_index],y[test_index])],
verbose=100, eval_metric='auc', early_stopping_rounds=self.early_stopping_rounds)
elif (isinstance(model, CatBoostClassifier) and self.early_stopping_rounds is not None):
model.od_wait=int(self.early_stopping_rounds)
model.fit(x[train_index], y[train_index], cat_features=self.categorical_features_indices,
eval_set=(x[test_index],y[test_index]), use_best_model=True, verbose=100)
elif isinstance(model,LogisticRegression):
model.fit(x[train_index],y[train_index])
else:
model.fit(x[train_index], y[train_index])
fitted_models.append(model)
else:
model = clone(self.model)
model.n_jobs = n_jobs
x_train, x_val, y_train, y_val = train_test_split(x, y, test_size =0.2, shuffle=True,
random_state=random.randint(0,1000) if self.random_state=='random' else self.random_state
)
if isinstance(model, LGBMClassifier):
if self.early_stopping_rounds is not None:
model.fit(X=x_train, y=y_train, eval_set=[(x_val,y_val)],
verbose=100, eval_metric='auc', early_stopping_rounds=self.early_stopping_rounds)
elif isinstance(model, XGBClassifier):
if self.early_stopping_rounds is not None:
model.fit(X=x_train, y=y_train, eval_set=[(x_val,y_val)],
verbose=100, eval_metric='auc', early_stopping_rounds=self.early_stopping_rounds)
model.fit(x, y)
fitted_models.append(model)
self.fitted_models = fitted_models
return self
def fit(self, x, y, use_oof=False, n_jobs=-1):
if not hasattr(self.model, 'fit'):
raise Exception("Model/algorithm needs to implement fit()")
fitted_models = []
scaler_models = []
# if (isinstance(self.model, CatBoostClassifier)):
# if (self.categorical_features_indices is None):
# temp_df=pd.DataFrame(x)
# temp_df=temp_df.apply(pd.to_numeric, errors='ignore')
# self.categorical_features_indices=get_categorical_column_indexes(temp_df,threshold=self.threshold).tolist()
# self.encoder = LabelEncoding(categorical_columns=self.categorical_features_indices)
# x = self.encoder.fit_transform(x)
if use_oof:
folds = CustomFolds(num_folds=self.n_splits,
random_state=self.random_state,
shuffle=self.shuffle,
validation_scheme=self.validation_scheme)
self.indices = folds.split(x, y, group=self.cv_group_col)
for i, (train_index, test_index) in enumerate(self.indices):
model = clone(self.model)
model.n_jobs = n_jobs
if (isinstance(model, LGBMClassifier)
and self.early_stopping_rounds is not None):
model.fit(X=x[train_index],
y=y[train_index],
eval_set=[(x[test_index], y[test_index]),
(x[train_index], y[train_index])],
verbose=100,
eval_metric='auc',
early_stopping_rounds=self.early_stopping_rounds)
elif (isinstance(model, XGBClassifier)
and self.early_stopping_rounds is not None):
model.fit(X=x[train_index],
y=y[train_index],
eval_set=[(x[test_index], y[test_index])],
verbose=50,
eval_metric='auc',
early_stopping_rounds=self.early_stopping_rounds)
elif (isinstance(model, CatBoostClassifier)
and self.early_stopping_rounds is not None):
model.od_wait = int(self.early_stopping_rounds)
model.fit(x[train_index],
y[train_index],
cat_features=self.categorical_features_indices,
eval_set=(x[test_index], y[test_index]),
use_best_model=True,
verbose=10)
elif isinstance(model, LogisticRegression):
scaler = RobustScaler()
xtrain = scaler.fit_transform(x[train_index])
scaler_models.append(scaler)
model.fit(xtrain, y[train_index])
else:
model.fit(x[train_index], y[train_index])
fitted_models.append(model)
else:
model = clone(self.model)
model.n_jobs = n_jobs
x_train, x_val, y_train, y_val = train_test_split(x,
y,
test_size=0.2,
shuffle=True,
random_state=100)
if isinstance(model, LGBMClassifier):
if self.early_stopping_rounds is not None:
model.fit(X=x_train,
y=y_train,
eval_set=[(x_val, y_val)],
verbose=False,
eval_metric='auc',
early_stopping_rounds=self.early_stopping_rounds)
elif isinstance(model, XGBClassifier):
if self.early_stopping_rounds is not None:
model.fit(X=x_train,
y=y_train,
eval_set=[(x_val, y_val)],
verbose=False,
eval_metric='auc',
early_stopping_rounds=self.early_stopping_rounds)
model.fit(x, y)
fitted_models.append(model)
self.fitted_models = fitted_models
self.scaler_models = scaler_models
return self
def fit(self, x, y, use_oof=False, n_jobs=-1):
if not hasattr(self.model, 'fit'):
raise Exception("Model/algorithm needs to implement fit()")
fitted_models = []
if use_oof:
# kf = StratifiedKFold(n_splits=self.kfolds, random_state=self.random_state, shuffle=self.shuffle)
# self.indices = [(train_index, test_index) for (train_index, test_index) in kf.split(x, y)]
folds = CustomFolds(num_folds=self.n_splits,
random_state=self.random_state,
shuffle=self.shuffle,
validation_scheme=self.validation_scheme)
self.indices = folds.split(x, y, group=self.cv_group_col)
for i, (train_index, test_index) in enumerate(self.indices):
model = clone(self.model)
model.n_jobs = n_jobs
if (isinstance(model, LGBMRegressor)
and self.early_stopping_rounds is not None):
model.fit(X=x[train_index],
y=y[train_index],
eval_set=[(x[test_index], y[test_index]),
(x[train_index], y[train_index])],
verbose=100,
eval_metric='rmse',
early_stopping_rounds=self.early_stopping_rounds)
elif (isinstance(model, XGBRegressor)
and self.early_stopping_rounds is not None):
model.fit(X=x[train_index],
y=y[train_index],
eval_set=[(x[test_index], y[test_index])],
verbose=100,
eval_metric='rmse',
early_stopping_rounds=self.early_stopping_rounds)
else:
# model.n_jobs=-1
model.fit(x[train_index], y[train_index])
print scoring_metric(y[test_index],
model.predict(x[test_index]))**0.5
fitted_models.append(model)
else:
model = clone(self.model)
model.n_jobs = n_jobs
x_train, x_val, y_train, y_val = train_test_split(x,
y,
test_size=0.2,
shuffle=True,
random_state=100)
if isinstance(model, LGBMRegressor):
if self.early_stopping_rounds is not None:
model.fit(X=x_train,
y=y_train,
eval_set=[(x_val, y_val)],
verbose=False,
eval_metric='rmse',
early_stopping_rounds=self.early_stopping_rounds)
elif isinstance(model, XGBRegressor):
if self.early_stopping_rounds is not None:
model.fit(X=x_train,
y=y_train,
eval_set=[(x_val, y_val)],
verbose=False,
eval_metric='rmse',
early_stopping_rounds=self.early_stopping_rounds)
model.fit(x, y)
fitted_models.append(model)
self.fitted_models = fitted_models
return self
