def stacker_data_v2(cutoff, num_least_correlated_cols):
X, y_train = load_data()
oof_predictions, lb_predictions, oof_ginis = load_predictions_with_cutoff(PREDICTION_PATH, cutoff)
X_train, X_test = oof_predictions, lb_predictions
new_cols = least_correlated_cols(X_train, num_least_correlated_cols)
X_train, X_test = X_train[new_cols], X_test[new_cols]
return X_train, y_train, X_test
def stacker_data_v2(cutoff, num_least_correlated_cols):
X, y_train = load_data()
oof_predictions, lb_predictions, oof_ginis = load_predictions_with_cutoff(
PREDICTION_PATH, cutoff)
X_train, X_test = oof_predictions, lb_predictions
new_cols = least_correlated_cols(X_train, num_least_correlated_cols)
X_train, X_test = X_train[new_cols], X_test[new_cols]
return X_train, y_train, X_test
def __init__(self, base_path, ensemble_func, cutoff=.38, column_picker_func=None,
include=[], verbose=True, **column_picker_kwargs):
self.ensemble_func = ensemble_func
self.base_path = base_path
self.cutoff = cutoff
self.include = include
self.verbose = verbose
self.oof_predictions, self.lb_predictions, self.oof_ginis = load_predictions_with_cutoff(
base_path, cutoff, include)
if column_picker_func is not None:
new_cols = column_picker_func(self.oof_predictions, **column_picker_kwargs)
self.oof_predictions = self.oof_predictions[new_cols]
self.lb_predictions = self.lb_predictions[new_cols]
def __init__(self, base_path, opt_func=fmin_powell, cutoff=.38, column_picker_func=None,
include=[], verbose=True, **kwargs):
self.opt_func = opt_func
self.base_path = base_path
self.cutoff = cutoff
self.include = include
self.verbose = verbose
self.kwargs = kwargs
self.oof_predictions, self.lb_predictions, self.oof_ginis = load_predictions_with_cutoff(
base_path, cutoff, include)
if column_picker_func is not None:
new_cols = column_picker_func(self.oof_predictions)
self.oof_predictions = self.oof_predictions[new_cols]
self.lb_predictions = self.lb_predictions[new_cols]
self.opt_weights = None
def __init__(self,
base_path,
ensemble_func,
cutoff=.38,
column_picker_func=None,
include=[],
verbose=True,
**column_picker_kwargs):
self.ensemble_func = ensemble_func
self.base_path = base_path
self.cutoff = cutoff
self.include = include
self.verbose = verbose
self.oof_predictions, self.lb_predictions, self.oof_ginis = load_predictions_with_cutoff(
base_path, cutoff, include)
if column_picker_func is not None:
new_cols = column_picker_func(self.oof_predictions,
**column_picker_kwargs)
self.oof_predictions = self.oof_predictions[new_cols]
self.lb_predictions = self.lb_predictions[new_cols]
def __init__(self,
base_path,
opt_func=fmin_powell,
cutoff=.38,
column_picker_func=None,
include=[],
verbose=True,
**kwargs):
self.opt_func = opt_func
self.base_path = base_path
self.cutoff = cutoff
self.include = include
self.verbose = verbose
self.kwargs = kwargs
self.oof_predictions, self.lb_predictions, self.oof_ginis = load_predictions_with_cutoff(
base_path, cutoff, include)
if column_picker_func is not None:
new_cols = column_picker_func(self.oof_predictions)
self.oof_predictions = self.oof_predictions[new_cols]
self.lb_predictions = self.lb_predictions[new_cols]
self.opt_weights = None
def stacker_data_v1(cutoff):
X, y_train = load_data()
oof_predictions, lb_predictions, oof_ginis = load_predictions_with_cutoff(PREDICTION_PATH, cutoff)
X_train, X_test = oof_predictions, lb_predictions
return X_train, y_train, X_test
def stacker_data_v1(cutoff):
X, y_train = load_data()
oof_predictions, lb_predictions, oof_ginis = load_predictions_with_cutoff(
PREDICTION_PATH, cutoff)
X_train, X_test = oof_predictions, lb_predictions
return X_train, y_train, X_test
