def meta_fitting(data_to_predict_local, data_input_2, strategy_dictionary):
fitting_inputs, fitting_targets = input_processing(data_to_predict_local, data_input_2, strategy_dictionary)
error = []
train_indices, test_indices = train_test_indices(fitting_inputs, strategy_dictionary['train_test_ratio'])
if strategy_dictionary['ml_mode'] == 'svm':
fitting_dictionary, error = svm_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'randomforest':
fitting_dictionary, error = random_forest_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'adaboost':
fitting_dictionary, error = adaboost_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'gradientboosting':
fitting_dictionary, error = gradient_boosting_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'extratreesfitting':
fitting_dictionary, error = extra_trees_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, strategy_dictionary)
fitting_dictionary['train_indices'] = train_indices
fitting_dictionary['test_indices'] = test_indices
fitting_dictionary['error'] = error
return fitting_dictionary
def meta_fitting(fitting_inputs, fitting_targets, strategy_dictionary):
error = []
fitting_dictionary = {}
train_indices, test_indices, validation_indices = train_test_validation_indices(
fitting_inputs, strategy_dictionary['train_test_validation_ratios'])
if strategy_dictionary['ml_mode'] == 'svm':
fitting_dictionary, error = svm_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'randomforest':
fitting_dictionary, error = random_forest_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'adaboost':
fitting_dictionary, error = adaboost_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'gradientboosting':
fitting_dictionary, error = gradient_boosting_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'extratreesfitting':
fitting_dictionary, error = extra_trees_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
fitting_dictionary['train_indices'] = train_indices
fitting_dictionary['test_indices'] = test_indices
fitting_dictionary['validation_indices'] = validation_indices
fitting_dictionary['error'] = error
return fitting_dictionary
def meta_fitting(fitting_inputs, fitting_targets, strategy_dictionary):
error = []
model = []
train_indices, test_indices, validation_indices = train_test_validation_indices(
fitting_inputs,
strategy_dictionary['train_test_validation_ratios'])
target_scaler = StandardScaler()
if strategy_dictionary['regression_mode'] == 'regression':
fitting_targets = target_scaler.fit_transform(fitting_targets.reshape(-1, 1)).ravel()
if strategy_dictionary['ml_mode'] == 'svm':
model, error = svm_fitting(
fitting_inputs,
fitting_targets,
train_indices,
strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'randomforest':
model, error = random_forest_fitting(
fitting_inputs,
fitting_targets,
train_indices,
strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'adaboost':
model, error = adaboost_fitting(
fitting_inputs,
fitting_targets,
train_indices,
strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'gradientboosting':
model, error = gradient_boosting_fitting(
fitting_inputs,
fitting_targets,
train_indices,
strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'extratreesfitting':
model, error = extra_trees_fitting(
fitting_inputs,
fitting_targets,
train_indices,
strategy_dictionary)
if len(test_indices) != 0:
training_strategy_score = model.predict(fitting_inputs[train_indices, :])
fitted_strategy_score = model.predict(fitting_inputs[test_indices, :])
validation_strategy_score = model.predict(fitting_inputs[validation_indices, :])
else:
fitted_strategy_score = []
validation_strategy_score = []
if strategy_dictionary['regression_mode'] == 'regression':
fitting_dictionary = {
'training_strategy_score': training_strategy_score,
'fitted_strategy_score': fitted_strategy_score,
'validation_strategy_score':validation_strategy_score,
'train_indices': train_indices,
'test_indices': test_indices,
'validation_indices': validation_indices,
'error': error}
elif strategy_dictionary['regression_mode'] == 'classification':
fitting_dictionary = {
'training_strategy_score': training_strategy_score,
'fitted_strategy_score': fitted_strategy_score,
'validation_strategy_score': validation_strategy_score,
'train_indices': train_indices,
'test_indices': test_indices,
'validation_indices': validation_indices,
'error': error}
return fitting_dictionary