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