def predict_and_save_csv(self,test_features,test_ids, avg_score, final_score = None):
title = get_filename(self.model_args['model'])
print(f'Saving predictions to {title}.csv...\n')
y_preds = self.model.predict(test_features)
y_ids = pd.DataFrame(test_ids, columns=['ID'])
y_preds_df = pd.DataFrame(y_preds, columns=['target'])
predictions = y_ids.join(y_preds_df)
predictions.to_csv(f'{self.config_args["output"]}/{title}.csv', index = False)
self.log_experiment(self.model_args['model'], title, avg_score, final_score)
def predict_and_save_csv(self, test_features):
print('Training Decision Tree Model...\n')
self.train_model()
print('Saving predictions to csv...\n')
title = get_filename('decision_tree')
output_directory = Config().get_config()['output_directory']
y_preds = self.model.predict(test_features)
y_ids = pd.DataFrame(self.test_ids, columns=['ID'])
y_preds_df = pd.DataFrame(y_preds, columns=['Label_Id'])
predictions = y_ids.join(y_preds_df)
predictions = self.label_encoder.decode(predictions)
predictions.to_csv(f'{output_directory}/{title}.csv', index=False)
def predict_and_save_csv(self, test_features, skip_pipeline=False):
print('Training XGBoost Classifier...\n')
self.train_model(skip_pipeline)
title = get_filename('xgboost')
print(f'Saving predictions to csv {title}...\n')
output_directory = Config().get_config()['output_directory']
y_preds = self.model.predict(test_features)
y_ids = pd.DataFrame(self.test_ids, columns=['ID'])
y_preds_df = pd.DataFrame(y_preds, columns=['Label_Id'])
predictions = y_ids.join(y_preds_df)
predictions = self.label_encoder.decode(predictions)
predictions.to_csv(f'{output_directory}/{title}.csv', index=False)
def predict_and_save_csv(self, test_features, model_type='Gaussian'):
print('Training NaiveBayesian model...\n')
self.train_model(model_type)
print('Saving predictions to csv...\n')
title = get_filename('naive_bayesian_multinom')
output_directory = Config().get_config()['output_directory']
y_preds = None
if model_type == 'Gaussian':
y_preds = self.model.predict(self.test_features.toarray())
else:
y_preds = self.model.predict(test_features)
y_ids = pd.DataFrame(self.test_ids, columns=['ID'])
y_preds_df = pd.DataFrame(y_preds, columns=['Label_Id'])
predictions = y_ids.join(y_preds_df)
predictions = self.label_encoder.decode(predictions)
predictions.to_csv(f'{output_directory}/{title}.csv', index=False)
def tileset_dialog(self, path_input: TextInput, name_input: Optional[TextInput] = None):
if not has_tk:
return
value = path_input.get_value()
initialfile = value or None
filepath = askopenfilename(
initialfile = initialfile,
filetypes = [
(
"Arquivos de Imagem",
(".png", ".jpg", ".bmp", ".tiff", ".webp")
)
]
)
if filepath:
path_input.set_value(filepath)
if name_input and not name_input.get_value():
name = utils.get_filename(filepath)
name_input.set_value(name)