def train():
# 1. Crear modelo
print('(TRAINER) Creating model...')
model = Model()
# 2. Entrenar clasificador
print('(TRAINER) Training model...')
model.train()
# 3. Guardar clasificador
print('(TRAINER) Saving model...')
model.save()
return model
def evaluate(grid_search=False):
# Lista de 6-uplas (model, params, accuracy, precision, recall, f1_score)
results_list = []
# Iterar segun tipos de modelo
for model_type in const.MODELS:
print()
print('(EVALUATOR) Evaluating model ' + model_type)
if grid_search:
# Lista de 6-uplas (model, params, accuracy, precision, recall, f1_score)
grid_search_list = []
param_space = get_parameter_space(model_type)
for params in param_space:
# 1. Crear modelo
model = Model(model=model_type, params={'model': model_type, 'params': params})
# 2. Entrenar clasificador
model.train()
# 3. Evaluar clasificador
accuracy, results, _, _ = model.evaluate()
grid_search_list.append((model_type, params, accuracy, results['precision'], results['recall'], results['f1_score']))
# Ordenar resultados segun f1_score
grid_search_list = sorted(grid_search_list, key=lambda x: x[5], reverse=True)
print()
print('(EVALUATOR) Grid search results -> Model - ', model_type)
for _, params, accuracy, precision, recall, f1_score in grid_search_list:
print()
print("Params - ", params)
print("-> F1 Score - ", "{0:.2f}".format(f1_score))
print("-> Precision - ", "{0:.2f}".format(precision))
print("-> Recall - ", "{0:.2f}".format(recall))
print("-> Accuracy - ", "{0:.2f}".format(accuracy))
print()
best_params = grid_search_list[0][1]
best_accuracy = grid_search_list[0][2]
best_precision = grid_search_list[0][3]
best_recall = grid_search_list[0][4]
best_f1_score = grid_search_list[0][5]
results_list.append((model_type, best_params, best_accuracy, best_precision, best_recall, best_f1_score))
else:
# 1. Crear modelo
model = Model(model=model_type)
# 2. Entrenar clasificador
model.train()
# 3. Evaluar clasificador
accuracy, results, _, _ = model.evaluate()
results_list.append((model_type, None, accuracy, results['precision'], results['recall'], results['f1_score']))
# Ordenar resultados segun f1_score
results_list = sorted(results_list, key=lambda x: x[5], reverse=True)
# Mostrar resultados
print()
print('(EVALUATOR) Sorted results: ')
for model, params, accuracy, precision, recall, f1_score in results_list:
print()
print("Model - ", model)
if params is not None:
print("Params - ", params)
print("-> F1 Score - ", "{0:.2f}".format(f1_score))
print("-> Precision - ", "{0:.2f}".format(precision))
print("-> Recall - ", "{0:.2f}".format(recall))
print("-> Accuracy - ", "{0:.2f}".format(accuracy))
print()
best_solution = {
'model': results_list[0][0],
'params': results_list[0][1]
}
# Elegir mejor modelo, entrenarlo por completo y guardarlo
model = Model(model=results_list[0][0], params=best_solution)
model.train()
model.save()
print('(EVALUATOR) Trained and saved best model')
