def train_by_bucket_data(*args, **kwargs): body = kwargs.get('body') bucket_id = body.get('bucket_id') model_id = body.get('model_id') features, target = get_data_by_bucket_id(bucket_id) features = features.T model_params_name = [ "weights_init", 'num_iters', 'batch_size', 'neighborhood', 'learning_rate', 'learning_decay_rate', 'sigma', 'sigma_decay_rate', 'num_clusters' ] model_params = { 'model__{}'.format(p): v for p, v in body.items() if p in model_params_name } print(model_params) model_params['model__verbose'] = True X_train = np.array(features) y_train = np.array(target).astype(int) try: result_ml = helper.model_train(model_id, X_train, y_train, **model_params) except Exception as err: config.logger.error(str(err)) # err_message = ml_models.result.ErrorResult() return {"message": str(err)}, 400 else: success_message = ml_models.get_result(model_id)(**result_ml) return success_message()
def train(*args, **kwargs): body = kwargs.get('body') model_id = body.get('model_id') features = body.get('features') target = body.get('target') model_params_name = [ 'weights_init', 'num_iters', 'batch_size', 'neighborhood', 'learning_rate', 'learning_decay_rate', 'sigma', 'sigma_decay_rate', 'num_clusters' ] model_params = { 'model__{}'.format(p): v for p, v in body.items() if p in model_params_name } print(model_params) model_params['model__verbose'] = True X_train = np.array(features).T y_train = np.array(target) try: result_ml = helper.model_train(model_id, X_train, y_train, **model_params) except Exception as err: config.logger.error(str(err)) err_message = ml_models.result.ErrorResult() return err_message() else: success_message = ml_models.get_result(model_id)(**result_ml) return success_message()
def train_by_bucket_data(model_id, bucket_id): features, target = get_data_by_bucket_id(bucket_id) features = features.T try: result_ml = helper.model_train(model_id, features, target) except Exception as err: config.logger.error(str(err)) config.logger.error(traceback.print_exc()) # err_message = ml_models.result.ErrorResult() return {"message": str(err)}, 400 else: success_message = ml_models.get_result(model_id)(**result_ml) return success_message()
def train(model_id, features, target): features = np.array(features).T target = np.array(target) try: result_ml = helper.model_train(model_id, features, target) except Exception as err: config.logger.error(str(err)) config.logger.error(traceback.print_exc()) err_message = ml_models.result.ErrorResult() return err_message() else: success_message = ml_models.get_result(model_id)(**result_ml) return success_message()
def train_by_bucket_data(model_id, bucket_id): features, target = get_data_by_bucket_id(bucket_id) print("-------------Feature-----------") for i in features: print(i) print("================================") print("--------------Target------------") for i in target: print(i) print("================================") features = features.T try: result_ml = helper.model_train(model_id, features, target) except Exception as err: config.logger.error(str(err)) config.logger.error(traceback.print_exc()) err_message = ml_models.result.ErrorResult() return err_message() else: success_message = ml_models.get_result(model_id)(**result_ml) return success_message()
def train_by_bucket_data(*args, **kwargs): # features, target = get_data_by_bucket_id(bucket_id) # features = features.T # try: # result_ml = helper.model_train(model_id, features, target) # except Exception as err: # config.logger.error(str(err)) # config.logger.error(traceback.print_exc()) # err_message = ml_models.result.ErrorResult() # return err_message() # else: # success_message = ml_models.get_result(model_id)(**result_ml) # return success_message() body = kwargs.get('body') bucket_id = body.get('bucket_id') model_id = body.get('model_id') features, target = get_data_by_bucket_id(bucket_id) features = features.T model_params_name = ["features_selection","subset_size", 'unsup_num_iters', 'unsup_batch_size', 'sup_num_iters', 'sup_batch_size', 'neighborhood', 'learning_rate', 'learning_decay_rate', 'sigma', 'sigma_decay_rate'] model_params = {'model__{}'.format(p): v for p, v in body.items() if p in model_params_name} print(model_params) model_params['model__verbose'] = True X_train = np.array(features) y_train = np.array(target).astype(int) print(X_train.shape, y_train.shape) try: result_ml = helper.model_train(model_id, X_train, y_train, **model_params) except Exception as err: config.logger.error(str(err)) # err_message = ml_models.result.ErrorResult() return {"message": str(err)}, 400 else: success_message = ml_models.get_result(model_id)(**result_ml) return success_message()