def gen_user_results_old(user_id, results): ratings = np.genfromtxt('postprocessed-data/user_ratings', delimiter=',', dtype=None) user_ratings = ratings[user_id-1] y_pred = results[2] nonzero_indices = np.nonzero(user_ratings) avg_ratings = um.get_avg_ratings(ratings) avg_ratings = avg_ratings[nonzero_indices][:,0] user_ratings = user_ratings[nonzero_indices] nonzero = nonzero_indices[0] error_from_average = np.abs(avg_ratings-user_ratings) error_from_pred = np.abs(user_ratings-y_pred) col_names = ["Movie ID" ,"Average Rating","Error from Avg","User Rating","Model Prediction","Model Error"] col_avg = ["Average:", np.average(avg_ratings),np.average(error_from_average),np.average(user_ratings),np.average(y_pred),np.average(error_from_pred)] user_result = np.column_stack((nonzero, avg_ratings,error_from_average, user_ratings, y_pred, error_from_pred)) user_result = user_result[np.argsort(user_result[:, 5])] user_result = np.vstack((user_result,col_avg)) df = pd.DataFrame(user_result, columns = col_names) #df = df.convert_objects(convert_numeric=True) return df
def get_model_test_ratings(user_id, results): ratings = np.genfromtxt('postprocessed-data/user_ratings', delimiter=',', dtype=None) user_ratings = ratings[user_id-1] nonzero_indices = np.nonzero(user_ratings) user_ratings = user_ratings[nonzero_indices] avg_ratings = um.get_avg_ratings(ratings) avg_ratings = avg_ratings[nonzero_indices] user_data = np.column_stack((avg_ratings, user_ratings, results[2])) return user_data