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
