recommender.py 文件源码

def get_recommendations():
    module_dir = os.path.dirname(__file__)

    train_df = build_training_set()
    if train_df is None:
        return []
    x_train = train_df.iloc[:, 5:]
    try:
        x_train = scale(x_train)
    except:
        print("First migrations")
    y_train = train_df.iloc[:, 3]
    x_train_labels = train_df.iloc[:, 0]

    target_df = pd.read_csv(os.path.join(module_dir,'data.csv'))
    target_df = pd.DataFrame(target_df)
    target_df = target_df.append(train_df)
    target_df = target_df.append(train_df)
    target_df = target_df.drop_duplicates('SeriesName', keep=False)

    x_target = scale(target_df.iloc[:, 5:])
    x_target_labels = target_df.iloc[:, 0]

    clf = RandomForestClassifier()
    clf.fit(x_train,y_train)

    y_target = clf.predict(x_target)

    new_df = pd.DataFrame()
    new_df['seriesName'] = x_target_labels
    new_df['tvdbID'] = target_df.iloc[:, 1]
    new_df['PredictedRating'] = y_target
    new_df['indicator'] = (target_df.iloc[:, 4]/target_df.iloc[:, 3])*new_df['PredictedRating']

    new_df = new_df.sort_values(['indicator'], ascending=False)
    initial_list = list(new_df.iloc[:4, 1])
    latter_list =  list(new_df.iloc[5:15, 1])
    shuffle(latter_list)
    return list(initial_list + latter_list[:5])