model2.py 文件源码

def main():
    df_test = pd.read_csv("test2.csv")

    df_train0 = pd.read_csv("train.csv")

    df_train_list = [df_train0,]
    random.shuffle(df_train_list)
    df_train = pd.concat(df_train_list)

    df_ts = pd.read_csv("ts_feature2_simple.csv")
    df_date = pd.read_csv("date.csv")

    df_train = df_train.merge(df_date, on="date", how="left")
    df_train = df_train.merge(df_ts, on=["tollgate_id", "hour", "miniute", "direction"], how="left")

    df_test = df_test.merge(df_date, on="date", how="left")
    df_test = df_test.merge(df_ts, on=["tollgate_id", "hour", "miniute", "direction"], how="left")

    df_train_grouped = df_train.groupby(["tollgate_id", "direction"])
    df_test_grouped = df_test.groupby(["tollgate_id", "direction"])
    result = []
    oob = []
    for key, train_data in df_train_grouped:

        test_data = df_test_grouped.get_group(key)
        len_train = len(train_data)
        train_data = train_data.append(test_data)[train_data.columns.tolist()]


        train_data = feature_transform_knn(key, train_data)

        regressor_cubic = KNeighborsRegressor(n_neighbors=8, algorithm="auto")

        train_data = pd.DataFrame.reset_index(train_data)
        train_data = train_data.drop("index", axis=1)
        y = train_data.ix[:len_train - 1, :]["volume"]


        x = train_data.ix[:len_train - 1, 8:]
        print x.head()
        x1 = train_data.ix[len_train:, 8:]

        regressor_cubic.fit(x, y)
        yhat = regressor_cubic.predict(x1)

        test_data["volume"] = yhat
        result.append(test_data[['tollgate_id', 'time_window', 'direction', 'volume']])


    df_result = pd.concat(result, axis=0)
    print np.mean(df_result["volume"])
    df_result.to_csv("result/result_split_knn"+str(np.mean(df_result["volume"]))+".csv", index=False)

    print np.mean(oob)