未來杯 城市-房產租金預測開源baseline

昨天跟隊友開源了一份線上0.85的baseline，發在我們的知乎專欄裏了。
知乎地址：
https://zhuanlan.zhihu.com/p/64715267

github地址：
https://github.com/leo6033/future-AI-challenge

下面貼一下baseline的主要代碼部分：

def parseData(df):
    """
    預處理數據
    """
    df['rentType'][df['rentType']=='--'] = '未知方式'
    # 轉換object類型數據
    columns = ['rentType', 'houseFloor', 'houseToward', 'houseDecoration', 'communityName', 'region', 'plate']
    for col in columns:
        df[col] = df[col].astype('category')
        
    # 將buildYear列轉換爲整型數據
    tmp = df['buildYear'].copy()
    tmp2 = tmp[tmp!='暫無信息'].astype('int')
    tmp[tmp=='暫無信息'] = tmp2.mode().iloc[0]
    df['buildYear'] = tmp
    df['buildYear'] = df['buildYear'].astype('int')
    
    # 處理pv和uv的空值
    df['pv'].fillna(df['pv'].mean(),inplace=True)
    df['uv'].fillna(df['uv'].mean(),inplace=True)
    df['pv'] = df['pv'].astype('int')
    df['uv'] = df['uv'].astype('int')
    
    # 去掉部分特徵
    df.drop('communityName',axis=1, inplace=True)
    df.drop('city',axis=1,inplace=True)
    
    return df

def washData(df_train, df_test):
    """
    清洗數據
    """
    df_train = df_train[df_train['area']<=700]
    df_train = df_train[df_train['tradeMoney']<=100000]
    
    df_train.drop('ID', axis=1, inplace=True)
    df_test.drop('ID', axis=1,inplace=True)
    
    return df_train, df_test

def feature(df):
    """
    特徵
    """
    # 將houseType轉化爲‘房間數’，‘廳數’，‘衛生間數’
    def parseRoom(info, index):
        res = int(info[index*2])
        return res
    df.insert(3,'室',None)
    df.insert(4, '廳', None)
    df.insert(5, '衛', None)
    df['室'] = df['houseType'].apply(parseRoom, index=0)
    df['廳'] = df['houseType'].apply(parseRoom, index=1)
    df['衛'] = df['houseType'].apply(parseRoom, index=2)
    
    df['交易月份'] = df['tradeTime'].apply(lambda x: int(x.split('/')[1]))
#     df['pv/uv'] = df['pv'] / df['uv']
#     df['房間總數'] = df['室'] + df['廳'] + df['衛']
    
    df.drop('houseType', axis=1, inplace=True)
    df.drop('tradeTime', axis=1, inplace=True)
    
    categorical_feats = ['rentType', 'houseFloor', 'houseToward', 'houseDecoration', 'region', 'plate']
    return df, categorical_feats

def getData(feature):
    """
    獲取數據
    """
    train = pd.read_csv('input/train_data.csv')
    test = pd.read_csv('input/test_a.csv')
    
    train = parseData(train)
    test = parseData(test)
    train, test = washData(train, test)
    
    train, col = feature(train)
    test, col = feature(test)
    
    target = train.pop('tradeMoney')
    features = train.columns
    categorical_feats = col
    
    return train, test, target, features, categorical_feats

lgb模型參數

params = {
    'num_leaves': 31,
    'min_data_in_leaf': 20,
    'min_child_samples':20,
    'objective': 'regression',
    'learning_rate': 0.01,
    "boosting": "gbdt",
    "feature_fraction": 0.8,
    "bagging_freq": 1,
    "bagging_fraction": 0.85,
    "bagging_seed": 23,
    "metric": 'rmse',
    "lambda_l1": 0.2,
    "nthread": 4,
}

folds = KFold(n_splits=5, shuffle=True, random_state=2333)

oof_lgb = np.zeros(len(train))
predictions_lgb = np.zeros(len(test))
feature_importance_df = pd.DataFrame()

for fold_, (trn_idx, val_idx) in enumerate(folds.split(train.values, target.values)):
    print("fold {}".format(fold_))
    trn_data = lgb.Dataset(train.iloc[trn_idx], label=target.iloc[trn_idx], categorical_feature=categorical_feats)
    val_data = lgb.Dataset(train.iloc[val_idx], label=target.iloc[val_idx], categorical_feature=categorical_feats)

    num_round = 10000
    clf = lgb.train(params, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=500, early_stopping_rounds = 200)
    
    oof_lgb[val_idx] = clf.predict(train.iloc[val_idx], num_iteration=clf.best_iteration)
    
    fold_importance_df = pd.DataFrame()
    fold_importance_df["feature"] = features
    fold_importance_df["importance"] = clf.feature_importance()
    fold_importance_df["fold"] = fold_ + 1
    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
    
    predictions_lgb += clf.predict(test, num_iteration=clf.best_iteration) / folds.n_splits
    
print("CV Score: {:<8.5f}".format(r2_score(target, oof_lgb)))