RandomForest.py 文件源码

def data_preprocess(train,test):
    outlier_idx = [4,11,13,20,46,66,70,167,178,185,199, 224,261, 309,313,318, 349,412,423,440,454,477,478, 523,540, 581,588,595,654,688, 691, 774, 798, 875, 898,926,970,987,1027,1109, 1169,1182,1239, 1256,1298,1324,1353,1359,1405,1442,1447]
    train.drop(train.index[outlier_idx],inplace=True)
    all_data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],
                          test.loc[:,'MSSubClass':'SaleCondition']))

    to_delete = ['Alley','FireplaceQu','PoolQC','Fence','MiscFeature']
    all_data = all_data.drop(to_delete,axis=1)

    train["SalePrice"] = np.log1p(train["SalePrice"])
    #log transform skewed numeric features
    numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index
    skewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness
    skewed_feats = skewed_feats[skewed_feats > 0.75]
    skewed_feats = skewed_feats.index
    all_data[skewed_feats] = np.log1p(all_data[skewed_feats])
    all_data = pd.get_dummies(all_data)
    all_data = all_data.fillna(all_data.mean())
    X_train = all_data[:train.shape[0]]
    X_test = all_data[train.shape[0]:]
    y = train.SalePrice

    return X_train,X_test,y