python - sci-kit学习:随机森林分类器给出ValueError

因此，在添加新功能之前，我的随机森林分类器运行良好。当我尝试运行它时，我不断收到以下错误代码：

   \Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in _assert_all_finite(X)
         56             and not np.isfinite(X).all()):
         57         raise ValueError("Input contains NaN, infinity"
    ---> 58                          " or a value too large for %r." % X.dtype)
         59
         60

    ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

训练和测试都是从csv文件读取的np.DataFrame对象。我试图添加更多功能以更好地预测变量，但是每当我尝试拟合时最终都会遇到上述错误。我确实尝试删除了NaN和无限值，但仍然遇到相同的错误。

下面是我的代码：

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
def features(df):
    df["num_photos"] = df["photos"].apply(len)
    df["num_features"] = df["features"].apply(len)
    df["year_created"] = df["created"].dt.year
    df["month_created"] = df["created"].dt.month
    df["day_created"] = df["created"].dt.day
    df["desc_len"] = df["description"].apply(lambda x: len(x.split(" ")))
    #New features begin here
    df["pricePerBed"] = df['price'] / df['bedrooms']
    df["pricePerBath"] = df['price'] / df['bathrooms']
    df["pricePerRoom"] = df['price'] / (df['bedrooms'] + df['bathrooms'])
    df["bedPerBath"] = df['bedrooms'] / df['bathrooms']
    df["bedBathDiff"] = df['bedrooms'] - df['bathrooms']
    df["bedBathSum"] = df["bedrooms"] + df['bathrooms']
    df["bedsPerc"] = df["bedrooms"] / (df['bedrooms'] + df['bathrooms'])

    df = df.replace([np.inf, -np.inf], np.nan)
    df = df.fillna(1)

    return df

features(train)
features(test)

key_features = ["bathrooms", "bedrooms", "latitude", "longitude", "year_created",
                "month_created", "day_created", "price", "num_photos", "num_features", "desc_len",
                "pricePerBed",
                "pricePerBath",
                "pricePerRoom",
                #"bedPerBath",
                "bedBathDiff",
                "bedBathSum"]

X = train[key_features]
y = train["interest_level"]

X.fillna(1) #I tried getting rid of NaN

X.isnull().any()

bedPerBath变量为isull（）。any（）提供了True，因此我将其遗漏了，其余的都给了我False。但是，当我尝试拟合估计量时，我仍然会收到“ ValueError”。

X_train, X_cv, y_train, y_cv = train_test_split(X, y, test_size = 0.3)

X_train.isnull().any()

clfRF = RandomForestClassifier(n_estimators = 1000)
clfRF.fit(X_train, y_train)

#CV
y_cv_pred = clfRF.predict_proba(X_cv)
log_loss(y_cv, y_cv_pred)

我注意到错误消息说对于dtype（'float32'）太大，而我的值主要是float64，这是否可能导致错误？如果可以，为什么？

谢谢。

最佳答案

尝试：

import numpy as np
X_train, X_cv, y_train, y_cv = train_test_split(np.nan_to_num(X), y, test_size = 0.3)

clfRF = RandomForestClassifier(n_estimators = 1000)
clfRF.fit(X_train, y_train)

#CV
y_cv_pred = clfRF.predict_proba(X_cv)
log_loss(y_cv, y_cv_pred)