字典散列内存错误和功能散列浮动错误

这是我的数据[作为熊猫df]：

print（X_train [numeric_predictors categorical_predictors] .head（））：

bathrooms  bedrooms   price                       building_id  \
10            1.5       3.0  3000.0  53a5b119ba8f7b61d4e010512e0dfc85   
10000         1.0       2.0  5465.0  c5c8a357cba207596b04d1afd1e4f130   
100004        1.0       1.0  2850.0  c3ba40552e2120b0acfc3cb5730bb2aa   
100007        1.0       1.0  3275.0  28d9ad350afeaab8027513a3e52ac8d5   
100013        1.0       4.0  3350.0                                 0  

99993         1.0       0.0   3350.0  ad67f6181a49bde19218929b401b31b7   
99994         1.0       2.0   2200.0  5173052db6efc0caaa4d817112a70f32   


                              manager_id  
10      5ba989232d0489da1b5f2c45f6688adc  
10000   7533621a882f71e25173b27e3139d83d  
100004  d9039c43983f6e564b1482b273bd7b01  
100007  1067e078446a7897d2da493d2f741316  
100013  98e13ad4b495b9613cef886d79a6291f  
...
99993   9fd3af5b2d23951e028059e8940a55d7  
99994   d7f57128272bfd82e33a61999b5f4c42

最后两列是分类预测变量 .

同样，打印pandas系列X_train [target]：

10        medium
10000        low
100004      high
100007       low
100013       low
...
99993        low
99994        low

我正在尝试使用管道模板并使用散列矢量化器获得错误 .

首先，这是我的字典hasher，它给了我一个MemoryError：

from sklearn.feature_extraction import DictVectorizer

dv = DictVectorizer(sparse=False)
feature_dict = X_train[categorical_predictors].to_dict(orient='records')
dv.fit(feature_dict)
out = pd.DataFrame(
    dv.transform(feature_dict),
    columns = dv.feature_names_
)

所以在下一个单元格中，我使用以下代码作为我的功能哈希编码器：

from sklearn.feature_extraction import FeatureHasher

fh = FeatureHasher(n_features=2)
feature_dict = X_train[categorical_predictors].to_dict(orient='records')
fh.fit(feature_dict)
out = pd.DataFrame(fh.transform(feature_dict).toarray())
#print out.head()

注释掉的打印行为我提供了一个DataFrame，其特征行包含每行2个单元格中的-1.0,0.0或1.0浮点数 .

这是我的vectorizer汇总字典和功能哈希：

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_extraction import FeatureHasher, DictVectorizer

class MyVectorizer(BaseEstimator, TransformerMixin):
    """
    Vectorize a set of categorical variables
    """

    def __init__(self, cols, hashing=None):
        """
        args:
            cols: a list of column names of the categorical variables
            hashing: 
                If None, then vectorization is a simple one-hot-encoding.
                If an integer, then hashing is the number of features in the output.
        """
        self.cols = cols
        self.hashing = hashing

    def fit(self, X, y=None):

        data = X[self.cols]

        # Choose a vectorizer
        if self.hashing is None:
            self.myvec = DictVectorizer(sparse=False)
        else:
            self.myvec = FeatureHasher(n_features = self.hashing)

        self.myvec.fit(X[self.cols].to_dict(orient='records'))
        return self

    def transform(self, X):

        # Vectorize Input
        if self.hashing is None:
            return pd.DataFrame(
                self.myvec.transform(X[self.cols].to_dict(orient='records')),
                columns = self.myvec.feature_names_
            )
        else:
            return pd.DataFrame(
                self.myvec.transform(X[self.cols].to_dict(orient='records')).toarray()
            )

我把它全部放在我的管道中：

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import FeatureUnion

pipeline = Pipeline([
    ('preprocess', FeatureUnion([
        ('numeric', Pipeline([
            ('scale', StandardScaler())
        ])
        ),
        ('categorical', Pipeline([
            ('vectorize', MyVectorizer(cols=['categorical_predictors'], hashing=None))
        ])
        )
    ])),
    ('predict', MultinomialNB(alphas))
])

和alpha参数：

alphas = {
    'predict__alpha': [.01, .1, 1, 2, 10]
}

并使用gridsearchCV，当我在第三行得到一个错误拟合它：

print X_train.head(), train_data[target]
grid_search = GridSearchCV(pipeline, param_grid=alphas,scoring='accuracy')
grid_search.fit(X_train[numeric_predictors + categorical_predictors], X_train[target])
grid_search.best_params_

ValueError：无法将字符串转换为float：d7f57128272bfd82e33a61999b5f4c42