SGD分類子異なる次元の入力データによる部分適合学習

Question

1つのホットエンコードされたデータに対してSGD分類を実行しようとしています。私はトレーニングの例に合っており、後で少しのデータでpartial_fitを実行したいと思っています。 fitデータとpartial_fitデータの間のディメンション変更のためにスローされるエラーを理解します。

私はhot_new_dfでデータ変換を実行する必要があることを理解していますが、わかりません。 [29]中

- 私は[32] IN fit()

をしています場所です - 私はpartial_fit()

をやっている場合、私はちょうどここに仮想的な例を提示してきた...私実際のデータは、40K行の図形の上で、〜200列

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Python 3.6.2 |Anaconda custom (64-bit)| (default, Sep 21 2017, 18:29:43) 
 
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IPython 6.1.0 -- An enhanced Interactive Python. Type '?' for help. 
 

 
In [27]: import pandas as pd 
 
    ...: 
 
    ...: input_df = pd.DataFrame(dict(fruit=['Apple', 'Orange', 'Pine'], 
 
    ...:        color=['Red', 'Orange','Green'], 
 
    ...:        is_sweet = [0,0,1], 
 
    ...:        country=['USA','India','Asia'], 
 
    ...:        is_valid = ['Valid', 'Valid', 'Invalid'])) 
 
    ...: input_df 
 
Out[27]: 
 
    color country fruit is_sweet is_valid 
 
0  Red  USA Apple   0 Valid 
 
1 Orange India Orange   0 Valid 
 
2 Green Asia Pine   1 Invalid 
 

 
In [28]: hot_df = pd.get_dummies(input_df, columns=['fruit','color','country']) 
 
    ...: hot_df 
 
Out[28]: 
 
    is_sweet is_valid fruit_Apple fruit_Orange fruit_Pine color_Green \ 
 
0   0 Valid   1    0   0   0 
 
1   0 Valid   0    1   0   0 
 
2   1 Invalid   0    0   1   1 
 

 
    color_Orange color_Red country_Asia country_India country_USA 
 
0    0   1    0    0   1 
 
1    1   0    0    1   0 
 
2    0   0    1    0   0 
 

 
In [29]: from sklearn.linear_model import SGDClassifier 
 
    ...: from sklearn.model_selection import train_test_split 
 
    ...: 
 
    ...: X_train, X_test, y_train, y_test = train_test_split(hot_df.drop(['is_valid'], axis=1), 
 
    ...:              hot_df['is_valid'], 
 
    ...:              test_size=0.1) 
 
    ...: clf = SGDClassifier(loss="log", penalty="l2") 
 
    ...: clf.fit(X_train, y_train) 
 
    ...: clf 
 
/Users/praj3/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:84: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3. 
 
    "and default tol will be 1e-3." % type(self), FutureWarning) 
 
Out[29]: 
 
SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1, 
 
     eta0=0.0, fit_intercept=True, l1_ratio=0.15, 
 
     learning_rate='optimal', loss='log', max_iter=5, n_iter=None, 
 
     n_jobs=1, penalty='l2', power_t=0.5, random_state=None, 
 
     shuffle=True, tol=None, verbose=0, warm_start=False) 
 

 
In [30]: new_df = pd.DataFrame(dict(fruit=['Banana'], 
 
    ...:       color=['Red'], 
 
    ...:       is_sweet=[1], 
 
    ...:       country=['India'], 
 
    ...:       is_valid=['Invalid'])) 
 
    ...: new_df 
 
Out[30]: 
 
    color country fruit is_sweet is_valid 
 
0 Red India Banana   1 Invalid 
 

 
In [31]: hot_new_df = pd.get_dummies(new_df, columns=['fruit','color','country']) 
 
    ...: hot_new_df 
 
Out[31]: 
 
    is_sweet is_valid fruit_Banana color_Red country_India 
 
0   1 Invalid    1   1    1 
 

 
In [32]: clf.partial_fit(hot_new_df.drop(['is_valid'], axis=1), hot_new_df['is_valid']) 
 
--------------------------------------------------------------------------- 
 
ValueError        Traceback (most recent call last) 
 
<ipython-input-32-088a54ade6f8> in <module>() 
 
----> 1 clf.partial_fit(hot_new_df.drop(['is_valid'], axis=1), hot_new_df['is_valid']) 
 

 
~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py in partial_fit(self, X, y, classes, sample_weight) 
 
    543         learning_rate=self.learning_rate, max_iter=1, 
 
    544         classes=classes, sample_weight=sample_weight, 
 
--> 545         coef_init=None, intercept_init=None) 
 
    546 
 
    547  def fit(self, X, y, coef_init=None, intercept_init=None, 
 

 
~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py in _partial_fit(self, X, y, alpha, C, loss, learning_rate, max_iter, classes, sample_weight, coef_init, intercept_init) 
 
    381   elif n_features != self.coef_.shape[-1]: 
 
    382    raise ValueError("Number of features %d does not match previous " 
 
--> 383        "data %d." % (n_features, self.coef_.shape[-1])) 
 
    384 
 
    385   self.loss_function_ = self._get_loss_function(loss) 
 

 
ValueError: Number of features 4 does not match previous data 10. 
 

 
In [33]:

Answer 1

sklearn.preprocessing.OneHotEncoderを使用してください。これに関するドキュメントはhereです。

は、使用をエンコードする前に、あなたのtrain_test_splitを行い、このようなものになります。私はこのことができます願ってい

from sklearn.preprocessing import OneHotEncoder 
encoder = OneHotEncoder() 
encoder.fit(X_train) 

X_train = encoder.transform(X_train) 
X_test = encoder.transform(X_test) 

！