reshapeをする箇所が分からない

Question

前提・実現したいこと

wineデータをL1正規化付きロジスティック回帰を適用したい。

発生している問題・エラーメッセージ

C:\Users\anaconda3\lib\site-packages\sklearn\utils\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-144-5bb30b05e054> in <module>
      1 lr = LogisticRegression(penalty = 'l1', C = 1.0, solver = 'liblinear')
      2 lr.fit(X_train_std, y_train)
----> 3 print('Training accuracy:', lr.score(X_test_std, y_train))
      4 print('Test accuracy:', lr.score(X_test_std, y_test))

~\anaconda3\lib\site-packages\sklearn\base.py in score(self, X, y, sample_weight)
    367         """
    368         from .metrics import accuracy_score
--> 369         return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
    370 
    371 

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in accuracy_score(y_true, y_pred, normalize, sample_weight)
    183 
    184     # Compute accuracy for each possible representation
--> 185     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
    186     check_consistent_length(y_true, y_pred, sample_weight)
    187     if y_type.startswith('multilabel'):

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in _check_targets(y_true, y_pred)
     78     y_pred : array or indicator matrix
     79     """
---> 80     check_consistent_length(y_true, y_pred)
     81     type_true = type_of_target(y_true)
     82     type_pred = type_of_target(y_pred)

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
    210     if len(uniques) > 1:
    211         raise ValueError("Found input variables with inconsistent numbers of"
--> 212                          " samples: %r" % [int(l) for l in lengths])
    213 
    214 

ValueError: Found input variables with inconsistent numbers of samples: [124, 54]



[ ]:


​

該当のソースコード

import pandas as pd
import numpy as np
df_wine = pd.read_csv(
    'https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Datasets/wine.data.csv', header = 0)

df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash', 
                   'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 
                   'Nonflavanoid phenols', 'Proanthocyanis', 'Color intenesity', 'Hue', 
                   'OD280/OD315 of diluted wines', 'Proline']


print('Class labels', np.unique(df_wine['Class label']))
df_wine.head()

from sklearn.model_selection import train_test_split
X, y = df_wine.iloc[:, 1].values, df_wine.iloc[:, 0].values

X_train, X_test, y_train, y_test = \
    train_test_split(X.reshape(-1, 1), y.reshape(-1, 1), test_size = 0.3, random_state = 0, stratify = y)
    
from sklearn.preprocessing import MinMaxScaler
mms = MinMaxScaler()
X_train_norm = mms.fit_transform(X_train)
X_test_norm = mms.transform(X_test)

ex = np.array([0, 1, 2, 3, 4, 5])
print('standardized:', (ex - ex.mean()) / ex.std())
print('normalized:', (ex - ex.min()) / (ex.max() - ex.min()))

from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)

from sklearn.linear_model import LogisticRegression
LogisticRegression(penalty = 'l1')

lr = LogisticRegression(penalty = 'l1', C = 1.0, solver = 'liblinear')
lr.fit(X_train_std, y_train)
print('Training accuracy:', lr.score(X_test_std, y_train))
print('Test accuracy:', lr.score(X_test_std, y_test))

試したこと

教科書が発売された時から、若干の仕様変更が行われているみたいなので、なかなかうまくいきません。わかる方お願いします。

補足情報（ツールのバージョンなど）

anaconda JupiterLab 1.2.6

Comments

[hoshi-takanori]

教科書の名前を教えてください。

[os-t]

[第2版]Python 機械学習プログラミング 達人データサイエンティストによる理論と実践 (impress top gear)
です。

Answer 1

以下の修正で動きましたけど、結果が正しいかどうかは分かりません。

 lr = LogisticRegression(penalty = 'l1', C = 1.0, solver = 'liblinear')
-lr.fit(X_train_std, y_train)
-print('Training accuracy:', lr.score(X_test_std, y_train))
+lr.fit(X_train_std, y_train.ravel())
+print('Training accuracy:', lr.score(X_train_std, y_train))
 print('Test accuracy:', lr.score(X_test_std, y_test))

Comments

[os-t]

値は多少違いましたが、動いたので先に進みます。ありがとうございます。