ロジスティック回帰のcode実装をしたくてyoutubeに上がっているものをそのまま写しているのですがYouTubeではできていて自分のPCではエラーが出てしまい意味が分かりません。どなたか分かる方ご教授お願いします。codeはipynbファイルをHTMLに書き直してerror箇所まですべて貼りました。
参考にしたyoutubeはhttps://youtu.be/mMMzDFttZ8Aです。
python
1import numpy as np 2import pandas as pd 3from sklearn.datasets import load_iris 4 iris=load_iris() 5#load_iris関数でロードしている 6iris 7'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 12 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 13 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 14 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 15 'DESCR': '.. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 150 (50 in each of three classes)\n :Number of Attributes: 4 numeric, predictive attributes and the class\n :Attribute Information:\n - sepal length in cm\n - sepal width in cm\n - petal length in cm\n - petal width in cm\n - class:\n - Iris-Setosa\n - Iris-Versicolour\n - Iris-Virginica\n \n :Summary Statistics:\n\n ============== ==== ==== ======= ===== ====================\n Min Max Mean SD Class Correlation\n ============== ==== ==== ======= ===== ====================\n sepal length: 4.3 7.9 5.84 0.83 0.7826\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n ============== ==== ==== ======= ===== ====================\n\n :Missing Attribute Values: None\n :Class Distribution: 33.3% for each of 3 classes.\n :Creator: R.A. Fisher\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n :Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\'s paper. Note that it\'s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature. Fisher\'s paper is a classic in the field and\nis referenced frequently to this day. (See Duda & Hart, for example.) The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant. One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. topic:: References\n\n - Fisher, R.A. "The use of multiple measurements in taxonomic problems"\n Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n Mathematical Statistics" (John Wiley, NY, 1950).\n - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n Structure and Classification Rule for Recognition in Partially Exposed\n Environments". IEEE Transactions on Pattern Analysis and Machine\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\n - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions\n on Information Theory, May 1972, 431-433.\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II\n conceptual clustering system finds 3 classes in the data.\n - Many, many more ...', 16 'feature_names': ['sepal length (cm)', 17 'sepal width (cm)', 18 'petal length (cm)', 19 'petal width (cm)'], 20 'filename': 'C:\Users\mkou0\Anaconda3\lib\site-packages\sklearn\datasets\data\iris.csv'} 21print(iris.target_names) 22#花の種類が格納されている 23['setosa' 'versicolor' 'virginica'] 24for data,target in zip(iris.data[:5],iris.target[:5]): 25 print(data,target) 26#がくの長さ、幅、花弁の長さ、幅の特徴量 27[5.1 3.5 1.4 0.2] 0 28[4.9 3. 1.4 0.2] 0 29[4.7 3.2 1.3 0.2] 0 30[4.6 3.1 1.5 0.2] 0 31[5. 3.6 1.4 0.2] 0 32df =pd.DataFrame(iris.data,columns=iris.feature_names) 33df['target']=iris.target 34df 35sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target 360 5.1 3.5 1.4 0.2 0 371 4.9 3.0 1.4 0.2 0 382 4.7 3.2 1.3 0.2 0 393 4.6 3.1 1.5 0.2 0 404 5.0 3.6 1.4 0.2 0 415 5.4 3.9 1.7 0.4 0 426 4.6 3.4 1.4 0.3 0 437 5.0 3.4 1.5 0.2 0 448 4.4 2.9 1.4 0.2 0 459 4.9 3.1 1.5 0.1 0 4610 5.4 3.7 1.5 0.2 0 4711 4.8 3.4 1.6 0.2 0 4812 4.8 3.0 1.4 0.1 0 4913 4.3 3.0 1.1 0.1 0 5014 5.8 4.0 1.2 0.2 0 5115 5.7 4.4 1.5 0.4 0 5216 5.4 3.9 1.3 0.4 0 5317 5.1 3.5 1.4 0.3 0 5418 5.7 3.8 1.7 0.3 0 5519 5.1 3.8 1.5 0.3 0 5620 5.4 3.4 1.7 0.2 0 5721 5.1 3.7 1.5 0.4 0 5822 4.6 3.6 1.0 0.2 0 5923 5.1 3.3 1.7 0.5 0 6024 4.8 3.4 1.9 0.2 0 6125 5.0 3.0 1.6 0.2 0 6226 5.0 3.4 1.6 0.4 0 6327 5.2 3.5 1.5 0.2 0 6428 5.2 3.4 1.4 0.2 0 6529 4.7 3.2 1.6 0.2 0 66... ... ... ... ... ... 67120 6.9 3.2 5.7 2.3 2 68121 5.6 2.8 4.9 2.0 2 69122 7.7 2.8 6.7 2.0 2 70123 6.3 2.7 4.9 1.8 2 71124 6.7 3.3 5.7 2.1 2 72125 7.2 3.2 6.0 1.8 2 73126 6.2 2.8 4.8 1.8 2 74127 6.1 3.0 4.9 1.8 2 75128 6.4 2.8 5.6 2.1 2 76129 7.2 3.0 5.8 1.6 2 77130 7.4 2.8 6.1 1.9 2 78131 7.9 3.8 6.4 2.0 2 79132 6.4 2.8 5.6 2.2 2 80133 6.3 2.8 5.1 1.5 2 81134 6.1 2.6 5.6 1.4 2 82135 7.7 3.0 6.1 2.3 2 83136 6.3 3.4 5.6 2.4 2 84137 6.4 3.1 5.5 1.8 2 85138 6.0 3.0 4.8 1.8 2 86139 6.9 3.1 5.4 2.1 2 87140 6.7 3.1 5.6 2.4 2 88141 6.9 3.1 5.1 2.3 2 89142 5.8 2.7 5.1 1.9 2 90143 6.8 3.2 5.9 2.3 2 91144 6.7 3.3 5.7 2.5 2 92145 6.7 3.0 5.2 2.3 2 93146 6.3 2.5 5.0 1.9 2 94147 6.5 3.0 5.2 2.0 2 95148 6.2 3.4 5.4 2.3 2 96149 5.9 3.0 5.1 1.8 2 97150 rows × 5 columns 98 99x=iris.data[50:,2].reshape(-1,1) 100y=iris.data[50:]-1 101#x,yの50行から二列取り出す 102 103from sklearn.linear_model import LogisticRegression 104from sklearn.preprocessing import StandardScaler 105from sklearn.model_selection import train_test_split 106#標準化を行う関数StandardScaler 107scaler=StandardScaler() 108x_scaled=scaler.fit_transform(x) 109 110X_train,x_test,Y_train,y_test=train_test_split(x_scaled,y,random_state=0) 111log_reg=LogisticRegression().fit(X_train,Y_train) 112C:\Users\mkou0\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. 113 FutureWarning) 114--------------------------------------------------------------------------- 115ValueError Traceback (most recent call last) 116<ipython-input-28-106621ea5937> in <module> 117----> 1 log_reg=LogisticRegression().fit(X_train,Y_train) 118 119~\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight) 120 1283 121 1284 X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order="C", 122-> 1285 accept_large_sparse=solver != 'liblinear') 123 1286 check_classification_targets(y) 124 1287 self.classes_ = np.unique(y) 125 126~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator) 127 759 dtype=None) 128 760 else: 129--> 761 y = column_or_1d(y, warn=True) 130 762 _assert_all_finite(y) 131 763 if y_numeric and y.dtype.kind == 'O': 132 133~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in column_or_1d(y, warn) 134 795 return np.ravel(y) 135 796 136--> 797 raise ValueError("bad input shape {0}".format(shape)) 137 798 138 799 139 140ValueError: bad input shape (75, 4)
コード全体を貼っていただけますか?
あとコードの記載は markdown 記法をお使いください。
https://qiita.com/Qiita/items/c686397e4a0f4f11683d#code---%E3%82%B3%E3%83%BC%E3%83%89%E3%81%AE%E6%8C%BF%E5%85%A5
回答2件
あなたの回答
tips
プレビュー