次の記事と同じことを scikit-learn 0.23.1 で行いました。
機械学習のライブラリ！scikit-learnとは【初心者向け】
Arch Linux で必要なライブラリーのインストール
sudo pacman -S python-scikit-learn
sudo pacman -S python-matplotlib
確認したバージョン
$ python
Python 3.11.3 (main, Jun 5 2023, 09:32:32) [GCC 13.1.1 20230429] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sklearn
>>> print(sklearn.__version__)
1.2.2
データの確認
show_data.py
#! /usr/bin/python
#
# show_data.py
#
# Sep/03/2020
#
from sklearn import datasets
import matplotlib.pyplot as plt
digits = datasets.load_digits()
plt.matshow(digits.images[0], cmap="Greys")
plt.show()
実行結果
SVM
svm01.py
#! /usr/bin/python
#
# svm01.py
#
# Sep/03/2020
#
from sklearn import datasets
from sklearn import svm
import sklearn.metrics as metrics
digits = datasets.load_digits()
X = digits.data
y = digits.target
X_train, y_train = X[0::2], y[0::2]
X_test, y_test = X[1::2], y[1::2]
clf = svm.SVC(gamma=0.001)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
print(f"正解率{accuracy}")
predicted = clf.predict(X_test)
print("classification report")
print(metrics.classification_report(y_test, predicted))
実行結果
$ ./svm01.py
正解率0.9866369710467706
classification report
precision recall f1-score support
0 1.00 0.99 0.99 88
1 0.98 1.00 0.99 89
2 1.00 1.00 1.00 91
3 1.00 0.98 0.99 93
4 0.99 1.00 0.99 88
5 0.98 0.97 0.97 91
6 0.99 1.00 0.99 90
7 0.99 1.00 0.99 91
8 0.97 0.97 0.97 86
9 0.98 0.97 0.97 91
accuracy 0.99 898
macro avg 0.99 0.99 0.99 898
weighted avg 0.99 0.99 0.99 898
ロジスティック回帰
logistic01.py
#! /usr/bin/python
#
# logistic.py
#
# Sep/03/2020
#
from sklearn import datasets
import sklearn.metrics as metrics
from sklearn.linear_model import LogisticRegression
digits = datasets.load_digits()
X = digits.data
y = digits.target
X_train, y_train = X[0::2], y[0::2]
X_test, y_test = X[1::2], y[1::2]
clf = LogisticRegression(max_iter=2000)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
print(f"正解率{accuracy}")
predicted = clf.predict(X_test)
print("classification report")
print(metrics.classification_report(y_test, predicted))
実行結果
$ ./logistic01.py
正解率0.9532293986636972
classification report
precision recall f1-score support
0 1.00 0.98 0.99 88
1 0.87 0.98 0.92 89
2 0.97 1.00 0.98 91
3 0.98 0.92 0.95 93
4 0.93 0.98 0.96 88
5 0.96 0.95 0.95 91
6 0.97 0.99 0.98 90
7 0.99 0.97 0.98 91
8 0.95 0.88 0.92 86
9 0.93 0.89 0.91 91
accuracy 0.95 898
macro avg 0.95 0.95 0.95 898
weighted avg 0.95 0.95 0.95 898
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