メモ > 技術 > プログラミング言語: Python > scikit-learn（機械学習）でアヤメの分類
■scikit-learn（機械学習）でアヤメの分類
■アヤメの分類
pandas/iris.csv at master - pandas-dev/pandas - GitHub
https://github.com/pandas-dev/pandas/blob/master/pandas/tests/data/iris.csv
GitHub - kujirahand/book-mlearn-gyomu: Book sample (AI Machine-learning Deep-learning)
https://github.com/kujirahand/book-mlearn-gyomu
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# アヤメデータの読み込み
iris_data = pd.read_csv("iris.csv", encoding="utf-8")
# アヤメデータをラベルと入力データに分離する
y = iris_data.loc[:,"Name"]
x = iris_data.loc[:,["SepalLength","SepalWidth","PetalLength","PetalWidth"]]
# 学習用とテスト用に分離する
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, train_size = 0.8, shuffle = True)
# 学習する
clf = SVC()
clf.fit(x_train, y_train)
# 評価する
y_pred = clf.predict(x_test)
print("正解率 = " , accuracy_score(y_test, y_pred))
以下、勉強メモ
train_test_split関数でデータ分割 - PyQ 1.0 ドキュメント
https://docs.pyq.jp/python/machine_learning/tips/train_test_split.html
iris.csv
SepalLength,SepalWidth,PetalLength,PetalWidth,Name
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
iris.py
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# アヤメデータの読み込み
iris_data = pd.read_csv("iris.csv", encoding="utf-8")
print("iris_test.csv")
print(iris_data)
# アヤメデータをラベルと入力データに分離する
y = iris_data.loc[:,"Name"]
x = iris_data.loc[:,["SepalLength","SepalWidth","PetalLength","PetalWidth"]]
print("y")
print(y)
print("x")
print(x)
# 学習用とテスト用に分離する
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, train_size = 0.8, shuffle = True)
print("x_train")
print(x_train)
print("x_test")
print(x_test)
print("y_train")
print(y_train)
print("y_test")
print(y_test)
以下のとおり実行
$ python3 iris_test.py
iris_test.csv
SepalLength SepalWidth PetalLength PetalWidth Name
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 7.0 3.2 4.7 1.4 Iris-versicolor
3 6.4 3.2 4.5 1.5 Iris-versicolor
4 6.3 3.3 6.0 2.5 Iris-virginica
5 5.8 2.7 5.1 1.9 Iris-virginica
y
0 Iris-setosa
1 Iris-setosa
2 Iris-versicolor
3 Iris-versicolor
4 Iris-virginica
5 Iris-virginica
Name: Name, dtype: object
x
SepalLength SepalWidth PetalLength PetalWidth
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 7.0 3.2 4.7 1.4
3 6.4 3.2 4.5 1.5
4 6.3 3.3 6.0 2.5
5 5.8 2.7 5.1 1.9
x_train
SepalLength SepalWidth PetalLength PetalWidth
5 5.8 2.7 5.1 1.9
2 7.0 3.2 4.7 1.4
0 5.1 3.5 1.4 0.2
3 6.4 3.2 4.5 1.5
x_test
SepalLength SepalWidth PetalLength PetalWidth
1 4.9 3.0 1.4 0.2
4 6.3 3.3 6.0 2.5
y_train
5 Iris-virginica
2 Iris-versicolor
0 Iris-setosa
3 Iris-versicolor
Name: Name, dtype: object
y_test
1 Iris-setosa
4 Iris-virginica
Name: Name, dtype: object