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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-gyomuimport 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
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