【簡単】pythonとScikit-learnを使用して、ロジスティック回帰を通じたアヤメの分類をしてみる

from sklearn.datasets import load_iris
import pandas as pd
import numpy as np

# Irisデータセットのロード
iris = load_iris()
iris_df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                       columns= iris['feature_names'] + ['target'])

import seaborn as sns
import matplotlib.pyplot as plt

# ターゲット列が整数型であることを確認し、整数型に変換
iris_df['target'] = iris_df['target'].astype(int)

# ターゲットの数値を種類名にマッピング
iris_df['species'] = iris_df['target'].map({0: 'setosa', 1: 'versicolor', 2: 'virginica'})

# データの視覚化
sns.pairplot(iris_df.drop('target', axis=1), hue='species')
plt.show()

# 相関行列を計算
corr = iris_df.drop('target', axis=1).corr()

# ヒートマップを作成
sns.heatmap(corr, annot=True)
plt.show()

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# データとラベルを準備
X = iris.data
y = iris.target

# 訓練セットとテストセットに分割
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# モデルの訓練
model_lr = LogisticRegression(max_iter=200)
model_lr.fit(X_train, y_train)

# テストデータの予測
predictions_lr = model_lr.predict(X_test)

# 正確度の計算
accuracy_lr = accuracy_score(y_test, predictions_lr)
print(f"Logistic Regression Accuracy: {accuracy_lr}")

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt

# Load the iris dataset
iris = load_iris()

# Prepare subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))

# First subplot for sepal features (sepal length and sepal width)
X_sepal = iris.data[:, :2]  # Selecting sepal length and sepal width
logreg_sepal = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')
logreg_sepal.fit(X_sepal, iris.target)
x_min, x_max = X_sepal[:, 0].min() - .5, X_sepal[:, 0].max() + .5
y_min, y_max = X_sepal[:, 1].min() - .5, X_sepal[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange(y_min, y_max, .02))
Z_sepal = logreg_sepal.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
axes[0].contourf(xx, yy, Z_sepal, cmap=plt.cm.Paired, alpha=0.8)
axes[0].scatter(X_sepal[:, 0], X_sepal[:, 1], c=iris.target, edgecolors='k', cmap=plt.cm.Paired)
axes[0].set_xlabel('Sepal length')
axes[0].set_ylabel('Sepal width')
axes[0].set_title('Logistic Regression on Sepal Features')

# Second subplot for petal features (petal length and petal width)
X_petal = iris.data[:, 2:4]  # Selecting petal length and petal width
logreg_petal = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')
logreg_petal.fit(X_petal, iris.target)
x_min, x_max = X_petal[:, 0].min() - .5, X_petal[:, 0].max() + .5
y_min, y_max = X_petal[:, 1].min() - .5, X_petal[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange(y_min, y_max, .02))
Z_petal = logreg_petal.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
axes.contourf(xx, yy, Z_petal, cmap=plt.cm.Paired, alpha=0.8)
axes.scatter(X_petal[:, 0], X_petal[:, 1], c=iris.target, edgecolors='k', cmap=plt.cm.Paired)
axes.set_xlabel('Petal length')
axes.set_ylabel('Petal width')
axes.set_title('Logistic Regression on Petal Features')

# Adjust the layout
plt.tight_layout()
plt.show()

from sklearn import datasets
from sklearn.linear_model import LogisticRegression

# Load the iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Train the LogisticRegression model
model = LogisticRegression(max_iter=200)
model.fit(X, y)

# Define a function to predict the class and probability of iris species
def predict_iris_species(features):
    species = ['setosa', 'versicolor', 'virginica']
    prediction = model.predict([features])
    probabilities = model.predict_proba([features])
    
    print(f"Predicted species: {species[prediction[0]]}")
    print("Probabilities for each class:")
    for specie, probability in zip(species, probabilities[0]):
        print(f"  {specie}: {probability:.4f}")

# Example: Predict the species and probabilities for a new iris with features
new_iris_features = [6.3, 2.5, 5, 1.5]
predict_iris_species(new_iris_features)