scientific-ml-classification

name	scientific-ml-classification
description	機械学習分類パイプラインのスキル。複数の分類モデル（Logistic Regression, Random Forest, SVM, XGBoost）を StratifiedKFold 交差検証で比較し、ROC 曲線・混同行列で評価する際に使用。 Scientific Skills Exp-03, 05 で確立したパターン。
tu_tools	[{"key":"openml","name":"OpenML","description":"分類ベンチマーク・データセット取得"}]

Scientific ML Classification Pipeline

バイナリ/マルチクラス分類タスクのための統一 ML パイプライン。 StratifiedKFold 交差検証で複数モデルを公平に比較し、ROC-AUC と混同行列で評価する。

When to Use

• カテゴリ予測タスク（二値分類・多クラス分類）
• がん分類、毒性予測、材料分類などの判別問題
• 複数分類モデルの性能比較が必要なとき

Quick Start

標準パイプライン

1. モデル定義

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import (accuracy_score, precision_score, recall_score,
                             f1_score, roc_auc_score, roc_curve,
                             confusion_matrix, classification_report)

MODEL_DEFS = {
    "Logistic Regression": LogisticRegression(max_iter=1000, random_state=42),
    "Random Forest": RandomForestClassifier(n_estimators=200, max_depth=15,
                                            random_state=42),
    "Gradient Boosting": GradientBoostingClassifier(n_estimators=200,
                                                     random_state=42),
    "SVM": SVC(kernel="rbf", probability=True, random_state=42),
}

2. StratifiedKFold 学習 & 評価

import numpy as np
import pandas as pd

def train_evaluate_classifiers(X_train, X_test, y_train, y_test,
                                model_defs=None, n_splits=5):
    """全分類モデルを StratifiedKFold で評価する。"""
    if model_defs is None:
        model_defs = MODEL_DEFS

    scaler = StandardScaler()
    X_train_sc = scaler.fit_transform(X_train)
    X_test_sc = scaler.transform(X_test)

    skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)
    results = []
    trained_models = {}

    for name, model in model_defs.items():
        import copy
        m = copy.deepcopy(model)
        m.fit(X_train_sc, y_train)
        y_pred = m.predict(X_test_sc)
        y_proba = m.predict_proba(X_test_sc)[:, 1] if hasattr(m, "predict_proba") else None

        cv_scores = cross_val_score(copy.deepcopy(model), X_train_sc, y_train,
                                    cv=skf, scoring="accuracy")

        results.append({
            "Model": name,
            "Accuracy": accuracy_score(y_test, y_pred),
            "Precision": precision_score(y_test, y_pred, average="weighted"),
            "Recall": recall_score(y_test, y_pred, average="weighted"),
            "F1": f1_score(y_test, y_pred, average="weighted"),
            "ROC_AUC": roc_auc_score(y_test, y_proba) if y_proba is not None else np.nan,
            : cv_scores.mean(),
            : cv_scores.std(),
        })
        trained_models[name] = {: m, : y_pred, : y_proba}

    results_df = pd.DataFrame(results)
    results_df.to_csv(, index=)
     results_df, trained_models

3. ROC 曲線

import matplotlib.pyplot as plt

def plot_roc_curves(y_test, trained_models, figsize=(8, 8)):
    """全モデルの ROC 曲線を重ねて描画する。"""
    fig, ax = plt.subplots(figsize=figsize)

    for name, info in trained_models.items():
        if info["y_proba"] is not None:
            fpr, tpr, _ = roc_curve(y_test, info["y_proba"])
            auc = roc_auc_score(y_test, info["y_proba"])
            ax.plot(fpr, tpr, linewidth=2, label=f"{name} (AUC={auc:.3f})")

    ax.plot([0, 1], [0, 1], "k--", linewidth=1)
    ax.set_xlabel("False Positive Rate")
    ax.set_ylabel("True Positive Rate")
    ax.set_title("ROC Curves", fontweight="bold")
    ax.legend()
    plt.tight_layout()
    plt.savefig("figures/roc_curves.png", dpi=300, bbox_inches="tight")
    plt.close()

4. 混同行列

import seaborn as sns

def plot_confusion_matrices(y_test, trained_models, class_names=None,
                            ncols=2, figsize=(14, 12)):
    """全モデルの混同行列をグリッド表示する。"""
    n_models = len(trained_models)
    nrows = (n_models + ncols - 1) // ncols
    fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
    axes = axes.flatten()

    for i, (name, info) in enumerate(trained_models.items()):
        cm = confusion_matrix(y_test, info["y_pred"])
        sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", ax=axes[i],
                    xticklabels=class_names, yticklabels=class_names)
        axes[i].set_title(name, fontweight="bold")
        axes[i].set_xlabel("Predicted")
        axes[i].set_ylabel("Actual")

    for j in range(i + 1, len(axes)):
        axes[j].set_visible(False)

    plt.tight_layout()
    plt.savefig("figures/confusion_matrices.png", dpi=300, bbox_inches="tight")
    plt.close()

5. Precision-Recall 曲線

from sklearn.metrics import precision_recall_curve, average_precision_score

def plot_precision_recall_curves(y_test, trained_models, figsize=(8, 8)):
    """全モデルの Precision-Recall 曲線を重ねて描画する。
    クラス不均衡データでは ROC よりも PR 曲線が適切な評価指標となる。"""
    fig, ax = plt.subplots(figsize=figsize)

    for name, info in trained_models.items():
        if info["y_proba"] is not None:
            precision, recall, _ = precision_recall_curve(y_test, info["y_proba"])
            ap = average_precision_score(y_test, info["y_proba"])
            ax.plot(recall, precision, linewidth=2,
                    label=f"{name} (AP={ap:.3f})")

    # ベースライン（陽性率）
    baseline = y_test.mean()
    ax.axhline(baseline, color="gray", linestyle="--", linewidth=1,
               label=f"Baseline ({baseline:.3f})")

    ax.set_xlabel("Recall")
    ax.set_ylabel("Precision")
    ax.set_title("Precision-Recall Curves", fontweight="bold")
    ax.legend()
    ax.set_xlim([0, 1])
    ax.set_ylim([0, 1.05])
    plt.tight_layout()
    plt.savefig("figures/precision_recall_curves.png", dpi=300,
                bbox_inches="tight")
    plt.close()

6. Partial Dependence Plot (PDP)

from sklearn.inspection import PartialDependenceDisplay

def plot_partial_dependence(model, X_train_scaled, feature_names,
                             top_n=6, figsize=(14, 8)):
    """重要な特徴量の部分依存プロットを描画する。
    モデルの予測が各特徴量に対してどのように変化するかを可視化。"""
    fig, ax = plt.subplots(figsize=figsize)
    features_idx = list(range(min(top_n, len(feature_names))))
    PartialDependenceDisplay.from_estimator(
        model, X_train_scaled, features=features_idx,
        feature_names=feature_names,
        ax=ax, grid_resolution=50
    )
    plt.suptitle("Partial Dependence Plots", fontweight="bold", y=1.02)
    plt.tight_layout()
    plt.savefig("figures/partial_dependence.png", dpi=300, bbox_inches="tight")
    plt.close()

7. Volcano Plot（差次発現/差次特徴量）

from scipy import stats

def volcano_plot(df, group_col, value_cols, group1, group2,
                 fc_threshold=1.0, p_threshold=0.05, figsize=(10, 8)):
    """Fold Change と p 値による Volcano Plot を描画する。"""
    results = []
    g1 = df[df[group_col] == group1]
    g2 = df[df[group_col] == group2]

    for col in value_cols:
        stat, pval = stats.mannwhitneyu(g1[col].dropna(), g2[col].dropna(),
                                        alternative="two-sided")
        fc = g2[col].mean() - g1[col].mean()  # or log2FC
        results.append({"Feature": col, "log2FC": fc, "pvalue": pval,
                        "neg_log10p": -np.log10(pval + 1e-300)})

    vdf = pd.DataFrame(results)

    fig, ax = plt.subplots(figsize=figsize)
    # 色分け: significant up / down / ns
    sig_up = (vdf["log2FC"] > fc_threshold) & (vdf["pvalue"] < p_threshold)
    sig_down = (vdf["log2FC"] < -fc_threshold) & (vdf["pvalue"] < p_threshold)
    ns = ~(sig_up | sig_down)

    ax.scatter(vdf.loc[ns, "log2FC"], vdf.loc[ns, "neg_log10p"],
              c="gray", alpha=0.5, s=20, label="NS")
    ax.scatter(vdf.loc[sig_up, "log2FC"], vdf.loc[sig_up, "neg_log10p"],
              c="red", alpha=0.7, s=30, label="Up")
    ax.scatter(vdf.loc[sig_down, "log2FC"], vdf.loc[sig_down, "neg_log10p"],
              c="blue", alpha=, s=, label=)
    ax.axhline(-np.log10(p_threshold), color=, linestyle=)
    ax.axvline(fc_threshold, color=, linestyle=)
    ax.axvline(-fc_threshold, color=, linestyle=)
    ax.set_xlabel()
    ax.set_ylabel()
    ax.set_title(, fontweight=)
    ax.legend()
    plt.tight_layout()
    plt.savefig(, dpi=, bbox_inches=)
    plt.close()
     vdf

ToolUniverse 連携

TU Key	ツール名	連携内容
openml	OpenML	分類ベンチマーク・データセット取得

References

Output Files

ファイル	形式
results/classification_metrics.csv	CSV
figures/roc_curves.png	PNG
figures/precision_recall_curves.png	PNG
figures/confusion_matrices.png	PNG
figures/partial_dependence.png	PNG
figures/volcano_plot.png	PNG

参照実験

• Exp-03: がん遺伝子発現の 4 モデル分類 + ROC + 混同行列
• Exp-05: 毒性予測の二値分類 + Volcano Plot + PR 曲線 + PDP