在 cross_val_score 和 GridSearchCV 上演示多指标评估

可以通过将 scoring 参数设置为指标评分器名称列表或将评分器名称映射到评分器可调用的字典来完成多指标参数搜索。

所有评分器的得分在 cv_results_ 字典中以 '_<scorer_name>' 结尾的键可用 ('mean_test_precision', 'rank_test_precision' 等…)

best_estimator_, best_index_, best_score_ 和 best_params_ 对应于设置为 refit 属性的评分器（键）。

# Author: Raghav RV <[email protected]>
# License: BSD

import numpy as np
from matplotlib import pyplot as plt

from sklearn.datasets import make_hastie_10_2
from sklearn.metrics import accuracy_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier

使用多个评估指标运行 GridSearchCV

X, y = make_hastie_10_2(n_samples=8000, random_state=42)

# The scorers can be either one of the predefined metric strings or a scorer
# callable, like the one returned by make_scorer
scoring = {"AUC": "roc_auc", "Accuracy": make_scorer(accuracy_score)}

# Setting refit='AUC', refits an estimator on the whole dataset with the
# parameter setting that has the best cross-validated AUC score.
# That estimator is made available at ``gs.best_estimator_`` along with
# parameters like ``gs.best_score_``, ``gs.best_params_`` and
# ``gs.best_index_``
gs = GridSearchCV(
    DecisionTreeClassifier(random_state=42),
    param_grid={"min_samples_split": range(2, 403, 20)},
    scoring=scoring,
    refit="AUC",
    n_jobs=2,
    return_train_score=True,
)
gs.fit(X, y)
results = gs.cv_results_

绘制结果

plt.figure(figsize=(13, 13))
plt.title("GridSearchCV evaluating using multiple scorers simultaneously", fontsize=16)

plt.xlabel("min_samples_split")
plt.ylabel("Score")

ax = plt.gca()
ax.set_xlim(0, 402)
ax.set_ylim(0.73, 1)

# Get the regular numpy array from the MaskedArray
X_axis = np.array(results["param_min_samples_split"].data, dtype=float)

for scorer, color in zip(sorted(scoring), ["g", "k"]):
    for sample, style in (("train", "--"), ("test", "-")):
        sample_score_mean = results["mean_%s_%s" % (sample, scorer)]
        sample_score_std = results["std_%s_%s" % (sample, scorer)]
        ax.fill_between(
            X_axis,
            sample_score_mean - sample_score_std,
            sample_score_mean + sample_score_std,
            alpha=0.1 if sample == "test" else 0,
            color=color,
        )
        ax.plot(
            X_axis,
            sample_score_mean,
            style,
            color=color,
            alpha=1 if sample == "test" else 0.7,
            label="%s (%s)" % (scorer, sample),
        )

    best_index = np.nonzero(results["rank_test_%s" % scorer] == 1)[0][0]
    best_score = results["mean_test_%s" % scorer][best_index]

    # Plot a dotted vertical line at the best score for that scorer marked by x
    ax.plot(
        [
            X_axis[best_index],
        ]
        * 2,
        [0, best_score],
        linestyle="-.",
        color=color,
        marker="x",
        markeredgewidth=3,
        ms=8,
    )

    # Annotate the best score for that scorer
    ax.annotate("%0.2f" % best_score, (X_axis[best_index], best_score + 0.005))

plt.legend(loc="best")
plt.grid(False)
plt.show()

相关示例

网格搜索和连续减半的比较

网格搜索和连续减半的比较

对成本敏感学习进行决策阈值后调整

对成本敏感学习进行决策阈值后调整

使用交叉验证的网格搜索的自定义重拟合策略

使用交叉验证的网格搜索的自定义重拟合策略

平衡模型复杂度和交叉验证得分

平衡模型复杂度和交叉验证得分