具有显示对象的可视化#

在这个例子中，我们将构建显示对象， ConfusionMatrixDisplay , RocCurveDisplay ，而且 PrecisionRecallDisplay 直接来自他们各自的指标。当模型的预测已经计算或计算成本很高时，这是使用相应图函数的替代方案。请注意，这是高级用途，一般来说，我们建议使用它们各自的绘图功能。

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

负载数据和列车模型#

对于本例，我们加载输血服务中心数据集 OpenML .这是一个二元分类问题，目标是个人是否献血。然后将数据分成训练和测试数据集，并将逻辑回归与训练数据集进行匹配。

from sklearn.datasets import fetch_openml
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

X, y = fetch_openml(data_id=1464, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)

clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0))
clf.fit(X_train, y_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('logisticregression', LogisticRegression(random_state=0))])

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创建 `ConfusionMatrixDisplay`#

使用拟合模型，我们计算模型在测试数据集上的预测。这些预测用于计算混淆矩阵，该混淆矩阵用 ConfusionMatrixDisplay

from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix

y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)

cm_display = ConfusionMatrixDisplay(cm).plot()

创建 `RocCurveDisplay`#

roc曲线需要来自估计器的概率或非阈值决策值。由于逻辑回归提供了决策函数，因此我们将使用它来绘制roc曲线：

from sklearn.metrics import RocCurveDisplay, roc_curve

y_score = clf.decision_function(X_test)

fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=clf.classes_[1])
roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()

/xpy/lib/python3.11/site-packages/sklearn/metrics/_plot/roc_curve.py:189: UserWarning:

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

创建 `PrecisionRecallDisplay`#

类似地，精确召回曲线可以使用 y_score 从预视部分。

from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve

prec, recall, _ = precision_recall_curve(y_test, y_score, pos_label=clf.classes_[1])
pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot()

将显示对象组合到单个地块中#

显示对象存储作为参数传递的计算值。这使得可以使用matplotlib的API轻松组合可视化。在下面的示例中，我们将显示器并排放置在一行中。

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))

roc_display.plot(ax=ax1)
pr_display.plot(ax=ax2)
plt.show()

/xpy/lib/python3.11/site-packages/sklearn/metrics/_plot/roc_curve.py:189: UserWarning:

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

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