Scikit-Learn

要运行 scikit-learn 示例，请确保您已安装以下库：

pip install -U scikit-learn

evaluate 中的指标可以轻松地与 Scikit-Learn 的估计器（estimator）或管道（pipeline）集成。

然而，这些指标需要我们从模型生成预测。估计器的预测和标签可以传递给 evaluate 指标以计算所需的值。

import numpy as np
np.random.seed(0)
import evaluate
from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

从 https://www.openml.org/d/40945 加载数据

X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)

或者，也可以直接从 frame 属性获取 X 和 y

X = titanic.frame.drop('survived', axis=1)
y = titanic.frame['survived']

我们为数值和分类数据创建预处理管道。请注意，pclass 既可以作为分类特征，也可以作为数值特征处理。

numeric_features = ["age", "fare"]
numeric_transformer = Pipeline(
    steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]
)

categorical_features = ["embarked", "sex", "pclass"]
categorical_transformer = OneHotEncoder(handle_unknown="ignore")

preprocessor = ColumnTransformer(
    transformers=[
        ("num", numeric_transformer, numeric_features),
        ("cat", categorical_transformer, categorical_features),
    ]
)

将分类器附加到预处理管道。现在我们有了一个完整的预测管道。

clf = Pipeline(
    steps=[("preprocessor", preprocessor), ("classifier", LogisticRegression())]
)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

由于 Evaluate 指标使用列表作为参考和预测的输入，我们需要将它们转换为 Python 列表。

# Evaluate metrics accept lists as inputs for values of references and predictions

y_test = y_test.tolist()
y_pred = y_pred.tolist()

# Accuracy

accuracy_metric = evaluate.load("accuracy")
accuracy = accuracy_metric.compute(references=y_test, predictions=y_pred)
print("Accuracy:", accuracy)
# Accuracy: 0.79

只要它们与任务和预测兼容，您就可以将任何合适的 evaluate 指标与估计器一起使用。

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