Scikit-Learn

要執行 scikit-learn 示例，請確保您已安裝以下庫：

pip install -U scikit-learn

evaluate 中的指標可以輕鬆地與 Scikit-Learn 的估計器（estimator）或管道（pipeline）整合。

然而，這些指標需要我們從模型生成預測。估計器的預測和標籤可以傳遞給 evaluate 指標以計算所需的值。

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

從 https://www.openml.org/d/40945 載入資料

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

或者，也可以直接從 frame 屬性獲取 X 和 y

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

我們為數值和分類資料建立預處理管道。請注意，pclass 既可以作為分類特徵，也可以作為數值特徵處理。

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),
    ]
)

將分類器附加到預處理管道。現在我們有了一個完整的預測管道。

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)

由於 Evaluate 指標使用列表作為參考和預測的輸入，我們需要將它們轉換為 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

只要它們與任務和預測相容，您就可以將任何合適的 evaluate 指標與估計器一起使用。

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