scikit-learn 1.4 的发布亮点#
我们很高兴宣布 scikit-learn 1.4 发布！添加了许多错误修复和改进，以及一些新的关键功能。我们将在下面详细介绍此版本的一些主要功能。**有关所有更改的详尽列表**，请参阅发行说明。
要安装最新版本（使用 pip）
pip install --upgrade scikit-learn
或使用 conda
conda install -c conda-forge scikit-learn
HistGradientBoosting 本机支持 DataFrame 中的分类 DType#
ensemble.HistGradientBoostingClassifier 和 ensemble.HistGradientBoostingRegressor 现在直接支持具有分类特征的 DataFrame。这里我们有一个包含分类和数值特征混合的数据集
from sklearn.datasets import fetch_openml
X_adult, y_adult = fetch_openml("adult", version=2, return_X_y=True)
# Remove redundant and non-feature columns
X_adult = X_adult.drop(["education-num", "fnlwgt"], axis="columns")
X_adult.dtypes
age int64
workclass category
education category
marital-status category
occupation category
relationship category
race category
sex category
capital-gain int64
capital-loss int64
hours-per-week int64
native-country category
dtype: object
通过设置 categorical_features="from_dtype"，梯度提升分类器将具有分类 DType 的列视为算法中的分类特征
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
X_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)
hist = HistGradientBoostingClassifier(categorical_features="from_dtype")
hist.fit(X_train, y_train)
y_decision = hist.decision_function(X_test)
print(f"ROC AUC score is {roc_auc_score(y_test, y_decision)}")
ROC AUC score is 0.9284491372325592
set_output 中的 Polars 输出#
scikit-learn 的转换器现在使用 set_output API 支持 polars 输出。
import polars as pl
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
df = pl.DataFrame(
{"height": [120, 140, 150, 110, 100], "pet": ["dog", "cat", "dog", "cat", "cat"]}
)
preprocessor = ColumnTransformer(
[
("numerical", StandardScaler(), ["height"]),
("categorical", OneHotEncoder(sparse_output=False), ["pet"]),
],
verbose_feature_names_out=False,
)
preprocessor.set_output(transform="polars")
df_out = preprocessor.fit_transform(df)
df_out
print(f"Output type: {type(df_out)}")
Output type: <class 'polars.dataframe.frame.DataFrame'>
随机森林的缺失值支持#
类 ensemble.RandomForestClassifier 和 ensemble.RandomForestRegressor 现在支持缺失值。在训练每棵单独的树时，分割器会使用缺失值分别进入左节点和右节点来评估每个潜在阈值。有关更多详细信息，请参阅用户指南。
import numpy as np
from sklearn.ensemble import RandomForestClassifier
X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
y = [0, 0, 1, 1]
forest = RandomForestClassifier(random_state=0).fit(X, y)
forest.predict(X)
array([0, 0, 1, 1])
在基于树的模型中添加对单调约束的支持#
虽然我们在 scikit-learn 0.23 中添加了对基于直方图的梯度提升的单调约束的支持，但我们现在支持所有其他基于树的模型，如树、随机森林、额外树和精确梯度提升。这里，我们展示了随机森林在回归问题上的此功能。
import matplotlib.pyplot as plt
from sklearn.inspection import PartialDependenceDisplay
from sklearn.ensemble import RandomForestRegressor
n_samples = 500
rng = np.random.RandomState(0)
X = rng.randn(n_samples, 2)
noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)
y = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise
rf_no_cst = RandomForestRegressor().fit(X, y)
rf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)
disp = PartialDependenceDisplay.from_estimator(
rf_no_cst,
X,
features=[0],
feature_names=["feature 0"],
line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"},
)
PartialDependenceDisplay.from_estimator(
rf_cst,
X,
features=[0],
line_kw={"linewidth": 4, "label": "constrained", "color": "tab:orange"},
ax=disp.axes_,
)
disp.axes_[0, 0].plot(
X[:, 0], y, "o", alpha=0.5, zorder=-1, label="samples", color="tab:green"
)
disp.axes_[0, 0].set_ylim(-3, 3)
disp.axes_[0, 0].set_xlim(-1, 1)
disp.axes_[0, 0].legend()
plt.show()
丰富的估计器显示#
估计器显示已得到丰富：如果我们查看上面定义的 forest
forest
可以通过单击图表右上角的“？”图标访问估计器的文档。
此外，当估计器拟合时，显示会更改颜色，从橙色变为蓝色。您也可以通过将鼠标悬停在“i”图标上获得此信息。
from sklearn.base import clone
clone(forest) # the clone is not fitted
元数据路由支持#
许多元估计器和交叉验证例程现在支持元数据路由，这些例程列在用户指南中。例如，您可以使用样本权重和GroupKFold进行嵌套交叉验证。
import sklearn
from sklearn.metrics import get_scorer
from sklearn.datasets import make_regression
from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold
# For now by default metadata routing is disabled, and need to be explicitly
# enabled.
sklearn.set_config(enable_metadata_routing=True)
n_samples = 100
X, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)
rng = np.random.RandomState(7)
groups = rng.randint(0, 10, size=n_samples)
sample_weights = rng.rand(n_samples)
estimator = Lasso().set_fit_request(sample_weight=True)
hyperparameter_grid = {"alpha": [0.1, 0.5, 1.0, 2.0]}
scoring_inner_cv = get_scorer("neg_mean_squared_error").set_score_request(
sample_weight=True
)
inner_cv = GroupKFold(n_splits=5)
grid_search = GridSearchCV(
estimator=estimator,
param_grid=hyperparameter_grid,
cv=inner_cv,
scoring=scoring_inner_cv,
)
outer_cv = GroupKFold(n_splits=5)
scorers = {
"mse": get_scorer("neg_mean_squared_error").set_score_request(sample_weight=True)
}
results = cross_validate(
grid_search,
X,
y,
cv=outer_cv,
scoring=scorers,
return_estimator=True,
params={"sample_weight": sample_weights, "groups": groups},
)
print("cv error on test sets:", results["test_mse"])
# Setting the flag to the default `False` to avoid interference with other
# scripts.
sklearn.set_config(enable_metadata_routing=False)
cv error on test sets: [-0.20966251 -0.24002621 -0.5348001 -0.38629346 -0.32892807]
改进稀疏数据 PCA 的内存和运行时效率#
PCA 现在能够通过利用 scipy.sparse.linalg.LinearOperator 来原生处理稀疏矩阵的 arpack 求解器，从而在执行数据集协方差矩阵的特征值分解时避免物化大型稀疏矩阵。
from sklearn.decomposition import PCA
import scipy.sparse as sp
from time import time
X_sparse = sp.random(m=1000, n=1000, random_state=0)
X_dense = X_sparse.toarray()
t0 = time()
PCA(n_components=10, svd_solver="arpack").fit(X_sparse)
time_sparse = time() - t0
t0 = time()
PCA(n_components=10, svd_solver="arpack").fit(X_dense)
time_dense = time() - t0
print(f"Speedup: {time_dense / time_sparse:.1f}x")
Speedup: 3.7x
脚本总运行时间：（0 分钟 42.896 秒）
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