PassiveAggressiveRegressor#

class sklearn.linear_model.PassiveAggressiveRegressor(*, C=1.0, fit_intercept=True, max_iter=1000, tol=0.001, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, shuffle=True, verbose=0, loss='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False)[source]#

Passive Aggressive 回归器。

Deprecated since version 1.8: The whole class PassiveAggressiveRegressor was deprecated in version 1.8 and will be removed in 1.10. Instead use

reg = SGDRegressor(
    loss="epsilon_insensitive",
    penalty=None,
    learning_rate="pa1",  # or "pa2"
    eta0=1.0,  # for parameter C
)

Read more in the User Guide.

参数:
Cfloat, default=1.0

Aggressiveness parameter for the passive-agressive algorithm, see [1]. For PA-I it is the maximum step size. For PA-II it regularizes the step size (the smaller C the more it regularizes). As a general rule-of-thumb, C should be small when the data is noisy.

fit_interceptbool, default=True

Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.

max_iterint, default=1000

The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.

Added in version 0.19.

tolfloat or None, default=1e-3

The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol).

Added in version 0.19.

early_stoppingbool, default=False

Whether to use early stopping to terminate training when validation. score is not improving. If set to True, it will automatically set aside a fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs.

0.20 版本新增。

validation_fractionfloat, default=0.1

The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.

0.20 版本新增。

n_iter_no_changeint, default=5

Number of iterations with no improvement to wait before early stopping.

0.20 版本新增。

shufflebool, default=True

Whether or not the training data should be shuffled after each epoch.

verboseint, default=0

详细程度。

lossstr, default=”epsilon_insensitive”

The loss function to be used: epsilon_insensitive: equivalent to PA-I in the reference paper. squared_epsilon_insensitive: equivalent to PA-II in the reference paper.

epsilonfloat, default=0.1

If the difference between the current prediction and the correct label is below this threshold, the model is not updated.

random_stateint, RandomState instance, default=None

Used to shuffle the training data, when shuffle is set to True. Pass an int for reproducible output across multiple function calls. See Glossary.

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.

Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled.

averagebool or int, default=False

When set to True, computes the averaged SGD weights and stores the result in the coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.

Added in version 0.19: parameter average to use weights averaging in SGD.

属性:
coef_array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features]

Weights assigned to the features.

intercept_array, shape = [1] if n_classes == 2 else [n_classes]

决策函数中的常数。

n_features_in_int

在 拟合 期间看到的特征数。

0.24 版本新增。

feature_names_in_shape 为 (n_features_in_,) 的 ndarray

在 fit 期间看到的特征名称。仅当 X 具有全部为字符串的特征名称时才定义。

1.0 版本新增。

n_iter_int

The actual number of iterations to reach the stopping criterion.

t_int

Number of weight updates performed during training. Same as (n_iter_ * n_samples + 1).

另请参阅

SGDRegressor

通过使用 SGD 最小化正则化经验损失来拟合的线性模型。

References

Online Passive-Aggressive Algorithms <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006).

示例

>>> from sklearn.linear_model import PassiveAggressiveRegressor
>>> from sklearn.datasets import make_regression

>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = PassiveAggressiveRegressor(max_iter=100, random_state=0,
... tol=1e-3)
>>> regr.fit(X, y)
PassiveAggressiveRegressor(max_iter=100, random_state=0)
>>> print(regr.coef_)
[20.48736655 34.18818427 67.59122734 87.94731329]
>>> print(regr.intercept_)
[-0.02306214]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-0.02306214]
densify()[source]#

Convert coefficient matrix to dense array format.

Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

返回:
self

拟合的估计器。

fit(X, y, coef_init=None, intercept_init=None)[source]#

Fit linear model with Passive Aggressive algorithm.

参数:
Xshape 为 (n_samples, n_features) 的 {array-like, sparse matrix}

训练数据。

ynumpy array of shape [n_samples]

目标值。

coef_initarray, shape = [n_features]

The initial coefficients to warm-start the optimization.

intercept_initarray, shape = [1]

The initial intercept to warm-start the optimization.

返回:
selfobject

拟合的估计器。

get_metadata_routing()[source]#

获取此对象的元数据路由。

请查阅 用户指南，了解路由机制如何工作。

返回:
routingMetadataRequest

封装路由信息的 MetadataRequest。

get_params(deep=True)[source]#

获取此估计器的参数。

参数:
deepbool, default=True

如果为 True，将返回此估计器以及包含的子对象（如果它们是估计器）的参数。

返回:
paramsdict

参数名称映射到其值。

partial_fit(X, y)[source]#

Fit linear model with Passive Aggressive algorithm.

参数:
Xshape 为 (n_samples, n_features) 的 {array-like, sparse matrix}

Subset of training data.

ynumpy array of shape [n_samples]

Subset of target values.

返回:
selfobject

拟合的估计器。

predict(X)[source]#

使用线性模型进行预测。

参数:
X{array-like, sparse matrix}, shape (n_samples, n_features)

Input data.

返回:
ndarray of shape (n_samples,)

Predicted target values per element in X.

score(X, y, sample_weight=None)[source]#

返回测试数据的 决定系数。

The coefficient of determination, \(R^2\), is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

参数:
Xshape 为 (n_samples, n_features) 的 array-like

测试样本。对于某些估计器，这可能是一个预先计算的核矩阵或一个通用对象列表，形状为 (n_samples, n_samples_fitted)，其中 n_samples_fitted 是用于估计器拟合的样本数。

yshape 为 (n_samples,) 或 (n_samples, n_outputs) 的 array-like

X 的真实值。

sample_weightshape 为 (n_samples,) 的 array-like, default=None

样本权重。

返回:
scorefloat

self.predict(X) 相对于 y 的 \(R^2\)。

注意事项

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, coef_init: bool | None | str = '$UNCHANGED$', intercept_init: bool | None | str = '$UNCHANGED$') → PassiveAggressiveRegressor[source]#

配置是否应请求元数据以传递给 fit 方法。

请注意，此方法仅在以下情况下相关：此估计器用作 元估计器 中的子估计器，并且通过 enable_metadata_routing=True 启用了元数据路由（请参阅 sklearn.set_config）。请查看 用户指南 以了解路由机制的工作原理。

每个参数的选项如下：

  • True：请求元数据，如果提供则传递给 fit。如果未提供元数据，则忽略该请求。

  • False：不请求元数据，元估计器不会将其传递给 fit。

  • None：不请求元数据，如果用户提供元数据，元估计器将引发错误。

  • str：应将元数据以给定别名而不是原始名称传递给元估计器。

默认值 (sklearn.utils.metadata_routing.UNCHANGED) 保留现有请求。这允许您更改某些参数的请求而不更改其他参数。

在版本 1.3 中新增。

参数:
coef_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for coef_init parameter in fit.

intercept_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for intercept_init parameter in fit.

返回:
selfobject

更新后的对象。

set_params(**params)[source]#

设置此估计器的参数。

此方法适用于简单的估计器以及嵌套对象（如 Pipeline）。后者具有 <component>__<parameter> 形式的参数，以便可以更新嵌套对象的每个组件。

参数:
**paramsdict

估计器参数。

返回:
selfestimator instance

估计器实例。

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PassiveAggressiveRegressor[source]#

Configure whether metadata should be requested to be passed to the partial_fit method.

请注意，此方法仅在以下情况下相关：此估计器用作 元估计器 中的子估计器，并且通过 enable_metadata_routing=True 启用了元数据路由（请参阅 sklearn.set_config）。请查看 用户指南 以了解路由机制的工作原理。

每个参数的选项如下：

  • True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to partial_fit.

  • None：不请求元数据，如果用户提供元数据，元估计器将引发错误。

  • str：应将元数据以给定别名而不是原始名称传递给元估计器。

默认值 (sklearn.utils.metadata_routing.UNCHANGED) 保留现有请求。这允许您更改某些参数的请求而不更改其他参数。

在版本 1.3 中新增。

参数:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in partial_fit.

返回:
selfobject

更新后的对象。

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PassiveAggressiveRegressor[source]#

配置是否应请求元数据以传递给 score 方法。

请注意，此方法仅在以下情况下相关：此估计器用作 元估计器 中的子估计器，并且通过 enable_metadata_routing=True 启用了元数据路由（请参阅 sklearn.set_config）。请查看 用户指南 以了解路由机制的工作原理。

每个参数的选项如下：

  • True：请求元数据，如果提供则传递给 score。如果未提供元数据，则忽略该请求。

  • False：不请求元数据，元估计器不会将其传递给 score。

  • None：不请求元数据，如果用户提供元数据，元估计器将引发错误。

  • str：应将元数据以给定别名而不是原始名称传递给元估计器。

默认值 (sklearn.utils.metadata_routing.UNCHANGED) 保留现有请求。这允许您更改某些参数的请求而不更改其他参数。

在版本 1.3 中新增。

参数:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

score 方法中 sample_weight 参数的元数据路由。

返回:
selfobject

更新后的对象。

sparsify()[source]#

Convert coefficient matrix to sparse format.

Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The intercept_ member is not converted.

返回:
self

拟合的估计器。

注意事项

For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.

After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.