sklearn的make_pipeline函数的代码解释、使用方法
为了简化构建变换和模型链的过程，Scikit-Learn提供了pipeline类，可以将多个处理步骤合并为单个Scikit-Learn估计器。pipeline类本身具有fit、predict和score方法，其行为与Scikit-Learn中的其他模型相同。
sklearn的make_pipeline函数的代码解释
def make_pipeline(*steps, **kwargs):
"""Construct a Pipeline from the given estimators.
This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.
Parameters
----------
*steps : list of estimators,
memory : None, str or object with the joblib.Memory interface, optional
Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.
根据给定的估算器构造一条管道。
这是管道构造函数的简写;它不需要，也不允许命名估算器。相反，它们的名称将自动设置为类型的小写。
参数
----------
*steps :评估表、
memory:无，str或带有joblib的对象。内存接口,可选
用于缓存安装在管道中的变压器。默认情况下，不执行缓存。如果给定一个字符串，它就是到缓存目录的路径。启用缓存会在安装前触发变压器的克隆。因此，给管线的变压器实例不能直接检查。使用属性' ' named_steps ' ' '或' ' steps ' '检查管道中的评估器。当装配耗时时，缓存变压器是有利的。
Examples
--------
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
... # doctest: +NORMALIZE_WHITESPACE
Pipeline(memory=None,
steps=[('standardscaler',
StandardScaler(copy=True, with_mean=True, with_std=True)),
('gaussiannb', GaussianNB(priors=None))])
Returns
-------
p : Pipeline
"""
memory = kwargs.pop('memory', None)
if kwargs:
raise TypeError('Unknown keyword arguments: "{}"'
.format(list(kwargs.keys())[0]))
return Pipeline(_name_estimators(steps), memory=memory)
sklearn的make_pipeline函数的使用方法
Examples
--------
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
... # doctest: +NORMALIZE_WHITESPACE
Pipeline(memory=None,
steps=[('standardscaler',
StandardScaler(copy=True, with_mean=True, with_std=True)),
('gaussiannb', GaussianNB(priors=None))])
Returns
-------
p : Pipeline
1、使用Pipeline类来表示在使用MinMaxScaler缩放数据之后再训练一个SVM的工作流程
from sklearn.pipeline import Pipeline
pipe = Pipeline([("scaler",MinMaxScaler()),("svm",SVC())])
pip.fit(X_train,y_train)
pip.score(X_test,y_test)
2、make_pipeline函数创建管道
用Pipeline类构建管道时语法有点麻烦，我们通常不需要为每一个步骤提供用户指定的名称，这种情况下，就可以用make_pipeline函数创建管道，它可以为我们创建管道并根据每个步骤所属的类为其自动命名。
from sklearn.pipeline import make_pipeline
pipe = make_pipeline(MinMaxScaler(),SVC())
参考文章
《Python机器学习基础教程》构建管道(make_pipeline)
Python sklearn.pipeline.make_pipeline() Examples
sklearn的RobustScaler函数的代码解释、使用方法
RobustScaler函数的代码解释
class RobustScaler(BaseEstimator, TransformerMixin):
"""Scale features using statistics that are robust to outliers.
This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range).
The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). Centering and scaling happen independently on each feature (or each sample, depending on the ``axis`` argument) by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the ``transform`` method.
Standardization of a dataset is a common requirement for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results.
.. versionadded:: 0.17
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
with_centering : boolean, True by default
If True, center the data before scaling. This will cause ``transform`` to raise an exception when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.
with_scaling : boolean, True by default
If True, scale the data to interquartile range.
quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0
Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR
Quantile range used to calculate ``scale_``.
.. versionadded:: 0.18
copy : boolean, optional, default is True
If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be
returned.
Attributes
----------
center_ : array of floats
The median value for each feature in the training set.
scale_ : array of floats
The (scaled) interquartile range for each feature in the training set.
.. versionadded:: 0.17
*scale_* attribute.
See also
--------
robust_scale: Equivalent function without the estimator API.
:class:`sklearn.decomposition.PCA`
Further removes the linear correlation across features with 'whiten=True'.
Notes
-----
For a comparison of the different scalers, transformers, and normalizers, see :ref:`examples/preprocessing/plot_all_scaling.py
<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
https://en.wikipedia.org/wiki/Median_(statistics)
https://en.wikipedia.org/wiki/Interquartile_range
"""
使用对离群值稳健的统计数据来衡量特征。
这个标量去除中值，并根据分位数范围(默认为IQR:四分位数范围)对数据进行缩放。
IQR是第1个四分位数(第25分位数)和第3个四分位数(第75分位数)之间的范围。通过计算训练集中样本的相关统计数据，在每个特征(或每个样本，取决于“轴”参数)上独立地进行定心和缩放。然后，中值和四分位范围被存储起来，以便使用“变换”方法在以后的数据中使用。
数据集的标准化是许多机器学习估计器的常见需求。这通常是通过去除平均值和缩放到单位方差来实现的。然而，异常值往往会对样本均值/方差产生负面影响。在这种情况下，中位数和四分位范围通常会得到更好的结果。
. .versionadded:: 0.17
详见:ref: ' User Guide '。</preprocessing_scaler>
参数
----------
with_center:boolean，默认为True
如果为真，在缩放前将数据居中。这将导致“转换”在尝试处理稀疏矩阵时引发异常，因为围绕它们需要构建一个密集的矩阵，在常见的用例中，这个矩阵可能太大而无法装入内存。
with_scaling:布尔值，默认为True
如果为真，将数据缩放到四分位范围。
quantile_range:元组(q_min, q_max)， 0.0 < q_min < q_max < 100.0
默认:(25.0,75.0)=(第1分位数，第3分位数)= IQR
用于计算' ' scale_ ' '的分位数范围。
. .versionadded:: 0.18
布尔值，可选，默认为真
如果为False，则尽量避免复制，而改为就地缩放。这并不能保证总是有效的;例如，如果数据不是一个NumPy数组或scipy。稀疏CSR矩阵，仍可复制
返回。
属性
----------
浮点数数组
训练集中每个特征的中值。
浮点数数组
训练集中每个特征的(缩放的)四分位范围。
. .versionadded:: 0.17
* scale_ *属性。
另请参阅
--------
没有estimator API的等价函数。
类:“sklearn.decomposition.PCA”
进一步消除了“whiten=True”特征之间的线性相关性。
笔记
-----
对于不同的标量、转换器和规整器的比较，请参见:ref: ' examples/preprocessing/ plot_all_scale .py
< sphx_glr_auto_examples_preprocessing_plot_all_scaling.py >”。
https://en.wikipedia.org/wiki/Median_(统计)
https://en.wikipedia.org/wiki/Interquartile_range
”“”
def __init__(self, with_centering=True, with_scaling=True,
quantile_range=(25.0, 75.0), copy=True):
self.with_centering = with_centering
self.with_scaling = with_scaling
self.quantile_range = quantile_range
self.copy = copy
def _check_array(self, X, copy):
"""Makes sure centering is not enabled for sparse matrices."""
X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
estimator=self, dtype=FLOAT_DTYPES)
if sparse.issparse(X):
if self.with_centering:
raise ValueError(
"Cannot center sparse matrices: use `with_centering=False`"
" instead. See docstring for motivation and alternatives.")
return X
def fit(self, X, y=None):
"""Compute the median and quantiles to be used for scaling.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data used to compute the median and quantiles
used for later scaling along the features axis.
"""
if sparse.issparse(X):
raise TypeError("RobustScaler cannot be fitted on sparse inputs")
X = self._check_array(X, self.copy)
if self.with_centering:
self.center_ = np.median(X, axis=0)
if self.with_scaling:
q_min, q_max = self.quantile_range
if not 0 <= q_min <= q_max <= 100:
raise ValueError("Invalid quantile range: %s" %
str(self.quantile_range))
q = np.percentile(X, self.quantile_range, axis=0)
self.scale_ = (q[1] - q[0])
self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False)
return self
def transform(self, X):
"""Center and scale the data.
Can be called on sparse input, provided that ``RobustScaler`` has been
fitted to dense input and ``with_centering=False``.
Parameters
----------
X : {array-like, sparse matrix}
The data used to scale along the specified axis.
"""
if self.with_centering:
check_is_fitted(self, 'center_')
if self.with_scaling:
check_is_fitted(self, 'scale_')
X = self._check_array(X, self.copy)
if sparse.issparse(X):
if self.with_scaling:
inplace_column_scale(X, 1.0 / self.scale_)
else:
if self.with_centering:
X -= self.center_
if self.with_scaling:
X /= self.scale_
return X
def inverse_transform(self, X):
"""Scale back the data to the original representation
Parameters
----------
X : array-like
The data used to scale along the specified axis.
"""
if self.with_centering:
check_is_fitted(self, 'center_')
if self.with_scaling:
check_is_fitted(self, 'scale_')
X = self._check_array(X, self.copy)
if sparse.issparse(X):
if self.with_scaling:
inplace_column_scale(X, self.scale_)
else:
if self.with_scaling:
X *= self.scale_
if self.with_centering:
X += self.center_
return X