Question

我正在尝试使用GridSearchCV创建管道以过滤数据（使用iforest）并使用StandarSclaler + MLPRegressor执行回归。

我做了一个FunctionTransformer，将我的iForest过滤器包含在管道中。我还为iForest过滤器定义了参数网格（使用kw_args方法）。

一切似乎都还可以，但是当进行安装时，什么也没发生...没有错误消息。没事。

之后，当我要进行预测时，出现消息：“此RandomizedSearchCV实例尚未安装”

from sklearn.preprocessing import FunctionTransformer

#Definition of the function auto_filter using the iForest algo
def auto_filter(DF, conta=0.1):
    #iForest made on the DF dataframe
    iforest = IsolationForest(behaviour='new', n_estimators=300, max_samples='auto', contamination=conta)
    iforest = iforest.fit(DF)

    # The DF (dataframe in input) is filtered taking into account only the inlier observations

data_filtered = DF[iforest.predict(DF) == 1]

    # Only few variables are kept for the next step (regression by MLPRegressor)
    # this function delivers X_filtered and y
    X_filtered = data_filtered[['SessionTotalTime','AverageHR','MaxHR','MinHR','EETotal','EECH','EEFat','TRIMP','BeatByBeatRMSSD','BeatByBeatSD','HFAverage','LFAverage','LFHFRatio','Weight']]
    y = data_filtered['MaxVO2']
    return (X_filtered, y)

#Pipeline definition ('auto_filter' --> 'scaler' --> 'MLPRegressor')    
pipeline_steps = [('auto_filter', FunctionTransformer(auto_filter)), ('scaler', StandardScaler()), ('MLPR', MLPRegressor(solver='lbfgs', activation='relu', early_stopping=True, n_iter_no_change=20, validation_fraction=0.2, max_iter=10000))]

#Gridsearch Definition with differents values of 'conta' for the first stage of the pipeline ('auto_filter)
parameters = {'auto_filter__kw_args': [{'conta': 0.1}, {'conta': 0.2}, {'conta': 0.3}], 'MLPR__hidden_layer_sizes':[(sp_randint.rvs(1, nb_features, 1),), (sp_randint.rvs(1, nb_features, 1), sp_randint.rvs(1, nb_features, 1))], 'MLPR__alpha':sp_rand.rvs(0, 1, 1)}   

pipeline = Pipeline(pipeline_steps)

estimator = RandomizedSearchCV(pipeline, parameters, cv=5, n_iter=10)
estimator.fit(X_train, y_train)

Answer 1

您可以尝试手动逐步查找问题：

auto_filter_transformer = FunctionTransformer(auto_filter)
X_train = auto_filter_transformer.fit_transform(X_train)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)

MLPR = MLPRegressor(solver='lbfgs', activation='relu', early_stopping=True, n_iter_no_change=20, validation_fraction=0.2, max_iter=10000)
MLPR.fit(X_train, y_train)

如果每个步骤都工作正常，请建立一个管道。检查管道。如果正常，请尝试使用RandomizedSearchCV。

Answer 2

func的{{1}}参数应该是可调用的，接受与FunctionTransformer方法的参数相同（形状类似于数组的transform X的{{1}}和(n_samples, n_features)），并返回经过转换的kwargs 相同形状。您的功能func不符合这些要求。

此外，无法使用scikit-learn的异常/异常检测技术由于管道组装在一起，因此在scikit-learn管道中用作中间步骤一个或多个 transformers 和一个可选的最终估算器。 X或，例如，OneClassSVM不是转换器：它实现了auto_filter和IsolationForest。因此，可能的解决方案是分别切除可能的异常值并构建由变压器和回归器组成的管道：

fit

问题是您将无法优化的超参数 predict。处理它的一种方法是定义超参数空间对于森林，请使用ParameterSampler或 ParameterGrid，预测离群值并拟合随机搜索：

>>> import warnings
>>> from sklearn.exceptions import ConvergenceWarning
>>> warnings.filterwarnings(category=ConvergenceWarning, action='ignore')
>>> import numpy as np
>>> from scipy import stats
>>> from sklearn.datasets import make_regression
>>> from sklearn.ensemble import IsolationForest
>>> from sklearn.model_selection import RandomizedSearchCV
>>> from sklearn.neural_network import MLPRegressor
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> X, y = make_regression(n_samples=50, n_features=2, n_informative=2)
>>> detect = IsolationForest(contamination=0.1, behaviour='new')
>>> inliers_mask = detect.fit_predict(X) == 1
>>> pipe = Pipeline([('scale', StandardScaler()),
...                  ('estimate', MLPRegressor(max_iter=500, tol=1e-5))])
>>> param_distributions = dict(estimate__alpha=stats.uniform(0, 0.1))
>>> search = RandomizedSearchCV(pipe, param_distributions,
...                             n_iter=2, cv=3, iid=True)
>>> search = search.fit(X[inliers_mask], y[inliers_mask])

如何在管道中使用适当的FunctionTransformer制作GridSearchCV？

2 个答案: