类型错误:get_params() 缺少 1 个必需的位置参数:'self' [英] TypeError: get_params() missing 1 required positional argument: 'self'

问题描述

我试图使用 scikit-learn 包和 python-3.4 进行网格搜索，

I was trying to use scikit-learn package with python-3.4 to do a grid search,

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.metrics import precision_score, recall_score, accuracy_score
from sklearn.preprocessing import LabelBinarizer
import numpy as np

pipeline = Pipeline([
    ('vect', TfidfVectorizer(stop_words='english')),
    ('clf', LogisticRegression)
])

parameters = {
    'vect__max_df': (0.25, 0.5, 0.75),
    'vect__stop_words': ('english', None),
    'vect__max_features': (2500, 5000, 10000, None),
    'vect__ngram_range': ((1, 1), (1, 2)),
    'vect__use_idf': (True, False),
    'vect__norm': ('l1', 'l2'),
    'clf__penalty': ('l1', 'l2'),
    'clf__C': (0.01, 0.1, 1, 10)
}

if __name__ == '__main__':
    grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, scoring='accuracy', cv = 3)
    df = pd.read_csv('SMS Spam Collection/SMSSpamCollection', delimiter='\t', header=None)
    lb = LabelBinarizer()
    X, y = df[1], np.array([number[0] for number in lb.fit_transform(df[0])])
    X_train, X_test, y_train, y_test = train_test_split(X, y)
    grid_search.fit(X_train, y_train)
    print('Best score: ', grid_search.best_score_)
    print('Best parameter set:')
    best_parameters = grid_search.best_estimator_.get_params()
    for param_name in sorted(best_parameters):
        print(param_name, best_parameters[param_name])

但是没有运行成功，报错信息是这样的:

However, it does not run successfully, the error message looks like this:

Fitting 3 folds for each of 1536 candidates, totalling 4608 fits
Traceback (most recent call last):
  File "/home/xiangru/PycharmProjects/machine_learning_note_with_sklearn/grid search.py", line 36, in <module>
    grid_search.fit(X_train, y_train)
  File "/usr/local/lib/python3.4/dist-packages/sklearn/grid_search.py", line 732, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "/usr/local/lib/python3.4/dist-packages/sklearn/grid_search.py", line 493, in _fit
    base_estimator = clone(self.estimator)
  File "/usr/local/lib/python3.4/dist-packages/sklearn/base.py", line 47, in clone
    new_object_params[name] = clone(param, safe=False)
  File "/usr/local/lib/python3.4/dist-packages/sklearn/base.py", line 35, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/usr/local/lib/python3.4/dist-packages/sklearn/base.py", line 35, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/usr/local/lib/python3.4/dist-packages/sklearn/base.py", line 35, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/usr/local/lib/python3.4/dist-packages/sklearn/base.py", line 35, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/usr/local/lib/python3.4/dist-packages/sklearn/base.py", line 45, in clone
    new_object_params = estimator.get_params(deep=False)
TypeError: get_params() missing 1 required positional argument: 'self'

我也试过只用

if __name__ == '__main__':
    pipeline.get_params()

它给出了相同的错误消息.谁知道如何解决这个问题?

It gives the same error message. Who knows how to fix this?

推荐答案

我终于把问题解决了.原因正如阿巴纳特所说.

I finally get the problem solved. The reason is exactly as what abarnert said.

首先我尝试过:

pipeline = LogisticRegression()

parameters = {
    'penalty': ('l1', 'l2'),
    'C': (0.01, 0.1, 1, 10)
}

而且效果很好.

凭着直觉，我将管道修改为:

With that intuition I modified the pipeline to be:

pipeline = Pipeline([
    ('vect', TfidfVectorizer(stop_words='english')),
    ('clf', LogisticRegression())
])

注意LogisticRegression后面有一个().这次成功了.

Note that there is a () after LogisticRegression. This time it works.

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