python并行调参——scikit-learn grid_search

关键词：scikit learn,python scikit learn,scikit learn 教程

上篇 应用scikit-learn做文本分类 中以20newsgroups为例讲了如何用三种方法提取训练集=测试集的文本feature，但是

vectorizer取多少个word呢？

预处理时候要过滤掉tf>max_df的words，max_df设多少呢？

tfidftransformer只用tf还是加idf呢？

classifier分类时迭代几次？学习率怎么设？

……

“循环一个个试过来啊”……啊好吧，matlab里就是这么做的……

好在scikit-learn中提供了pipeline(for estimator connection) & grid_search(searching best parameters)进行并行调参。

官网上pipeline 解释如下：

Pipeline  can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification.  Pipeline  serves two purposes here:

Convenience : You only have to call  fit  and  predict  once on your data to fit a whole sequence of estimators.

Joint parameter selection : You can  grid search  over parameters of all estimators in the pipeline at once.

调用方式：

仍以20newsgroups为例，上一篇文章中有讲数据集加载方式，这里不予赘述，

pipeline+gridsearchcv (grid search parameters with cross validation)代码：

1. pipeline定义，输入备选parameter

print '*************************\nFeature Extraction\n*************************'
from sklearn.pipeline import Pipeline
from sklearn.linear_model import SGDClassifier
from sklearn.grid_search import GridSearchCV

pipeline = Pipeline([
('vect',CountVectorizer()),
('tfidf',TfidfTransformer()),
('clf',SGDClassifier()),
]);

parameters = {
  'vect__max_df': (0.5, 0.75),
  'vect__max_features': (None, 5000, 10000),
  'tfidf__use_idf': (True, False),
#	'tfidf__norm': ('l1', 'l2'),
  'clf__alpha': (0.00001, 0.000001),
#	'clf__penalty': ('l2', 'elasticnet'),
  'clf__n_iter': (10, 50),
}

2. gridsearch寻找vectorizer词频统计, tfidftransformer特征变换和SGD classifier的最优参数

GridSearchCV 函数定义见官网。

grid_search = GridSearchCV(pipeline,parameters,n_jobs = 1,verbose=1);
print("Performing grid search...")
print("pipeline:", [name for name, _ in pipeline.steps])
print("parameters:")
pprint(parameters)
from time import time
t0 = time()
grid_search.fit(newsgroup_train.data, newsgroup_train.target)
print("done in %0.3fs" % (time() - t0))
print()
print("Best score: %0.3f" % grid_search.best_score_)

结果：

Performing grid search…
(‘pipeline:’, [‘vect’, ‘tfidf’, ‘clf’])
parameters:
{‘clf__alpha’: (1e-05, 1e-06),
‘clf__n_iter’: (10, 50),
‘tfidf__use_idf’: (True, False),
‘vect__max_df’: (0.5, 0.75),
‘vect__max_features’: (None, 5000, 10000)}
Fitting 3 folds for each of 48 candidates, totalling 144 fits
[Parallel(n_jobs=1)]: Done   1 jobs       | elapsed:    1.1s
[Parallel(n_jobs=1)]: Done  50 jobs       | elapsed:  1.1min
[Parallel(n_jobs=1)]: Done 144 out of 144 | elapsed:  3.1min finished
done in 189.978s
()
Best score: 0.871
3. 输出最佳参数，在此基础上求最佳结果

from sklearn import metrics
best_parameters = dict();
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
    print("\t%s: %r" % (param_name, best_parameters[param_name]));
pipeline.set_params(clf__alpha = 1e-05, 
  clf__n_iter = 50, 
  tfidf__use_idf = True,
  vect__max_df = 0.5,
  vect__max_features = None);
pipeline.fit(newsgroup_train.data, newsgroup_train.target);
pred = pipeline.predict(newsgroups_test.data)
calculate_result(newsgroups_test.target,pred);

结果：

clf__alpha: 1e-05
clf__n_iter: 50
tfidf__use_idf: True
vect__max_df: 0.5
vect__max_features: None
predict info:
precision:0.806
recall:0.805
f1-score:0.804

Other references:

grid search + cross validation

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