CODEWITHSUNDEEP
python-3.xmachine-learningscikit-learn
C. DAVID
C. DAVID

Reputation: 61

tuning models with grid search

I ran across an example on parameters tuning with Grid search and text data using TfidfVectorizer() in the pipeline. As far as I've understood is that when we call grid_search.fit(X_train, y_train) it will transform the data then fit the model as it is described in a dictionary. However during the evaluation, I'm a bit confused with the test dataset, since when we call grid_search.predict(X_test) I don't know whether/(how) the TfidfVectorizer() is applied on this test chunk.

Thanks

David

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import train_test_split
from sklearn.metrics import precision_score, recall_score, accuracy_
score
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('data/sms.csv')
X, y, = df['message'], df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y)
grid_search.fit(X_train, y_train)
print 'Best score: %0.3f' % grid_search.best_score_
print 'Best parameters set:'
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])
predictions = grid_search.predict(X_test)
print 'Accuracy:', accuracy_score(y_test, predictions)
print 'Precision:', precision_score(y_test, predictions)
print 'Recall:', recall_score(y_test, predictions)

Upvotes: 0

Views: 71

Answers (1)

Claygirl
Claygirl

Reputation: 349

This is an example of scikit-learn pipelines magic. It works like this:

  1. First, you define elements of a pipeline with Pipeline constructor - all data, whether on train or test (predict) stage, will be processed through all the defined steps - in this case by TfidfVectorizer and then the output will be passed to LogisticRegression model.
  2. Passing defined pipeline to GridSearchCV constructor allows you to use the method fit, that not only performs grid search but also internally sets both TfidfVectorizer and LogisticRegression to best found parameters, so later running predict does so on best-found models.

You can find more info on creating pipelines in scikit-learn documentation.

Upvotes: 1

Related Questions