def train(model_name, category_type, dump=False):
clf = tfidf_pipeline.make(model_name)
categories = names.categories[category_type]
print 'Loading data...'
data = data_loader.load('full', categories)
train_X, train_y, test_X, test_y = data_loader.split(data, 0.1)
print 'Done.'
print 'Training...'
clf.fit(train_X, train_y)
print 'Done.'
print 'Testing...'
predicted = clf.predict(test_X)
if model_name in ['svr', 'linreg']:
predicted = np.clip(np.round(predicted), 0, 7)
accuracy = scorers.err1(test_y, predicted)
print 'Off-by-one accuracy: ' + str(accuracy)
else:
accuracy = scorers.err0(test_y, predicted)
print 'Exact accuracy: ' + str(accuracy)
print classification_report(test_y, predicted, target_names=categories)
cm = confusion_matrix(test_y, predicted)
print cm
plot.plot_confusion_matrix(cm, category_type)
if dump:
print 'Saving classifier...'
if not exists('dumps'):
makedirs('dumps')
joblib.dump(clf, join('dumps', category_type + '_' + model_name + '_classifier.pkl'))
print 'Done.'
return clf
import sys
from sklearn.externals.joblib import dump
import data_loader
import names
import tfidf_pipeline
import model_presets
if __name__ == '__main__':
for (category_name, model_name) in [('stars', 'linreg'), ('binary', 'svc')]:
print 'Loading ' + category_name + ' data'
train,_ = data_loader.load('split', names.categories[category_name])
print 'Training ' + model_name
clf = tfidf_pipeline.make(model_name)
clf.fit(train.data, train.target)
print 'Dumping ' + model_name
dump(clf, 'web_clf_' + category_name + '.pkl')
