def main():
if len(sys.argv) != 3:
print "Usage " + sys.argv[0] + " file_train model_file"
sys.exit(1)
train_filename = sys.argv[1]
model_filename = sys.argv[2]
featureIndexes = processData(train_filename)
trainFeatures, trainTargets, trainItemIds = processData(train_filename, featureIndexes)
# joblib.dump((trainFeatures, trainTargets, trainItemIds), os.path.join(dataFolder, "train_data.pkl"))
# trainFeatures, trainTargets, trainItemIds = joblib.load(os.path.join(dataFolder, "train_data.pkl"))
trainTargets = np.asarray(trainTargets)
logging.info("Feature preparation done, fitting model...")
clf = SGDClassifier(loss="log", penalty="l2", alpha=1e-4, class_weight="auto")
# clf.fit(trainFeatures, trainTargets)
# joblib.dump(clf, model_filename)
print utils.xvalidation(trainFeatures, trainTargets, clf)
return c_features, y#, ids, [vectorizer]
if __name__ == '__main__':
if len(sys.argv) != 3:
print "Usage " + sys.argv[0] + " file_train model_file"
sys.exit(1)
train_filename = sys.argv[1]
model_filename = sys.argv[2]
X, y = read_train_nb(train_filename)
xv = []
sz = []
for i in range(10):
sz.append(X[i].shape[0])
nb = MultinomialNB()
xv.append(utils.xvalidation(X[i], y[i], nb))
total_sz = float(sum(sz))
for i in range(10):
print category_dict_r[i], sz[i]/total_sz, xv[i]
#utils.xvalidation_result(train_filename, 'nb.ng2.txt', X, y, ids, nb)
# nb.fit(X, y)
# joblib.dump((nb, transformers), model_filename)
