问Logistic回归中的参数微调

EN

from sklearn import metrics,preprocessing,cross_validation
from sklearn.feature_extraction.text import TfidfVectorizer
import sklearn.linear_model as lm
import pandas as p
loadData = lambda f: np.genfromtxt(open(f, 'r'), delimiter=' ')

print "loading data.."
traindata = list(np.array(p.read_table('train.tsv'))[:, 2])
testdata = list(np.array(p.read_table('test.tsv'))[:, 2])
y = np.array(p.read_table('train.tsv'))[:, -1]

tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode',
                      analyzer='word', token_pattern=r'\w{1,}', 
                      ngram_range=(1, 2), use_idf=1, smooth_idf=1, 
                      sublinear_tf=1)

rd = lm.LogisticRegression(penalty='l2', dual=True, tol=0.0001, 
                           C=1, fit_intercept=True, intercept_scaling=1.0, 
                           class_weight=None, random_state=None)

X_all = traindata + testdata
lentrain = len(traindata)

print "fitting pipeline"
tfv.fit(X_all)
print "transforming data"
X_all = tfv.transform(X_all)

X = X_all[:lentrain]
X_test = X_all[lentrain:]

print "20 Fold CV Score: ", np.mean(cross_validation.cross_val_score(rd, X, y, cv=20, scoring='roc_auc'))

print "training on full data"
rd.fit(X, y)
pred = rd.predict_proba(X_test)[:, 1]
testfile = p.read_csv('test.tsv', sep="\t", na_values=['?'], index_col=1)
pred_df = p.DataFrame(pred, index=testfile.index, columns=['label'])
pred_df.to_csv('benchmark.csv')
print "submission file created.."