在Logistic回归中微调参数

Simon Kiely 发表于 Dev

西蒙·基利

我正在使用在文本列上运行的tf-idf运行逻辑回归。这是我在逻辑回归中使用的唯一列。如何确保对此参数进行最佳调整？

我希望能够执行一组步骤，这些步骤最终将使我说我的Logistic回归分类器正在尽可能地运行。

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.."

lennon310

您可以使用网格搜索来找到最C适合您的价值。基本上较小的值C指定更强的正则化。

>>> param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000] }
>>> clf = GridSearchCV(LogisticRegression(penalty='l2'), param_grid)
GridSearchCV(cv=None,
             estimator=LogisticRegression(C=1.0, intercept_scaling=1,   
               dual=False, fit_intercept=True, penalty='l2', tol=0.0001),
             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]})

有关您的应用程序的更多详细信息，请参见GridSearchCv文档。

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