tr = StandardScaler()
pca = PCA(n_components=10)
lr = LinearSVC(penalty='l1',dual=False)
pipe = Pipeline([('tr',tr),('pca',pca),('lr',lr)])
clf = GridSearchCV(pipe, parms, scoring='f1', n_jobs=5)
clf.fit(X,Y)
import pickle
f = open('%s.model' % __fname__,'wb')
pickle.dump(clf, f)
f.close()
pred = clf.predict(X)
summary.clf_summary(clf, feature_names)
summary.summary(Y, pred)
F1, P, R = TestModel()
util.notify_me('%s.F1:%.2f,P:%.2f,R:%.2f' % (__fname__, F1*100, P*100, R*100))
f.close()
return clf
if __name__ == '__main__':
X, Y = GetData()
parms = {
'C':np.logspace(-6,0,10),
#'class_weight':[{0:1,1:200}] #[{0:1,1:50},{0:1,1:70},{0:1,1:85},{0:1,1:100},{0:1,1:120},{0:1,1:150}]
}
lr = LogisticRegression()
clf = GridSearchCV(lr, parms, scoring='f1', n_jobs=10)
clf.fit(X,Y)
import pickle
f = open('model0.model','wb')
pickle.dump(clf, f)
f.close()
pred = clf.predict(X)
from summary import summary,clf_summary,TestModel
clf_summary(clf)
summary(Y, pred)
TestModel('model0')
if __name__ == '__main__':
X, Y = GetData()
parms = {
'C':np.logspace(-6,0,10),
#'class_weight':[{0:1,1:200}] #[{0:1,1:50},{0:1,1:70},{0:1,1:85},{0:1,1:100},{0:1,1:120},{0:1,1:150}]
}
lr = LogisticRegression(penalty='l1')
clf = GridSearchCV(lr, parms, scoring='f1', n_jobs=10)
clf.fit(X,Y)
import pickle
f = open('model4.model','wb')
pickle.dump(clf, f)
f.close()
pred = clf.predict(X)
from summary import summary,clf_summary,TestModel
clf_summary(clf)
summary(Y, pred)
TestModel('model4')
