class LinearSVRImpl():
def __init__(self,
epsilon=0.0,
tol=0.0001,
C=1.0,
loss='epsilon_insensitive',
fit_intercept=True,
intercept_scaling=1.0,
dual=True,
verbose=0,
random_state=None,
max_iter=1000):
self._hyperparams = {
'epsilon': epsilon,
'tol': tol,
'C': C,
'loss': loss,
'fit_intercept': fit_intercept,
'intercept_scaling': intercept_scaling,
'dual': dual,
'verbose': verbose,
'random_state': random_state,
'max_iter': max_iter
}
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def predict(self, X):
return self._sklearn_model.predict(X)
X = scalerX.transform(X)
#print(userDF.userData)
y =userFeature.ix[:,feature]
lars_cv = linear_model.LassoLarsCV(cv=6).fit(X,y)
selector = feature_selection.SelectFromModel(lars_cv,prefit=True)
X = selector.transform(X)
selectors.append(selector)
print(feature)
print(X.shape)
print(y.shape)
clff.fit(X,y)
clfPersonality.append(clff)
i=i+1
samples = inputTools.sampleInputPd(inputFile)
lt.predictAgeLikesid(samples, clfAge, vectorizer= vectorizerA)
lt.predictGenderLikesid(samples, clfGender,vectorizer= vectorizerG)
samples.featureData.rename(columns={'userId':'userid'},inplace=True)
personalityData = pd.merge(samples.featureData,samples.userData,on='userid',how= 'right')