def do_svr(X, Y):
Y.ravel()
"""
Call the Support Vector Regressor,
Fit the weight on the training set
Input
------
X: dataframe, n*m, n is number of data points,
m is number of features
y: experimental electrical conductivity
Returns
------
svr: objective, the regressor objective
"""
svr = SVR(kernel='rbf',
degree=3,
gamma='auto',
coef0=0.0,
tol=0.001,
C=1.0,
epsilon=0.01,
shrinking=True,
cache_size=200,
verbose=False,
max_iter=-1)
grid_search = GridSearchCV(svr,
cv=5,
param_grid={
"C": [1e0, 1e1, 1e2, 1e3],
"gamma": np.logspace(-2, 2, 5)
})
grid_search.fit(X, Y)
svr.alpha_ = grid_search.best_params_['alpha']
svr.fit(X, Y)
return svr
