c_weights = ['auto']
param_grid = tuple([c_vals, c_weights, g_vals])
param_grid = list(product(*param_grid))
# storage structure for forecasts
mvalid = np.zeros((xtrain.shape[0],len(param_grid)))
mfull = np.zeros((xtest.shape[0],len(param_grid)))
## build 2nd level forecasts
for i in range(len(param_grid)):
print("processing parameter combo:", param_grid[i])
# configure model with j-th combo of parameters
x = param_grid[i]
model.C = x[0]
model.class_weight = x[1]
model.gamma = x[2]
# loop over folds
for j in range(0,n_folds):
print("Running fold", j+1)
idx0 = np.where(fold_index != j)
idx1 = np.where(fold_index == j)
x0 = np.array(xtrain)[idx0,:][0];
x1 = np.array(xtrain)[idx1,:][0]
y0 = np.array(ytrain)[idx0];
y1 = np.array(ytrain)[idx1]
# fit the model on observations associated with subject whichSubject in this fold
model.fit(x0, y0)
mvalid[idx1,i] = model.predict_proba(x1)[:,1]
c_weights = ['auto']
param_grid = tuple([c_vals, c_weights, g_vals])
param_grid = list(product(*param_grid))
# storage structure for forecasts
mvalid = np.zeros((xtrain.shape[0],len(param_grid)))
mfull = np.zeros((xtest.shape[0],len(param_grid)))
## build 2nd level forecasts
for i in range(len(param_grid)):
print "processing parameter combo:", param_grid[i]
# configure model with j-th combo of parameters
x = param_grid[i]
model.C = x[0]
model.class_weight = x[1]
model.gamma = x[2]
# loop over folds
for j in range(0,n_folds):
print "Running fold", j+1
idx0 = np.where(fold_index != j)
idx1 = np.where(fold_index == j)
x0 = np.array(xtrain)[idx0,:][0];
x1 = np.array(xtrain)[idx1,:][0]
y0 = np.array(ytrain)[idx0];
y1 = np.array(ytrain)[idx1]
# fit the model on observations associated with subject whichSubject in this fold
model.fit(x0, y0)
mvalid[idx1,i] = model.predict_proba(x1)[:,1]
