def plot_predictions(prog_num, bp, inX, iny):
plt.cla()
X_bp, y_bp = ascdata.get_bp_data(prog_num, bp, inX, iny)
iny_pred = clf.predict(X_bp)
plot_inputs = range(1,11)
plt.plot(plot_inputs, y_bp.flatten(), 'bx')
plt.plot(plot_inputs, iny_pred.flatten(),'ro')
plt.savefig("multitask_" + str(prog_num) + "_" + str(bp) + ".png")
bp_rms = ascdata.multi_RMSE(y_bp, iny_pred)
bp_r2 = ascdata.multi_r2(y_bp, iny_pred)
print "rms total for prognum", prog_num, "at breakpoint", bp, bp_rms
print "r2 total for prognum", prog_num, "at breakpoint", bp, bp_r2
def get_kfold_scores(inX, iny, k):
N = inX.shape[0]
kf = KFold(N, k, shuffle=True)
mses=[]
r2s = []
for train_index, test_index in kf:
kf_X_train, kf_X_test = inX[train_index], inX[test_index]
kf_y_train, kf_y_test = iny[train_index], iny[test_index]
kf_y_test_pred = clf.predict(kf_X_test)
mse = ascdata.multi_MSE(kf_y_test, kf_y_test_pred)
mses.append(mse)
print "mse:", mse
r2 = ascdata.multi_r2(kf_y_test, kf_y_test_pred)
r2s.append(r2)
print "r2:", r2
overall_mse = np.mean(mses)
overall_rmse = sqrt(overall_mse)
overall_r2 = np.mean(r2s)
print "KFold cross validation MSE:", overall_mse
print "KFold cross validation RMSE:", overall_rmse
print "KFold cross validation r2:", overall_r2
