n_estimators=args.n_estimators,
learning_rate=args.lr,
natural_gradient=args.natural,
minibatch_frac=args.minibatch_frac,
verbose=True,
)
ngb.fit(x_tr, y_tr)
x_te, y_te, _ = gen_data(n=1000, bound=1.3)
x_te = poly_transform.transform(x_te)
preds = ngb.pred_dist(x_te)
pctles, obs, _, _ = calibration_regression(preds, y_te)
all_preds = ngb.staged_pred_dist(x_te)
preds = all_preds[-1]
plt.figure(figsize=(6, 3))
plt.scatter(x_tr[:, 1], y_tr, color="black", marker=".", alpha=0.5)
plt.plot(
x_te[:, 1],
preds.loc,
color="black",
linestyle="-",
linewidth=1,
label="Predicted mean",
)
plt.plot(
x_te[:, 1],
preds.loc - 1.96 * preds.scale,
color="black",
y_true += list(y_test.flatten())
ngb = NGBoost(Base=base_name_to_learner[args.base],
Dist=eval(args.distn),
Score=score_name_to_score[args.score](64),
n_estimators=args.n_est,
learning_rate=args.lr,
natural_gradient=args.natural,
minibatch_frac=args.minibatch_frac,
verbose=args.verbose)
train_loss, val_loss = ngb.fit(X_train, y_train) #, X_val, y_val)
y_preds = ngb.staged_predict(X_val)
y_forecasts = ngb.staged_pred_dist(X_val)
val_rmse = [mean_squared_error(y_pred, y_val) for y_pred in y_preds]
val_nll = [-y_forecast.logpdf(y_val.flatten()).mean() for y_forecast in y_forecasts]
best_itr = np.argmin(val_rmse) + 1
best_itr = np.argmin(val_nll) + 1
full_retrain = True
if full_retrain:
ngb = NGBoost(Base=base_name_to_learner[args.base],
Dist=eval(args.distn),
Score=score_name_to_score[args.score](64),
n_estimators=args.n_est,
learning_rate=args.lr,
natural_gradient=args.natural,
minibatch_frac=args.minibatch_frac,
verbose=args.verbose)
