Diminishing returns of external regressors

[royrvd]

Discussed in #635

^{Originally posted by royrvd November 28, 2021}
Hey Orbit team!

While trying to calculate the diminishing returns of each regressor from the demo using the following code:

def calc_regressor_diminishing_return(model, df, regressor):
    reg_max = df[regressor].max()
    reg_min = df[regressor].min()
    preds = []
    sample_df = df.iloc[[0]].copy()
    for i in np.linspace(reg_min, reg_max, num=50):
        sample_df[regressor] = i
        pred = np.exp(model.predict(sample_df)["prediction"].values[0])
        preds.append(pred)
    return preds

def plot_diminishing_returns(model, df, regressors):
    ncols = 2
    nrows = int(np.ceil(len(regressors) / 2))
    fig, axes = plt.subplots(figsize=(15, 15), ncols=ncols, nrows=nrows)
    for ax, reg in zip(axes.flatten(), regressors):
        diminishing_returns = calc_regressor_diminishing_return(model, df, reg)
        ax.plot(diminishing_returns)
        ax.set_title(reg)
    return fig

plot_diminishing_returns(ktr, rw_data, regressor_col)

I got non-monotonic curves. Does that make sense or could that be an error?

[edwinnglabs]

Hi @royrvd , if I understand correctly, you are performing some non-linear operation on the MCMC / Full Bayesian model prediction. I guess there are couple of sources you need to look into:

1. you probably want to do a point estimate instead since full Bayesian prediction involves the quantile function which is not associative with non-linear operations; so the fix is to use the arg point_method='median'
2. if you really want to use Full Bayesian, you should get the whole posterior distributions out and perform non-linear process first and then the quantile function. However, it may not solve the problem completely
3. there could be randomness due to the seed in various call of MCMC prediction, chance is you need to fix the seed in order to iteratively call the prediction

Hope that makes sense!

[royrvd]

I'll try it out, thanks!