Quantile regression forest PyStata

[Madu Abuchi]

Please can someone suggest an approach to get the predictions from Quantile regression foress from python into Stata for further analysis. I am running python inside Stata. The example code is below:

python
# random forest
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=200, random_state=0,min_samples_split=10)
rf.fit(X_train, Y_train)

# Get the predictions of all trees for all observations
# Each observation has N predictions from the N trees
pred_Q = pd.DataFrame()
for pred in rf.estimators_:
    temp = pd.Series(pred.predict(X_test).round(2))
    pred_Q = pd.concat([pred_Q,temp],axis=1)
pred_Q.head()

RF_actual_pred = pd.DataFrame()

for q in quantiles:
    s = pred_Q.quantile(q=q, axis=1)
    RF_actual_pred = pd.concat([RF_actual_pred,s],axis=1,sort=False)
   
RF_actual_pred.columns=quantiles
RF_actual_pred['actual'] = Y_test
RF_actual_pred['interval'] = RF_actual_pred[np.max(quantiles)] - RF_actual_pred[np.min(quantiles)]
RF_actual_pred = RF_actual_pred.sort_values('interval')
RF_actual_pred = RF_actual_pred.round(2)
RF_actual_pred

# Get the R-squared
r2 = metrics.r2_score(RF_actual_pred['actual'], RF_actual_pred[0.5]).round(2)
print('R2 score is {}'.format(r2) )  # 0.81

# Get the correct percentage
correctPcnt(RF_actual_pred) # 0.9509

# Show the intervals
showIntervals(RF_actual_pred)

end