radix-ai / conformal-tights Goto Github PK

Hi Laurent,

 I have trained a conformal predictor but I need to save it. I have tried pickle dump and save model but it does not work. Is there a way to save the model? This is really important because I am getting really good results and I want to save the model for use later again in production. Let me know if this is possible. Thanks.

~ Charlie

Hi there. I am trying to run the regression example but I when I try to load the library below

from conformal_tights import ConformalCoherentQuantileRegressor

I get the error

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
[<ipython-input-2-ee841138eade>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from conformal_tights import ConformalCoherentQuantileRegressor
      2 from sklearn.datasets import fetch_openml
      3 from sklearn.model_selection import train_test_split
      4 from xgboost import XGBRegressor

2 frames
[/usr/local/lib/python3.10/dist-packages/conformal_tights/_darts_forecaster.py](https://localhost:8080/#) in DartsForecaster()
     79         *,
     80         # Default darts.models.RegressionModel parameters.
---> 81         lags: LAGS_TYPE | None = None,
     82         lags_past_covariates: LAGS_TYPE | None = None,
     83         lags_future_covariates: FUTURE_LAGS_TYPE | None = None,

TypeError: unsupported operand type(s) for |: 'NoneType' and 'NoneType'

Not sure why it is trying to load the DartsForecaster here. Any suggestions? Thanks.

~ C

Hi,

I was keen to use this package since when I've tried to do conformal prediction using quantile regression in the past I've encountered the common issue of quantile forecast results not increasing monotonically.

I see this package aimed to solve that but I am still seeing some NOT monotonic results in my quantiles! It seemed to work before this change feat: support pre-fitted estimators (https://github.com/radix-ai/conformal-tights/pull/19).

I think it could be because I'm passing a fitted model lgbm model into ConformalCoherentQuantileRegressor and so the part that uses XGBoost to model the quantiles doesn't happen possibly.

Hi, I am trying to run conformal tights on my local machine, however even after a pip install conformal tights and pip install darts, I get the above error message (in title). Is there something I'm missing?

Hi, interesting work!
I will keep an eye on this project for sure!

Quick question: Will this be valid for timeseries? I am specifically thinking about whether you have considered if the splitting should be shuffled=false?

(Also the classification addition from another post sounds interesting!)

Great package. Is this approach adaptable for classification tasks?