code: def test_asymmetrical_quantiles():
      log.info(
          "testing: Naive Split Conformal Prediction and Conformalized Quantile Regression "
          "with asymmetrical quantiles"
      )
      df = pd.read_csv(AIR_FILE)
      m = NeuralProphet(
          seasonality_mode="multiplicative",
          loss_func="MSE",
          quantiles=[0.05, 0.95],
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
  
      train_df, test_df = m.split_df(df, freq="MS", valid_p=0.2)
      train_df, cal_df = m.split_df(train_df, freq="MS", valid_p=0.15)
      m.fit(train_df, freq="MS")
  
      alpha = (0.03, 0.07)
      decompose = False
      future = m.make_future_dataframe(
          test_df,
          periods=50,
          n_historic_predictions=len(test_df),
      )
  
      # should raise value error if method is naive and alpha is not a float
      method = "naive"
      with pytest.raises(ValueError):
          forecast = m.conformal_predict(
              future,
              calibration_df=cal_df,
              alpha=alpha,
              method=method,
              decompose=decompose,
          )
  
      # should not raise value error if method is cqr and alpha is not a float
      method = "cqr"
      forecast = m.conformal_predict(
          future,
          calibration_df=cal_df,
          alpha=alpha,
          method=method,
          decompose=decompose,
      )
      uncertainty_evaluate(forecast)
  
file_path: neural_prophet/tests/test_uncertainty.py
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet, uncertainty_evaluate
label_desc: Use image node_id 6 for calling a global function with example usage: test_asymmetrical_quantiles() without return types