docstr: Test 1: VAR without CV
docstr_tok: ["Test","1",":","VAR","without","CV"]
code: def test_noCV(self):
      """
      Test 1: VAR without CV
      """
      print("\n\n" + "*" * 50)
      print("Performing Unit Test: 'test_noCV'")
      print("*" * 50 + "\n\n")
  
      automl_model = ATS(
          score_type="rmse",
          forecast_period=self.forecast_period,
          time_interval="Month",
          non_seasonal_pdq=None,
          seasonality=False,
          seasonal_period=12,
          model_type="VAR",
          verbose=0,
      )
      automl_model.fit(
          traindata=self.train_multivar,
          ts_column=self.ts_column,
          target=self.target,
          cv=None,
          sep=self.sep,
      )
  
      ml_dict = automl_model.get_ml_dict()
  
      ######################
      ## External Results ##
      ######################
  
      # Simple forecast with forecast window = the one used in training
      # Using named model
      test_predictions = automl_model.predict(
          testdata=self.test_multivar[
              [self.ts_column] + self.preds
          ],  # Not needed for VAR
          forecast_period=self.forecast_period,
          model="VAR",
      )
      assert_series_equal(
          test_predictions["mean"].round(6),
          self.forecast_gold_var_multivar_series,
      )
  
      # Simple forecast with forecast window != the one used in training
      # Using named model
      test_predictions = automl_model.predict(
          testdata=self.test_multivar[
              [self.ts_column] + self.preds
          ],  # Not needed for VAR
          forecast_period=10,
          model="VAR",
      )
      assert_series_equal(
          test_predictions["mean"].round(6),
          self.forecast_gold_var_multivar_series_10,
      )
  
      # Complex forecasts (returns confidence intervals, etc.)
      test_predictions = automl_model.predict(
          testdata=self.test_multivar[
              [self.ts_column] + self.preds
          ],  # Not needed for VAR
          forecast_period=self.forecast_period,
          model="VAR",
          simple=False,
      )
      self.assertIsNone(
          np.testing.assert_array_equal(
              test_predictions.columns.values,
              self.expected_pred_col_names,
          )
      )
  
      ###################
      ## ML Dictionary ##
      ###################
      self.assertIsNone(
          np.testing.assert_array_equal(
              np.round(
                  ml_dict.get("VAR")
                  .get("forecast")[0]["mean"]
                  .values.astype(np.double),
                  6,
              ),
              self.forecast_gold_var_multivar_internal_val_cv_fold2,
          ),
          "(Multivar Test) VAR Forecast does not match up with expected values.",
      )
  
      self.assertEqual(
          round(ml_dict.get("VAR").get("rmse")[0], 8),
          self.rmse_gold_var_multivar_cv_fold2,
          "(Multivar Test) VAR RMSE does not match up with expected values.",
      )
  
file_path: Auto_TS/auto_ts/test/test_var.py
imports: import sys
  import os
  import unittest
  import math
  import numpy
  import pandas
  from pandas.testing import assert_series_equal
  from pandas.testing import assert_frame_equal
  from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
  from auto_ts import auto_timeseries
label_desc: Use image node_id 2 for calling the TestVAR obj's underlying member method code with example usage: obj.test_noCV() without return types