Predict fix (#281)

* fix regressor matrix extraction

* unit test for cases with mixed pos&reg regressors

orbit/models/dlt.py

@@ -193,7 +193,7 @@ def _predict(self, posterior_estimates, df=None, include_error=False, decompose=
         # calculate regression component
         if self.regressor_col is not None and len(self.regressor_col) > 0:
             regressor_beta = regressor_beta.t()
-            regressor_matrix = df[self.regressor_col].values
+            regressor_matrix = df[self._regressor_col].values
             regressor_torch = torch.from_numpy(regressor_matrix).double()
             regressor_component = torch.matmul(regressor_torch, regressor_beta)
             regressor_component = regressor_component.t()

orbit/models/lgt.py

@@ -224,6 +224,8 @@ def _set_static_regression_attributes(self):
                 self._regular_regressor_beta_prior.append(self._regressor_beta_prior[index])
                 self._regular_regressor_sigma_prior.append(self._regressor_sigma_prior[index])
 
+        self._regressor_col = self._positive_regressor_col + self._regular_regressor_col
+
     def _set_with_mcmc(self):
         estimator_type = self.estimator_type
         # set `_with_mcmc` attribute based on estimator type
@@ -521,7 +523,7 @@ def _predict(self, posterior_estimates, df, include_error=False, decompose=False
         # calculate regression component
         if self.regressor_col is not None and len(self.regressor_col) > 0:
             regressor_beta = regressor_beta.t()
-            regressor_matrix = df[self.regressor_col].values
+            regressor_matrix = df[self._regressor_col].values
             regressor_torch = torch.from_numpy(regressor_matrix).double()
             regressor_component = torch.matmul(regressor_torch, regressor_beta)
             regressor_component = regressor_component.t()

tests/orbit/models/test_dlt.py

@@ -1,4 +1,6 @@
 import pytest
+import numpy as np
+
 from orbit.models.dlt import BaseDLT, DLTFull, DLTAggregated, DLTMAP
 from orbit.estimators.stan_estimator import StanEstimatorMCMC, StanEstimatorVI, StanEstimatorMAP
 
@@ -251,3 +253,30 @@ def test_dlt_predict_all_positive_reg(iclaims_training_data):
     predicted_df = dlt.predict(df, decompose=True)
 
     assert any(predicted_df['regression'].values)
+
+def test_dlt_predict_mixed_regular_positive(iclaims_training_data):
+    df = iclaims_training_data
+
+    dlt = DLTMAP(
+        response_col='claims',
+        date_col='week',
+        regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
+        regressor_sign=['=', '+', '='],
+        seasonality=52,
+        seed=8888,
+    )
+    dlt.fit(df)
+    predicted_df = dlt.predict(df)
+
+    dlt_new = DLTMAP(
+        response_col='claims',
+        date_col='week',
+        regressor_col=['trend.unemploy', 'trend.job', 'trend.filling'],
+        regressor_sign=['=', '=', '+'],
+        seasonality=52,
+        seed=8888,
+    )
+    dlt_new.fit(df)
+    predicted_df_new = dlt_new.predict(df)
+
+    assert np.allclose(predicted_df['prediction'].values, predicted_df_new['prediction'].values)

tests/orbit/models/test_lgt.py

@@ -1,4 +1,5 @@
 import pytest
+import numpy as np
 
 from orbit.estimators.pyro_estimator import PyroEstimator, PyroEstimatorVI, PyroEstimatorMAP
 from orbit.estimators.stan_estimator import StanEstimator, StanEstimatorMCMC, StanEstimatorVI, StanEstimatorMAP
@@ -313,6 +314,33 @@ def test_lgt_predict_all_positive_reg(iclaims_training_data):
 
     assert any(predicted_df['regression'].values)
 
+def test_lgt_predict_mixed_regular_positive(iclaims_training_data):
+    df = iclaims_training_data
+
+    lgt = LGTMAP(
+        response_col='claims',
+        date_col='week',
+        regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
+        regressor_sign=['=', '+', '='],
+        seasonality=52,
+        seed=8888,
+    )
+    lgt.fit(df)
+    predicted_df = lgt.predict(df)
+
+    lgt_new = LGTMAP(
+        response_col='claims',
+        date_col='week',
+        regressor_col=['trend.unemploy', 'trend.job', 'trend.filling'],
+        regressor_sign=['=', '=', '+'],
+        seasonality=52,
+        seed=8888,
+    )
+    lgt_new.fit(df)
+    predicted_df_new = lgt_new.predict(df)
+
+    assert np.allclose(predicted_df['prediction'].values, predicted_df_new['prediction'].values)
+
 
 @pytest.mark.parametrize("prediction_percentiles", [None, [5, 10, 95]])
 def test_prediction_percentiles(iclaims_training_data, prediction_percentiles):