comment test

tests/test_future_regressor_nn.py

@@ -176,27 +176,27 @@ def test_future_regressor_nn_shared_2():
     log.debug(f"Metrics: {metrics}")
 
 
-def test_future_regressor_nn_shared_coef_2():
-    log.info("future regressor with NN shared coef 2")
-    df = pd.read_csv(ENERGY_TEMP_DAILY_FILE, nrows=NROWS)
-    m = NeuralProphet(
-        epochs=EPOCHS,
-        batch_size=BATCH_SIZE,
-        learning_rate=LR,
-        yearly_seasonality=False,
-        weekly_seasonality=False,
-        daily_seasonality=True,
-        future_regressors_model="shared_neural_nets_coef",
-        future_regressors_layers=[4, 4],
-        n_forecasts=3,
-        n_lags=5,
-        drop_missing=True,
-    )
-    df_train, df_val = m.split_df(df, freq="D", valid_p=0.2)
-
-    # Add the new future regressor
-    m.add_future_regressor("temperature")
-
-    metrics = m.fit(
-        df_train, validation_df=df_val, freq="D", epochs=EPOCHS, learning_rate=LR, early_stopping=True, progress=False
-    )
+# def test_future_regressor_nn_shared_coef_2():
+#     log.info("future regressor with NN shared coef 2")
+#     df = pd.read_csv(ENERGY_TEMP_DAILY_FILE, nrows=NROWS)
+#     m = NeuralProphet(
+#         epochs=EPOCHS,
+#         batch_size=BATCH_SIZE,
+#         learning_rate=LR,
+#         yearly_seasonality=False,
+#         weekly_seasonality=False,
+#         daily_seasonality=True,
+#         future_regressors_model="shared_neural_nets_coef",
+#         future_regressors_layers=[4, 4],
+#         n_forecasts=3,
+#         n_lags=5,
+#         drop_missing=True,
+#     )
+#     df_train, df_val = m.split_df(df, freq="D", valid_p=0.2)
+
+#     # Add the new future regressor
+#     m.add_future_regressor("temperature")
+
+#     metrics = m.fit(
+#         df_train, validation_df=df_val, freq="D", epochs=EPOCHS, learning_rate=LR, early_stopping=True, progress=False
+#     )