uncomment future reg tests

ourownstory · Aug 31, 2024 · ca1cb84 · ca1cb84
1 parent a4a277a
commit ca1cb84
Showing 1 changed file with 87 additions and 85 deletions.
diff --git a/tests/test_future_regressor_nn.py b/tests/test_future_regressor_nn.py
@@ -82,69 +82,69 @@ def test_future_reg_nn_shared():
         plt.show()
 
 
-# def test_future_reg_nn_shared_coef():
-#     log.info("testing: Future Regressors modelled with NNs shared coef")
-#     df = pd.read_csv(PEYTON_FILE, nrows=NROWS + 50)
-#     m = NeuralProphet(
-#         epochs=EPOCHS, batch_size=BATCH_SIZE, learning_rate=LR, future_regressors_model="shared_neural_nets_coef"
-#     )
-#     df["A"] = df["y"].rolling(7, min_periods=1).mean()
-#     df["B"] = df["y"].rolling(30, min_periods=1).mean()
-#     df["C"] = df["y"].rolling(7, min_periods=1).mean()
-#     df["D"] = df["y"].rolling(30, min_periods=1).mean()
-
-#     regressors_df_future = pd.DataFrame(
-#         data={"A": df["A"][-50:], "B": df["B"][-50:], "C": df["C"][-50:], "D": df["D"][-50:]}
-#     )
-#     df = df[:-50]
-#     m = m.add_future_regressor(name="A")
-#     m = m.add_future_regressor(name="B", mode="additive")
-#     m = m.add_future_regressor(name="C", mode="multiplicative")
-#     m = m.add_future_regressor(name="D", mode="multiplicative")
-#     m.fit(df, freq="D")
-#     future = m.make_future_dataframe(df=df, regressors_df=regressors_df_future, n_historic_predictions=10, periods=50)
-#     forecast = m.predict(df=future)
-#     if PLOT:
-#         m.plot(forecast)
-#         m.plot_components(forecast)
-#         m.plot_parameters()
-#         plt.show()
-
-
-# def test_future_regressor_nn_2():
-#     log.info("future regressor with NN")
-
-#     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="neural_nets",  # 'linear' default or 'neural_nets'
-#         future_regressors_layers=[4, 4],
-#         n_forecasts=3,
-#         n_lags=5,
-#         drop_missing=True,
-#         # trainer_config={"accelerator": "gpu"},
-#     )
-#     df_train, df_val = m.split_df(df, freq="D", valid_p=0.2)
-
-#     # Use static plotly in notebooks
-#     # m.set_plotting_backend("plotly")
-
-#     # Add the new future regressor
-#     m.add_future_regressor("temperature")
-
-#     # Add counrty holidays
-#     m.add_country_holidays("IT", mode="additive", lower_window=-1, upper_window=1)
-
-#     metrics = m.fit(
-#         df_train, validation_df=df_val, freq="D", epochs=EPOCHS, learning_rate=LR, early_stopping=True, progress=False
-#     )
-#     log.debug(f"Metrics: {metrics}")
+def test_future_reg_nn_shared_coef():
+    log.info("testing: Future Regressors modelled with NNs shared coef")
+    df = pd.read_csv(PEYTON_FILE, nrows=NROWS + 50)
+    m = NeuralProphet(
+        epochs=EPOCHS, batch_size=BATCH_SIZE, learning_rate=LR, future_regressors_model="shared_neural_nets_coef"
+    )
+    df["A"] = df["y"].rolling(7, min_periods=1).mean()
+    df["B"] = df["y"].rolling(30, min_periods=1).mean()
+    df["C"] = df["y"].rolling(7, min_periods=1).mean()
+    df["D"] = df["y"].rolling(30, min_periods=1).mean()
+
+    regressors_df_future = pd.DataFrame(
+        data={"A": df["A"][-50:], "B": df["B"][-50:], "C": df["C"][-50:], "D": df["D"][-50:]}
+    )
+    df = df[:-50]
+    m = m.add_future_regressor(name="A")
+    m = m.add_future_regressor(name="B", mode="additive")
+    m = m.add_future_regressor(name="C", mode="multiplicative")
+    m = m.add_future_regressor(name="D", mode="multiplicative")
+    m.fit(df, freq="D")
+    future = m.make_future_dataframe(df=df, regressors_df=regressors_df_future, n_historic_predictions=10, periods=50)
+    forecast = m.predict(df=future)
+    if PLOT:
+        m.plot(forecast)
+        m.plot_components(forecast)
+        m.plot_parameters()
+        plt.show()
+
+
+def test_future_regressor_nn_2():
+    log.info("future regressor with NN")
+
+    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="neural_nets",  # 'linear' default or 'neural_nets'
+        future_regressors_layers=[4, 4],
+        n_forecasts=3,
+        n_lags=5,
+        drop_missing=True,
+        # trainer_config={"accelerator": "gpu"},
+    )
+    df_train, df_val = m.split_df(df, freq="D", valid_p=0.2)
+
+    # Use static plotly in notebooks
+    # m.set_plotting_backend("plotly")
+
+    # Add the new future regressor
+    m.add_future_regressor("temperature")
+
+    # Add counrty holidays
+    m.add_country_holidays("IT", mode="additive", lower_window=-1, upper_window=1)
+
+    metrics = m.fit(
+        df_train, validation_df=df_val, freq="D", epochs=EPOCHS, learning_rate=LR, early_stopping=True, progress=False
+    )
+    log.debug(f"Metrics: {metrics}")
 
 
 def test_future_regressor_nn_shared_2():
@@ -176,25 +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
+    )