Removed commented tests in test_uncertainty.py. (#1032)

Co-authored-by: Richard Stromer <noxan@users.noreply.github.com>
ourownstory · Dec 3, 2022 · 9f90fcc · 9f90fcc
1 parent cc19b2f
commit 9f90fcc
Showing 1 changed file with 0 additions and 131 deletions.
diff --git a/tests/test_uncertainty.py b/tests/test_uncertainty.py
@@ -215,134 +215,3 @@ def test_split_conformal_prediction():
             fig2 = m.plot_components(forecast)
             fig3 = m.plot_parameters()
             plt.show()
-
-
-"""
-def test_uncertainty_estimation_plot():
-    log.info("testing: Quantile Regression Plotting")
-    df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
-    # Without auto-regression enabled
-    m = NeuralProphet(
-        n_forecasts=7,
-        quantiles=[0.25, 0.75],
-        epochs=EPOCHS,
-        batch_size=BATCH_SIZE,
-        learning_rate=LR,
-    )
-    metrics_df = m.fit(df, freq="D")
-    future = m.make_future_dataframe(df, periods=m.n_forecasts, n_historic_predictions=10)
-    forecast = m.predict(future)
-    fig0 = m.plot(forecast)
-    # fig0 = m.plot_latest_forecast(forecast, include_previous_forecasts=10)
-    fig1 = m.plot_components(forecast)
-    fig2 = m.plot_parameters()
-    if PLOT:
-        plt.show()
-    # With auto-regression enabled
-    m = NeuralProphet(
-        n_forecasts=7,
-        n_lags=14,
-        quantiles=[0.25, 0.75],
-        epochs=EPOCHS,
-        batch_size=BATCH_SIZE,
-        learning_rate=LR,
-    )
-    metrics_df = m.fit(df, freq="D")
-    future = m.make_future_dataframe(df, periods=m.n_forecasts, n_historic_predictions=10)
-    forecast = m.predict(future)
-    m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
-    fig0 = m.plot(forecast)
-    fig1 = m.plot_latest_forecast(forecast, include_previous_forecasts=10)
-    fig2 = m.plot_latest_forecast(forecast, include_previous_forecasts=10, plot_history_data=True)
-    fig3 = m.plot_latest_forecast(forecast, include_previous_forecasts=10, plot_history_data=False)
-    fig4 = m.plot_components(forecast)
-    fig5 = m.plot_parameters()
-    if PLOT:
-        plt.show()
-    ## Global Model Plot
-    df1 = df.copy(deep=True)
-    df1["ID"] = "df1"
-    df2 = df.copy(deep=True)
-    df2["ID"] = "df2"
-    df_global = pd.concat((df1, df2))
-    m = NeuralProphet(
-        n_forecasts=7,
-        n_lags=14,
-        quantiles=[0.25, 0.75],
-        epochs=EPOCHS,
-        batch_size=BATCH_SIZE,
-        learning_rate=LR,
-    )
-    metrics_df = m.fit(df_global, freq="D")
-    future = m.make_future_dataframe(df_global, periods=m.n_forecasts, n_historic_predictions=10)
-    forecast = m.predict(future)
-    m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
-    log.info("Plot forecast with many IDs - Raise exceptions")
-    with pytest.raises(Exception):
-        m.plot(forecast)
-    with pytest.raises(Exception):
-        m.plot_latest_forecast(forecast, include_previous_forecasts=10)
-    with pytest.raises(Exception):
-        m.plot_components(forecast)
-    forecast = m.predict(df_global)
-    with pytest.raises(Exception):
-        m.plot(forecast)
-    with pytest.raises(Exception):
-        m.plot_latest_forecast(forecast, include_previous_forecasts=10)
-    with pytest.raises(Exception):
-        m.plot_components(forecast)
-
-
-def test_split_conformal_prediction_plot():
-    log.info("testing: Split Conformal Prediction Plotting")
-    df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
-    # Without auto-regression enabled
-    m = NeuralProphet(
-        n_forecasts=7,
-        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)
-    metrics_df = m.fit(train_df, freq="D")
-    alpha = 0.1
-    for method in ["naive", "cqr"]:  # Naive and CQR SCP methods
-        m.conformalize(cal_df, alpha=alpha, method=method)
-        future = m.make_future_dataframe(test_df, periods=m.n_forecasts, n_historic_predictions=10)
-        forecast = m.predict(future)
-        fig0 = m.plot(forecast)
-        # fig0 = m.plot_latest_forecast(forecast, include_previous_forecasts=10)
-        fig1 = m.plot_components(forecast)
-        fig2 = m.plot_parameters()
-        if PLOT:
-            plt.show()
-    # With auto-regression enabled
-    # TO-DO: Fix Assertion error n_train >= 1
-    # m = NeuralProphet(
-    #     n_forecasts=7,
-    #     n_lags=14,
-    #     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)
-    # metrics_df = m.fit(train_df, freq="D")
-    # alpha = 0.1
-    # for method in ["naive", "cqr"]:  # Naive and CQR SCP methods
-    #     m.conformalize(cal_df, alpha=alpha, method=method)
-    #     future = m.make_future_dataframe(df, periods=m.n_forecasts, n_historic_predictions=10)
-    #     forecast = m.predict(future)
-    #     m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
-    #     fig0 = m.plot(forecast)
-    #     fig1 = m.plot_latest_forecast(forecast, include_previous_forecasts=10)
-    #     fig2 = m.plot_latest_forecast(forecast, include_previous_forecasts=10, plot_history_data=True)
-    #     fig3 = m.plot_latest_forecast(forecast, include_previous_forecasts=10, plot_history_data=False)
-    #     fig4 = m.plot_components(forecast)
-    #     fig5 = m.plot_parameters()
-    #     if PLOT:
-    #         plt.show()
-"""