def test_global_modeling_global_normalization():
### GLOBAL MODELLING - NO EXOGENOUS VARIABLES - GLOBAL NORMALIZATION
log.info("Global Modeling + Global Normalization")
df = pd.read_csv(PEYTON_FILE, nrows=512)
df1_0 = df.iloc[:128, :].copy(deep=True)
df2_0 = df.iloc[128:256, :].copy(deep=True)
df3_0 = df.iloc[256:384, :].copy(deep=True)
m = NeuralProphet(n_forecasts=2,
n_lags=10,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
global_normalization=True)
train_dict = {"df1": df1_0, "df2": df2_0}
test_dict = {"df3": df3_0}
m.fit(train_dict)
future = m.make_future_dataframe(test_dict)
forecast = m.predict(future)
metrics = m.test(test_dict)
forecast_trend = m.predict_trend(test_dict)
forecast_seasonal_componets = m.predict_seasonal_components(test_dict)
def test_global_modeling_no_exogenous_variable():
### GLOBAL MODELLING - NO EXOGENOUS VARIABLE
log.info("Global Modeling - No exogenous variables")
df = pd.read_csv(PEYTON_FILE, nrows=512)
df1_0 = df.iloc[:128, :].copy(deep=True)
df2_0 = df.iloc[128:256, :].copy(deep=True)
df3_0 = df.iloc[256:384, :].copy(deep=True)
df4_0 = df.iloc[384:, :].copy(deep=True)
train_input = {
0: df1_0,
1: {
"df1": df1_0,
"df2": df2_0
},
2: {
"df1": df1_0,
"df2": df2_0
}
}
test_input = {0: df3_0, 1: {"df1": df3_0}, 2: {"df1": df3_0, "df2": df4_0}}
info_input = {
0: "Testing df train / df test - no events, no regressors",
1: "Testing dict df train / df test - no events, no regressors",
2: "Testing dict df train / dict df test - no events, no regressors",
}
for i in range(0, 3):
log.info(info_input[i])
m = NeuralProphet(n_forecasts=2,
n_lags=10,
epochs=EPOCHS,
batch_size=BATCH_SIZE)
metrics = m.fit(train_input[i], freq="D")
forecast = m.predict(df=test_input[i])
forecast_trend = m.predict_trend(df=test_input[i])
forecast_seasonal_componets = m.predict_seasonal_components(
df=test_input[i])
if PLOT:
forecast = forecast if isinstance(forecast, list) else [forecast]
for key in forecast:
fig1 = m.plot(forecast[key])
fig2 = m.plot(forecast[key])
with pytest.raises(ValueError):
forecast = m.predict({"df4": df4_0})
log.info(
"Error - dict with names not provided in the train dict (not in the data params dict)"
)
with pytest.raises(ValueError):
metrics = m.test({"df4": df4_0})
log.info(
"Error - dict with names not provided in the train dict (not in the data params dict)"
)
m = NeuralProphet(
n_forecasts=2,
n_lags=10,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
m.fit({"df1": df1_0, "df2": df2_0}, freq="D")
with pytest.raises(ValueError):
forecast = m.predict({"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
with pytest.raises(ValueError):
metrics = m.test({"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
with pytest.raises(ValueError):
forecast_trend = m.predict_trend({"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
with pytest.raises(ValueError):
forecast_seasonal_componets = m.predict_seasonal_components(
{"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
# Set unknown_data_normalization to True - now there should be no errors
m.config_normalization.unknown_data_normalization = True
forecast = m.predict({"df4": df4_0})
metrics = m.test({"df4": df4_0})
forecast_trend = m.predict_trend({"df4": df4_0})
forecast_seasonal_componets = m.predict_seasonal_components({"df4": df4_0})
m.plot_parameters(df_name="df1")
m.plot_parameters()
