def test_train_eval_test(self):
log.info("testing: Train Eval Test")
m = NeuralProphet(
n_lags=10,
n_forecasts=3,
ar_sparsity=0.1,
epochs=3,
batch_size=32,
)
df = pd.read_csv(PEYTON_FILE, nrows=95)
df = df_utils.check_dataframe(df, check_y=False)
df = m._handle_missing_data(df, freq="D", predicting=False)
df_train, df_test = m.split_df(df,
freq="D",
valid_p=0.1,
inputs_overbleed=True)
metrics = m.fit(df_train,
freq="D",
validate_each_epoch=True,
valid_p=0.1)
metrics = m.fit(df_train, freq="D")
val_metrics = m.test(df_test)
log.debug("Metrics: train/eval: \n {}".format(
metrics.to_string(float_format=lambda x: "{:6.3f}".format(x))))
log.debug("Metrics: test: \n {}".format(
val_metrics.to_string(float_format=lambda x: "{:6.3f}".format(x))))
def check_split(df_in,
df_len_expected,
n_lags,
n_forecasts,
freq,
p=0.1):
m = NeuralProphet(
n_lags=n_lags,
n_forecasts=n_forecasts,
)
df_in = df_utils.check_dataframe(df_in, check_y=False)
df_in = m._handle_missing_data(df_in, freq=freq, predicting=False)
assert df_len_expected == len(df_in)
total_samples = len(df_in) - n_lags - 2 * n_forecasts + 2
df_train, df_test = m.split_df(df_in,
freq=freq,
valid_p=0.1,
inputs_overbleed=True)
n_train = len(df_train) - n_lags - n_forecasts + 1
n_test = len(df_test) - n_lags - n_forecasts + 1
assert total_samples == n_train + n_test
n_test_expected = max(1, int(total_samples * p))
n_train_expected = total_samples - n_test_expected
assert n_train == n_train_expected
assert n_test == n_test_expected
