def test_loader():
df = pd.read_csv(PEYTON_FILE, nrows=100)
df["A"] = np.arange(len(df))
df["B"] = np.arange(len(df)) * 0.1
df1 = df[:50]
df2 = df[50:]
m = NeuralProphet(
yearly_seasonality=True,
weekly_seasonality=True,
daily_seasonality=True,
n_lags=3,
n_forecasts=2,
)
m.add_future_regressor("A")
m.add_lagged_regressor("B")
config_normalization = configure.Normalization("auto", False, True, False)
df_dict = {"df1": df1.copy(), "df2": df2.copy()}
config_normalization.init_data_params(df_dict, m.config_covar,
m.regressors_config, m.events_config)
m.config_normalization = config_normalization
df_dict = m._normalize(df_dict)
dataset = m._create_dataset(df_dict, predict_mode=False)
loader = DataLoader(dataset,
batch_size=min(1024, len(df)),
shuffle=True,
drop_last=False)
for inputs, targets, meta in loader:
assert set(meta["df_name"]) == set(df_dict.keys())
break
def test_normalize():
length = 100
days = pd.date_range(start="2017-01-01", periods=length)
y = np.ones(length)
y[1] = 0
y[2] = 2
y[3] = 3.3
df = pd.DataFrame({"ds": days, "y": y})
m = NeuralProphet(normalize="soft", )
# with config
m.config_normalization.init_data_params(
df_utils.prep_copy_df_dict(df)[0], m.config_covar, m.regressors_config,
m.events_config)
df_norm = m._normalize(df_utils.prep_copy_df_dict(df)[0])
m.config_normalization.unknown_data_normalization = True
df_norm = m._normalize(df_utils.prep_copy_df_dict(df)[0])
m.config_normalization.unknown_data_normalization = False
# using config for utils
df_norm = df_utils.normalize(df.copy(deep=True),
m.config_normalization.global_data_params)
df_norm = df_utils.normalize(
df_utils.prep_copy_df_dict(df)[0]["__df__"],
m.config_normalization.local_data_params["__df__"])
# with utils
local_data_params, global_data_params = df_utils.init_data_params(
df_dict=df_utils.prep_copy_df_dict(df)[0],
normalize=m.config_normalization.normalize,
covariates_config=m.config_covar,
regressor_config=m.regressors_config,
events_config=m.events_config,
global_normalization=m.config_normalization.global_normalization,
global_time_normalization=m.config_normalization.
global_time_normalization,
)
df_norm = df_utils.normalize(df.copy(deep=True), global_data_params)
df_norm = df_utils.normalize(
df_utils.prep_copy_df_dict(df)[0]["__df__"],
local_data_params["__df__"])
