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[typing] Add ConfigEvents type annotations #989

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merged 3 commits into from
Nov 30, 2022
Merged

[typing] Add ConfigEvents type annotations #989

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d3be88d
add ConfigEvents type annotations
noxan Nov 24, 2022
48593eb
resolve weird import error
noxan Nov 24, 2022
4df76c3
fix isort
noxan Nov 24, 2022
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2 changes: 1 addition & 1 deletion neuralprophet/configure.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ def init_data_params(
df,
config_lagged_regressors: Optional[ConfigLaggedRegressors] = None,
config_regressors=None,
config_events=None,
config_events: Optional[ConfigEvents] = None,
):
if len(df["ID"].unique()) == 1:
if not self.global_normalization:
Expand Down
17 changes: 12 additions & 5 deletions neuralprophet/df_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
import pandas as pd

if TYPE_CHECKING:
from neuralprophet.configure import ConfigLaggedRegressors
from neuralprophet.configure import ConfigEvents, ConfigLaggedRegressors


log = logging.getLogger("NP.df_utils")
Expand Down Expand Up @@ -140,7 +140,7 @@ def data_params_definition(
normalize,
config_lagged_regressors: Optional[ConfigLaggedRegressors] = None,
config_regressors=None,
config_events=None,
config_events: Optional[ConfigEvents] = None,
):
"""
Initialize data scaling values.
Expand Down Expand Up @@ -229,7 +229,7 @@ def init_data_params(
normalize="auto",
config_lagged_regressors: Optional[ConfigLaggedRegressors] = None,
config_regressors=None,
config_events=None,
config_events: Optional[ConfigEvents] = None,
global_normalization=False,
global_time_normalization=False,
):
Expand Down Expand Up @@ -947,7 +947,14 @@ def split_df(df, n_lags, n_forecasts, valid_p=0.2, inputs_overbleed=True, local_


def make_future_df(
df_columns, last_date, periods, freq, config_events=None, events_df=None, config_regressors=None, regressors_df=None
df_columns,
last_date,
periods,
freq,
config_events: Optional[ConfigEvents] = None,
events_df=None,
config_regressors=None,
regressors_df=None,
):
"""Extends df periods number steps into future.

Expand Down Expand Up @@ -996,7 +1003,7 @@ def make_future_df(
return future_df


def convert_events_to_features(df, config_events, events_df):
def convert_events_to_features(df, config_events: Optional[ConfigEvents], events_df):
"""
Converts events information into binary features of the df

Expand Down
4 changes: 2 additions & 2 deletions neuralprophet/time_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -204,7 +204,7 @@ def tabularize_univariate_datetime(
n_forecasts=1,
predict_steps=1,
config_season=None,
config_events=None,
config_events: Optional[configure.ConfigEvents] = None,
config_country_holidays=None,
config_lagged_regressors: Optional[configure.ConfigLaggedRegressors] = None,
config_regressors: Optional[configure.ConfigFutureRegressors] = None,
Expand Down Expand Up @@ -477,7 +477,7 @@ def make_country_specific_holidays_df(year_list, country):
return country_specific_holidays_dict


def make_events_features(df, config_events=None, config_country_holidays=None):
def make_events_features(df, config_events: Optional[configure.ConfigEvents] = None, config_country_holidays=None):
"""
Construct arrays of all event features

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6 changes: 3 additions & 3 deletions neuralprophet/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
from neuralprophet import utils_torch

if TYPE_CHECKING:
from neuralprophet.configure import ConfigLaggedRegressors
from neuralprophet.configure import ConfigEvents, ConfigLaggedRegressors

log = logging.getLogger("NP.utils")

Expand Down Expand Up @@ -116,7 +116,7 @@ def reg_func_season(weights):
return reg_func_abs(weights)


def reg_func_events(config_events, config_country_holidays, model):
def reg_func_events(config_events: Optional[ConfigEvents], config_country_holidays, model):
"""
Regularization of events coefficients to induce sparcity

Expand Down Expand Up @@ -307,7 +307,7 @@ def get_holidays_from_country(country, df=None):
return set(holiday_names)


def config_events_to_model_dims(config_events, config_country_holidays):
def config_events_to_model_dims(config_events: Optional[ConfigEvents], config_country_holidays):
"""
Convert user specified events configurations along with country specific
holidays to input dims for TimeNet model.
Expand Down