Removing neuralprophet

Summary:
Removing neuralprophet in its own diff to isolate downstream impact

(second version of D63468756 which was reverted)

Reviewed By: islijepcevic

Differential Revision: D64188388

fbshipit-source-id: 16c8394214db0a719cd0a905638ce74c21ad99c0

kats/models/metalearner/metalearner_hpt.py

@@ -29,7 +29,6 @@
 from sklearn.model_selection import train_test_split
 
 _MODELS = {
-    "neuralprophet",
     "prophet",
     "arima",
     "sarima",
@@ -56,8 +55,6 @@ class DefaultModelParams:
     theta_numerical_idx: List[str] = field(default_factory=list)
     stlf_categorical_idx: List[str] = field(default_factory=list)
     stlf_numerical_idx: List[str] = field(default_factory=list)
-    neuralprophet_categorical_idx: List[str] = field(default_factory=list)
-    neuralprophet_numerical_idx: List[str] = field(default_factory=list)
     prophet_categorical_idx: List[str] = field(default_factory=list)
     prophet_numerical_idx: List[str] = field(default_factory=list)
     cusum_categorical_idx: List[str] = field(default_factory=list)
@@ -81,14 +78,6 @@ def __init__(self) -> None:
         self.theta_numerical_idx = []
         self.stlf_categorical_idx = ["method", "m"]
         self.stlf_numerical_idx = []
-        self.neuralprophet_categorical_idx = [
-            "yearly_seasonality",
-            "weekly_seasonality",
-            "daily_seasonality",
-            "seasonality_mode",
-            "changepoints_range",
-        ]
-        self.neuralprophet_numerical_idx = []
         self.prophet_categorical_idx = [
             "yearly_seasonality",
             "weekly_seasonality",
@@ -126,9 +115,6 @@ class DefaultModelNetworks:
     stlf_n_hidden_shared: List[int] = field(default_factory=list)
     stlf_n_hidden_cat_combo: List[List[int]] = field(default_factory=list)
     stlf_n_hidden_num: List[int] = field(default_factory=list)
-    neuralprophet_n_hidden_shared: List[int] = field(default_factory=list)
-    neuralprophet_n_hidden_cat_combo: List[List[int]] = field(default_factory=list)
-    neuralprophet_n_hidden_num: List[int] = field(default_factory=list)
     prophet_n_hidden_shared: List[int] = field(default_factory=list)
     prophet_n_hidden_cat_combo: List[List[int]] = field(default_factory=list)
     prophet_n_hidden_num: List[int] = field(default_factory=list)
@@ -155,9 +141,6 @@ def __init__(self) -> None:
         self.stlf_n_hidden_shared = [20]
         self.stlf_n_hidden_cat_combo = [[5], [5]]
         self.stlf_n_hidden_num = []
-        self.neuralprophet_n_hidden_shared = [40]
-        self.neuralprophet_n_hidden_cat_combo = [[5], [5], [2], [3], [5]]
-        self.neuralprophet_n_hidden_num = []
         self.prophet_n_hidden_shared = [40]
         self.prophet_n_hidden_cat_combo = [[5], [5], [2], [3], [5], [5], [5]]
         self.prophet_n_hidden_num = []
@@ -187,7 +170,7 @@ class MetaLearnHPT:
         categorical_idx: Optional; A list of strings of the names of the categorical hyper-parameters. Default is None.
         numerical_idx: Optional; A list of strings of the names of the numerical hyper-parameters. Default is None.
         default_model: Optional; A string of the name of the forecast model whose default settings will be used.
-                       Can be 'arima', 'sarima', 'theta', 'neuralprophet', 'prophet', 'holtwinters', 'stlf' or None. Default is None.
+                       Can be 'arima', 'sarima', 'theta', 'prophet', 'holtwinters', 'stlf' or None. Default is None.
         scale: Optional; A boolean to specify whether or not to normalize time series features to zero mean and unit variance. Default is True.
         load_model: Optional; A boolean to specify whether or not to load a trained model. Default is False.
 
@@ -259,7 +242,7 @@ def __init__(
                     categorical_idx = getattr(default_model_params, categorical_idx_var)
                     numerical_idx = getattr(default_model_params, numerical_idx_var)
                 else:
-                    msg = f"default_model={default_model} is not available! Please choose one from 'neuralprophet', 'prophet', 'arima', 'sarima', 'holtwinters', 'stlf', 'theta', 'cusum', 'statsig'"
+                    msg = f"default_model={default_model} is not available! Please choose one from 'prophet', 'arima', 'sarima', 'holtwinters', 'stlf', 'theta', 'cusum', 'statsig'"
                     raise _log_error(msg)
 
             if (not numerical_idx) and (not categorical_idx):

kats/tests/models/test_metalearner.py

@@ -24,7 +24,6 @@
 from kats.models.metalearner.metalearner_hpt import MetaLearnHPT
 from kats.models.metalearner.metalearner_modelselect import MetaLearnModelSelect
 from kats.models.metalearner.metalearner_predictability import MetaLearnPredictability
-from kats.models.neuralprophet import NeuralProphetModel, NeuralProphetParams
 from kats.models.prophet import ProphetModel, ProphetParams
 from kats.models.sarima import SARIMAModel, SARIMAParams
 from kats.models.stlf import STLFModel, STLFParams
@@ -77,7 +76,6 @@
     "arima": ARIMAModel,
     "holtwinters": HoltWintersModel,
     "sarima": SARIMAModel,
-    "neuralprophet": NeuralProphetModel,
     "prophet": ProphetModel,
     "stlf": STLFModel,
     "theta": ThetaModel,
@@ -160,14 +158,12 @@ def generate_meta_data_by_model(model, n, d=num_features):
         "sarima",
         "theta",
         "stlf",
-        "neuralprophet",
         "prophet",
     ]
 }
 
 candidate_models = {
     "holtwinters": HoltWintersModel,
-    "neuralprophet": NeuralProphetModel,
     "prophet": ProphetModel,
     "theta": ThetaModel,
     "stlf": STLFModel,
@@ -176,7 +172,6 @@ def generate_meta_data_by_model(model, n, d=num_features):
 
 candidate_params = {
     "holtwinters": HoltWintersParams,
-    "neuralprophet": NeuralProphetParams,
     "prophet": ProphetParams,
     "theta": ThetaParams,
     "stlf": STLFParams,
@@ -455,7 +450,6 @@ def test_default_models(self) -> None:
             feature3.copy(),
         )
         for model in [
-            "neuralprophet",
             "prophet",
             "arima",
             "sarima",