Revert D63468756: Removing neuralprophet

Differential Revision:
D63468756

Original commit changeset: 8480bb727d3a

Original Phabricator Diff: D63468756

fbshipit-source-id: 93c1a55bf5936533b0b3d185779ee952141a305d

kats/models/metalearner/metalearner_hpt.py

@@ -29,6 +29,7 @@
 from sklearn.model_selection import train_test_split
 
 _MODELS = {
+    "neuralprophet",
     "prophet",
     "arima",
     "sarima",
@@ -55,6 +56,8 @@ 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)
@@ -78,6 +81,14 @@ 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",
@@ -115,6 +126,9 @@ 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)
@@ -141,6 +155,9 @@ 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 = []
@@ -170,7 +187,7 @@ 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', 'prophet', 'holtwinters', 'stlf' or None. Default is None.
+                       Can be 'arima', 'sarima', 'theta', 'neuralprophet', '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.
 
@@ -242,7 +259,7 @@ 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 'prophet', 'arima', 'sarima', 'holtwinters', 'stlf', 'theta', 'cusum', 'statsig'"
+                    msg = f"default_model={default_model} is not available! Please choose one from 'neuralprophet', '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

@@ -24,6 +24,7 @@
 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
@@ -76,6 +77,7 @@
     "arima": ARIMAModel,
     "holtwinters": HoltWintersModel,
     "sarima": SARIMAModel,
+    "neuralprophet": NeuralProphetModel,
     "prophet": ProphetModel,
     "stlf": STLFModel,
     "theta": ThetaModel,
@@ -158,12 +160,14 @@ 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,
@@ -172,6 +176,7 @@ def generate_meta_data_by_model(model, n, d=num_features):
 
 candidate_params = {
     "holtwinters": HoltWintersParams,
+    "neuralprophet": NeuralProphetParams,
     "prophet": ProphetParams,
     "theta": ThetaParams,
     "stlf": STLFParams,
@@ -450,6 +455,7 @@ def test_default_models(self) -> None:
             feature3.copy(),
         )
         for model in [
+            "neuralprophet",
             "prophet",
             "arima",
             "sarima",