2/n remove fbprophet from Kats

Summary:
This diff fully removes the `fbprophet` dependency from Kats.

bye

Differential Revision: D67025007

fbshipit-source-id: 875ee7278e48042cf1d17ae69a14a62490fb0d7b

kats/detectors/prophet_detector.py

@@ -18,12 +18,6 @@
 
 import numpy as np
 import pandas as pd
-from fbprophet import Prophet as FbProphet
-from fbprophet.make_holidays import make_holidays_df as make_holidays_df_0
-from fbprophet.serialize import (
-    model_from_json as model_from_json_0,
-    model_to_json as model_to_json_0,
-)
 from kats.consts import (
     DataError,
     DataInsufficientError,
@@ -36,11 +30,8 @@
 from kats.detectors.detector_consts import AnomalyResponse, ConfidenceBand
 from kats.models.prophet import predict
 from prophet import Prophet
-from prophet.make_holidays import make_holidays_df as make_holidays_df_1
-from prophet.serialize import (
-    model_from_json as model_from_json_1,
-    model_to_json as model_to_json_1,
-)
+from prophet.make_holidays import make_holidays_df
+from prophet.serialize import model_from_json, model_to_json
 from pyre_extensions import ParameterSpecification
 from scipy.stats import norm
 
@@ -172,35 +163,6 @@ class ProphetScoreFunction(Enum):
     z_score = "z_score"
 
 
-class ProphetVersion(Enum):
-    fbprophet = "fbprophet"
-    prophet = "prophet"
-
-    def make_holidays_df(self, *args: P.args, **kwargs: P.kwargs) -> pd.DataFrame:
-        if self == ProphetVersion.fbprophet:
-            return make_holidays_df_0(*args, **kwargs)
-        else:
-            return make_holidays_df_1(*args, **kwargs)
-
-    def model_from_json(self, *args: P.args, **kwargs: P.kwargs) -> FbProphet | Prophet:
-        if self == ProphetVersion.fbprophet:
-            return model_from_json_0(*args, **kwargs)
-        else:
-            return model_from_json_1(*args, **kwargs)
-
-    def model_to_json(self, *args: P.args, **kwargs: P.kwargs) -> str:
-        if self == ProphetVersion.fbprophet:
-            return model_to_json_0(*args, **kwargs)
-        else:
-            return model_to_json_1(*args, **kwargs)
-
-    def create_prophet(self, *args: P.args, **kwargs: P.kwargs) -> FbProphet | Prophet:
-        if self == ProphetVersion.fbprophet:
-            return FbProphet(*args, **kwargs)
-        else:
-            return Prophet(*args, **kwargs)  # pyre-ignore
-
-
 SCORE_FUNC_DICT: Dict[str, Any] = {
     ProphetScoreFunction.deviation_from_predicted_val.value: deviation_from_predicted_val,
     ProphetScoreFunction.z_score.value: z_score,
@@ -324,7 +286,6 @@ def get_holiday_dates(
     holidays: Optional[pd.DataFrame] = None,
     country_holidays: Optional[str] = None,
     dates: Optional[pd.Series] = None,
-    prophet_version: ProphetVersion = ProphetVersion.prophet,
 ) -> pd.Series:
     if dates is None:
         return pd.Series()
@@ -333,7 +294,7 @@ def get_holiday_dates(
     if holidays is not None:
         all_holidays = holidays.copy()
     if country_holidays:
-        country_holidays_df = prophet_version.make_holidays_df(
+        country_holidays_df = make_holidays_df(
             year_list=year_list, country=country_holidays
         )
         all_holidays = pd.concat((all_holidays, country_holidays_df), sort=False)
@@ -343,17 +304,6 @@ def get_holiday_dates(
     return all_holidays
 
 
-def deserialize_model(
-    serialized_model: bytes,
-) -> Tuple[FbProphet | Prophet, ProphetVersion]:
-    model_json = json.loads(serialized_model)
-    if "__fbprophet_version" in model_json:
-        prophet_version = ProphetVersion.fbprophet
-    else:
-        prophet_version = ProphetVersion.prophet
-    return prophet_version.model_from_json(serialized_model), prophet_version
-
-
 class ProphetDetectorModel(DetectorModel):
     """Prophet based anomaly detection model.
 
@@ -373,8 +323,7 @@ class ProphetDetectorModel(DetectorModel):
 
     """
 
-    model: Optional[FbProphet | Prophet] = None
-    prophet_version: ProphetVersion = ProphetVersion.prophet
+    model: Optional[Prophet] = None
     seasonalities: Dict[SeasonalityTypes, Union[bool, str]] = {}
     seasonalities_to_fit: Dict[SeasonalityTypes, Union[bool, str]] = {}
 
@@ -423,7 +372,7 @@ def __init__(
         """
 
         if serialized_model:
-            self.model, self.prophet_version = deserialize_model(serialized_model)
+            self.model = model_from_json(serialized_model)
         else:
             self.model = None
 
@@ -465,7 +414,7 @@ def serialize(self) -> bytes:
         Returns:
             json containing information of the model.
         """
-        return str.encode(self.prophet_version.model_to_json(self.model))
+        return str.encode(model_to_json(self.model))
 
     def fit_predict(
         self,
@@ -529,7 +478,6 @@ def fit(
                 self.outlier_threshold,
                 uncertainty_samples=self.outlier_removal_uncertainty_samples,
                 vectorize=self.vectorize,
-                prophet_version=self.prophet_version,
             )
         # seasonalities depends on current time series
         self.seasonalities_to_fit = seasonalities_processing(
@@ -559,7 +507,7 @@ def fit(
             self.holidays = pd.DataFrame(self.holidays_list)
 
         # No incremental training. Create a model and train from scratch
-        model = self.prophet_version.create_prophet(
+        model = Prophet(
             interval_width=self.scoring_confidence_interval,
             uncertainty_samples=self.uncertainty_samples,
             daily_seasonality=self.seasonalities_to_fit[SeasonalityTypes.DAY],
@@ -651,7 +599,7 @@ def predict(
                 pd.DataFrame(self.holidays_list) if self.holidays_list else None
             )
             holidays_df: Optional[pd.Series] = get_holiday_dates(
-                custom_holidays, self.country_holidays, data.time, self.prophet_version
+                custom_holidays, self.country_holidays, data.time
             )
             if holidays_df is not None:
                 scores_ts = pd.Series(list(scores.value), index=data.time)
@@ -679,7 +627,6 @@ def _remove_outliers(
         outlier_ci_threshold: float = 0.99,
         uncertainty_samples: float = OUTLIER_REMOVAL_UNCERTAINTY_SAMPLES,
         vectorize: bool = False,
-        prophet_version: ProphetVersion = ProphetVersion.prophet,
     ) -> pd.DataFrame:
         """
         Remove outliers from the time series by fitting a Prophet model to the time series
@@ -689,7 +636,7 @@ def _remove_outliers(
 
         ts_dates_df = pd.DataFrame({PROPHET_TIME_COLUMN: ts_df.iloc[:, 0]})
 
-        model = prophet_version.create_prophet(
+        model = Prophet(
             interval_width=outlier_ci_threshold, uncertainty_samples=uncertainty_samples
         )
         with ExitStack() as stack:
@@ -711,8 +658,7 @@ def _remove_outliers(
 class ProphetTrendDetectorModel(DetectorModel):
     """Prophet based trend detection model."""
 
-    model: Optional[FbProphet | Prophet] = None
-    prophet_version: ProphetVersion = ProphetVersion.prophet
+    model: Optional[Prophet] = None
 
     def __init__(
         self,
@@ -722,7 +668,7 @@ def __init__(
         changepoint_prior_scale: float = 0.01,
     ) -> None:
         if serialized_model:
-            self.model, self.prophet_version = deserialize_model(serialized_model)
+            self.model = model_from_json(serialized_model)
         else:
             self.model = None
 
@@ -738,7 +684,7 @@ def serialize(self) -> bytes:
         Returns:
             json containing information of the model.
         """
-        return str.encode(self.prophet_version.model_to_json(self.model))
+        return str.encode(model_to_json(self.model))
 
     def _zeros_ts(self, data: pd.DataFrame) -> TimeSeriesData:
         return TimeSeriesData(
@@ -755,7 +701,7 @@ def fit_predict(
         historical_data: Optional[TimeSeriesData] = None,
         **kwargs: Any,
     ) -> AnomalyResponse:
-        model = self.prophet_version.create_prophet(
+        model = Prophet(
             changepoint_range=self.changepoint_range,
             weekly_seasonality=self.weekly_seasonality,
             changepoint_prior_scale=self.changepoint_prior_scale,

kats/detectors/threshold_detector.py

@@ -9,11 +9,11 @@
 
 import numpy as np
 import pandas as pd
-from fbprophet import Prophet
-from fbprophet.serialize import model_from_json, model_to_json
 from kats.consts import DEFAULT_VALUE_NAME, TimeSeriesData
 from kats.detectors.detector import DetectorModel
 from kats.detectors.detector_consts import AnomalyResponse
+from prophet import Prophet
+from prophet.serialize import model_from_json, model_to_json
 
 
 class StaticThresholdModel(DetectorModel):

kats/models/prophet.py

@@ -12,14 +12,12 @@
 
 try:
     # Prophet is an optional dependency for kats.
-    from fbprophet import Prophet as FbProphet
     from prophet import Prophet
 
     _no_prophet = False
 except ImportError:
     _no_prophet = True
     Prophet = Dict[str, Any]  # for Pyre
-    FbProphet = Dict[str, Any]  # for Pyre
 
 import numpy as np
 import numpy.typing as npt
@@ -138,7 +136,7 @@ def __init__(
         extra_regressors: Optional[List[Dict[str, Any]]] = None,
     ) -> None:
         if _no_prophet:
-            raise RuntimeError("requires fbprophet to be installed")
+            raise RuntimeError("requires prophet to be installed")
         super().__init__()
         self.growth = growth
         self.changepoints = changepoints
@@ -252,7 +250,7 @@ class ProphetModel(Model[ProphetParams]):
     def __init__(self, data: TimeSeriesData, params: ProphetParams) -> None:
         super().__init__(data, params)
         if _no_prophet:
-            raise RuntimeError("requires fbprophet to be installed")
+            raise RuntimeError("requires prophet to be installed")
         self.data: TimeSeriesData = data
         self._data_params_validation()
 
@@ -517,7 +515,7 @@ def get_parameter_search_space() -> List[Dict[str, object]]:
 
 # From now on, the main logics are from github PR https://github.com/facebook/prophet/pull/2186 with some modifications.
 def predict_uncertainty(
-    prophet_model: Prophet | FbProphet, df: pd.DataFrame, vectorized: bool
+    prophet_model: Prophet, df: pd.DataFrame, vectorized: bool
 ) -> pd.DataFrame:
     """Prediction intervals for yhat and trend.
 
@@ -548,7 +546,7 @@ def predict_uncertainty(
 
 
 def _sample_predictive_trend_vectorized(
-    prophet_model: Prophet | FbProphet,
+    prophet_model: Prophet,
     df: pd.DataFrame,
     n_samples: int,
     iteration: int = 0,
@@ -594,7 +592,7 @@ def _sample_predictive_trend_vectorized(
 
 
 def _sample_trend_uncertainty(
-    prophet_model: Prophet | FbProphet,
+    prophet_model: Prophet,
     n_samples: int,
     df: pd.DataFrame,
     iteration: int = 0,
@@ -683,7 +681,7 @@ def _make_trend_shift_matrix(
 
 
 def predict(
-    prophet_model: Prophet | FbProphet,
+    prophet_model: Prophet,
     df: Optional[pd.DataFrame] = None,
     vectorized: bool = False,
 ) -> pd.DataFrame:
@@ -730,7 +728,7 @@ def predict(
 
 
 def sample_model_vectorized(
-    prophet_model: Prophet | FbProphet,
+    prophet_model: Prophet,
     df: pd.DataFrame,
     seasonal_features: pd.DataFrame,
     iteration: int,
@@ -761,7 +759,7 @@ def sample_model_vectorized(
 
 
 def sample_posterior_predictive(
-    prophet_model: Prophet | FbProphet, df: pd.DataFrame, vectorized: bool
+    prophet_model: Prophet, df: pd.DataFrame, vectorized: bool
 ) -> Dict[str, npt.NDArray]:
     """Generate posterior samples of a trained Prophet model.
 
@@ -836,7 +834,7 @@ def _make_historical_mat_time(
 
 
 def _logistic_uncertainty(
-    prophet_model: Prophet | FbProphet,
+    prophet_model: Prophet,
     mat: npt.NDArray,
     deltas: npt.NDArray,
     k: float,
@@ -905,7 +903,7 @@ def _piecewise_linear_vectorize(
 
 
 def sample_linear_predictive_trend_vectorize(
-    prophet_model: Prophet | FbProphet,
+    prophet_model: Prophet,
     df: pd.DataFrame,
     sample_size: int,
     iteration: int,