Fix saving/loading ProphetModel

CHANGELOG.md

@@ -34,6 +34,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 -
 -
 - Remove documentation warning about using pickle in saving/loading catboost ([#1020](https://github.com/tinkoff-ai/etna/pull/1020))
+- Fix saving/loading ProphetModel ([#1019](https://github.com/tinkoff-ai/etna/pull/1019))
+-
 -
 ## [1.13.0] - 2022-10-10
 ### Added

etna/models/prophet.py

@@ -1,3 +1,4 @@
+from copy import deepcopy
 from datetime import datetime
 from typing import Dict
 from typing import Iterable
@@ -16,6 +17,8 @@
 
 if SETTINGS.prophet_required:
     from prophet import Prophet
+    from prophet.serialize import model_from_dict
+    from prophet.serialize import model_to_dict
 
 
 class _ProphetAdapter(BaseAdapter):
@@ -62,11 +65,16 @@ def __init__(
         self.stan_backend = stan_backend
         self.additional_seasonality_params = additional_seasonality_params
 
-        self.model = Prophet(
+        self.model = self._create_model()
+
+        self.regressor_columns: Optional[List[str]] = None
+
+    def _create_model(self) -> "Prophet":
+        model = Prophet(
             growth=self.growth,
-            changepoints=changepoints,
-            n_changepoints=n_changepoints,
-            changepoint_range=changepoint_range,
+            changepoints=self.changepoints,
+            n_changepoints=self.n_changepoints,
+            changepoint_range=self.changepoint_range,
             yearly_seasonality=self.yearly_seasonality,
             weekly_seasonality=self.weekly_seasonality,
             daily_seasonality=self.daily_seasonality,
@@ -84,7 +92,7 @@ def __init__(
         for seasonality_params in self.additional_seasonality_params:
             self.model.add_seasonality(**seasonality_params)
 
-        self.regressor_columns: Optional[List[str]] = None
+        return model
 
     def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_ProphetAdapter":
         """
@@ -154,6 +162,33 @@ def get_model(self) -> Prophet:
         """
         return self.model
 
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        try:
+            model_dict = model_to_dict(self.model)
+            is_fitted = True
+        except ValueError:
+            is_fitted = False
+            model_dict = {}
+        del state["model"]
+        state["_is_fitted"] = is_fitted
+        state["_model_dict"] = model_dict
+        return state
+
+    def __setstate__(self, state):
+        local_state = deepcopy(state)
+        is_fitted = local_state["_is_fitted"]
+        model_dict = local_state["_model_dict"]
+        del local_state["_is_fitted"]
+        del local_state["_model_dict"]
+
+        self.__dict__.update(local_state)
+
+        if is_fitted:
+            self.model = model_from_dict(model_dict)
+        else:
+            self.model = self._create_model()
+
 
 class ProphetModel(
     PerSegmentModelMixin, PredictionIntervalContextIgnorantModelMixin, PredictionIntervalContextIgnorantAbstractModel
@@ -165,11 +200,6 @@ class ProphetModel(
     Original Prophet can use features 'cap' and 'floor',
     they should be added to the known_future list on dataset initialization.
 
-    Warning
-    -------
-    Currently, pickle is used in ``save`` and  ``load`` methods.
-    It can work unreliably according to `documentation <https://facebook.github.io/prophet/docs/additional_topics.html>`_.
-
     Examples
     --------
     >>> from etna.datasets import generate_periodic_df

etna/transforms/outliers/point_outliers.py

@@ -122,13 +122,7 @@ def detect_outliers(self, ts: TSDataset) -> Dict[str, List[pd.Timestamp]]:
 
 
 class PredictionIntervalOutliersTransform(OutliersTransform):
-    """Transform that uses :py:func:`~etna.analysis.outliers.prediction_interval_outliers.get_anomalies_prediction_interval` to find anomalies in data.
-
-    Warning
-    -------
-    Currently, pickle is used in ``save`` and  ``load`` methods.
-    It can work unreliably according to `documentation <https://facebook.github.io/prophet/docs/additional_topics.html>`_.
-    """
+    """Transform that uses :py:func:`~etna.analysis.outliers.prediction_interval_outliers.get_anomalies_prediction_interval` to find anomalies in data."""
 
     def __init__(
         self,

tests/test_models/test_prophet.py

@@ -2,9 +2,11 @@
 import pandas as pd
 import pytest
 from prophet import Prophet
+from prophet.serialize import model_to_dict
 
 from etna.datasets.tsdataset import TSDataset
 from etna.models import ProphetModel
+from etna.models.prophet import _ProphetAdapter
 from etna.pipeline import Pipeline
 from tests.test_models.utils import assert_model_equals_loaded_original
 
@@ -123,6 +125,78 @@ def test_get_model_after_training(example_tsds):
         assert isinstance(models_dict[segment], Prophet)
 
 
+@pytest.fixture
+def prophet_default_params():
+    params = {
+        "growth": "linear",
+        "changepoints": None,
+        "n_changepoints": 25,
+        "changepoint_range": 0.8,
+        "yearly_seasonality": "auto",
+        "weekly_seasonality": "auto",
+        "daily_seasonality": "auto",
+        "holidays": None,
+        "seasonality_mode": "additive",
+        "seasonality_prior_scale": 10.0,
+        "holidays_prior_scale": 10.0,
+        "changepoint_prior_scale": 0.05,
+        "mcmc_samples": 0,
+        "interval_width": 0.8,
+        "uncertainty_samples": 1000,
+        "stan_backend": None,
+        "additional_seasonality_params": (),
+    }
+    return params
+
+
+def test_getstate_not_fitted(prophet_default_params):
+    model = _ProphetAdapter()
+    state = model.__getstate__()
+    expected_state = {
+        "_is_fitted": False,
+        "_model_dict": {},
+        "regressor_columns": None,
+        **prophet_default_params,
+    }
+    assert state == expected_state
+
+
+def test_getstate_fitted(example_tsds, prophet_default_params):
+    model = _ProphetAdapter()
+    df = example_tsds.to_pandas()["segment_1"].reset_index()
+    model.fit(df, regressors=[])
+    state = model.__getstate__()
+    expected_state = {
+        "_is_fitted": True,
+        "_model_dict": model_to_dict(model.model),
+        "regressor_columns": [],
+        **prophet_default_params,
+    }
+    assert state == expected_state
+
+
+def test_setstate_not_fitted():
+    model_1 = _ProphetAdapter(n_changepoints=25)
+    initial_state = model_1.__getstate__()
+
+    model_2 = _ProphetAdapter(n_changepoints=20)
+    model_2.__setstate__(initial_state)
+    new_state = model_2.__getstate__()
+    assert new_state == initial_state
+
+
+def test_setstate_fitted(example_tsds):
+    model_1 = _ProphetAdapter()
+    df = example_tsds.to_pandas()["segment_1"].reset_index()
+    model_1.fit(df, regressors=[])
+    initial_state = model_1.__getstate__()
+
+    model_2 = _ProphetAdapter()
+    model_2.__setstate__(initial_state)
+    new_state = model_2.__getstate__()
+    assert new_state == initial_state
+
+
 @pytest.mark.xfail(reason="Non native serialization, should be fixed in inference-v2.0")
 def test_save_load(example_tsds):
     model = ProphetModel()

tests/test_transforms/test_outliers/test_outliers_transform.py

@@ -178,7 +178,6 @@ def test_save_load(transform, outliers_solid_tsds):
     assert_transformation_equals_loaded_original(transform=transform, ts=outliers_solid_tsds)
 
 
-@pytest.mark.xfail(reason="Non native serialization, should be fixed in inference-v2.0")
 @pytest.mark.parametrize(
     "transform",
     (