upgrade pyre version in fbcode/kats - batch 1

Differential Revision: D60993726

fbshipit-source-id: 9577a16262dc4a1f322639fdb0ba4507530df895

kats/detectors/cusum_model.py

@@ -348,8 +348,10 @@ def _if_normal(
             check_decrease = 0 if decrease else np.inf
 
         return (
+            # pyre-fixme[61]: `check_decrease` is undefined, or not always defined.
             self.pre_mean - check_decrease * self.pre_std
             <= cur_mean
+            # pyre-fixme[61]: `check_increase` is undefined, or not always defined.
             <= self.pre_mean + check_increase * self.pre_std
         )
 

kats/detectors/rolling_stats_model.py

@@ -166,6 +166,9 @@ def calculate_iqr_median_deviation(
         numerator, m, out=np.zeros_like(numerator), where=m != 0
     )
 
+    # pyre-fixme[16]: `int` has no attribute `__getitem__`.
+    # pyre-fixme[6]: For 1st argument expected `pyre_extensions.ReadOnly[Sized]` but
+    #  got `int`.
     return result[0] if len(result) == 1 else result
 
 

kats/models/globalmodel/model.py

@@ -552,6 +552,7 @@ def _format_fcst(
                 first_time[i], freq=self.params.freq, periods=steps
             )
             if "actual" in fcst_store:
+                # pyre-fixme[61]: `actual` is undefined, or not always defined.
                 df["actual"] = actual[i]
             ans[idx] = df
         return ans

kats/models/ml_ar.py

@@ -1228,7 +1228,9 @@ def _predict(
                 # fill rest of the data with forecasts
                 if new_data_is_forecast:
                     in_window.update(
-                        fc_wide[fc_wide.index <= fc_origin], overwrite=False
+                        # pyre-fixme[61]: `fc_wide` is undefined, or not always defined.
+                        fc_wide[fc_wide.index <= fc_origin],
+                        overwrite=False,
                     )
                 else:
                     # in case the new_data is actually earlier than the training data, we need to remove any data

kats/models/prophet.py

@@ -926,12 +926,14 @@ def sample_linear_predictive_trend_vectorize(
 
         # sample change points
         changepoint_ts_new = 1 + np.random.rand(sample_size, max_possion_num) * (T - 1)
+        # pyre-fixme[16]: `int` has no attribute `sort`.
         changepoint_ts_new.sort(axis=1)
 
         # create mask for deltas -> to mute some deltas based on number of change points
         mask = np.random.uniform(
             0, max_possion_num, max_possion_num * sample_size
         ).reshape(sample_size, -1)
+        # pyre-fixme[61]: `possion_sample` is undefined, or not always defined.
         mask = mask < possion_sample[:, None]
 
         # Sample deltas

kats/tests/detectors/test_prophet_detector.py

@@ -132,6 +132,7 @@ def create_weekend_seasonality_ts(
                 ts.extend(sim.stl_sim() + trend)
             rest_days_weekend = 2
             if i >= weeks:
+                # pyre-fixme[61]: `rest_days` is undefined, or not always defined.
                 rest_days_weekend = rest_days - rest_days_weekday
                 if rest_days_weekend == 0:
                     break

kats/tests/tsfeatures/test_tsfeatures.py

@@ -534,6 +534,7 @@ def test_IntegerArrays(self) -> None:
                     ],
                 }
             )
+        # pyre-fixme[61]: `df` is undefined, or not always defined.
         df["value"] = df["value"].astype(dtype=pd.Int64Dtype())
         # pyre-fixme[61]: `df` may not be initialized here.
         ts = TimeSeriesData(df)

kats/utils/decomposition.py

@@ -433,6 +433,7 @@ def _decompose_multi(self) -> None:
                 trend_jump=max(int((self.period + 1) * self.trend_jump_factor), 1),
             )
             assert self.decomp is not None
+            # pyre-fixme[16]: `Optional` has no attribute `__setitem__`.
             self.decomp[str(i)] = decomposer.decomposer()
 
     def remove_seasonality(self) -> TimeSeriesData: