re-enable Kats linting

Summary: Apply `pyfmt` to Kats.

Reviewed By: iamxiaodong

Differential Revision: D38137035

fbshipit-source-id: 876881d16c8a6b9fd1067f2f0abf9a593aeef941

kats/compat/sklearn.py

@@ -21,7 +21,13 @@ def mean_squared_error(
     squared: bool = True,
 ) -> float:
     # sklearn >= 1.0 expects args beyond the first two to be passed as named kwargs
-    return mse(y_true, y_pred, sample_weight=sample_weight, multioutput=multioutput, squared=squared)
+    return mse(
+        y_true,
+        y_pred,
+        sample_weight=sample_weight,
+        multioutput=multioutput,
+        squared=squared,
+    )
 
 
 def mean_squared_log_error(
@@ -32,11 +38,19 @@ def mean_squared_log_error(
     squared: bool = True,
 ) -> float:
     if version <= "0.24":
-        result = msle(y_true, y_pred, sample_weight=sample_weight, multioutput=multioutput)
+        result = msle(
+            y_true, y_pred, sample_weight=sample_weight, multioutput=multioutput
+        )
         if not squared:
             result = np.sqrt(result)
     else:
         # sklearn >= 1.0 expects args beyond the first two to be passed as named kwargs
         # pyre-ignore
-        result = msle(y_true, y_pred, sample_weight=sample_weight, multioutput=multioutput, squared=squared)
+        result = msle(
+            y_true,
+            y_pred,
+            sample_weight=sample_weight,
+            multioutput=multioutput,
+            squared=squared,
+        )
     return result

kats/compat/statsmodels.py

@@ -38,7 +38,6 @@ def __init__(self, results: holtwinters.HoltWintersResults) -> None:
                 results.level,
                 results.trend,
                 results.season,
-
                 results.params_formatted,
                 results.resid,
                 results.k,
@@ -69,7 +68,7 @@ def __init__(
         initial_trend: Optional[float] = None,
         missing: str = "none",
         seasonal_periods: Optional[int] = None,
-        use_boxcox: bool = False
+        use_boxcox: bool = False,
     ) -> None:
         if version < "0.12":
             self._use_boxcox = use_boxcox

kats/data/utils.py

@@ -22,7 +22,7 @@
 import os
 import pkgutil
 import sys
-from typing import Union, overload
+from typing import overload, Union
 
 if sys.version_info >= (3, 8):
     from typing import Literal
@@ -33,7 +33,7 @@
 from kats.consts import TimeSeriesData
 
 
-def load_data(file_name: str, reset_columns:bool=False) -> pd.DataFrame:
+def load_data(file_name: str, reset_columns: bool = False) -> pd.DataFrame:
     """load data for tests and tutorial notebooks"""
     ROOT = "kats"
     if "kats" in os.getcwd().lower():

kats/detectors/bocpd.py

@@ -16,24 +16,24 @@
 from enum import Enum
 from typing import (
     Any,
+    Callable,
+    cast,
     Dict,
     List,
     Optional,
     Sequence,
     Tuple,
     Type,
     Union,
-    cast,
-    Callable,
 )
 
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from kats.consts import TimeSeriesChangePoint, TimeSeriesData, SearchMethodEnum
+from kats.consts import SearchMethodEnum, TimeSeriesChangePoint, TimeSeriesData
 from kats.detectors.detector import Detector
 from scipy.special import logsumexp  # @manual
-from scipy.stats import invgamma, linregress, norm, nbinom  # @manual
+from scipy.stats import invgamma, linregress, nbinom, norm  # @manual
 
 try:
     import kats.utils.time_series_parameter_tuning as tpt
@@ -758,7 +758,9 @@ def __init__(
             self.P = 1
             self._ts_slice = 0
             data_df = self.data.to_dataframe()
-            self._ts_names = [x for x in data_df.columns if x != self.data.time_col_name]
+            self._ts_names = [
+                x for x in data_df.columns if x != self.data.time_col_name
+            ]
 
             self.data_values = np.expand_dims(data.value.values, axis=1)
 

kats/detectors/bocpd_model.py

@@ -104,7 +104,12 @@ def fit_predict(
         if historical_data is not None:
             historical_data.extend(data, validate=False)
             data = TimeSeriesData(
-                pd.DataFrame({"time": list(historical_data.time), "value": list(historical_data.value)})
+                pd.DataFrame(
+                    {
+                        "time": list(historical_data.time),
+                        "value": list(historical_data.value),
+                    }
+                )
             )
 
         bocpd_model = BOCPDetector(data=data)