condense code

evalml/pipelines/components/transformers/preprocessing/decomposer.py

@@ -190,6 +190,34 @@ def _detrend_on_fly(X, y):
         relative_maxima = _get_rel_max_from_acf(y_detrended)
         return relative_maxima
 
+    # def set_period(
+    #     self,
+    #     X: pd.DataFrame,
+    #     y: pd.Series,
+    #     acf_threshold: float = 0.01,
+    #     rel_max_order: int = 5,
+    # ):
+    #     """Function to set the component's seasonal period based on the target's seasonality.
+
+    #     Args:
+    #         X (pandas.DataFrame): The feature data of the time series problem.
+    #         y (pandas.Series): The target data of a time series problem.
+    #         acf_threshold (float) : The threshold for the autocorrelation function to determine the period. Any values below
+    #             the threshold are considered to be 0 and will not be considered for the period. Defaults to 0.01.
+    #         rel_max_order (int) : The order of the relative maximum to determine the period. Defaults to 5.
+
+    #     """
+    #     self.periods = {}
+    #     if len(y.columns) == 1:
+    #         self.period = self.determine_periodicity(X, y, acf_threshold, rel_max_order)
+    #         self.update_parameters({"period": self.period})
+    #         self.periods[id] = self.period
+    #         return
+    #     else:
+    #         for id in y.columns:
+    #             self.periods[id] = self.determine_periodicity(X, y[id], acf_threshold, rel_max_order)
+    #     self.update_parameters({"periods": self.periods})
+
     def set_period(
         self,
         X: pd.DataFrame,
@@ -356,9 +384,6 @@ def plot_decomposition(
             fig, axs = plt.subplots(4)
             fig.set_size_inches(18.5, 14.5)
 
-            for ax in axs:
-                ax.cla()
-
             if len(y.columns) > 1:
                 results = decomposition_results[id]
             else:

evalml/pipelines/components/transformers/preprocessing/stl_decomposer.py

@@ -45,12 +45,14 @@ def __init__(
         series_id: str = None,
         degree: int = 1,  # Currently unused.
         period: int = None,
+        periods: dict = None,
         seasonal_smoother: int = 7,
         random_seed: int = 0,
         **kwargs,
     ):
         self.logger = logging.getLogger(__name__)
         self.series_id = series_id
+        self.periods = periods
         # Programmatically adjust seasonal_smoother to fit underlying STL requirements,
         # that seasonal_smoother must be odd.
         if seasonal_smoother % 2 == 0:
@@ -64,6 +66,7 @@ def __init__(
         parameters = {
             "degree": degree,
             "period": period,
+            "periods": periods,
             "seasonal_smoother": seasonal_smoother,
             "time_index": time_index,
             "series_id": series_id,
@@ -189,10 +192,22 @@ def fit(
         self.frequency = y.index.freqstr or pd.infer_freq(y.index)
         # Iterate through each id group
         self.seasonals = {}
-        self.periods = {}
         self.seasonalities = {}
         self.trends = {}
         self.residuals = {}
+        self.periods = {}
+
+        # # Determine the period of the seasonal component
+        # # Set the period if it is single series and period is given
+        # if self.period is not None and len(y.columns) == 1:
+        #     self.periods = {0: self.period}
+        # # Set periods if it is single series and period is
+        # if self.periods is None or self.period is None:
+        #     self.set_period(X, y)
+
+        # if self.period is None:
+        #     self.set_period(X, y)
+
         for id in y.columns:
             series_y = y[id]
 
@@ -347,14 +362,13 @@ def inverse_transform(
         if isinstance(y_t, pd.Series):
             y_t = y_t.to_frame()
 
+        index = self._choose_proper_index(y_t)
         y = []
         for id in y_t.columns:
             y_in_sample = pd.Series([])
             y_out_of_sample = pd.Series([])
             series_y = y_t[id]
 
-            index = self._choose_proper_index(series_y)
-
             if len(y_t.columns) > 1:
                 old_trend = self.trends[id]
                 old_seasonal = self.seasonals[id]
@@ -454,13 +468,9 @@ def get_trend_dataframe(self, X, y):
 
         def _decompose_target(X, y, fh, trend, seasonal, residual, period, id):
             """Function to generate a single DataFrame with trend, seasonality and residual components."""
-            if len(y.index) == len(trend.index) and all(
+            if len(y.index) != len(trend.index) or not all(
                 y.index == trend.index,
             ):
-                trend = trend
-                seasonal = seasonal
-                residual = residual
-            else:
                 # TODO: Do a better job cloning.
                 decomposer = STLDecomposer(
                     seasonal_smoother=self.seasonal_smoother,