added plot_series

atom/atom.py

@@ -2078,7 +2078,7 @@ def _run(self, trainer: BaseRunner):
                 self._delete_models(model.name)
                 self._log(
                     f"Consecutive runs of model {model.name}. "
-                    "The former model has been overwritten.", 1,
+                    "The former model has been overwritten.", 3,
                 )
 
         self._models.extend(trainer._models)

atom/basemodel.py

@@ -50,6 +50,7 @@
 )
 from sklearn.utils import resample
 from sklearn.utils.metaestimators import available_if
+from sktime.forecasting.base import ForecastingHorizon
 from sktime.forecasting.compose import make_reduction
 from sktime.proba.normal import Normal
 from sktime.split import SingleWindowSplitter
@@ -61,18 +62,18 @@
 from atom.plots import RunnerPlot
 from atom.utils.constants import DF_ATTRS
 from atom.utils.types import (
-    HT, Backend, Bool, DataFrame, Engine, FHSelector, Float, FloatZeroToOneExc,
-    Index, Int, IntLargerEqualZero, MetricConstructor, MetricFunction, NJobs,
-    Pandas, PredictionMethods, PredictionMethodsTS, Predictor, RowSelector,
-    Scalar, Scorer, Sequence, Stages, TargetSelector, Verbose, Warnings,
-    XSelector, YSelector, dataframe_t, float_t, int_t,
+    HT, Backend, Bool, DataFrame, Engine, Float, FloatZeroToOneExc, Index, Int,
+    IntLargerEqualZero, MetricConstructor, MetricFunction, NJobs, Pandas,
+    PredictionMethods, PredictionMethodsTS, Predictor, RowSelector, Scalar,
+    Scorer, Sequence, Stages, TargetSelector, Verbose, Warnings, XSelector,
+    YSelector, dataframe_t, float_t, int_t,
 )
 from atom.utils.utils import (
     ClassMap, DataConfig, Goal, PlotCallback, ShapExplanation, Task,
     TrialsCallback, adjust_verbosity, bk, cache, check_dependency, check_empty,
     check_is_fitted, check_scaling, composed, crash, estimator_has_attr,
     fit_and_score, flt, get_cols, get_custom_scorer, has_task, it, lst, merge,
-    method_to_log, rnd, sign, time_to_str, to_df, to_pandas, to_series,
+    method_to_log, rnd, sign, time_to_str, to_pandas,
 )
 
 
@@ -540,7 +541,7 @@ def _fit_estimator(
                 # Multi-objective optimization doesn't support pruning
                 if trial and len(self._metric) == 1:
                     trial.report(
-                        self._score_from_est(self._metric[0], estimator, *validation),
+                        value=float(self._score_from_est(self._metric[0], estimator, *validation)),
                         step=step,
                     )
 
@@ -726,15 +727,16 @@ def _score_from_est(
 
         """
         if self.task.is_forecast:
-            # Sktime uses signature estimator.predict(fh, X)
-            y_pred = to_series(estimator.predict(y.index, check_empty(X)), index=y.index)
-            return self._score_from_pred(scorer, y, y_pred, **kwargs)
-        elif self.task is Task.multiclass_multioutput_classification:
-            # Calculate predictions with shape=(n_samples, n_targets)
-            y_pred = to_df(estimator.predict(X), index=y.index, columns=y.columns)
-            return self._score_from_pred(scorer, y, y_pred, **kwargs)
+            y_pred = estimator.predict(fh=y.index, X=check_empty(X))
         else:
-            return scorer(estimator, X, y, **kwargs)
+            y_pred = to_pandas(
+                data=estimator.predict(X),
+                index=y.index,
+                columns=getattr(y, "columns", None),
+                name=getattr(y, "name", None),
+            )
+
+        return self._score_from_pred(scorer, y, y_pred, **kwargs)
 
     def _score_from_pred(
         self,
@@ -3012,13 +3014,17 @@ def _prediction(
             called.
 
         """
-        Xt, yt = X, y  # self.transform(X, y, verbose=verbose)  TODO: Fix pipeline ts
+        Xt, yt = self.transform(X, y, verbose=verbose)
 
         if method != "score":
+            fh = kwargs.get("fh")
+            if fh is not None and not isinstance(fh, ForecastingHorizon):
+                kwargs["fh"] = self.branch._get_rows(fh).index
+
             if "y" in sign(func := getattr(self.estimator, method)):
-                return self.memory.cache(func)(y=yt, X=Xt, **kwargs)
+                return self.memory.cache(func)(fh=fh, y=yt, X=Xt, **kwargs)
             else:
-                return self.memory.cache(func)(X=Xt, **kwargs)
+                return self.memory.cache(func)(fh=fh, X=Xt, **kwargs)
         else:
             if metric is None:
                 scorer = self._metric[0]
@@ -3031,7 +3037,7 @@ def _prediction(
     @composed(crash, method_to_log, beartype)
     def predict(
         self,
-        fh: FHSelector,
+        fh: RowSelector | ForecastingHorizon,
         X: XSelector | None = None,
         *,
         verbose: Int | None = None,
@@ -3046,9 +3052,9 @@ def predict(
 
         Parameters
         ----------
-        fh: int, range, sequence or [ForecastingHorizon][]
-            The forecasting horizon encoding the time stamps to
-            forecast at.
+        fh: hashable, segment, sequence, dataframe or [ForecastingHorizon][]
+            The [forecasting horizon][row-and-column-selection] encoding
+            the time stamps to forecast at.
 
         X: hashable, segment, sequence, dataframe-like or None, default=None
             Exogenous time series corresponding to `fh`.
@@ -3070,7 +3076,7 @@ def predict(
     @composed(crash, method_to_log, beartype)
     def predict_interval(
         self,
-        fh: FHSelector,
+        fh: RowSelector | ForecastingHorizon,
         X: XSelector | None = None,
         *,
         coverage: Float | Sequence[Float] = 0.9,
@@ -3086,9 +3092,9 @@ def predict_interval(
 
         Parameters
         ----------
-        fh: int, range, sequence or [ForecastingHorizon][]
-            The forecasting horizon encoding the time stamps to
-            forecast at.
+        fh: hashable, segment, sequence, dataframe or [ForecastingHorizon][]
+            The [forecasting horizon][row-and-column-selection] encoding
+            the time stamps to forecast at.
 
         X: hashable, segment, sequence, dataframe-like or None, default=None
             Exogenous time series corresponding to `fh`.
@@ -3119,7 +3125,7 @@ def predict_interval(
     @composed(crash, method_to_log, beartype)
     def predict_proba(
         self,
-        fh: FHSelector,
+        fh: RowSelector | ForecastingHorizon,
         X: XSelector | None = None,
         *,
         marginal: Bool = True,
@@ -3135,9 +3141,9 @@ def predict_proba(
 
         Parameters
         ----------
-        fh: int, range, sequence or [ForecastingHorizon][]
-            The forecasting horizon encoding the time stamps to
-            forecast at.
+        fh: hashable, segment, sequence, dataframe or [ForecastingHorizon][]
+            The [forecasting horizon][row-and-column-selection] encoding
+            the time stamps to forecast at.
 
         X: hashable, segment, sequence, dataframe-like or None, default=None
             Exogenous time series corresponding to `fh`.
@@ -3167,7 +3173,7 @@ def predict_proba(
     @composed(crash, method_to_log, beartype)
     def predict_quantiles(
         self,
-        fh: FHSelector,
+        fh: RowSelector | ForecastingHorizon,
         X: XSelector | None = None,
         *,
         alpha: Float | Sequence[Float] = (0.05, 0.95),
@@ -3183,9 +3189,9 @@ def predict_quantiles(
 
         Parameters
         ----------
-        fh: int, range, sequence or [ForecastingHorizon][]
-            The forecasting horizon encoding the time stamps to
-            forecast at.
+        fh: hashable, segment, sequence, dataframe or [ForecastingHorizon][]
+            The [forecasting horizon][row-and-column-selection] encoding
+            the time stamps to forecast at.
 
         X: hashable, segment, sequence, dataframe-like or None, default=None
             Exogenous time series corresponding to `fh`.
@@ -3256,7 +3262,7 @@ def predict_residuals(
     @composed(crash, method_to_log, beartype)
     def predict_var(
         self,
-        fh: RowSelector | FHSelector,
+        fh: RowSelector | ForecastingHorizon,
         X: XSelector | None = None,
         *,
         cov: Bool = False,
@@ -3272,9 +3278,9 @@ def predict_var(
 
         Parameters
         ----------
-        fh: int, range, sequence or [ForecastingHorizon][]
-            The forecasting horizon encoding the time stamps to
-            forecast at.
+        fh: hashable, segment, sequence, dataframe or [ForecastingHorizon][]
+            The [forecasting horizon][row-and-column-selection] encoding
+            the time stamps to forecast at.
 
         X: hashable, segment, sequence, dataframe-like or None, default=None
             Exogenous time series corresponding to `fh`.
@@ -3308,7 +3314,7 @@ def score(
         self,
         y: RowSelector | YSelector,
         X: XSelector | None = None,
-        fh: FHSelector | None = None,
+        fh: RowSelector | ForecastingHorizon | None = None,
         *,
         metric: str | MetricFunction | Scorer | None = None,
         verbose: Int | None = None,
@@ -3334,9 +3340,9 @@ def score(
         X: hashable, segment, sequence, dataframe-like or None, default=None
             Exogenous time series corresponding to `fh`.
 
-        fh: int, sequence or [ForecastingHorizon][] or None, default=None
-            The forecasting horizon encoding the time stamps to
-            forecast at.
+        fh: hashable, segment, sequence, dataframe, [ForecastingHorizon][] or None, default=None
+            The [forecasting horizon][row-and-column-selection] encoding
+            the time stamps to forecast at.
 
         metric: str, func, scorer or None, default=None
             Metric to calculate. Choose from any of sklearn's scorers,

atom/baserunner.py

@@ -212,15 +212,15 @@ def holdout(self) -> DataFrame | None:
     @property
     def models(self) -> str | list[str] | None:
         """Name of the model(s)."""
-        if isinstance(self._models, ClassMap):
+        if self._models:
             return flt(self._models.keys())
         else:
             return None
 
     @property
     def metric(self) -> str | list[str] | None:
         """Name of the metric(s)."""
-        if isinstance(self._metric, ClassMap):
+        if self._metric:
             return flt(self._metric.keys())
         else:
             return None

atom/basetransformer.py

@@ -378,7 +378,7 @@ def _inherit(self, obj: T_Estimator) -> T_Estimator:
         # Add seasonal period to the estimator
         if hasattr(self, "_config") and self._config.sp:
             if "sp" in signature and getattr(obj, "sp", "<!>") == signature["sp"]._default:
-                obj.sp = self._config.sp
+                obj.sp = self._config.sp if self.multiple_seasonality else flt(self._config.sp)
 
         return obj
 

atom/models/ts.py

@@ -706,6 +706,30 @@ def _get_est(self, params: dict[str, Any]) -> Predictor:
         """
         return super()._get_est({"season_length": self._config.sp or 1} | params)
 
+    def _get_parameters(self, trial: Trial) -> dict:
+        """Get the trial's hyperparameters.
+
+        Parameters
+        ----------
+        trial: [Trial][]
+            Current trial.
+
+        Returns
+        -------
+        dict
+            Trial's hyperparameters.
+
+        """
+        params = super()._get_parameters(trial)
+
+        # MSTL has stl_kwargs, that takes a dict of hyperparameters
+        if "stl_kwargs" in self._est_params:
+            new_params = {}
+        else:
+            new_params = {"stl_kwargs": params}
+
+        return new_params
+
     @staticmethod
     def _get_distributions() -> dict[str, BaseDistribution]:
         """Get the predefined hyperparameter distributions.

atom/plots/baseplot.py

@@ -418,17 +418,21 @@ def _draw_line(
 
         """
         return go.Scatter(
-            line={
-                "width": self.line_width,
-                "color": BasePlot._fig.get_elem(parent),
-                "dash": BasePlot._fig.get_elem(child, "dash"),
-            },
-            marker={
-                "symbol": BasePlot._fig.get_elem(child, "marker"),
-                "size": self.marker_size,
-                "color": BasePlot._fig.get_elem(parent),
-                "line": {"width": 1, "color": "rgba(255, 255, 255, 0.9)"},
-            },
+            line=kwargs.pop(
+                "line", {
+                    "width": self.line_width,
+                    "color": BasePlot._fig.get_elem(parent),
+                    "dash": BasePlot._fig.get_elem(child, "dash"),
+                }
+            ),
+            marker=kwargs.pop(
+                "marker", {
+                    "symbol": BasePlot._fig.get_elem(child, "marker"),
+                    "size": self.marker_size,
+                    "color": BasePlot._fig.get_elem(parent),
+                    "line": {"width": 1, "color": "rgba(255, 255, 255, 0.9)"},
+                }
+            ),
             hovertemplate=kwargs.pop(
                 "hovertemplate",
                 f"(%{{x}}, %{{y}})<extra>{parent}{f' - {child}' if child else ''}</extra>",