Add plot_features_relevance (#579)

CHANGELOG.md

@@ -27,6 +27,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - Create `BasePipeline`, add prediction intervals to all the pipelines, move parameter n_fold to forecast ([#578](https://github.com/tinkoff-ai/etna/pull/578))
 - Add stl_plot ([#575](https://github.com/tinkoff-ai/etna/pull/575))
+- Add plot_features_relevance ([#579](https://github.com/tinkoff-ai/etna/pull/579))
 - Add community section to README.md ([#580](https://github.com/tinkoff-ai/etna/pull/580))
 - Create `AbstaractPipeline` ([#573](https://github.com/tinkoff-ai/etna/pull/573))
 - 

etna/analysis/__init__.py

@@ -21,6 +21,7 @@
 from etna.analysis.plotters import plot_backtest_interactive
 from etna.analysis.plotters import plot_clusters
 from etna.analysis.plotters import plot_correlation_matrix
+from etna.analysis.plotters import plot_feature_relevance
 from etna.analysis.plotters import plot_forecast
 from etna.analysis.plotters import plot_residuals
 from etna.analysis.plotters import plot_time_series_with_change_points

etna/analysis/feature_selection/__init__.py

@@ -1 +1,3 @@
+from etna.analysis.feature_selection.mrmr import AGGREGATION_FN
+from etna.analysis.feature_selection.mrmr import AggregationMode
 from etna.analysis.feature_selection.mrmr import mrmr

etna/analysis/feature_selection/mrmr.py

@@ -14,7 +14,7 @@ class AggregationMode(str, Enum):
     median = "median"
 
 
-aggregation_fn = {
+AGGREGATION_FN = {
     AggregationMode.mean: np.mean,
     AggregationMode.max: np.max,
     AggregationMode.min: np.min,
@@ -58,8 +58,8 @@ def mrmr(
     selected_features: List[str]
         list of `top_k` selected regressors, sorted by their importance
     """
-    relevance_aggregation_fn = aggregation_fn[AggregationMode(relevance_aggregation_mode)]
-    redundancy_aggregation_fn = aggregation_fn[AggregationMode(redundancy_aggregation_mode)]
+    relevance_aggregation_fn = AGGREGATION_FN[AggregationMode(relevance_aggregation_mode)]
+    redundancy_aggregation_fn = AGGREGATION_FN[AggregationMode(redundancy_aggregation_mode)]
 
     relevance = relevance_table.apply(relevance_aggregation_fn).fillna(0)
 

etna/analysis/plotters.py

@@ -2,6 +2,7 @@
 import warnings
 from copy import deepcopy
 from typing import TYPE_CHECKING
+from typing import Any
 from typing import Callable
 from typing import Dict
 from typing import List
@@ -19,6 +20,9 @@
 import seaborn as sns
 from typing_extensions import Literal
 
+from etna.analysis import RelevanceTable
+from etna.analysis.feature_selection import AGGREGATION_FN
+from etna.analysis.feature_selection import AggregationMode
 from etna.transforms import Transform
 
 if TYPE_CHECKING:
@@ -799,3 +803,83 @@ def plot_trend(
         ax[i].set_title(segment)
         ax[i].tick_params("x", rotation=45)
         ax[i].legend()
+
+
+def plot_feature_relevance(
+    ts: "TSDataset",
+    relevance_table: RelevanceTable,
+    normalized: bool = False,
+    relevance_aggregation_mode: Union[str, Literal["per-segment"]] = AggregationMode.mean,
+    relevance_params: Optional[Dict[str, Any]] = None,
+    top_k: Optional[int] = None,
+    segments: Optional[List[str]] = None,
+    columns_num: int = 2,
+    figsize: Tuple[int, int] = (10, 5),
+):
+    """
+    Plot relevance of the features.
+
+    The most important features are at the top, the least important are at the bottom.
+
+    Parameters
+    ----------
+    ts:
+        TSDataset with timeseries data
+    relevance_table:
+        method to evaluate the feature relevance
+    normalized:
+        whether obtained relevances should be normalized to sum up to 1
+    relevance_aggregation_mode:
+        aggregation strategy for obtained feature relevance table;
+        all the strategies can be examined at `etna.analysis.feature_selection.AggregationMode`
+    relevance_params:
+        additional keyword arguments for `__call__` method of `RelevanceTable` instances
+    top_k:
+        number of best features to plot, if None plot all the features
+    segments:
+        segments to use
+    columns_num:
+        if `relevance_aggregation_mode="per-segment"` number of columns in subplots, otherwise the value is ignored
+    figsize:
+        size of the figure per subplot with one segment in inches
+    """
+    if relevance_params is None:
+        relevance_params = {}
+    if not segments:
+        segments = sorted(ts.segments)
+
+    is_ascending = not relevance_table.greater_is_better
+    features = list(set(ts.columns.get_level_values("feature")) - {"target"})
+    relevance_df = relevance_table(df=ts[:, :, "target"], df_exog=ts[:, :, features], **relevance_params).loc[segments]
+
+    if relevance_aggregation_mode == "per-segment":
+        ax = prepare_axes(segments=segments, columns_num=columns_num, figsize=figsize)
+        for i, segment in enumerate(segments):
+            relevance = relevance_df.loc[segment].sort_values(ascending=is_ascending)
+            # warning about NaNs
+            if relevance.isna().any():
+                na_relevance_features = relevance[relevance.isna()].index.tolist()
+                warnings.warn(
+                    f"Relevances on segment: {segment} of features: {na_relevance_features} can't be calculated."
+                )
+            relevance = relevance.dropna()[:top_k]
+            if normalized:
+                relevance = relevance / relevance.sum()
+            sns.barplot(x=relevance.values, y=relevance.index, orient="h", ax=ax[i])
+            ax[i].set_title(f"Feature relevance: {segment}")
+
+    else:
+        relevance_aggregation_fn = AGGREGATION_FN[AggregationMode(relevance_aggregation_mode)]
+        relevance = relevance_df.apply(lambda x: relevance_aggregation_fn(x[~x.isna()]))  # type: ignore
+        relevance = relevance.sort_values(ascending=is_ascending)
+        # warning about NaNs
+        if relevance.isna().any():
+            na_relevance_features = relevance[relevance.isna()].index.tolist()
+            warnings.warn(f"Relevances of features: {na_relevance_features} can't be calculated.")
+        # if top_k == None, all the values are selected
+        relevance = relevance.dropna()[:top_k]
+        if normalized:
+            relevance = relevance / relevance.sum()
+        _, ax = plt.subplots(figsize=figsize, constrained_layout=True)
+        sns.barplot(x=relevance.values, y=relevance.index, orient="h", ax=ax)
+        ax.set_title("Feature relevance")  # type: ignore