Merge branch 'master' into issue-425

CHANGELOG.md

@@ -15,6 +15,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add `get_anomalies_mad` function for anomaly detection ([#398](https://github.com/etna-team/etna/pull/398))
 - Add `TSDataset.features` property to get list of all features in a dataset ([#405](https://github.com/etna-team/etna/pull/405))
 - Add `MADOutlierTransform` class for anomaly detection ([#415](https://github.com/etna-team/etna/pull/415))
+- Add `MeanEncoderTransform` ([#413](https://github.com/etna-team/etna/pull/413))
 - Add `FourierDecomposeTransform` transform for series decomposition using DFT ([#430](https://github.com/etna-team/etna/pull/430))
 
 ### Changed
@@ -34,7 +35,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fix rendering in 210 tutorial ([#386](https://github.com/etna-team/etna/pull/386))
 - Fix typo in 103 tutorial ([#408](https://github.com/etna-team/etna/pull/408))
 - Remove sorting of `ts.df` by timestamps in `plot_forecast` and `plot_forecast_decomposition` ([#410](https://github.com/etna-team/etna/pull/410))
-- 
+- Fix forecast visualization with `horizon=1` ([#426](https://github.com/etna-team/etna/pull/426))
 - 
 - 
 - Fix passing custom model to `STLTransform` ([#412](https://github.com/etna-team/etna/pull/412))

docs/source/api_reference/transforms.rst

@@ -53,6 +53,7 @@ Categorical encoding transforms:
    :template: class.rst
 
    SegmentEncoderTransform
+   MeanEncoderTransform
    MeanSegmentEncoderTransform
    LabelEncoderTransform
    OneHotEncoderTransform

etna/analysis/forecast/plots.py

@@ -143,20 +143,27 @@ def plot_forecast(
             plot_df = pd.DataFrame(columns=["timestamp", "target", "segment"])
 
         if (train_ts is not None) and (n_train_samples != 0):
-            ax[i].plot(plot_df.index.values, plot_df.target.values, label="train")
+            marker = None if len(plot_df) > 1 else "o"
+            ax[i].plot(plot_df.index.values, plot_df.target.values, label="train", marker=marker)
         if test_ts is not None:
-            ax[i].plot(segment_test_df.index.values, segment_test_df.target.values, color="purple", label="test")
+            marker = None if len(segment_test_df) > 1 else "o"
+            ax[i].plot(
+                segment_test_df.index.values, segment_test_df.target.values, color="purple", label="test", marker=marker
+            )
 
         # plot forecast plot for each of given forecasts
         for forecast_name, forecast in forecast_results.items():
             legend_prefix = f"{forecast_name}: " if num_forecasts > 1 else ""
 
             segment_forecast_df = forecast[:, segment, :][segment]
+            marker = None if len(segment_forecast_df) > 1 else "o"
+
             line = ax[i].plot(
                 segment_forecast_df.index.values,
                 segment_forecast_df.target.values,
                 linewidth=1,
                 label=f"{legend_prefix}forecast",
+                marker=marker,
             )
             forecast_color = line[0].get_color()
 
@@ -182,7 +189,8 @@ def plot_forecast(
                             segment_borders_df.index.values,
                             values_low,
                             values_high,
-                            facecolor=forecast_color,
+                            linewidth=3,
+                            color=forecast_color,
                             alpha=alpha[interval_idx],
                             label=f"{legend_prefix}{low_border}-{high_border}",
                         )
@@ -196,7 +204,8 @@ def plot_forecast(
                             segment_borders_df.index.values,
                             values_low,
                             values_next,
-                            facecolor=forecast_color,
+                            linewidth=3,
+                            color=forecast_color,
                             alpha=alpha[interval_idx],
                             label=f"{legend_prefix}{low_border}-{high_border}",
                         )
@@ -205,17 +214,19 @@ def plot_forecast(
                             segment_borders_df.index.values,
                             values_high,
                             values_prev,
-                            facecolor=forecast_color,
+                            linewidth=3,
+                            color=forecast_color,
                             alpha=alpha[interval_idx],
                         )
                 # when we can't find pair for border, we plot it separately
                 if len(prediction_intervals_names) % 2 != 0:
                     remaining_border = prediction_intervals_names[len(prediction_intervals_names) // 2]
                     values = segment_borders_df[remaining_border].values
+                    marker = "--" if len(values) > 1 else "d"
                     ax[i].plot(
                         segment_borders_df.index.values,
                         values,
-                        "--",
+                        marker,
                         color=forecast_color,
                         label=f"{legend_prefix}{remaining_border}",
                     )

etna/transforms/__init__.py

@@ -22,6 +22,7 @@
 from etna.transforms.embeddings import EmbeddingSegmentTransform
 from etna.transforms.embeddings import EmbeddingWindowTransform
 from etna.transforms.encoders import LabelEncoderTransform
+from etna.transforms.encoders import MeanEncoderTransform
 from etna.transforms.encoders import MeanSegmentEncoderTransform
 from etna.transforms.encoders import OneHotEncoderTransform
 from etna.transforms.encoders import SegmentEncoderTransform

etna/transforms/encoders/__init__.py

@@ -1,4 +1,5 @@
 from etna.transforms.encoders.categorical import LabelEncoderTransform
 from etna.transforms.encoders.categorical import OneHotEncoderTransform
+from etna.transforms.encoders.mean_encoder import MeanEncoderTransform
 from etna.transforms.encoders.mean_segment_encoder import MeanSegmentEncoderTransform
 from etna.transforms.encoders.segment_encoder import SegmentEncoderTransform