Change Point Interactive

CHANGELOG.md

@@ -11,7 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 -
 -
 -
--
+- Add `plot_change_points_interactive` ([#988](https://github.com/tinkoff-ai/etna/pull/988))
 -
 -
 -

etna/analysis/__init__.py

@@ -28,6 +28,7 @@
 from etna.analysis.plotters import plot_anomalies_interactive
 from etna.analysis.plotters import plot_backtest
 from etna.analysis.plotters import plot_backtest_interactive
+from etna.analysis.plotters import plot_change_points_interactive
 from etna.analysis.plotters import plot_clusters
 from etna.analysis.plotters import plot_correlation_matrix
 from etna.analysis.plotters import plot_feature_relevance

etna/analysis/plotters.py

@@ -23,6 +23,9 @@
 import plotly.graph_objects as go
 import seaborn as sns
 from matplotlib.lines import Line2D
+from ruptures.base import BaseCost
+from ruptures.base import BaseEstimator
+from ruptures.exceptions import BadSegmentationParameters
 from scipy.signal import periodogram
 from typing_extensions import Literal
 
@@ -1819,3 +1822,139 @@ def metric_per_segment_distribution_plot(
 
     plt.title("Metric per-segment distribution plot")
     plt.grid()
+
+
+def plot_change_points_interactive(
+    ts,
+    change_point_model: BaseEstimator,
+    model: BaseCost,
+    params_bounds: Dict[str, Tuple[Union[int, float], Union[int, float], Union[int, float]]],
+    model_params: List[str],
+    predict_params: List[str],
+    in_column: str = "target",
+    segments: Optional[List[str]] = None,
+    columns_num: int = 2,
+    figsize: Tuple[int, int] = (10, 5),
+    start: Optional[str] = None,
+    end: Optional[str] = None,
+):
+    """Plot a time series with indicated change points.
+
+    Change points are obtained using the specified method. The method parameters values
+    can be changed using the corresponding sliders.
+
+    Parameters
+    ----------
+    ts:
+        TSDataset with timeseries data
+    change_point_model:
+        model to get trend change points
+    model:
+        binseg segment model, ["l1", "l2", "rbf",...]. Not used if 'custom_cost' is not None
+    params_bounds:
+        Parameters ranges of the change points detection. Bounds for the parameter are (min,max,step)
+    model_params:
+        List of iterable parameters for initialize the model
+    predict_params:
+        List of iterable parameters for predict method
+    in_column:
+        column to plot
+    segments:
+        segments to use
+    columns_num:
+        number of subplots columns
+    figsize:
+        size of the figure in inches
+    start:
+        start timestamp for plot
+    end:
+        end timestamp for plot
+
+    Notes
+    -----
+    Jupyter notebook might display the results incorrectly,
+    in this case try to use ``!jupyter nbextension enable --py widgetsnbextension``.
+
+    Examples
+    --------
+    >>> from etna.datasets import TSDataset
+    >>> from etna.datasets import generate_ar_df
+    >>> from etna.analysis import plot_change_points_interactive
+    >>> from ruptures.detection import Binseg
+    >>> classic_df = generate_ar_df(periods=1000, start_time="2021-08-01", n_segments=2)
+    >>> df = TSDataset.to_dataset(classic_df)
+    >>> ts = TSDataset(df, "D")
+    >>> params_bounds = {"n_bkps": [0, 5, 1], "min_size":[1,10,3]}
+    >>> plot_change_points_interactive(ts=ts, change_point_model=Binseg, model="l2", params_bounds=params_bounds, model_params=["min_size"], predict_params=["n_bkps"], figsize=(20, 10)) # doctest: +SKIP
+    """
+    from ipywidgets import FloatSlider
+    from ipywidgets import IntSlider
+    from ipywidgets import interact
+
+    if segments is None:
+        segments = sorted(ts.segments)
+
+    cache = {}
+
+    sliders = dict()
+    style = {"description_width": "initial"}
+    for param, bounds in params_bounds.items():
+        min_, max_, step = bounds
+        if isinstance(min_, float) or isinstance(max_, float) or isinstance(step, float):
+            sliders[param] = FloatSlider(min=min_, max=max_, step=step, continuous_update=False, style=style)
+        else:
+            sliders[param] = IntSlider(min=min_, max=max_, step=step, continuous_update=False, style=style)
+
+    def update(**kwargs):
+        _, ax = prepare_axes(num_plots=len(segments), columns_num=columns_num, figsize=figsize)
+
+        key = "_".join([str(val) for val in kwargs.values()])
+
+        is_fitted = False
+
+        if key not in cache:
+            m_params = {x: kwargs[x] for x in model_params}
+            p_params = {x: kwargs[x] for x in predict_params}
+            cache[key] = {}
+        else:
+            is_fitted = True
+
+        for i, segment in enumerate(segments):
+            ax[i].cla()
+            segment_df = ts[start:end, segment, :][segment]
+            timestamp = segment_df.index.values
+            target = segment_df[in_column].values
+
+            if not is_fitted:
+                try:
+                    algo = change_point_model(model=model, **m_params).fit(signal=target)
+                    bkps = algo.predict(**p_params)
+                    cache[key][segment] = bkps
+                    cache[key][segment].insert(0, 1)
+                except BadSegmentationParameters:
+                    cache[key][segment] = None
+
+            segment_bkps = cache[key][segment]
+
+            if segment_bkps is not None:
+                for idx in range(len(segment_bkps[:-1])):
+                    bkp = segment_bkps[idx] - 1
+                    start_time = timestamp[bkp]
+                    end_time = timestamp[segment_bkps[idx + 1] - 1]
+                    selected_indices = (timestamp >= start_time) & (timestamp <= end_time)
+                    cur_timestamp = timestamp[selected_indices]
+                    cur_target = target[selected_indices]
+                    ax[i].plot(cur_timestamp, cur_target)
+                    if bkp != 0:
+                        ax[i].axvline(timestamp[bkp], linestyle="dashed", c="grey")
+
+            else:
+                box = {"facecolor": "grey", "edgecolor": "red", "boxstyle": "round"}
+                ax[i].text(
+                    0.5, 0.4, "Parameters\nError", bbox=box, horizontalalignment="center", color="white", fontsize=50
+                )
+            ax[i].set_title(segment)
+            ax[i].tick_params("x", rotation=45)
+        plt.show()
+
+    interact(update, **sliders)

examples/README.md

@@ -21,6 +21,9 @@ We have prepared a set of tutorials for an easy introduction:
 - Outliers
      - Median method
      - Density method
+- Change Points
+     - Change points plot
+     - Interactive change points plot
 #### 04. [Outliers](https://github.com/tinkoff-ai/etna/tree/master/examples/outliers.ipynb) 
 - Point outliers
     - Median method