Visualization module

This module contains functions for plotting time series. It can be imported as follows:

>>> from dtaianomaly import visualization

The functions within this module offer alternative manners to nicely plot the time series along with the ground truth or predicted anomalies.

dtaianomaly.visualization.plot_demarcated_anomalies(X: ndarray, y: array, ax: Axes = None, time_steps: array = None, feature_names: List[str] = None, color_anomaly: str = 'red', alpha_anomaly: float = 0.2, **kwargs) → Figure

Plot the given time series and binary anomaly labels. Each anomalous interval is marked by a colored area, depending on the provided parameters.

Parameters:

• X (np.ndarray of shape (n_samples, n_attributes)) – The time series to plot

• y (np.array of shape (n_samples)) – The binary anomaly scores.

• ax (plt.Axes, default=None) – The axes onto which the plot should be made. If None, then a new figure and axis will be created.

• time_steps (np.array of shape (n_samples), default=None) – The time steps to plot. If no time steps are provided, then the default range [0, ..., n_samples-1] will be used.

• feature_names (list of str of shape (n_attributes), default=None) – The names of each feature in the given time series X.

• color_anomaly (str, default='red') – The color in which the anomaly should be marked.

• alpha_anomaly (float, default=0.2) – The alpha value for marking the anomaly, to adjust transparency.

• **kwargs – Arguments to be passed to plt.Figure(), in case ax=None.

Returns:

fig – The figure containing the plotted data.

Return type:

plt.Figure

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.visualization import plot_demarcated_anomalies
>>> X, y = demonstration_time_series()
>>> fig = plot_demarcated_anomalies(X, y, figsize=(10, 3))
>>> fig.suptitle("Example of 'plot_demarcated_anomalies'")

dtaianomaly.visualization.plot_time_series_colored_by_score(X: ndarray, y: ndarray, time_steps: array = None, feature_names: List[str] = None, ax: Axes = None, nb_colors: int = 100, **kwargs) → Figure

Plots the given time series, and color it according to the given scores. Higher scores will be colored red, and lower scores will be colored green. Thus, if the ground truth anomaly scores are passed, red corresponds to anomalies and green to normal observations.

Parameters:

• X (np.ndarray of shape (n_samples, n_attributes)) – The time series to plot

• y (np.ndarray of shape (n_samples)) – The scores, according to which the plotted data should be colored.

• time_steps (np.array of shape (n_samples), default=None) – The time steps to plot. If no time steps are provided, then the default range [0, ..., n_samples-1] will be used.

• feature_names (list of str of shape (n_attributes), default=None) – The names of each feature in the given time series X. Because the color of each attribute varies over time (to indicate y), the labels are not shown for simplicity. The parameter is available for compatability reasons.

• ax (plt.Axes, default=None) – The axes onto which the plot should be made. If None, then a new figure and axis will be created.

• nb_colors (int, default=100) – The number of colors to use for plotting the time series.

• **kwargs – Arguments to be passed to plt.Figure(), in case ax=None.

Returns:

fig – The figure containing the plotted data.

Return type:

plt.Figure

Notes

Each segment in the time series will be plotted independently. Thus, for time series with many observations, plotting the data using this method can cost a huge amount of time.

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.visualization import plot_time_series_colored_by_score
>>> X, y = demonstration_time_series()
>>> fig = plot_time_series_colored_by_score(X, y, figsize=(10, 3))
>>> fig.suptitle("Example of 'plot_time_series_colored_by_score' on the ground truth")

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.visualization import plot_time_series_colored_by_score
>>> from dtaianomaly.anomaly_detection import IsolationForest
>>> X, _ = demonstration_time_series()
>>> y_pred = IsolationForest(window_size=100).fit(X).predict_proba(X)
>>> fig = plot_time_series_colored_by_score(X, y_pred, figsize=(10, 3))
>>> fig.suptitle("Example of 'plot_time_series_colored_by_score' on predictions")

dtaianomaly.visualization.plot_anomaly_scores(X: ~numpy.array, y: ~numpy.array, y_pred: ~numpy.array, time_steps: ~numpy.array = None, feature_names: ~typing.List[str] = None, method_to_plot=<function plot_demarcated_anomalies>, confidence: ~numpy.array = None, **kwargs) → Figure

Plot the given data with the ground truth anomalies, and compare the predicted anomaly scores.

Parameters:

• X (np.ndarray of shape (n_samples, n_attributes)) – The time series to plot

• y (np.array of shape (n_samples)) – The binary anomaly scores.

• y_pred (np.array of shape (n_samples)) – The predicted anomaly scores to plot.

• time_steps (np.array of shape (n_samples), default=None) – The time steps to plot. If no time steps are provided, then the default range [0, ..., n_samples-1] will be used.

• feature_names (list of str of shape (n_attributes), default=None) – The names of each feature in the given time series X.

• method_to_plot (callable, default=:py:autofunc:~dtaianomaly.visualization.plot_demarcated_anomalies) – Method used for plotting the data along with the ground truth anomaly scores. Should take as inputs the values X (the time series data), y (the anomaly labels``), time_steps (the time steps at which there was an observation) and ax (the axis on which the plot should be made).

• confidence (np.array of shape (n_samples), default=None) – The confidence of the anomaly scores.

• **kwargs – Arguments to be passed to plt.subplots().

Returns:

fig – The figure containing the plotted data.

Return type:

plt.Figure

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.visualization import plot_anomaly_scores, plot_time_series_colored_by_score
>>> from dtaianomaly.anomaly_detection import IsolationForest
>>> X, y = demonstration_time_series()
>>> y_pred = IsolationForest(window_size=100).fit(X).predict_proba(X)
>>> fig = plot_anomaly_scores(X, y, y_pred, figsize=(10, 3), method_to_plot=plot_time_series_colored_by_score)
>>> fig.suptitle("Example of 'plot_anomaly_scores'")

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.visualization import plot_anomaly_scores, plot_time_series_colored_by_score
>>> from dtaianomaly.anomaly_detection import IsolationForest
>>> X, y = demonstration_time_series()
>>> detector = IsolationForest(window_size=100).fit(X)
>>> y_pred = detector.predict_proba(X)
>>> confidence = detector.predict_confidence(X)
>>> fig = plot_anomaly_scores(X, y, y_pred, confidence=confidence, figsize=(10, 3), method_to_plot=plot_time_series_colored_by_score)
>>> fig.suptitle("Example of 'plot_anomaly_scores' with confidence ranges")

dtaianomaly.visualization.plot_time_series_anomalies(X: ndarray, y: ndarray, y_pred: ndarray, time_steps: array = None, feature_names: List[str] = None, ax: Axes = None, **kwargs) → Figure

Visualizes time series data with true and predicted anomalies, highlighting true positives (TP), false positives (FP), and false negatives (FN).

Parameters:

• X (np.ndarray of shape (n_samples, n_attributes)) – The time series to plot

• y (np.ndarray of shape (n_samples,)) – Ground truth anomaly labels (binary values: 0 or 1).

• y_pred (np.ndarray of shape (n_samples,)) – Predicted anomaly labels (binary values: 0 or 1).

• time_steps (np.array of shape (n_samples), default=None) – The time steps to plot. If no time steps are provided, then the default range [0, ..., n_samples-1] will be used.

• feature_names (list of str of shape (n_attributes), default=None) – The names of each feature in the given time series X.

• ax (plt.Axes, default=None) – The axes onto which the plot should be made. If None, then a new figure and axis will be created.

• **kwargs – Arguments to be passed to plt.Figure(), in case ax=None.

Returns:

fig – The figure containing the plotted data.

Return type:

plt.Figure

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.visualization import plot_time_series_anomalies
>>> from dtaianomaly.anomaly_detection import IsolationForest
>>> from dtaianomaly.thresholding import FixedCutoff
>>> X, y = demonstration_time_series()
>>> y_pred = IsolationForest(window_size=100).fit(X).predict_proba(X)
>>> y_pred_binary = FixedCutoff(cutoff=0.9).threshold(y_pred)
>>> fig = plot_time_series_anomalies(X, y, y_pred_binary, figsize=(10, 3))
>>> fig.suptitle("Example of 'plot_time_series_anomalies'")

dtaianomaly.visualization.plot_with_zoom(X: ~numpy.ndarray, start_zoom: int, end_zoom: int, y: ~numpy.array = None, y_pred: ~numpy.array = None, time_steps: ~numpy.array = None, feature_names: ~typing.List[str] = None, method_to_plot=<function plot_demarcated_anomalies>, color: str = 'blue', linewidth: float = 3, linestyle: str = '--', **kwargs) → Figure

Plot the given data in two axes, one showing the entire time series and one zooming in on a specific area of the time series.

Parameters:

• X (np.ndarray of shape (n_samples, n_attributes)) – The time series to plot

• start_zoom (int) – The index in the data at which the zoom starts.

• end_zoom (int) – The index in the data at which the zoom ends.

• y (np.array of shape (n_samples), default=None) – The anomaly ground truth anomaly scores, to be passed to the method_to_plot function.

• y_pred (np.array of shape (n_samples), default=None) – The predicted anomaly scores to plot. Is necessary if the method_to_plot requires predicted anomaly scores.

• time_steps (np.array of shape (n_samples), default=None) – The time steps to plot. If no time steps are provided, then the default range [0, ..., n_samples-1] will be used.

• feature_names (list of str of shape (n_attributes), default=None) – The names of each feature in the given time series X.

• method_to_plot (callable, default=:py:autofunc:~dtaianomaly.visualization.plot_demarcated_anomalies) – Method used for plotting the data. Should take as inputs the values X (the time series data), y (the anomaly labels``), time_steps (the time steps at which there was an observation) and ax (the axis on which the plot should be made). Optionally, the method takes as input a value y_pred for the predicted anomaly scores.

• color (string, default='blue') – The color of the lines to demarcate the area of zooming.

• linewidth (float, default=3) – The width of the lines to demarcate the area of zooming.

• linestyle (str, default='--') – The style of the lines to demarcate the area of zooming.

• **kwargs – Arguments to be passed to plt.subplots().

Returns:

fig – The figure containing the plotted data.

Return type:

plt.Figure

>>> from dtaianomaly.data import demonstration_time_series
>>> from dtaianomaly.visualization import plot_with_zoom
>>> X, y = demonstration_time_series()
>>> fig = plot_with_zoom(X, y=y, start_zoom=700, end_zoom=1200, figsize=(10, 3))
>>> fig.suptitle("Example of 'plot_with_zoom'")