From e5049309c455cbc0d35b53eab0cd97c9a8694358 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Fri, 27 Sep 2024 11:18:09 +0800
Subject: [PATCH 01/14] Add new feature to plot each series's component
separately
---
darts/ad/anomaly_model/anomaly_model.py | 2 +
darts/ad/anomaly_model/forecasting_am.py | 2 +
darts/ad/scorers/scorers.py | 4 +
darts/ad/utils.py | 315 +++++++++++++++++------
4 files changed, 244 insertions(+), 79 deletions(-)
diff --git a/darts/ad/anomaly_model/anomaly_model.py b/darts/ad/anomaly_model/anomaly_model.py
index be66758a0f..f93ef3301e 100644
--- a/darts/ad/anomaly_model/anomaly_model.py
+++ b/darts/ad/anomaly_model/anomaly_model.py
@@ -250,6 +250,7 @@ def show_anomalies(
names_of_scorers: Union[str, Sequence[str]] = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
+ multivariate_plot: bool = False,
**score_kwargs,
):
"""Plot the results of the anomaly model.
@@ -313,6 +314,7 @@ def show_anomalies(
names_of_scorers=names_of_scorers,
title=title,
metric=metric,
+ multivariate_plot=multivariate_plot,
)
@property
diff --git a/darts/ad/anomaly_model/forecasting_am.py b/darts/ad/anomaly_model/forecasting_am.py
index fd3eb9a33a..42dee0960c 100644
--- a/darts/ad/anomaly_model/forecasting_am.py
+++ b/darts/ad/anomaly_model/forecasting_am.py
@@ -447,6 +447,7 @@ def show_anomalies(
names_of_scorers: Union[str, Sequence[str]] = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
+ multivariate_plot: bool = False,
**score_kwargs,
):
"""Plot the results of the anomaly model.
@@ -535,6 +536,7 @@ def show_anomalies(
names_of_scorers=names_of_scorers,
title=title,
metric=metric,
+ multivariate_plot=multivariate_plot,
**score_kwargs,
)
diff --git a/darts/ad/scorers/scorers.py b/darts/ad/scorers/scorers.py
index 1afae77d21..ba573229bb 100644
--- a/darts/ad/scorers/scorers.py
+++ b/darts/ad/scorers/scorers.py
@@ -175,6 +175,7 @@ def show_anomalies_from_prediction(
anomalies: TimeSeries = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
+ multivariate_plot: bool = False,
):
"""Plot the results of the scorer.
@@ -229,6 +230,7 @@ def show_anomalies_from_prediction(
names_of_scorers=scorer_name,
title=title,
metric=metric,
+ multivariate_plot=multivariate_plot,
)
@property
@@ -579,6 +581,7 @@ def show_anomalies(
scorer_name: str = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
+ multivariate_plot: bool = False,
):
"""Plot the results of the scorer.
@@ -632,6 +635,7 @@ def show_anomalies(
names_of_scorers=scorer_name,
title=title,
metric=metric,
+ multivariate_plot=multivariate_plot,
)
@property
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index 0d5260b1f4..0ee00ca6f7 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -310,6 +310,7 @@ def show_anomalies_from_scores(
names_of_scorers: Union[str, Sequence[str]] = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
+ multivariate_plot: bool = False,
):
"""Plot the results generated by an anomaly model.
@@ -352,6 +353,7 @@ def show_anomalies_from_scores(
Only effective when `pred_scores` is not `None`.
Default: "AUC_ROC".
"""
+
series = _check_input(
series,
name="series",
@@ -362,6 +364,7 @@ def show_anomalies_from_scores(
title = "Anomaly results"
nbr_plots = 1
+
if anomalies is not None:
nbr_plots = nbr_plots + 1
elif metric is not None:
@@ -421,105 +424,259 @@ def show_anomalies_from_scores(
nbr_plots = nbr_plots + len(set(window))
- fig, axs = plt.subplots(
- nbr_plots,
- figsize=(8, 4 + 2 * (nbr_plots - 1)),
- sharex=True,
- gridspec_kw={"height_ratios": [2] + [1] * (nbr_plots - 1)},
- squeeze=False,
- )
+ if multivariate_plot:
+ series = _check_input(
+ series,
+ name="series",
+ check_multivariate=True,
+ )[0]
+ series_width = series.n_components
+
+ if pred_series is not None:
+ pred_series = _check_input(
+ pred_series,
+ name="pred_series",
+ width_expected=series.width,
+ check_multivariate=True,
+ )[0]
+
+ if anomalies is not None:
+ anomalies = _check_input(
+ anomalies,
+ name="anomalies",
+ width_expected=series.width,
+ check_multivariate=True,
+ )[0]
+
+ if pred_scores is not None:
+ for pred_score in pred_scores:
+ pred_score = _check_input(
+ pred_score,
+ name="pred_score",
+ width_expected=series.width,
+ check_multivariate=True,
+ )[0]
+
+ series_width = series.n_components
+ fig, axs = plt.subplots(
+ nbr_plots * series_width,
+ figsize=(8, 4 + 2 * (nbr_plots * series_width - 1)),
+ sharex=True,
+ gridspec_kw={"height_ratios": [2] + [1] * (nbr_plots * series_width - 1)},
+ squeeze=False,
+ )
- index_ax = 0
+ for i in range(series_width):
+ index_ax = i * nbr_plots
- _plot_series(series=series, ax_id=axs[index_ax][0], linewidth=0.5, label_name="")
+ _plot_series(
+ series=series[series.components[i]],
+ ax_id=axs[index_ax][0],
+ linewidth=0.5,
+ label_name="",
+ )
- if pred_series is not None:
- _plot_series(
- series=pred_series,
- ax_id=axs[index_ax][0],
- linewidth=0.5,
- label_name="model output",
- )
+ if pred_series[pred_series.components[i]] is not None:
+ _plot_series(
+ series=pred_series[pred_series.components[i]],
+ ax_id=axs[index_ax][0],
+ linewidth=0.5,
+ label_name=pred_series.components[i] + " model_output",
+ )
- axs[index_ax][0].set_title("")
+ axs[index_ax][0].set_title("")
- if anomalies is not None or pred_scores is not None:
- axs[index_ax][0].set_xlabel("")
+ if anomalies is not None or pred_scores is not None:
+ axs[index_ax][0].set_xlabel("")
- axs[index_ax][0].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
+ axs[index_ax][0].legend(
+ loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2
+ )
- if pred_scores is not None:
- dict_input = {}
-
- for idx, (score, w) in enumerate(zip(pred_scores, window)):
- dict_input[idx] = {"series_score": score, "window": w, "name_id": idx}
-
- for index, elem in enumerate(
- sorted(dict_input.items(), key=lambda x: x[1]["window"])
- ):
- if index == 0:
- current_window = elem[1]["window"]
- index_ax = index_ax + 1
-
- idx = elem[1]["name_id"]
- w = elem[1]["window"]
-
- if w != current_window:
- current_window = w
- index_ax = index_ax + 1
-
- if metric is not None:
- value = round(
- eval_metric_from_scores(
- anomalies=anomalies,
- pred_scores=pred_scores[idx],
- window=w,
- metric=metric,
- ),
- 3,
+ if pred_scores is not None:
+ dict_input = {}
+
+ for idx, (score, w) in enumerate(zip(pred_scores, window)):
+ dict_input[idx] = {
+ "series_score": score,
+ "window": w,
+ "name_id": idx,
+ }
+
+ for index, elem in enumerate(
+ sorted(dict_input.items(), key=lambda x: x[1]["window"])
+ ):
+ if index == 0:
+ current_window = elem[1]["window"]
+ index_ax = index_ax + 1
+
+ idx = elem[1]["name_id"]
+ w = elem[1]["window"]
+
+ if w != current_window:
+ current_window = w
+ index_ax = index_ax + 1
+
+ if metric is not None:
+ value = round(
+ eval_metric_from_scores(
+ anomalies=anomalies[anomalies.components[i]],
+ pred_scores=pred_scores[idx][
+ pred_scores[idx].components[i]
+ ],
+ window=w,
+ metric=metric,
+ ),
+ 3,
+ )
+ else:
+ value = None
+
+ if names_of_scorers is not None:
+ label = (
+ names_of_scorers[idx] + [f" ({value})", ""][value is None]
+ )
+ else:
+ label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
+
+ _plot_series(
+ series=elem[1]["series_score"][
+ elem[1]["series_score"].components[i]
+ ],
+ ax_id=axs[index_ax][0],
+ linewidth=0.5,
+ label_name=label,
+ )
+
+ axs[index_ax][0].legend(
+ loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
+ )
+ axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
+ axs[index_ax][0].set_title("")
+ axs[index_ax][0].set_xlabel("")
+
+ if anomalies is not None:
+ _plot_series(
+ series=anomalies[anomalies.components[i]],
+ ax_id=axs[index_ax + 1][0],
+ linewidth=1,
+ label_name=anomalies.components[i],
+ color="red",
)
- else:
- value = None
- if names_of_scorers is not None:
- label = names_of_scorers[idx] + [f" ({value})", ""][value is None]
+ axs[index_ax + 1][0].set_title("")
+ axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
+ axs[index_ax + 1][0].set_yticks([0, 1])
+ axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
+ axs[index_ax + 1][0].legend(
+ loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
+ )
else:
- label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
+ axs[index_ax][0].set_xlabel("timestamp")
+
+ fig.suptitle(title)
+ else:
+ fig, axs = plt.subplots(
+ nbr_plots,
+ figsize=(8, 4 + 2 * (nbr_plots - 1)),
+ sharex=True,
+ gridspec_kw={"height_ratios": [2] + [1] * (nbr_plots - 1)},
+ squeeze=False,
+ )
+ index_ax = 0
+
+ _plot_series(
+ series=series, ax_id=axs[index_ax][0], linewidth=0.5, label_name=""
+ )
+
+ if pred_series is not None:
_plot_series(
- series=elem[1]["series_score"],
+ series=pred_series,
ax_id=axs[index_ax][0],
linewidth=0.5,
- label_name=label,
+ label_name="model output",
)
- axs[index_ax][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
- )
- axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
- axs[index_ax][0].set_title("")
+ axs[index_ax][0].set_title("")
+
+ if anomalies is not None or pred_scores is not None:
axs[index_ax][0].set_xlabel("")
- if anomalies is not None:
- _plot_series(
- series=anomalies,
- ax_id=axs[index_ax + 1][0],
- linewidth=1,
- label_name="anomalies",
- color="red",
- )
+ axs[index_ax][0].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
+
+ if pred_scores is not None:
+ dict_input = {}
+
+ for idx, (score, w) in enumerate(zip(pred_scores, window)):
+ dict_input[idx] = {"series_score": score, "window": w, "name_id": idx}
+
+ for index, elem in enumerate(
+ sorted(dict_input.items(), key=lambda x: x[1]["window"])
+ ):
+ if index == 0:
+ current_window = elem[1]["window"]
+ index_ax = index_ax + 1
+
+ idx = elem[1]["name_id"]
+ w = elem[1]["window"]
+
+ if w != current_window:
+ current_window = w
+ index_ax = index_ax + 1
+
+ if metric is not None:
+ value = round(
+ eval_metric_from_scores(
+ anomalies=anomalies,
+ pred_scores=pred_scores[idx],
+ window=w,
+ metric=metric,
+ ),
+ 3,
+ )
+ else:
+ value = None
+
+ if names_of_scorers is not None:
+ label = names_of_scorers[idx] + [f" ({value})", ""][value is None]
+ else:
+ label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
+
+ _plot_series(
+ series=elem[1]["series_score"],
+ ax_id=axs[index_ax][0],
+ linewidth=0.5,
+ label_name=label,
+ )
- axs[index_ax + 1][0].set_title("")
- axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
- axs[index_ax + 1][0].set_yticks([0, 1])
- axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
- axs[index_ax + 1][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
- )
- else:
- axs[index_ax][0].set_xlabel("timestamp")
+ axs[index_ax][0].legend(
+ loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
+ )
+ axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
+ axs[index_ax][0].set_title("")
+ axs[index_ax][0].set_xlabel("")
+
+ if anomalies is not None:
+ _plot_series(
+ series=anomalies,
+ ax_id=axs[index_ax + 1][0],
+ linewidth=1,
+ label_name="anomalies",
+ color="red",
+ )
+
+ axs[index_ax + 1][0].set_title("")
+ axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
+ axs[index_ax + 1][0].set_yticks([0, 1])
+ axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
+ axs[index_ax + 1][0].legend(
+ loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
+ )
+ else:
+ axs[index_ax][0].set_xlabel("timestamp")
- fig.suptitle(title)
+ fig.suptitle(title)
def _assert_binary(series: TimeSeries, name: str):
From b05eb8a2bc241cbc8e6bbbb439cd046527ee1bd2 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Tue, 31 Dec 2024 09:06:50 +0800
Subject: [PATCH 02/14] Update the docstring
---
darts/ad/anomaly_model/anomaly_model.py | 2 ++
darts/ad/anomaly_model/forecasting_am.py | 2 ++
darts/ad/scorers/scorers.py | 2 ++
3 files changed, 6 insertions(+)
diff --git a/darts/ad/anomaly_model/anomaly_model.py b/darts/ad/anomaly_model/anomaly_model.py
index 49ebe63ba9..d75ad18ab0 100644
--- a/darts/ad/anomaly_model/anomaly_model.py
+++ b/darts/ad/anomaly_model/anomaly_model.py
@@ -284,6 +284,8 @@ def show_anomalies(
Default: "AUC_ROC".
score_kwargs
parameters for the `score()` method.
+ multivariate_plot
+ If True, it will separately plot each component in multivariate series.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
predict_kwargs = predict_kwargs if predict_kwargs is not None else {}
diff --git a/darts/ad/anomaly_model/forecasting_am.py b/darts/ad/anomaly_model/forecasting_am.py
index 348e025e9f..7fee356cb6 100644
--- a/darts/ad/anomaly_model/forecasting_am.py
+++ b/darts/ad/anomaly_model/forecasting_am.py
@@ -507,6 +507,8 @@ def show_anomalies(
Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
+ multivariate_plot
+ If True, it will separately plot each component in multivariate series.
score_kwargs
parameters for the `score()` method.
"""
diff --git a/darts/ad/scorers/scorers.py b/darts/ad/scorers/scorers.py
index d797c93212..66f01b6692 100644
--- a/darts/ad/scorers/scorers.py
+++ b/darts/ad/scorers/scorers.py
@@ -209,6 +209,8 @@ def show_anomalies_from_prediction(
Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
+ multivariate_plot
+ If True, it will separately plot each component in multivariate series.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
pred_series = _check_input(
From 33006d44e3967f9d2e59e61d7a8367af45dde9ff Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Tue, 31 Dec 2024 10:19:18 +0800
Subject: [PATCH 03/14] refactor the show_anomalies_from_scores()
---
darts/ad/utils.py | 347 +++++++++++++++++++++-------------------------
1 file changed, 155 insertions(+), 192 deletions(-)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index 510ef3f924..6f2821a656 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -469,112 +469,23 @@ def show_anomalies_from_scores(
for i in range(series_width):
index_ax = i * nbr_plots
-
- _plot_series(
+ _plot_series_and_anomalies(
series=series[series.components[i]],
- ax_id=axs[index_ax][0],
- linewidth=0.5,
- label_name="",
- )
-
- if pred_series[pred_series.components[i]] is not None:
- _plot_series(
- series=pred_series[pred_series.components[i]],
- ax_id=axs[index_ax][0],
- linewidth=0.5,
- label_name=pred_series.components[i] + " model_output",
- )
-
- axs[index_ax][0].set_title("")
-
- if anomalies is not None or pred_scores is not None:
- axs[index_ax][0].set_xlabel("")
-
- axs[index_ax][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2
+ anomalies=anomalies[anomalies.components[i]]
+ if anomalies is not None
+ else None,
+ pred_series=pred_series[pred_series.components[i]]
+ if pred_series is not None
+ else None,
+ pred_scores=pred_scores,
+ window=window,
+ names_of_scorers=names_of_scorers,
+ metric=metric,
+ axs=axs,
+ index_ax=index_ax,
+ nbr_plots=nbr_plots,
)
- if pred_scores is not None:
- dict_input = {}
-
- for idx, (score, w) in enumerate(zip(pred_scores, window)):
- dict_input[idx] = {
- "series_score": score,
- "window": w,
- "name_id": idx,
- }
-
- for index, elem in enumerate(
- sorted(dict_input.items(), key=lambda x: x[1]["window"])
- ):
- if index == 0:
- current_window = elem[1]["window"]
- index_ax = index_ax + 1
-
- idx = elem[1]["name_id"]
- w = elem[1]["window"]
-
- if w != current_window:
- current_window = w
- index_ax = index_ax + 1
-
- if metric is not None:
- value = round(
- eval_metric_from_scores(
- anomalies=anomalies[anomalies.components[i]],
- pred_scores=pred_scores[idx][
- pred_scores[idx].components[i]
- ],
- window=w,
- metric=metric,
- ),
- 3,
- )
- else:
- value = None
-
- if names_of_scorers is not None:
- label = (
- names_of_scorers[idx] + [f" ({value})", ""][value is None]
- )
- else:
- label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
-
- _plot_series(
- series=elem[1]["series_score"][
- elem[1]["series_score"].components[i]
- ],
- ax_id=axs[index_ax][0],
- linewidth=0.5,
- label_name=label,
- )
-
- axs[index_ax][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
- )
- axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
- axs[index_ax][0].set_title("")
- axs[index_ax][0].set_xlabel("")
-
- if anomalies is not None:
- _plot_series(
- series=anomalies[anomalies.components[i]],
- ax_id=axs[index_ax + 1][0],
- linewidth=1,
- label_name=anomalies.components[i],
- color="red",
- )
-
- axs[index_ax + 1][0].set_title("")
- axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
- axs[index_ax + 1][0].set_yticks([0, 1])
- axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
- axs[index_ax + 1][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
- )
- else:
- axs[index_ax][0].set_xlabel("timestamp")
-
fig.suptitle(title)
else:
fig, axs = plt.subplots(
@@ -586,97 +497,19 @@ def show_anomalies_from_scores(
)
index_ax = 0
-
- _plot_series(
- series=series, ax_id=axs[index_ax][0], linewidth=0.5, label_name=""
+ _plot_series_and_anomalies(
+ series=series,
+ anomalies=anomalies,
+ pred_series=pred_series,
+ pred_scores=pred_scores,
+ window=window,
+ names_of_scorers=names_of_scorers,
+ metric=metric,
+ axs=axs,
+ index_ax=index_ax,
+ nbr_plots=nbr_plots,
)
- if pred_series is not None:
- _plot_series(
- series=pred_series,
- ax_id=axs[index_ax][0],
- linewidth=0.5,
- label_name="model output",
- )
-
- axs[index_ax][0].set_title("")
-
- if anomalies is not None or pred_scores is not None:
- axs[index_ax][0].set_xlabel("")
-
- axs[index_ax][0].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
-
- if pred_scores is not None:
- dict_input = {}
-
- for idx, (score, w) in enumerate(zip(pred_scores, window)):
- dict_input[idx] = {"series_score": score, "window": w, "name_id": idx}
-
- for index, elem in enumerate(
- sorted(dict_input.items(), key=lambda x: x[1]["window"])
- ):
- if index == 0:
- current_window = elem[1]["window"]
- index_ax = index_ax + 1
-
- idx = elem[1]["name_id"]
- w = elem[1]["window"]
-
- if w != current_window:
- current_window = w
- index_ax = index_ax + 1
-
- if metric is not None:
- value = round(
- eval_metric_from_scores(
- anomalies=anomalies,
- pred_scores=pred_scores[idx],
- window=w,
- metric=metric,
- ),
- 3,
- )
- else:
- value = None
-
- if names_of_scorers is not None:
- label = names_of_scorers[idx] + [f" ({value})", ""][value is None]
- else:
- label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
-
- _plot_series(
- series=elem[1]["series_score"],
- ax_id=axs[index_ax][0],
- linewidth=0.5,
- label_name=label,
- )
-
- axs[index_ax][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
- )
- axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
- axs[index_ax][0].set_title("")
- axs[index_ax][0].set_xlabel("")
-
- if anomalies is not None:
- _plot_series(
- series=anomalies,
- ax_id=axs[index_ax + 1][0],
- linewidth=1,
- label_name="anomalies",
- color="red",
- )
-
- axs[index_ax + 1][0].set_title("")
- axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
- axs[index_ax + 1][0].set_yticks([0, 1])
- axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
- axs[index_ax + 1][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
- )
- else:
- axs[index_ax][0].set_xlabel("timestamp")
-
fig.suptitle(title)
@@ -937,3 +770,133 @@ def _assert_fit_called(fit_called: bool, name: str):
),
logger=logger,
)
+
+
+def _plot_series_and_anomalies(
+ series: TimeSeries,
+ anomalies: TimeSeries,
+ pred_series: TimeSeries,
+ pred_scores: Sequence[TimeSeries],
+ window: Sequence[int],
+ names_of_scorers: Sequence[str],
+ metric: str,
+ axs: plt.Axes,
+ index_ax: int,
+ nbr_plots: int,
+):
+ """Helper function to plot series and anomalies.
+
+ Parameters
+ ----------
+ series
+ The actual series to visualize anomalies from.
+ anomalies
+ The ground truth of the anomalies (1 if it is an anomaly and 0 if not).
+ pred_series
+ Output of the model given as input the `series` (can be stochastic).
+ pred_scores
+ Output of the scorers given the output of the model and `series`.
+ window
+ Window parameter for each anomaly scores.
+ names_of_scorers
+ Name of the scores.
+ metric
+ The name of the metric function to use.
+ axs
+ The axes to plot on.
+ index_ax
+ The index of the current axis.
+ nbr_plots
+ The number of plots.
+ """
+ _plot_series(series=series, ax_id=axs[index_ax][0], linewidth=0.5, label_name="")
+
+ if pred_series is not None:
+ _plot_series(
+ series=pred_series,
+ ax_id=axs[index_ax][0],
+ linewidth=0.5,
+ label_name="model output",
+ )
+
+ axs[index_ax][0].set_title("")
+
+ if anomalies is not None or pred_scores is not None:
+ axs[index_ax][0].set_xlabel("")
+
+ axs[index_ax][0].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
+
+ if pred_scores is not None:
+ dict_input = {}
+
+ for idx, (score, w) in enumerate(zip(pred_scores, window)):
+ dict_input[idx] = {"series_score": score, "window": w, "name_id": idx}
+
+ for index, elem in enumerate(
+ sorted(dict_input.items(), key=lambda x: x[1]["window"])
+ ):
+ if index == 0:
+ current_window = elem[1]["window"]
+ index_ax = index_ax + 1
+
+ idx = elem[1]["name_id"]
+ w = elem[1]["window"]
+
+ if w != current_window:
+ current_window = w
+ index_ax = index_ax + 1
+
+ if metric is not None:
+ value = round(
+ eval_metric_from_scores(
+ anomalies=anomalies,
+ pred_scores=pred_scores[idx][
+ pred_scores[idx].components[index_ax // nbr_plots]
+ ],
+ window=w,
+ metric=metric,
+ ),
+ 3,
+ )
+ else:
+ value = None
+
+ if names_of_scorers is not None:
+ label = names_of_scorers[idx] + [f" ({value})", ""][value is None]
+ else:
+ label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
+
+ _plot_series(
+ series=elem[1]["series_score"][
+ elem[1]["series_score"].components[index_ax // nbr_plots]
+ ],
+ ax_id=axs[index_ax][0],
+ linewidth=0.5,
+ label_name=label,
+ )
+
+ axs[index_ax][0].legend(
+ loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
+ )
+ axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
+ axs[index_ax][0].set_title("")
+ axs[index_ax][0].set_xlabel("")
+
+ if anomalies is not None:
+ _plot_series(
+ series=anomalies,
+ ax_id=axs[index_ax + 1][0],
+ linewidth=1,
+ label_name="anomalies",
+ color="red",
+ )
+
+ axs[index_ax + 1][0].set_title("")
+ axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
+ axs[index_ax + 1][0].set_yticks([0, 1])
+ axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
+ axs[index_ax + 1][0].legend(
+ loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
+ )
+ else:
+ axs[index_ax][0].set_xlabel("timestamp")
From e6b68545bd10a2d904242da97ea520850ce878b4 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Tue, 31 Dec 2024 10:55:24 +0800
Subject: [PATCH 04/14] Update changelog
---
CHANGELOG.md | 1 +
1 file changed, 1 insertion(+)
diff --git a/CHANGELOG.md b/CHANGELOG.md
index cf805cad84..8439668f75 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -12,6 +12,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
**Improved**
- New model: `StatsForecastAutoTBATS`. This model offers the [AutoTBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats) model from Nixtla's `statsforecasts` library. [#2611](https://github.com/unit8co/darts/pull/2611) by [He Weilin](https://github.com/cnhwl).
+- Added `multivariate_plot` parameter in `show_anomalies()` to separately plot each component in multivariate series. [#2544](https://github.com/unit8co/darts/pull/2544) by [He Weilin](https://github.com/cnhwl).
**Fixed**
From 8468f6d851d9ce4c1d94473414c045bf9f169b09 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Tue, 31 Dec 2024 17:21:59 +0800
Subject: [PATCH 05/14] Improve code in utils.py
---
darts/ad/utils.py | 60 +++++++++++++++++++++--------------------------
1 file changed, 27 insertions(+), 33 deletions(-)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index 6f2821a656..672a3a818a 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -353,13 +353,23 @@ def show_anomalies_from_scores(
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Only effective when `pred_scores` is not `None`.
Default: "AUC_ROC".
+ multivariate_plot
+ If True, it will separately plot each component in multivariate series.
"""
- series = _check_input(
- series,
- name="series",
- num_series_expected=1,
- )[0]
+ series = (
+ _check_input(
+ series,
+ name="series",
+ check_multivariate=True,
+ )[0]
+ if multivariate_plot
+ else _check_input(
+ series,
+ name="series",
+ num_series_expected=1,
+ )[0]
+ )
if title is None and pred_scores is not None:
title = "Anomaly results"
@@ -424,15 +434,17 @@ def show_anomalies_from_scores(
)
nbr_plots = nbr_plots + len(set(window))
-
- if multivariate_plot:
- series = _check_input(
- series,
- name="series",
- check_multivariate=True,
- )[0]
series_width = series.n_components
+ plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
+ fig, axs = plt.subplots(
+ plots_per_ts,
+ figsize=(8, 4 + 2 * (plots_per_ts - 1)),
+ sharex=True,
+ gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
+ squeeze=False,
+ )
+ if multivariate_plot:
if pred_series is not None:
pred_series = _check_input(
pred_series,
@@ -446,6 +458,7 @@ def show_anomalies_from_scores(
anomalies,
name="anomalies",
width_expected=series.width,
+ check_binary=True,
check_multivariate=True,
)[0]
@@ -458,17 +471,7 @@ def show_anomalies_from_scores(
check_multivariate=True,
)[0]
- series_width = series.n_components
- fig, axs = plt.subplots(
- nbr_plots * series_width,
- figsize=(8, 4 + 2 * (nbr_plots * series_width - 1)),
- sharex=True,
- gridspec_kw={"height_ratios": [2] + [1] * (nbr_plots * series_width - 1)},
- squeeze=False,
- )
-
for i in range(series_width):
- index_ax = i * nbr_plots
_plot_series_and_anomalies(
series=series[series.components[i]],
anomalies=anomalies[anomalies.components[i]]
@@ -482,21 +485,12 @@ def show_anomalies_from_scores(
names_of_scorers=names_of_scorers,
metric=metric,
axs=axs,
- index_ax=index_ax,
+ index_ax=i * nbr_plots,
nbr_plots=nbr_plots,
)
fig.suptitle(title)
else:
- fig, axs = plt.subplots(
- nbr_plots,
- figsize=(8, 4 + 2 * (nbr_plots - 1)),
- sharex=True,
- gridspec_kw={"height_ratios": [2] + [1] * (nbr_plots - 1)},
- squeeze=False,
- )
-
- index_ax = 0
_plot_series_and_anomalies(
series=series,
anomalies=anomalies,
@@ -506,7 +500,7 @@ def show_anomalies_from_scores(
names_of_scorers=names_of_scorers,
metric=metric,
axs=axs,
- index_ax=index_ax,
+ index_ax=0,
nbr_plots=nbr_plots,
)
From da1e644d9ac3c3937417819ab2b2c2ed3d16d6f9 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Thu, 2 Jan 2025 09:21:43 +0800
Subject: [PATCH 06/14] Improve code in utils.py
---
darts/ad/utils.py | 72 ++++++++++++++++++++++-------------------------
1 file changed, 33 insertions(+), 39 deletions(-)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index 672a3a818a..8bba465551 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -357,25 +357,17 @@ def show_anomalies_from_scores(
If True, it will separately plot each component in multivariate series.
"""
- series = (
- _check_input(
- series,
- name="series",
- check_multivariate=True,
- )[0]
- if multivariate_plot
- else _check_input(
- series,
- name="series",
- num_series_expected=1,
- )[0]
- )
+ series = _check_input(
+ series,
+ name="series",
+ num_series_expected=1,
+ check_multivariate=multivariate_plot,
+ )[0]
if title is None and pred_scores is not None:
title = "Anomaly results"
nbr_plots = 1
-
if anomalies is not None:
nbr_plots = nbr_plots + 1
elif metric is not None:
@@ -433,7 +425,7 @@ def show_anomalies_from_scores(
logger=logger,
)
- nbr_plots = nbr_plots + len(set(window))
+ nbr_plots += len(set(window))
series_width = series.n_components
plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
fig, axs = plt.subplots(
@@ -444,33 +436,36 @@ def show_anomalies_from_scores(
squeeze=False,
)
- if multivariate_plot:
- if pred_series is not None:
- pred_series = _check_input(
- pred_series,
- name="pred_series",
- width_expected=series.width,
- check_multivariate=True,
- )[0]
+ if pred_series is not None:
+ pred_series = _check_input(
+ pred_series,
+ name="pred_series",
+ width_expected=series.width,
+ num_series_expected=1,
+ check_multivariate=True,
+ )[0]
- if anomalies is not None:
- anomalies = _check_input(
- anomalies,
- name="anomalies",
+ if anomalies is not None:
+ anomalies = _check_input(
+ anomalies,
+ name="anomalies",
+ width_expected=series.width,
+ num_series_expected=1,
+ check_binary=True,
+ check_multivariate=True,
+ )[0]
+
+ if pred_scores is not None:
+ for pred_score in pred_scores:
+ pred_score = _check_input(
+ pred_score,
+ name="pred_score",
width_expected=series.width,
- check_binary=True,
+ num_series_expected=1,
check_multivariate=True,
)[0]
- if pred_scores is not None:
- for pred_score in pred_scores:
- pred_score = _check_input(
- pred_score,
- name="pred_score",
- width_expected=series.width,
- check_multivariate=True,
- )[0]
-
+ if multivariate_plot:
for i in range(series_width):
_plot_series_and_anomalies(
series=series[series.components[i]],
@@ -489,7 +484,6 @@ def show_anomalies_from_scores(
nbr_plots=nbr_plots,
)
- fig.suptitle(title)
else:
_plot_series_and_anomalies(
series=series,
@@ -504,7 +498,7 @@ def show_anomalies_from_scores(
nbr_plots=nbr_plots,
)
- fig.suptitle(title)
+ fig.suptitle(title)
def _assert_binary(series: TimeSeries, name: str):
From 8e51e2c57d80be72da4126d653dc716a8286c3ae Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Thu, 2 Jan 2025 09:59:13 +0800
Subject: [PATCH 07/14] make check_multivariate depend on multivariate_plot
---
darts/ad/utils.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index 8bba465551..a0a4dfc40a 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -442,7 +442,7 @@ def show_anomalies_from_scores(
name="pred_series",
width_expected=series.width,
num_series_expected=1,
- check_multivariate=True,
+ check_multivariate=multivariate_plot,
)[0]
if anomalies is not None:
@@ -452,7 +452,7 @@ def show_anomalies_from_scores(
width_expected=series.width,
num_series_expected=1,
check_binary=True,
- check_multivariate=True,
+ check_multivariate=multivariate_plot,
)[0]
if pred_scores is not None:
@@ -462,7 +462,7 @@ def show_anomalies_from_scores(
name="pred_score",
width_expected=series.width,
num_series_expected=1,
- check_multivariate=True,
+ check_multivariate=multivariate_plot,
)[0]
if multivariate_plot:
From f3e0db21206547bb42447f8163a283ad3c00342d Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Fri, 3 Jan 2025 13:55:01 +0100
Subject: [PATCH 08/14] update utils
---
darts/ad/scorers/scorers.py | 2 +
darts/ad/utils.py | 87 ++++++++++++++++++-------------------
2 files changed, 44 insertions(+), 45 deletions(-)
diff --git a/darts/ad/scorers/scorers.py b/darts/ad/scorers/scorers.py
index 66f01b6692..7e935c56dc 100644
--- a/darts/ad/scorers/scorers.py
+++ b/darts/ad/scorers/scorers.py
@@ -615,6 +615,8 @@ def show_anomalies(
Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
+ multivariate_plot
+ If True, it will separately plot each component in multivariate series.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
pred_scores = self.score(series)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index a0a4dfc40a..26a2a6f42f 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -356,7 +356,6 @@ def show_anomalies_from_scores(
multivariate_plot
If True, it will separately plot each component in multivariate series.
"""
-
series = _check_input(
series,
name="series",
@@ -426,78 +425,80 @@ def show_anomalies_from_scores(
)
nbr_plots += len(set(window))
- series_width = series.n_components
- plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
- fig, axs = plt.subplots(
- plots_per_ts,
- figsize=(8, 4 + 2 * (plots_per_ts - 1)),
- sharex=True,
- gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
- squeeze=False,
- )
+ series_width = series.n_components
if pred_series is not None:
pred_series = _check_input(
pred_series,
name="pred_series",
- width_expected=series.width,
+ width_expected=series_width,
num_series_expected=1,
check_multivariate=multivariate_plot,
)[0]
- if anomalies is not None:
+ if anomalies is not None and multivariate_plot:
anomalies = _check_input(
anomalies,
name="anomalies",
- width_expected=series.width,
+ width_expected=series_width,
num_series_expected=1,
check_binary=True,
check_multivariate=multivariate_plot,
)[0]
- if pred_scores is not None:
+ if pred_scores is not None and multivariate_plot:
for pred_score in pred_scores:
- pred_score = _check_input(
+ _ = _check_input(
pred_score,
name="pred_score",
- width_expected=series.width,
+ width_expected=series_width,
num_series_expected=1,
check_multivariate=multivariate_plot,
)[0]
- if multivariate_plot:
- for i in range(series_width):
- _plot_series_and_anomalies(
- series=series[series.components[i]],
- anomalies=anomalies[anomalies.components[i]]
- if anomalies is not None
- else None,
- pred_series=pred_series[pred_series.components[i]]
+ plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
+ fig, axs = plt.subplots(
+ plots_per_ts,
+ figsize=(8, 4 + 2 * (plots_per_ts - 1)),
+ sharex=True,
+ gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
+ squeeze=False,
+ )
+
+ for i in range(series_width if multivariate_plot else 1):
+ if multivariate_plot:
+ series_ = series[series.components[i]]
+ anomalies_ = (
+ anomalies[anomalies.components[i]] if anomalies is not None else None
+ )
+ pred_series_ = (
+ pred_series[pred_series.components[i]]
if pred_series is not None
- else None,
- pred_scores=pred_scores,
- window=window,
- names_of_scorers=names_of_scorers,
- metric=metric,
- axs=axs,
- index_ax=i * nbr_plots,
- nbr_plots=nbr_plots,
+ else None
+ )
+ pred_scores_ = (
+ [pc[pc.components[i]] for pc in pred_scores]
+ if pred_scores is not None
+ else None
)
+ else:
+ series_ = series
+ anomalies_ = anomalies
+ pred_series_ = pred_series
+ pred_scores_ = pred_scores
- else:
_plot_series_and_anomalies(
- series=series,
- anomalies=anomalies,
- pred_series=pred_series,
- pred_scores=pred_scores,
+ series=series_,
+ anomalies=anomalies_,
+ pred_series=pred_series_,
+ pred_scores=pred_scores_,
window=window,
names_of_scorers=names_of_scorers,
metric=metric,
axs=axs,
- index_ax=0,
+ index_ax=i * nbr_plots,
nbr_plots=nbr_plots,
)
-
fig.suptitle(title)
@@ -838,9 +839,7 @@ def _plot_series_and_anomalies(
value = round(
eval_metric_from_scores(
anomalies=anomalies,
- pred_scores=pred_scores[idx][
- pred_scores[idx].components[index_ax // nbr_plots]
- ],
+ pred_scores=pred_scores[idx],
window=w,
metric=metric,
),
@@ -855,9 +854,7 @@ def _plot_series_and_anomalies(
label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
_plot_series(
- series=elem[1]["series_score"][
- elem[1]["series_score"].components[index_ax // nbr_plots]
- ],
+ series=elem[1]["series_score"],
ax_id=axs[index_ax][0],
linewidth=0.5,
label_name=label,
From 27156f467cdaf04b0428ec850846a0a05fb9c48b Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Mon, 6 Jan 2025 11:02:21 +0800
Subject: [PATCH 09/14] improve the spacing between suptitle and axes
---
darts/ad/utils.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index 26a2a6f42f..d0e3fb4a3b 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -463,6 +463,7 @@ def show_anomalies_from_scores(
sharex=True,
gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
squeeze=False,
+ constrained_layout=True,
)
for i in range(series_width if multivariate_plot else 1):
From 1fa81e2b66b02c4b4d3dee99d75cd04be076f555 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Mon, 6 Jan 2025 14:55:39 +0800
Subject: [PATCH 10/14] update utils
---
darts/ad/scorers/scorers.py | 2 +
darts/ad/utils.py | 87 ++++++++++++++++++-------------------
2 files changed, 44 insertions(+), 45 deletions(-)
diff --git a/darts/ad/scorers/scorers.py b/darts/ad/scorers/scorers.py
index 66f01b6692..7e935c56dc 100644
--- a/darts/ad/scorers/scorers.py
+++ b/darts/ad/scorers/scorers.py
@@ -615,6 +615,8 @@ def show_anomalies(
Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
+ multivariate_plot
+ If True, it will separately plot each component in multivariate series.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
pred_scores = self.score(series)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index a0a4dfc40a..26a2a6f42f 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -356,7 +356,6 @@ def show_anomalies_from_scores(
multivariate_plot
If True, it will separately plot each component in multivariate series.
"""
-
series = _check_input(
series,
name="series",
@@ -426,78 +425,80 @@ def show_anomalies_from_scores(
)
nbr_plots += len(set(window))
- series_width = series.n_components
- plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
- fig, axs = plt.subplots(
- plots_per_ts,
- figsize=(8, 4 + 2 * (plots_per_ts - 1)),
- sharex=True,
- gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
- squeeze=False,
- )
+ series_width = series.n_components
if pred_series is not None:
pred_series = _check_input(
pred_series,
name="pred_series",
- width_expected=series.width,
+ width_expected=series_width,
num_series_expected=1,
check_multivariate=multivariate_plot,
)[0]
- if anomalies is not None:
+ if anomalies is not None and multivariate_plot:
anomalies = _check_input(
anomalies,
name="anomalies",
- width_expected=series.width,
+ width_expected=series_width,
num_series_expected=1,
check_binary=True,
check_multivariate=multivariate_plot,
)[0]
- if pred_scores is not None:
+ if pred_scores is not None and multivariate_plot:
for pred_score in pred_scores:
- pred_score = _check_input(
+ _ = _check_input(
pred_score,
name="pred_score",
- width_expected=series.width,
+ width_expected=series_width,
num_series_expected=1,
check_multivariate=multivariate_plot,
)[0]
- if multivariate_plot:
- for i in range(series_width):
- _plot_series_and_anomalies(
- series=series[series.components[i]],
- anomalies=anomalies[anomalies.components[i]]
- if anomalies is not None
- else None,
- pred_series=pred_series[pred_series.components[i]]
+ plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
+ fig, axs = plt.subplots(
+ plots_per_ts,
+ figsize=(8, 4 + 2 * (plots_per_ts - 1)),
+ sharex=True,
+ gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
+ squeeze=False,
+ )
+
+ for i in range(series_width if multivariate_plot else 1):
+ if multivariate_plot:
+ series_ = series[series.components[i]]
+ anomalies_ = (
+ anomalies[anomalies.components[i]] if anomalies is not None else None
+ )
+ pred_series_ = (
+ pred_series[pred_series.components[i]]
if pred_series is not None
- else None,
- pred_scores=pred_scores,
- window=window,
- names_of_scorers=names_of_scorers,
- metric=metric,
- axs=axs,
- index_ax=i * nbr_plots,
- nbr_plots=nbr_plots,
+ else None
+ )
+ pred_scores_ = (
+ [pc[pc.components[i]] for pc in pred_scores]
+ if pred_scores is not None
+ else None
)
+ else:
+ series_ = series
+ anomalies_ = anomalies
+ pred_series_ = pred_series
+ pred_scores_ = pred_scores
- else:
_plot_series_and_anomalies(
- series=series,
- anomalies=anomalies,
- pred_series=pred_series,
- pred_scores=pred_scores,
+ series=series_,
+ anomalies=anomalies_,
+ pred_series=pred_series_,
+ pred_scores=pred_scores_,
window=window,
names_of_scorers=names_of_scorers,
metric=metric,
axs=axs,
- index_ax=0,
+ index_ax=i * nbr_plots,
nbr_plots=nbr_plots,
)
-
fig.suptitle(title)
@@ -838,9 +839,7 @@ def _plot_series_and_anomalies(
value = round(
eval_metric_from_scores(
anomalies=anomalies,
- pred_scores=pred_scores[idx][
- pred_scores[idx].components[index_ax // nbr_plots]
- ],
+ pred_scores=pred_scores[idx],
window=w,
metric=metric,
),
@@ -855,9 +854,7 @@ def _plot_series_and_anomalies(
label = f"score_{str(idx)}" + [f" ({value})", ""][value is None]
_plot_series(
- series=elem[1]["series_score"][
- elem[1]["series_score"].components[index_ax // nbr_plots]
- ],
+ series=elem[1]["series_score"],
ax_id=axs[index_ax][0],
linewidth=0.5,
label_name=label,
From 8aea4c07dccc6aed2ac1bb4138b79d1f4d81d930 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Mon, 6 Jan 2025 15:49:23 +0800
Subject: [PATCH 11/14] improve the spacing between suptitle and axes
---
darts/ad/utils.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index 26a2a6f42f..d0e3fb4a3b 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -463,6 +463,7 @@ def show_anomalies_from_scores(
sharex=True,
gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
squeeze=False,
+ constrained_layout=True,
)
for i in range(series_width if multivariate_plot else 1):
From c6b799e03bfb103f789c833c3baf42af35013a06 Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Mon, 6 Jan 2025 17:10:21 +0800
Subject: [PATCH 12/14] Fix the height_ratios of each subplot
---
darts/ad/utils.py | 8 +++++---
1 file changed, 5 insertions(+), 3 deletions(-)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index d0e3fb4a3b..bc3588cb77 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -459,11 +459,13 @@ def show_anomalies_from_scores(
plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
fig, axs = plt.subplots(
plots_per_ts,
- figsize=(8, 4 + 2 * (plots_per_ts - 1)),
+ figsize=(8, 4 * (plots_per_ts // nbr_plots) + 2 * (nbr_plots - 1)),
sharex=True,
- gridspec_kw={"height_ratios": [2] + [1] * (plots_per_ts - 1)},
+ gridspec_kw={
+ "height_ratios": ([2] + [1] * (nbr_plots - 1)) * (plots_per_ts // nbr_plots)
+ },
squeeze=False,
- constrained_layout=True,
+ layout="constrained",
)
for i in range(series_width if multivariate_plot else 1):
From 78baeec4c4e89eeae88d6541e75934449568dd5a Mon Sep 17 00:00:00 2001
From: he weilin <heweilin19971212@163.com>
Date: Mon, 6 Jan 2025 17:18:38 +0800
Subject: [PATCH 13/14] Change "multivariate_plot" parameter name to
"component_wise"
---
darts/ad/anomaly_model/anomaly_model.py | 6 +++---
darts/ad/anomaly_model/forecasting_am.py | 6 +++---
darts/ad/scorers/scorers.py | 12 ++++++------
darts/ad/utils.py | 22 +++++++++++-----------
4 files changed, 23 insertions(+), 23 deletions(-)
diff --git a/darts/ad/anomaly_model/anomaly_model.py b/darts/ad/anomaly_model/anomaly_model.py
index d75ad18ab0..1b13d0553a 100644
--- a/darts/ad/anomaly_model/anomaly_model.py
+++ b/darts/ad/anomaly_model/anomaly_model.py
@@ -247,7 +247,7 @@ def show_anomalies(
names_of_scorers: Union[str, Sequence[str]] = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
- multivariate_plot: bool = False,
+ component_wise: bool = False,
**score_kwargs,
):
"""Plot the results of the anomaly model.
@@ -284,7 +284,7 @@ def show_anomalies(
Default: "AUC_ROC".
score_kwargs
parameters for the `score()` method.
- multivariate_plot
+ component_wise
If True, it will separately plot each component in multivariate series.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
@@ -313,7 +313,7 @@ def show_anomalies(
names_of_scorers=names_of_scorers,
title=title,
metric=metric,
- multivariate_plot=multivariate_plot,
+ component_wise=component_wise,
)
@property
diff --git a/darts/ad/anomaly_model/forecasting_am.py b/darts/ad/anomaly_model/forecasting_am.py
index 7fee356cb6..318fe3361a 100644
--- a/darts/ad/anomaly_model/forecasting_am.py
+++ b/darts/ad/anomaly_model/forecasting_am.py
@@ -440,7 +440,7 @@ def show_anomalies(
names_of_scorers: Union[str, Sequence[str]] = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
- multivariate_plot: bool = False,
+ component_wise: bool = False,
**score_kwargs,
):
"""Plot the results of the anomaly model.
@@ -507,7 +507,7 @@ def show_anomalies(
Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
- multivariate_plot
+ component_wise
If True, it will separately plot each component in multivariate series.
score_kwargs
parameters for the `score()` method.
@@ -530,7 +530,7 @@ def show_anomalies(
names_of_scorers=names_of_scorers,
title=title,
metric=metric,
- multivariate_plot=multivariate_plot,
+ component_wise=component_wise,
**score_kwargs,
)
diff --git a/darts/ad/scorers/scorers.py b/darts/ad/scorers/scorers.py
index 7e935c56dc..f5887c8314 100644
--- a/darts/ad/scorers/scorers.py
+++ b/darts/ad/scorers/scorers.py
@@ -176,7 +176,7 @@ def show_anomalies_from_prediction(
anomalies: TimeSeries = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
- multivariate_plot: bool = False,
+ component_wise: bool = False,
):
"""Plot the results of the scorer.
@@ -209,7 +209,7 @@ def show_anomalies_from_prediction(
Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
- multivariate_plot
+ component_wise
If True, it will separately plot each component in multivariate series.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
@@ -233,7 +233,7 @@ def show_anomalies_from_prediction(
names_of_scorers=scorer_name,
title=title,
metric=metric,
- multivariate_plot=multivariate_plot,
+ component_wise=component_wise,
)
@property
@@ -584,7 +584,7 @@ def show_anomalies(
scorer_name: str = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
- multivariate_plot: bool = False,
+ component_wise: bool = False,
):
"""Plot the results of the scorer.
@@ -615,7 +615,7 @@ def show_anomalies(
Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
- multivariate_plot
+ component_wise
If True, it will separately plot each component in multivariate series.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
@@ -640,7 +640,7 @@ def show_anomalies(
names_of_scorers=scorer_name,
title=title,
metric=metric,
- multivariate_plot=multivariate_plot,
+ component_wise=component_wise,
)
@property
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index bc3588cb77..f338d54b79 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -311,7 +311,7 @@ def show_anomalies_from_scores(
names_of_scorers: Union[str, Sequence[str]] = None,
title: str = None,
metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
- multivariate_plot: bool = False,
+ component_wise: bool = False,
):
"""Plot the results generated by an anomaly model.
@@ -353,14 +353,14 @@ def show_anomalies_from_scores(
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Only effective when `pred_scores` is not `None`.
Default: "AUC_ROC".
- multivariate_plot
+ component_wise
If True, it will separately plot each component in multivariate series.
"""
series = _check_input(
series,
name="series",
num_series_expected=1,
- check_multivariate=multivariate_plot,
+ check_multivariate=component_wise,
)[0]
if title is None and pred_scores is not None:
@@ -433,30 +433,30 @@ def show_anomalies_from_scores(
name="pred_series",
width_expected=series_width,
num_series_expected=1,
- check_multivariate=multivariate_plot,
+ check_multivariate=component_wise,
)[0]
- if anomalies is not None and multivariate_plot:
+ if anomalies is not None and component_wise:
anomalies = _check_input(
anomalies,
name="anomalies",
width_expected=series_width,
num_series_expected=1,
check_binary=True,
- check_multivariate=multivariate_plot,
+ check_multivariate=component_wise,
)[0]
- if pred_scores is not None and multivariate_plot:
+ if pred_scores is not None and component_wise:
for pred_score in pred_scores:
_ = _check_input(
pred_score,
name="pred_score",
width_expected=series_width,
num_series_expected=1,
- check_multivariate=multivariate_plot,
+ check_multivariate=component_wise,
)[0]
- plots_per_ts = nbr_plots * series_width if multivariate_plot else nbr_plots
+ plots_per_ts = nbr_plots * series_width if component_wise else nbr_plots
fig, axs = plt.subplots(
plots_per_ts,
figsize=(8, 4 * (plots_per_ts // nbr_plots) + 2 * (nbr_plots - 1)),
@@ -468,8 +468,8 @@ def show_anomalies_from_scores(
layout="constrained",
)
- for i in range(series_width if multivariate_plot else 1):
- if multivariate_plot:
+ for i in range(series_width if component_wise else 1):
+ if component_wise:
series_ = series[series.components[i]]
anomalies_ = (
anomalies[anomalies.components[i]] if anomalies is not None else None
From 95dd225faa91bef62595ec0066ec65e21ded9d68 Mon Sep 17 00:00:00 2001
From: dennisbader <dennis.bader@gmx.ch>
Date: Wed, 8 Jan 2025 14:40:38 +0100
Subject: [PATCH 14/14] make title fit better
---
CHANGELOG.md | 2 +-
darts/ad/anomaly_model/anomaly_model.py | 2 +-
darts/ad/anomaly_model/forecasting_am.py | 2 +-
darts/ad/scorers/scorers.py | 4 +-
darts/ad/utils.py | 63 +++++++++++-------------
5 files changed, 34 insertions(+), 39 deletions(-)
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 500c10a09f..1e2ac21663 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -12,7 +12,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
**Improved**
- New model: `StatsForecastAutoTBATS`. This model offers the [AutoTBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats) model from Nixtla's `statsforecasts` library. [#2611](https://github.com/unit8co/darts/pull/2611) by [He Weilin](https://github.com/cnhwl).
-- Added `multivariate_plot` parameter in `show_anomalies()` to separately plot each component in multivariate series. [#2544](https://github.com/unit8co/darts/pull/2544) by [He Weilin](https://github.com/cnhwl).
+- Added parameter `component_wise` to `show_anomalies()` to separately plot each component in multivariate series. [#2544](https://github.com/unit8co/darts/pull/2544) by [He Weilin](https://github.com/cnhwl).
**Fixed**
- Fixed a bug when performing optimized historical forecasts with `stride=1` using a `RegressionModel` with `output_chunk_shift>=1` and `output_chunk_length=1`, where the forecast time index was not properly shifted. [#2634](https://github.com/unit8co/darts/pull/2634) by [Mattias De Charleroy](https://github.com/MattiasDC).
diff --git a/darts/ad/anomaly_model/anomaly_model.py b/darts/ad/anomaly_model/anomaly_model.py
index 1b13d0553a..63655db40c 100644
--- a/darts/ad/anomaly_model/anomaly_model.py
+++ b/darts/ad/anomaly_model/anomaly_model.py
@@ -285,7 +285,7 @@ def show_anomalies(
score_kwargs
parameters for the `score()` method.
component_wise
- If True, it will separately plot each component in multivariate series.
+ If True, will separately plot each component in case of multivariate anomaly detection.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
predict_kwargs = predict_kwargs if predict_kwargs is not None else {}
diff --git a/darts/ad/anomaly_model/forecasting_am.py b/darts/ad/anomaly_model/forecasting_am.py
index 318fe3361a..8b4339cd9c 100644
--- a/darts/ad/anomaly_model/forecasting_am.py
+++ b/darts/ad/anomaly_model/forecasting_am.py
@@ -508,7 +508,7 @@ def show_anomalies(
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
component_wise
- If True, it will separately plot each component in multivariate series.
+ If True, will separately plot each component in case of multivariate anomaly detection.
score_kwargs
parameters for the `score()` method.
"""
diff --git a/darts/ad/scorers/scorers.py b/darts/ad/scorers/scorers.py
index f5887c8314..3fadee463a 100644
--- a/darts/ad/scorers/scorers.py
+++ b/darts/ad/scorers/scorers.py
@@ -210,7 +210,7 @@ def show_anomalies_from_prediction(
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
component_wise
- If True, it will separately plot each component in multivariate series.
+ If True, will separately plot each component in case of multivariate anomaly detection.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
pred_series = _check_input(
@@ -616,7 +616,7 @@ def show_anomalies(
Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
Default: "AUC_ROC".
component_wise
- If True, it will separately plot each component in multivariate series.
+ If True, will separately plot each component in case of multivariate anomaly detection.
"""
series = _check_input(series, name="series", num_series_expected=1)[0]
pred_scores = self.score(series)
diff --git a/darts/ad/utils.py b/darts/ad/utils.py
index f338d54b79..4395afdfeb 100644
--- a/darts/ad/utils.py
+++ b/darts/ad/utils.py
@@ -354,7 +354,7 @@ def show_anomalies_from_scores(
Only effective when `pred_scores` is not `None`.
Default: "AUC_ROC".
component_wise
- If True, it will separately plot each component in multivariate series.
+ If True, will separately plot each component in case of multivariate anomaly detection.
"""
series = _check_input(
series,
@@ -457,15 +457,13 @@ def show_anomalies_from_scores(
)[0]
plots_per_ts = nbr_plots * series_width if component_wise else nbr_plots
+ height_ratios = ([2] + [1] * (nbr_plots - 1)) * (plots_per_ts // nbr_plots)
+ height_total = 2 * sum(height_ratios)
fig, axs = plt.subplots(
- plots_per_ts,
- figsize=(8, 4 * (plots_per_ts // nbr_plots) + 2 * (nbr_plots - 1)),
+ nrows=plots_per_ts,
+ figsize=(8, height_total),
sharex=True,
- gridspec_kw={
- "height_ratios": ([2] + [1] * (nbr_plots - 1)) * (plots_per_ts // nbr_plots)
- },
- squeeze=False,
- layout="constrained",
+ gridspec_kw={"height_ratios": height_ratios},
)
for i in range(series_width if component_wise else 1):
@@ -500,9 +498,13 @@ def show_anomalies_from_scores(
metric=metric,
axs=axs,
index_ax=i * nbr_plots,
- nbr_plots=nbr_plots,
)
- fig.suptitle(title)
+ # make title fit nicely on plot
+ title_height = 0.1
+ title_y = 1 - title_height / height_total
+
+ fig.suptitle(title, y=title_y)
+ fig.tight_layout()
def _assert_binary(series: TimeSeries, name: str):
@@ -774,7 +776,6 @@ def _plot_series_and_anomalies(
metric: str,
axs: plt.Axes,
index_ax: int,
- nbr_plots: int,
):
"""Helper function to plot series and anomalies.
@@ -798,25 +799,23 @@ def _plot_series_and_anomalies(
The axes to plot on.
index_ax
The index of the current axis.
- nbr_plots
- The number of plots.
"""
- _plot_series(series=series, ax_id=axs[index_ax][0], linewidth=0.5, label_name="")
+ _plot_series(series=series, ax_id=axs[index_ax], linewidth=0.5, label_name="")
if pred_series is not None:
_plot_series(
series=pred_series,
- ax_id=axs[index_ax][0],
+ ax_id=axs[index_ax],
linewidth=0.5,
label_name="model output",
)
- axs[index_ax][0].set_title("")
+ axs[index_ax].set_title("")
if anomalies is not None or pred_scores is not None:
- axs[index_ax][0].set_xlabel("")
+ axs[index_ax].set_xlabel("")
- axs[index_ax][0].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
+ axs[index_ax].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2)
if pred_scores is not None:
dict_input = {}
@@ -858,33 +857,29 @@ def _plot_series_and_anomalies(
_plot_series(
series=elem[1]["series_score"],
- ax_id=axs[index_ax][0],
+ ax_id=axs[index_ax],
linewidth=0.5,
label_name=label,
)
- axs[index_ax][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2
- )
- axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left")
- axs[index_ax][0].set_title("")
- axs[index_ax][0].set_xlabel("")
+ axs[index_ax].legend(loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2)
+ axs[index_ax].set_title(f"Window: {str(w)}", loc="left")
+ axs[index_ax].set_title("")
+ axs[index_ax].set_xlabel("")
if anomalies is not None:
_plot_series(
series=anomalies,
- ax_id=axs[index_ax + 1][0],
+ ax_id=axs[index_ax + 1],
linewidth=1,
label_name="anomalies",
color="red",
)
- axs[index_ax + 1][0].set_title("")
- axs[index_ax + 1][0].set_ylim([-0.1, 1.1])
- axs[index_ax + 1][0].set_yticks([0, 1])
- axs[index_ax + 1][0].set_yticklabels(["no", "yes"])
- axs[index_ax + 1][0].legend(
- loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2
- )
+ axs[index_ax + 1].set_title("")
+ axs[index_ax + 1].set_ylim([-0.1, 1.1])
+ axs[index_ax + 1].set_yticks([0, 1])
+ axs[index_ax + 1].set_yticklabels(["no", "yes"])
+ axs[index_ax + 1].legend(loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2)
else:
- axs[index_ax][0].set_xlabel("timestamp")
+ axs[index_ax].set_xlabel("timestamp")