-1. **Detection Accuracy (DA)** — Precision, Recall, and F1 score over matched
- behavioral segments (TP/FP/FN at the bout level).
-
-2. **Segment Overlap (SO)** — Temporal Intersection over Union (tIoU) between each
- matched predicted segment and its ground-truth counterpart.
-
-3. **Temporal Precision (TP)** — Penalizes start/end time errors:
- `1 / (1 + |start deviation| + |end deviation|)`.
-
-4. **Intra-bout Continuity (IC)** — Measures label stability inside each
- ground-truth bout: `1 - (label switches / bout length)`.
+Annotations are binary: one column per behavior, one row per frame, 1 when the behavior is
+present. A **bout** is a run of consecutive frames with value 1. Real bouts come from the
+ground truth, predicted bouts from the annotation being evaluated. A predicted bout
+**overlaps** a real bout when they share at least one frame.
+
+1. **Detection Accuracy (DA)** — precision, recall and F1 over bouts. Predicted bouts are
+ taken in time order, and each is matched to the first real bout it overlaps that is not
+ matched yet (greedy matching). TP is the number of matched pairs, FP the number of
+ predicted bouts left unmatched, FN the number of real bouts left unmatched (TP here is
+ a count, not the Temporal Precision metric).
+ Precision = TP / (TP + FP), recall = TP / (TP + FN),
+ F1 = 2 · precision · recall / (precision + recall). F1 is 0 when no bout is matched
+ (TP = 0) but some bouts were predicted or missed (FP + FN > 0).
+
+2. **Segment Overlap (SO)** — the temporal intersection over union (shared frames / frames
+ in the union) of every overlapping (predicted, real) bout pair, averaged over all such
+ pairs.
+
+3. **Temporal Precision (TP)** — `1 / (1 + |start difference| + |end difference|)`, in
+ frames, for every overlapping (predicted, real) bout pair, averaged over all such pairs.
+ It falls quickly: a bout predicted 2 frames late at both ends scores 1 / 5 = 0.2.
+
+4. **Intra-bout Continuity (IC)** — label stability inside the real bouts that are found,
+ that is, with at least one predicted frame inside. For each found real bout longer than
+ one frame: `1 - switches / (bout length - 1)`, where the bout length is in frames and
+ the switches are the changes of the predicted value between consecutive frames of the
+ bout (a bout has bout length - 1 such frame pairs). IC is the mean over these bouts.
+ One-frame real bouts have no frame pair and are left out.
+
+SO and TP average over pairs, not over real bouts: a real bout overlapped by two predicted
+bouts gives two terms, and a predicted bout that overlaps two real bouts gives one term with
+each. A real bout with no predicted frame inside it (a missed bout) gives no SO, TP or IC
+term by default (see the `missed_bouts` option below).
+
+**Undefined values.** For a behavior with real bouts in a recording (or, with
+`aggregation="pooled"`, in at least one recording), recall and F1 are always defined, and
+a metric is NaN when it is undefined:
+
+- precision, when nothing is predicted (TP + FP = 0);
+- SO and TP, when no predicted bout overlaps a real bout;
+- IC, when no real bout longer than one frame is found.
+
+With `missed_bouts="zero"`, each missed bout adds a term equal to 0: SO and TP are then
+always defined, and IC is NaN only when no real bout is longer than one frame. Per
+recording, a behavior without real bouts follows the absent-behavior rule (see
+`aggregation` below). Pooled, a behavior with no real bout in any recording has an
+undefined recall, SO, TP and IC, and an undefined precision and F1 too when nothing is
+predicted either (TP = FP = FN = 0).
+
+NaN values are left out of every mean, over recordings or behaviors: a NaN never counts as
+0 or 1, and a mean with no defined value is NaN.
+
+**Matching.** `matching="optimal"` replaces greedy matching with the Hungarian algorithm
+(`scipy.optimize.linear_sum_assignment`), which finds the largest possible number of
+matched pairs. On bouts read from 0/1 columns, as in `banos.score()` and the pipeline
+functions, greedy matching already finds that number, so both options give the same counts
+and the same metrics. They can differ only when `banos.core.calculate_tp_fp_fn` is given
+bouts directly and the bouts of one side overlap one another or are not in time order;
+`banos.score()` always passes disjoint bouts in time order, so it is unaffected.
## Installation
@@ -52,8 +100,17 @@ BANOS addresses these questions with four complementary metrics.
pip install banos
```
-Requires Python 3.11+. Core dependency: `pandas`. Optional: `scipy` (for
-`matching='optimal'`).
+Requires Python 3.11+. Dependencies: `numpy` (>= 1.24), `pandas` (>= 2.0) and
+`scipy` (>= 1.10).
+
+Two optional extras install what the tutorial notebooks need (the notebooks are in the
+[GitHub repository](https://github.com/BelloneLab/BANOS/tree/main/demo), not in the
+package):
+
+- `pip install "banos[notebook]"`: jupyter, nbformat, matplotlib and seaborn, for
+ `demo/tutorial_python.ipynb`;
+- `pip install "banos[calms21]"`: tensorflow, scikit-learn and tqdm, for
+ `demo/tutorial_calms21_task1.ipynb` (together with the `notebook` extra).
## Quick Start
@@ -61,76 +118,183 @@ Requires Python 3.11+. Core dependency: `pandas`. Optional: `scipy` (for
import banos
import pandas as pd
-# Load annotations as DataFrames (rows=frames, columns=behaviors, values=0/1)
+# One recording: one row per frame, one 0/1 column per behavior
pred = pd.read_csv("machine_annotations.csv")
gt = pd.read_csv("human_annotations.csv")
-# Score a single recording
metrics = banos.score(pred, gt)
-# {'behavior1': {'precision': 0.9, 'recall': 0.85, 'f1_score': 0.87,
-# 'so': 0.76, 'tp': 0.82, 'ic': 0.91}, ...}
+# {'attack': {'precision': ..., 'recall': ..., 'f1_score': ..., 'so': ..., 'tp': ..., 'ic': ...}, ...}
-# Score multiple recordings
+# Several recordings: a dict of (prediction, ground truth) pairs
group_metrics, overall_metrics = banos.score({
- 'recording_1': (pred_1, gt_1),
- 'recording_2': (pred_2, gt_2),
+ "recording_1": (pred_1, gt_1),
+ "recording_2": (pred_2, gt_2),
})
```
-`banos.score()` returns:
+**Input.** For each recording, the prediction and the ground truth are DataFrames with the
+same number of rows (frames). Rows are read by position: the index is ignored. Columns are
+matched by name and hold 0 and 1 (integers, floats or booleans). NaN values are replaced by
+0, and columns that cannot be scored are dropped, each with a warning (see Errors and
+warnings below).
-- **Single recording**: `dict[behavior → dict[metric → float]]`
-- **Multiple recordings**: `(per-recording metrics, overall averaged metrics)`
+**Returns.** `banos.score()` returns, with either aggregation:
-Absent-behavior scoring (v0.2.0): when a behavior has no ground-truth bouts,
-BANOS checks whether the machine also predicted nothing — if so, all metrics = **1.0**
-(correct absence); if the machine predicted bouts that don't exist in GT, all metrics = **0.0**
-(false detection). This is excluded from NaN-averaging correctly.
+- for one recording (a DataFrame pair): `{behavior: {metric: value}}`, with the metrics
+ `precision`, `recall`, `f1_score` (DA), `so`, `tp` and `ic`;
+- for several recordings (a dict): a tuple `(group_metrics, overall_metrics)`, where
+ `group_metrics` is `{behavior: {metric: value}}` over all recordings and
+ `overall_metrics` is `{metric: value}`.
-## Examples & Tutorials
+## Options
-| Resource | Description |
-|----------|-------------|
-| [`demo/quickstart.py`](demo/quickstart.py) | Minimal Python example — score annotations in two lines |
-| [`demo/quickstart.m`](demo/quickstart.m) | Minimal MATLAB example |
-| [`demo/tutorial_python.ipynb`](demo/tutorial_python.ipynb) | Full Python tutorial: classic BANOS usage + frame F1 vs BANOS comparison |
-| [`demo/tutorial_matlab.m`](demo/tutorial_matlab.m) | Full MATLAB tutorial mirroring the Python notebook |
-| [`demo/tutorial_calms21_task1.ipynb`](demo/tutorial_calms21_task1.ipynb) | Human vs machine: CalMS21 Task 1 baseline, F1 + BANOS (Python only) |
+`banos.score(data, gt=None, *, matching="greedy", missed_bouts="exclude", aggregation="per_file")`
+
+### Combining recordings: `aggregation`
+
+**`aggregation="per_file"` (default).** Each recording is scored on its own, then the
+values are averaged: `group_metrics` is the mean of each behavior over the recordings,
+`overall_metrics` the mean of every (recording, behavior) value, NaN left out. Each
+recording weighs the same, whatever its number of bouts.
-## Advanced Usage
+*Absent-behavior rule:* when the ground truth of a recording has no bout of a behavior,
+that behavior scores 1.0 on all six metrics if nothing is predicted (correct absence), and
+0.0 on all six if at least one bout is predicted (false detection).
-### Optimal Matching
+The per-file mean therefore mixes detection quality with the share of recordings without
+the behavior. A model that predicts nothing scores 1.0 on every recording without the
+behavior and F1 = 0 on the others: on the bundled data, its per-file F1 is 0.5 for attack
+(absent from 5 of the 10 recordings) and 0.4 for mount (absent from 4), while it finds no
+bout at all.
-By default, predicted segments are matched to ground-truth bouts greedily (by
-overlap). For a globally optimal assignment, use the Hungarian algorithm:
+Use it to know how good the annotation of each recording is: look at the value of each
+recording (`calculate_banos_for_each_file`, see Lower-level API) and at their mean, with
+the share of recordings without each behavior in mind.
+
+**`aggregation="pooled"`.** Each behavior is scored once over all recordings: TP, FP and
+FN are summed over the recordings and give precision, recall and F1, and the SO, TP and IC
+terms of all recordings are averaged together. There is no absent-behavior rule: a
+recording without the behavior only adds its predicted bouts to FP. A behavior with no
+real and no predicted bout in any recording is NaN on all six metrics. `overall_metrics` is
+the mean over the behaviors, NaN left out. Every bout weighs the same, and predicting
+nothing gives F1 = 0: this is the right choice to compare or rank models and methods on
+the same recordings.
```python
-metrics = banos.score(pred, gt, matching='optimal')
+import pandas as pd
+import banos
+
+gt = pd.DataFrame({"attack": [0, 1, 1, 1, 0, 0, 1, 1, 1, 0]}) # two real bouts
+silent = pd.DataFrame({"attack": [0] * 10}) # a model that predicts nothing
+
+data = {
+ "video_1": (silent, gt), # both real bouts missed
+ "video_2": (silent, silent), # no attack in this video: correct absence
+}
+
+group, overall = banos.score(data) # per_file
+# group["attack"]: precision 1.0, recall 0.5, f1_score 0.5, so 1.0, tp 1.0, ic 1.0
+
+group, overall = banos.score(data, aggregation="pooled")
+# group["attack"]: precision nan, recall 0.0, f1_score 0.0, so nan, tp nan, ic nan
```
-Requires `scipy` (`pip install scipy`).
+Per file, `video_2` scores 1.0 everywhere, while `video_1` has recall and F1 0 and its
+precision, SO, TP and IC undefined: every mean but recall and F1 is 1.0. Pooled, the model
+has found 0 of the 2 bouts.
+
+### Missed bouts: `missed_bouts`
-### Lower-level API
+**`missed_bouts="exclude"` (default).** A real bout with no predicted frame inside it gives
+no SO, TP or IC term. SO, TP and IC then describe the quality of the bouts that were found,
+and are read together with recall, the share of real bouts found.
+
+**`missed_bouts="zero"`.** Each missed real bout adds a term equal to 0 to SO, to TP and,
+if it is longer than one frame, to IC, so these metrics also drop when bouts are missed.
+
+Precision, recall, F1 and the absent-behavior rule do not depend on this option.
```python
-from banos import preprocess_data, calculate_banos_for_each_file, aggregate_metrics
+gt = pd.DataFrame({"attack": [0, 1, 1, 1, 0, 0, 1, 1, 1, 0]}) # two real bouts
+pred = pd.DataFrame({"attack": [0, 1, 1, 1, 0, 0, 0, 0, 0, 0]}) # finds the first only
+
+banos.score(pred, gt)["attack"]
+# precision 1.0, recall 0.5, f1_score 0.667, so 1.0, tp 1.0, ic 1.0
+
+banos.score(pred, gt, missed_bouts="zero")["attack"]
+# precision 1.0, recall 0.5, f1_score 0.667, so 0.5, tp 0.5, ic 0.5
+```
+
+The two options combine: `banos.score(data, missed_bouts="zero", aggregation="pooled")`.
-data_dict = {
- 'file1': (pred_df_1, gt_df_1),
- 'file2': (pred_df_2, gt_df_2),
+### Errors and warnings
+
+`banos.score()` raises `ValueError`:
+
+- for an unknown `matching`, `missed_bouts` or `aggregation` value, before reading the data;
+- when `data` is a DataFrame and `gt` is missing;
+- when the prediction and the ground truth of a recording have different numbers of
+ frames, or a recording has no frame.
+
+It emits a `UserWarning` naming the recording and the column:
+
+- for each column whose NaN values are replaced by 0;
+- for each column dropped because it holds values other than 0 and 1 (probabilities, for
+ example);
+- for each column dropped because it is missing, or was dropped, on the other side.
+
+A single DataFrame pair is named `'single'` in these messages. Valid input gives no
+warning. `preprocess_data()` raises and warns in the same way.
+
+## Lower-level API
+
+`banos.score()` chains three functions; each step can be called on its own, for example
+to keep the results of each recording.
+
+```python
+from banos import (
+ preprocess_data,
+ calculate_banos_for_each_file, aggregate_metrics, # per_file
+ calculate_components_for_each_file, pool_components, # pooled
+)
+
+data = {
+ "recording_1": (pred_1, gt_1),
+ "recording_2": (pred_2, gt_2),
}
+clean, dropped = preprocess_data(data) # dropped: the columns removed from each recording
+
+# Per file: the metrics of each recording, then their means
+file_metrics = calculate_banos_for_each_file(clean) # {recording: {behavior: {metric: value}}}
+group_metrics, overall_metrics = aggregate_metrics(file_metrics)
-preprocessed, dropped = preprocess_data(data_dict)
-per_file_metrics = calculate_banos_for_each_file(preprocessed)
-group_metrics, overall = aggregate_metrics(per_file_metrics)
+# Pooled: the bout counts and terms of each recording, then one score per behavior
+file_components = calculate_components_for_each_file(clean) # {recording: {behavior: components}}
+group_metrics, overall_metrics = pool_components(file_components)
```
+`calculate_banos_for_each_file` and `calculate_components_for_each_file` take the
+`matching` and `missed_bouts` options of `banos.score()`. The components of a behavior in a
+recording are what its metrics are computed from:
+`{"n_tp": ..., "n_fp": ..., "n_fn": ..., "so_terms": [...], "tp_terms": [...], "ic_terms": [...]}`,
+the bout counts and the lists of terms that SO, TP and IC average. For a single recording
+that has already been preprocessed, `compute_behavior_metrics(pred, gt)` and
+`compute_behavior_components(pred, gt)` return its per-behavior metrics and components.
+
+`banos.core` holds the building blocks: `identify_bouts`, `calculate_tp_fp_fn`,
+`calculate_precision_recall_f1`, `calculate_tiou`, `calculate_so` and
+`calculate_so_terms`, `calculate_tp_metric` and `calculate_tp_terms`, `calculate_ic` and
+`calculate_ic_terms`, `count_switches_within_bout`, and the preprocessing functions
+`is_logical_column`, `drop_non_logical_columns`, `match_headers` and `preprocess_data`.
+Import the metric functions from `banos.core`.
+
## MATLAB
-A MATLAB implementation is provided in `matlab/BANOS/`. Usage mirrors the Python API.
+A MATLAB implementation is provided in `matlab/BANOS/`. It needs base MATLAB only, no
+toolbox. Usage mirrors the Python API.
```matlab
-% Add toolbox to path
+% Add the toolbox to the path for this session
run('matlab/BANOS/setup.m');
% Score a single recording (pred/gt = cell arrays: row 1 = headers, rows 2+ = binary data)
@@ -140,33 +304,80 @@ metrics = BANOS_score(pred, gt);
dataStruct.Recording_1 = {pred1, gt1};
dataStruct.Recording_2 = {pred2, gt2};
[groupMetrics, overallMetrics] = BANOS_score(dataStruct);
+
+% Options as name-value pairs (names and values are case-insensitive)
+metrics = BANOS_score(pred, gt, 'missed_bouts', 'zero');
+[groupMetrics, overallMetrics] = BANOS_score(dataStruct, 'aggregation', 'pooled');
```
-See [`demo/tutorial_matlab.m`](demo/tutorial_matlab.m) for a full walkthrough.
+The MATLAB code computes the same metrics with the same options and rules; the parity
+tests compare the Python results with golden values written by the MATLAB code, within
+1e-6. MATLAB uses greedy matching only (`'matching', 'optimal'` raises an error). A
+single recording is named `single_recording` in messages, and errors and warnings carry
+identifiers: `BANOS:lengthMismatch`, `BANOS:noFrame`, `BANOS:nanReplaced`,
+`BANOS:columnDropped`, `BANOS:unknownOption`, `BANOS:missingOptionValue`,
+`BANOS:invalidOption`, `BANOS:optimalNotImplemented`, `BANOS:missingGroundTruth` and
+`BANOS:invalidData`. `BANOScalculateBANOSForEachFile` returns the components of each
+recording as a second output, and `BANOSpoolComponents` pools them.
+
+See [`demo/tutorial_matlab.m`](https://github.com/BelloneLab/BANOS/blob/main/demo/tutorial_matlab.m)
+for a full walkthrough.
Also available on
[MATLAB File Exchange](https://www.mathworks.com/matlabcentral/fileexchange/157916-banos).
+## Examples & Tutorials
+
+| Resource | Description |
+|----------|-------------|
+| [`demo/quickstart.py`](https://github.com/BelloneLab/BANOS/blob/main/demo/quickstart.py) | Minimal Python example — score annotations in two lines |
+| [`demo/quickstart.m`](https://github.com/BelloneLab/BANOS/blob/main/demo/quickstart.m) | Minimal MATLAB example |
+| [`demo/tutorial_python.ipynb`](https://github.com/BelloneLab/BANOS/blob/main/demo/tutorial_python.ipynb) | Full Python tutorial: classic BANOS usage + frame F1 vs BANOS comparison (`notebook` extra) |
+| [`demo/tutorial_matlab.m`](https://github.com/BelloneLab/BANOS/blob/main/demo/tutorial_matlab.m) | Full MATLAB tutorial mirroring the Python notebook |
+| [`demo/tutorial_calms21_task1.ipynb`](https://github.com/BelloneLab/BANOS/blob/main/demo/tutorial_calms21_task1.ipynb) | Human vs machine: CalMS21 Task 1 baseline, F1 + BANOS (Python only; `calms21` and `notebook` extras) |
+
+The demos run from a clone of the repository. The quickstarts and the two full tutorials
+score the example recordings in `data/dataset_human_vs_human/`; the CalMS21 notebook
+downloads its test data on first run.
+
## Contributing
BANOS is functionally complete by design. We welcome **bug reports** and
-**documentation improvements**. See [CONTRIBUTING.md](CONTRIBUTING.md) for details on
-how to report issues, submit fixes, and the project's maintenance lifecycle.
+**documentation improvements**. See
+[CONTRIBUTING.md](https://github.com/BelloneLab/BANOS/blob/main/CONTRIBUTING.md) for
+details on how to report issues, submit fixes, and the project's maintenance lifecycle.
-To report a security vulnerability, see [SECURITY.md](SECURITY.md) — please do not use
-public GitHub Issues for security reports.
+To report a security vulnerability, see
+[SECURITY.md](https://github.com/BelloneLab/BANOS/blob/main/SECURITY.md) — please do not
+use public GitHub Issues for security reports.
## Citation
-If you use BANOS, please cite:
+If you use BANOS, please cite the article:
-> Chindemi G., Bellone C., Girard B. *From eye to AI: studying rodent social behavior
-> in the era of machine learning.* arXiv:2508.04255 (2025).
+> Chindemi G, Bellone C, Girard B. *From eye to AI: Studying rodent social behavior in the
+> era of machine learning.* Neuroscience & Biobehavioral Reviews 188 (2026) 106766.
+> https://doi.org/10.1016/j.neubiorev.2026.106766
+
+The preprint of this article is:
+
+> Chindemi G, Bellone C, Girard B. *From eye to AI: studying rodent social behavior in the
+> era of machine Learning.* arXiv:2508.04255 (2025).
> https://doi.org/10.48550/arXiv.2508.04255
```bibtex
+@article{chindemi2026eye,
+ title = {From eye to {AI}: Studying rodent social behavior in the era of machine learning},
+ author = {Chindemi, Giuseppe and Bellone, Camilla and Girard, Benoit},
+ journal = {Neuroscience \& Biobehavioral Reviews},
+ volume = {188},
+ pages = {106766},
+ year = {2026},
+ doi = {10.1016/j.neubiorev.2026.106766}
+}
+
@article{chindemi2025eye,
- title = {From eye to {AI}: studying rodent social behavior in the era of machine learning},
+ title = {From eye to {AI}: studying rodent social behavior in the era of machine {L}earning},
author = {Chindemi, Giuseppe and Bellone, Camilla and Girard, Benoit},
journal = {arXiv preprint arXiv:2508.04255},
year = {2025},
@@ -174,10 +385,15 @@ If you use BANOS, please cite:
}
```
+The same references are in
+[CITATION.cff](https://github.com/BelloneLab/BANOS/blob/main/CITATION.cff), which GitHub
+offers as "Cite this repository".
+
## Changelog
-See [CHANGELOG.md](CHANGELOG.md) for release history.
+See [CHANGELOG.md](https://github.com/BelloneLab/BANOS/blob/main/CHANGELOG.md) for release
+history.
## License
-MIT — see [LICENSE](LICENSE).
+MIT — see [LICENSE](https://github.com/BelloneLab/BANOS/blob/main/LICENSE).
diff --git a/RELEASE.md b/RELEASE.md
index 94ef1c6..f46e116 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -4,46 +4,65 @@
Before tagging a release, verify:
-- [ ] All CI tests pass on `main` (`pytest` green, coverage >= 80%)
-- [ ] `uv run pytest tests/test_regression.py` passes (verifies golden fixtures)
-- [ ] Version bumped consistently in `pyproject.toml` and `src/banos/__init__.py`
-- [ ] `CHANGELOG.md` has an entry for this version with a summary of changes
+- [ ] CI is green on `main`: `Lint (ruff)`, `Type check (mypy)` and `Test (Python 3.11)`
+ to `Test (Python 3.14)`, which run the whole suite, regression and parity tests
+ included, with coverage >= 80%
+- [ ] The version is the same in `pyproject.toml`, `src/banos/__init__.py`, `uv.lock`
+ (run `uv lock`), `CITATION.cff` and the `Version` line of `matlab/BANOS/Contents.m`
+ (`test_score_version` compares the first two)
+- [ ] `CHANGELOG.md` has an entry for this version, dated on release day; the same date
+ is set in `CITATION.cff` (`date-released`) and in `matlab/BANOS/Contents.m`
- [ ] README examples are up to date
---
## Python Package — PyPI
-Publishing is fully automated via GitHub Actions on version tag push.
+Publishing is automated by GitHub Actions when a version tag is pushed. `main` only
+accepts changes through a pull request, so the version is bumped on a branch.
```bash
-# 1. Bump version in two places
-# pyproject.toml: version = "X.Y.Z"
-# src/banos/__init__.py: __version__ = "X.Y.Z"
+# 1. On a branch, bump the version
+# pyproject.toml: version = "X.Y.Z"
+# src/banos/__init__.py: __version__ = "X.Y.Z"
+# CITATION.cff: version: X.Y.Z and date-released: YYYY-MM-DD
+# matlab/BANOS/Contents.m: % Version X.Y.Z DD-Mon-YYYY
+uv lock
-# 2. Update CHANGELOG.md
+# 2. Date the CHANGELOG.md entry
-# 3. Commit
-git commit -am "chore: bump version to vX.Y.Z"
+# 3. Commit, open a pull request, and merge it once CI is green, as a single
+# squashed commit whose subject is "chore: release vX.Y.Z (#N)", N being
+# the pull request number, and whose body is empty
+git commit -am "chore: release vX.Y.Z"
-# 4. Tag
-git tag vX.Y.Z
+# 4. Tag the merged commit on main
+git fetch origin
+git tag -a vX.Y.Z origin/main -m "BANOS X.Y.Z"
-# 5. Push tag — this triggers the publish workflow automatically
+# 5. Push the tag — this triggers the publish workflow
git push origin vX.Y.Z
+
+# 6. Once the package is on PyPI, create the GitHub release for the tag,
+# with the CHANGELOG.md entry of the version as notes (notes.md)
+gh release create vX.Y.Z --verify-tag --title "vX.Y.Z" --notes-file notes.md
```
The `publish.yml` workflow:
-1. Runs the full test suite (excluding parity tests)
-2. Builds the package with `uv build`
-3. Publishes to PyPI using OIDC trusted publishing (no API token required)
+1. Checks that the tag is `v` followed by the version in `pyproject.toml`, and stops
+ otherwise
+2. Runs the whole CI workflow (`ci.yml`): lint, type check, and the test matrix on
+ Python 3.11 to 3.14, parity tests included
+3. Builds the package with `uv build` and publishes it to PyPI using OIDC trusted
+ publishing (no API token required). This job runs in the `pypi` environment, which
+ waits for the maintainer's approval
The `README.md` is automatically bundled because `pyproject.toml` specifies
`readme = "README.md"` — no manual copying needed.
**Note:** OIDC trusted publishing requires a one-time setup in your PyPI account:
-go to the project page → Publishing → Add a new pending publisher, and configure
-it for this GitHub repository and the `publish.yml` workflow.
+go to the project page → Publishing → Add a new publisher, and configure it for this
+GitHub repository, the `publish.yml` workflow and the `pypi` environment.
---
@@ -52,8 +71,10 @@ it for this GitHub repository and the `publish.yml` workflow.
MATLAB File Exchange does not have a public API for automated uploads.
Updates are done manually when a new version is released.
-1. Download the release ZIP from the GitHub releases page:
- `https://github.com/BelloneLab/BANOS/releases`
+1. Download the source ZIP of the tag:
+ `https://github.com/BelloneLab/BANOS/archive/refs/tags/vX.Y.Z.zip`
+ (also linked from the tag on the GitHub releases page,
+ `https://github.com/BelloneLab/BANOS/releases`)
2. Log in to MATLAB File Exchange:
`https://www.mathworks.com/matlabcentral/fileexchange/`
@@ -72,12 +93,28 @@ Updates are done manually when a new version is released.
| File | Represents |
|------|-----------|
-| `tests/fixtures/golden_python_v020.json` | **Current Python (v0.2.0)** — score 1.0 for correct absence, 0.0 for false detection, corrected IC counting. Canonical reference. |
-| `tests/fixtures/golden_matlab_v020.json` | **Current Matlab (v0.2.0)** — same semantics as Python. Used for parity tests. |
-| `tests/fixtures/golden_python_v015.json` | **Original Python (v0.1.5)** — returns 0 for absent behaviors, IC misses last frame pair. Kept for historical comparison only. |
-
-To regenerate fixtures after a deliberate algorithm change:
+| `tests/fixtures/golden_python_v110.json` | **Current Python (v1.1.0)** — detection F1 0 when no real bout is matched, IC averaged over the found real bouts. Each `Recording_N` entry scores one recording alone with the default options; `all_recordings` scores the 10 recordings together for each combination of `missed_bouts` and `aggregation`. Canonical reference, used by the regression tests. |
+| `tests/fixtures/golden_matlab_v110.json` | **Current MATLAB (v1.1.0)** — the same entries, computed by the MATLAB code. Used by the parity tests. |
+| `tests/fixtures/golden_python_v020.json` | **Python v0.2.0** (archive) — 1.0 for correct absence, 0.0 for false detection, NaN for undefined metrics (F1 included), IC counting the last frame pair. |
+| `tests/fixtures/golden_matlab_v020.json` | **MATLAB v0.2.0** (archive) — same semantics as Python v0.2.0. |
+| `tests/fixtures/golden_python_v015.json` | **Original Python (v0.1.5)** (archive) — 0 for absent behaviors and undefined metrics, IC misses the last frame pair. Kept for historical comparison only. |
+
+To regenerate the current fixtures after a deliberate algorithm change, from the project
+root:
```bash
-python tests/fixtures/python/generate_golden_fixtures.py
+uv run python tests/fixtures/python/generate_golden_fixtures.py
matlab -batch "run('tests/fixtures/matlab/generate_matlab_fixtures.m')"
```
+
+Then regenerate the comparison report (`reports/fixture_comparison.html`), which
+`tests/test_compare_fixtures.py` checks against the fixtures:
+```bash
+uv run python tests/fixtures/compare_fixtures.py --html
+```
+
+The archives are not regenerated. The v0.1.5 file can be reproduced with the BANOS 0.1.5
+package from PyPI and Python 3.12 or later (with Python 3.11, about a hundred values
+differ in their last digit):
+```bash
+uv run --isolated --no-project --python 3.12 --with "BANOS==0.1.5" python tests/fixtures/python/generate_archive_fixtures.py
+```
diff --git a/SECURITY.md b/SECURITY.md
index a988ae6..e8ee213 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -32,22 +32,26 @@ BANOS is a pure-Python / MATLAB computation library. It:
- Has **no network access**
- Has **no authentication or authorization layer**
- Has **no persistent data storage**
-- Reads only local CSV files passed explicitly by the user
+- Reads **no file**: it takes pandas DataFrames (Python) or cell arrays (MATLAB) that the
+ caller has already loaded. Only the demos and tutorials read files, the CSV files of
+ the repository and, for the CalMS21 tutorial, a dataset it downloads
The attack surface is minimal. Security reports are most relevant to dependency
vulnerabilities (e.g., malicious `pandas` or `scipy` releases) or unsafe handling of
-user-supplied CSV input.
+user-supplied data.
## Dependency Policy
BANOS accompanies a peer-reviewed publication and is maintained in security-only mode.
Dependency handling follows two rules.
-**Numerical results must not change.** Runtime dependencies are pinned in `uv.lock`.
-Every update is validated by the golden regression tests
+**Numerical results must not change.** `uv.lock` pins the development and CI
+environment. Every update of the lock is validated by the golden regression tests
(`tests/test_regression.py`, tolerance `1e-10`) across the whole supported Python
matrix before it is merged. An update that moves a metric is rejected — the golden
-fixtures are never regenerated to accommodate a dependency bump.
+fixtures are never regenerated to accommodate a dependency bump. Users who install
+`banos` are not bound by the lock: they get any version of its dependencies within the
+bounds declared in `pyproject.toml`.
**Updates are deliberate, never automatic.** Dependabot alerts are enabled for
monitoring, but automatic security updates and version updates are disabled. Each
@@ -61,5 +65,6 @@ publishing, and unlike Python packages they cannot affect a computed result.
One caveat when reading alerts: Dependabot reports every entry of `uv.lock` as
`runtime` scope. In practice the BANOS runtime closure is limited to `numpy`,
-`pandas`, `scipy`, `matplotlib` and `seaborn`. The `notebook` and `calms21` extras
-are optional tutorial environments and are not needed to import or use BANOS.
+`pandas` and `scipy`, and the packages they depend on. The `notebook` and `calms21`
+extras are optional tutorial environments, and the `dev` dependency group is the
+development environment; neither is needed to import or use BANOS.
diff --git a/SETUP.md b/SETUP.md
index ef68420..572dd42 100644
--- a/SETUP.md
+++ b/SETUP.md
@@ -2,8 +2,18 @@
## GitHub Repository Settings
-- **Branch protection on `main`**: require PR review + CI passing before merge
-- **Actions permissions**: allow all actions (needed for CI and PyPI publish)
+- **Branch protection on `main`**: require PR review + CI passing before merge. The
+ required checks are the CI job names `Lint (ruff)` and `Test (Python 3.11)` to
+ `Test (Python 3.14)`, so these jobs must keep their names. `Type check (mypy)` runs
+ too, and blocks a merge only once it is added to the required checks
+- **Tags `v*`**: only the maintainer may create them, since a pushed tag starts the
+ publish workflow
+- **Actions permissions**: allow only selected actions — actions created by GitHub, plus
+ `actions/checkout`, `astral-sh/setup-uv` and `pypa/gh-action-pypi-publish` — and
+ require actions to be pinned to a full commit SHA. The workflows pin each action by
+ SHA, with its version in a comment; Dependabot updates them
+- **Environment `pypi`**: requires the maintainer's approval before the publishing job
+ runs
- **Secrets**: none required (OIDC replaces API tokens for PyPI)
---
@@ -14,23 +24,54 @@
# Install uv (package manager)
pip install uv
-# Install the package in editable mode with dev dependencies
-uv sync --extra dev
+# Install the package in editable mode with the dev dependency group
+uv sync
-# Verify everything works
+# Verify everything works (the parity tests need no MATLAB)
uv run pytest -q
```
+The checks run by CI:
+```bash
+uv run ruff check src/ tests/ demo/quickstart.py
+uv run ruff format --check src/ tests/ demo/quickstart.py
+uv run mypy src/
+uv run pytest tests/ -v
+```
+
+`uv build` builds the sdist and the wheel into `dist/`.
+
### Optional: pre-commit hooks
```bash
uv run pre-commit install
```
+### Python tutorial
+
+`demo/tutorial_python.ipynb` needs the `notebook` extra:
+```bash
+uv run --extra notebook jupyter notebook demo/tutorial_python.ipynb
+```
+
+The notebook is generated by `scripts/create_tutorial_notebook.py`. To regenerate it and
+store its outputs, from the project root:
+```bash
+uv run python scripts/create_tutorial_notebook.py
+uv run --extra notebook jupyter nbconvert --to notebook --execute --inplace \
+ --ExecutePreprocessor.record_timing=False demo/tutorial_python.ipynb
+```
+
---
## Human-vs-Machine Tutorial — CalMS21 Task 1
-The `demo/tutorial_calms21_task1.ipynb` notebook requires:
+The `demo/tutorial_calms21_task1.ipynb` notebook runs with the `calms21` and `notebook`
+extras:
+```bash
+uv run --extra calms21 --extra notebook jupyter notebook demo/tutorial_calms21_task1.ipynb
+```
+
+It requires:
### 1. Model weights (already in repo)
@@ -74,13 +115,14 @@ cp results/task1_baseline/task1_seed_42_model.h5 ../../demo/models/
### 2. CalMS21 test data (downloaded at runtime)
-The test JSON (~600 MB) is **not** included in the repo. The notebook downloads
-it automatically on first run from CaltechDATA to `.cache/calms21/` (gitignored).
+The test JSON (~600 MB) is **not** included in the repo. On first run, the notebook
+downloads the CalMS21 Task 1 zip (~457 MB) from CaltechDATA to `.cache/calms21/`
+(gitignored) and extracts the test JSON from it.
-If the automatic download fails, download manually from:
-https://data.caltech.edu/records/s0vdx-0k302
+If the automatic download fails, download `task1_classic_classification.zip` manually
+from: https://data.caltech.edu/records/s0vdx-0k302
-Place the file at: `.cache/calms21/calms21_task1_test.json`
+Place the file at: `.cache/calms21/task1_classic_classification.zip`
### 3. Optional — publish model to GitHub Releases
@@ -93,4 +135,6 @@ gh release create calms21-assets-v1 demo/models/task1_seed_42_model.h5 \
--notes "Pre-trained Conv-1D baseline model (seed 42) for tutorial_calms21_task1.ipynb"
```
-Then update `MODEL_URL` in the notebook's setup cell to the release asset URL.
+The notebook loads the model from `demo/models/` (`MODEL_PATH` in its setup cell); it
+has no download step for the model, so a copy downloaded from the release must be placed
+there, or `MODEL_PATH` changed.
diff --git a/demo/quickstart.py b/demo/quickstart.py
index a59f29f..5912de3 100644
--- a/demo/quickstart.py
+++ b/demo/quickstart.py
@@ -7,9 +7,10 @@
from pathlib import Path
-import banos
import pandas as pd
+import banos
+
DATASET = Path(__file__).parent.parent / "data" / "dataset_human_vs_human"
diff --git a/demo/tutorial_calms21_task1.ipynb b/demo/tutorial_calms21_task1.ipynb
index baa7e92..414524b 100644
--- a/demo/tutorial_calms21_task1.ipynb
+++ b/demo/tutorial_calms21_task1.ipynb
@@ -61,18 +61,18 @@
"output_type": "stream",
"text": [
"TensorFlow 2.21.0\n",
- "BANOS 0.2.0\n",
- "Model: C:\\Users\\GA401\\Documents\\Pro\\Code\\GithubPerso\\Tl_BANOS_dev\\demo\\models\\task1_seed_42_model.h5 (959 KB)\n",
- "Cache: C:\\Users\\GA401\\Documents\\Pro\\Code\\GithubPerso\\Tl_BANOS_dev\\.cache\\calms21\n"
+ "BANOS 1.1.0\n",
+ "Model: demo/models/task1_seed_42_model.h5 (959 KB)\n",
+ "Cache: .cache/calms21\n"
]
}
],
"source": [
+ "import math\n",
"import sys\n",
+ "import urllib.request\n",
"import zipfile\n",
"from pathlib import Path\n",
- "import math\n",
- "import urllib.request\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
@@ -84,9 +84,10 @@
" print(\"TensorFlow not found. Install with: pip install banos[calms21]\")\n",
" sys.exit(1)\n",
"\n",
- "import banos\n",
"from sklearn.metrics import f1_score\n",
"\n",
+ "import banos\n",
+ "\n",
"print(f\"BANOS {banos.__version__}\")\n",
"\n",
"\n",
@@ -106,6 +107,7 @@
"\n",
"\n",
"DEMO_DIR = _find_demo_dir()\n",
+ "REPO_DIR = DEMO_DIR.parent\n",
"\n",
"MODEL_PATH = DEMO_DIR / \"models\" / \"task1_seed_42_model.h5\"\n",
"\n",
@@ -123,8 +125,8 @@
" f\"Model not found at {MODEL_PATH}\\n\"\n",
" f\"Make sure you cloned the full repo (demo/models/task1_seed_42_model.h5 must exist).\"\n",
")\n",
- "print(f\"Model: {MODEL_PATH} ({MODEL_PATH.stat().st_size / 1e3:.0f} KB)\")\n",
- "print(f\"Cache: {CACHE_DIR}\")"
+ "print(f\"Model: {MODEL_PATH.relative_to(REPO_DIR).as_posix()} ({MODEL_PATH.stat().st_size / 1e3:.0f} KB)\")\n",
+ "print(f\"Cache: {CACHE_DIR.relative_to(REPO_DIR).as_posix()}\")"
]
},
{
@@ -159,7 +161,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "CalMS21 task1 zip: 457MB [00:41, 11.1MB/s] \n"
+ "CalMS21 task1 zip: 457MB [00:13, 34.5MB/s] \n"
]
},
{
@@ -173,6 +175,13 @@
}
],
"source": [
+ "# data.caltech.edu refuses the default user agent of urllib (HTTP 403):\n",
+ "# the requests name this tutorial instead.\n",
+ "_opener = urllib.request.build_opener()\n",
+ "_opener.addheaders = [(\"User-Agent\", \"BANOS tutorial (https://github.com/BelloneLab/BANOS)\")]\n",
+ "urllib.request.install_opener(_opener)\n",
+ "\n",
+ "\n",
"def download_with_progress(url, dest_path, label):\n",
" \"\"\"Download a file once; skip if already present.\"\"\"\n",
" dest_path = Path(dest_path)\n",
@@ -288,7 +297,7 @@
"for rec in recordings[:3]:\n",
" n = dataset[rec][\"gt\"].shape[0]\n",
" labels, counts = np.unique(dataset[rec][\"gt\"], return_counts=True)\n",
- " beh_str = \" \".join(f\"{list(VOCAB)[l]}={int(c)}\" for l, c in zip(labels, counts))\n",
+ " beh_str = \" \".join(f\"{list(VOCAB)[label]}={int(c)}\" for label, c in zip(labels, counts))\n",
" print(f\" {rec.split('/')[-1]}: {n} frames | {beh_str}\")\n",
"print(\" ...\")"
]
@@ -579,17 +588,17 @@
"text": [
"BANOS per-behavior metrics (nanmean across 19 recordings):\n",
" DA (F1) SO TP IC Precision Recall\n",
- "attack 0.5745 0.5272 0.4800 0.7287 0.5548 0.6076\n",
- "investigation 0.6476 0.4556 0.0651 0.9734 0.6166 0.7061\n",
- "mount 0.7006 0.6357 0.2586 0.9374 0.6801 0.7264\n",
+ "attack 0.5745 0.5272 0.4800 0.7228 0.5548 0.6076\n",
+ "investigation 0.6476 0.4556 0.0651 0.9652 0.6166 0.7061\n",
+ "mount 0.6638 0.6357 0.2586 0.9308 0.6801 0.7264\n",
"\n",
"BANOS overall metrics (nanmean across behaviors and recordings):\n",
" precision 0.6172\n",
" recall 0.6801\n",
- " f1_score 0.6398\n",
+ " f1_score 0.6286\n",
" so 0.5378\n",
" tp 0.2681\n",
- " ic 0.8798\n"
+ " ic 0.8719\n"
]
}
],
@@ -645,10 +654,10 @@
"---------------------------------------------------------------------------\n",
"CalMS21 F1 (pooled) 0.7792 Global frame overlap; matches leaderboard method\n",
"Our Frame F1 0.7267 Per-recording avg; explicit absent-behavior scoring\n",
- "BANOS DA (F1) 0.6398 Bout-level detection accuracy\n",
- "BANOS SO 0.5378 Temporal overlap quality of matched bouts\n",
+ "BANOS DA (F1) 0.6286 Bout-level detection accuracy\n",
+ "BANOS SO 0.5378 Temporal quality of overlapping bouts\n",
"BANOS TP 0.2681 Boundary precision (start/end frame accuracy)\n",
- "BANOS IC 0.8798 Label stability within detected bouts\n"
+ "BANOS IC 0.8719 Label stability within detected bouts\n"
]
}
],
@@ -671,7 +680,7 @@
" (\"BANOS DA (F1)\", banos_da,\n",
" \"Bout-level detection accuracy\"),\n",
" (\"BANOS SO\", banos_so,\n",
- " \"Temporal overlap quality of matched bouts\"),\n",
+ " \"Temporal quality of overlapping bouts\"),\n",
" (\"BANOS TP\", banos_tp,\n",
" \"Boundary precision (start/end frame accuracy)\"),\n",
" (\"BANOS IC\", banos_ic,\n",
@@ -701,7 +710,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.9"
+ "version": "3.12.12"
}
},
"nbformat": 4,
diff --git a/demo/tutorial_matlab.m b/demo/tutorial_matlab.m
index 59a9b10..e115b66 100644
--- a/demo/tutorial_matlab.m
+++ b/demo/tutorial_matlab.m
@@ -14,7 +14,7 @@
clear; clc;
-% Detect project root (works whether run from root or example/)
+% Detect project root from the location of this file (demo/)
thisDir = fileparts(mfilename('fullpath'));
if exist(fullfile(thisDir, 'matlab'), 'dir')
projectRoot = thisDir;
@@ -40,9 +40,9 @@
%% 1.1 Setup — already done above (addpath, BEHAVIORS, datasetDir)
%% 1.2 Single Recording -------------------------------------------------------
-% Recording_1, behaviors event1/event2/event3 (dropping 'other')
+% Recording_1, behaviors attack/investigation/mount (dropping 'other')
%
-% v0.2.0 absent-behavior rule:
+% Absent-behavior rule (since v0.2.0; per-file scoring, the default):
% - GT has no bouts AND machine predicts nothing -> all metrics = 1.0 (correct absence)
% - GT has no bouts BUT machine predicts bouts -> all metrics = 0.0 (false detection)
@@ -50,7 +50,7 @@
predTable = readtable(fullfile(recDir, 'humanAnnotation_2.csv'));
gtTable = readtable(fullfile(recDir, 'humanAnnotation_1.csv'));
-% Keep only event1/event2/event3
+% Keep only attack/investigation/mount
predTable = predTable(:, BEHAVIORS);
gtTable = gtTable(:, BEHAVIORS);
@@ -125,7 +125,7 @@
fprintf(' SO : Temporal IoU -- how well do detected bouts overlap GT?\n');
fprintf(' TP : Boundary precision -- are bout start/end frames accurate?\n');
fprintf(' IC : Intra-bout continuity -- is each detected bout clean?\n\n');
-fprintf('Absent-behavior rule (v0.2.0):\n');
+fprintf('Absent-behavior rule (since v0.2.0; per-file scoring, the default):\n');
fprintf(' GT has no bouts AND machine predicts nothing -> all metrics = 1.0 (correct absence)\n');
fprintf(' GT has no bouts BUT machine predicts bouts -> all metrics = 0.0 (false detection)\n\n');
@@ -171,7 +171,7 @@
end
framef1_overall = mean(perRecF1, 'omitnan');
-fprintf('Frame-based F1 (our approach): %.3f\n', framef1_overall); % Expected: ~0.791
+fprintf('Frame-based F1 (our approach): %.3f\n', framef1_overall); % Expected: 0.815
%% 2b. BANOS metrics: same recordings, same behaviors ------------------------
@@ -217,8 +217,13 @@
fprintf(' 2. Explicit absent-behavior scoring: we reward correct absence (score 1)\n');
fprintf(' and penalize false detection (score 0). CalMS21 implicitly handles\n');
fprintf(' this via pooling.\n\n');
-fprintf('We do NOT align with CalMS21 pooled approach because BANOS quantifies\n');
-fprintf('annotation quality per recording, not globally only.\n\n');
+fprintf('By default, we do NOT align with CalMS21 pooled approach because BANOS\n');
+fprintf('quantifies annotation quality per recording, not globally only.\n');
+fprintf('To compare or rank models on a whole dataset,\n');
+fprintf('BANOS_score(data, ''aggregation'', ''pooled'') scores each behavior once on\n');
+fprintf('the bout counts and terms of all recordings, without the absent-behavior\n');
+fprintf('rule. The per-file mean, by contrast, mixes detection quality with the\n');
+fprintf('share of recordings without the behavior (see the README).\n\n');
fprintf('For publications: state both the frame F1 methodology and BANOS metrics.\n\n');
%% 2e. Thought experiment -- the shifted annotator ---------------------------
@@ -255,7 +260,7 @@
fprintf('%s\n', repmat('-', 1, 52));
for pi = 1:3
p = preds{pi};
- ff1 = local_frame_f1(p, gt_s);
+ ff1 = local_frame_f1_raw(p, gt_s);
pred_cell = [{'beh'}; num2cell(p)];
b_m = BANOS_score(pred_cell, gt_cell);
b = b_m.beh;
@@ -264,18 +269,25 @@
end
fprintf('\nInterpretation:\n');
-fprintf(' Frame F1 collapses to 0 for a 15-frame shift -- same as completely missing.\n');
-fprintf(' BANOS DA = 1.0 for all predictions (both bouts are still detected).\n');
-fprintf(' BANOS TP quantifies the temporal shift: large shift -> low TP.\n');
-fprintf(' BANOS SO measures fractional temporal IoU of matched bouts.\n\n');
-fprintf('Frame F1 says "shifted 15f is equally wrong as missing entirely".\n');
-fprintf('BANOS tells you HOW the system is wrong -- and by how much.\n\n');
+fprintf(' Frame F1 collapses to 0 for a 15-frame shift (B); a 2-frame shift (C)\n');
+fprintf(' gives 0.80.\n');
+fprintf(' BANOS DA counts bouts: a predicted bout detects a GT bout when they share\n');
+fprintf(' at least one frame. C detects both GT bouts: DA = 1.0. B shares no frame\n');
+fprintf(' with them: both GT bouts are missed and both predicted bouts are false,\n');
+fprintf(' so DA = 0, and SO and TP are undefined (NaN), having no overlapping pair\n');
+fprintf(' (with ''missed_bouts'', ''zero'' they would be 0).\n');
+fprintf(' BANOS TP quantifies the boundary error, 1 / (1 + |start diff| + |end diff|):\n');
+fprintf(' C, 2 frames late at both ends, gets 1 / 5 = 0.2.\n');
+fprintf(' BANOS SO measures the temporal IoU of overlapping bouts: for C, 8 shared\n');
+fprintf(' frames out of 12, 0.667.\n\n');
+fprintf('Frame F1 gives C a single 0.80.\n');
+fprintf('BANOS tells you HOW the system is wrong -- every bout found (DA), but late (SO, TP).\n\n');
fprintf('Tutorial complete!\n');
%% =========================================================================
-%% Local helper: frame-level F1 with explicit absent-behavior scoring
+%% Local helpers: frame-level F1, with and without absent-behavior scoring
%% =========================================================================
function f1 = local_frame_f1(pred_col, gt_col)
@@ -310,3 +322,24 @@
f1 = NaN;
end
end
+
+function f1 = local_frame_f1_raw(pred_col, gt_col)
+ % Frame-level F1 without absent-behavior scoring, as frame_f1_raw in the
+ % Python tutorial: precision, recall and F1 are 0 when undefined.
+ tp_f = sum(pred_col == 1 & gt_col == 1);
+ fp_f = sum(pred_col == 1 & gt_col == 0);
+ fn_f = sum(pred_col == 0 & gt_col == 1);
+ prec = 0;
+ rec = 0;
+ if (tp_f + fp_f) > 0
+ prec = tp_f / (tp_f + fp_f);
+ end
+ if (tp_f + fn_f) > 0
+ rec = tp_f / (tp_f + fn_f);
+ end
+ if (prec + rec) > 0
+ f1 = 2 * prec * rec / (prec + rec);
+ else
+ f1 = 0;
+ end
+end
diff --git a/demo/tutorial_python.ipynb b/demo/tutorial_python.ipynb
index 627be15..8445d01 100644
--- a/demo/tutorial_python.ipynb
+++ b/demo/tutorial_python.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "7766bc43",
+ "id": "bc5c61b1",
"metadata": {},
"source": [
"# BANOS Tutorial — Python\n",
@@ -18,7 +18,7 @@
},
{
"cell_type": "markdown",
- "id": "85d2fa80",
+ "id": "2b5469e0",
"metadata": {},
"source": [
"---\n",
@@ -27,7 +27,7 @@
},
{
"cell_type": "markdown",
- "id": "9ff378d4",
+ "id": "087abc03",
"metadata": {},
"source": [
"### 1.1 Setup"
@@ -36,24 +36,24 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "c2ce5a40",
+ "id": "e11e85f3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "banos version: 0.2.0\n"
+ "banos version: 1.1.0\n"
]
}
],
"source": [
- "from pathlib import Path\n",
"import math\n",
+ "from pathlib import Path\n",
"\n",
+ "import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"import banos\n",
@@ -66,14 +66,14 @@
},
{
"cell_type": "markdown",
- "id": "67656986",
+ "id": "65aef272",
"metadata": {},
"source": [
"### 1.2 Single Recording\n",
"\n",
"Recording_1, behaviors `attack`/`investigation`/`mount` (dropping `other`).\n",
"\n",
- "> **Note (v0.2.0 absent-behavior rule):** \n",
+ "> **Note (absent-behavior rule, since v0.2.0; per-file scoring, the default):** \n",
"> - GT has no bouts AND machine predicts nothing → all metrics = **1.0** (correct absence) \n",
"> - GT has no bouts BUT machine predicts bouts → all metrics = **0.0** (false detection)"
]
@@ -81,7 +81,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "eb84e32f",
+ "id": "d671d64c",
"metadata": {},
"outputs": [
{
@@ -110,7 +110,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "f59929aa",
+ "id": "f68e54f7",
"metadata": {},
"outputs": [
{
@@ -118,17 +118,17 @@
"output_type": "stream",
"text": [
"Per-behavior metrics — Recording 1\n",
- " precision recall da (F1) so tp ic\n",
+ " precision recall DA (F1) so tp ic\n",
"attack 1.0 1.0000 1.0000 1.0000 1.0000 1.0000\n",
- "investigation 0.8 0.7347 0.7660 0.5444 0.0846 0.9829\n",
- "mount 1.0 0.5333 0.6957 0.7333 0.0785 0.9909\n"
+ "investigation 0.8 0.7347 0.7660 0.5444 0.0846 0.9806\n",
+ "mount 1.0 0.5333 0.6957 0.7333 0.0785 0.9829\n"
]
}
],
"source": [
"metrics = banos.score(pred, gt)\n",
"\n",
- "metrics_df = pd.DataFrame(metrics).T.rename(columns={\"f1_score\": \"da (F1)\"})\n",
+ "metrics_df = pd.DataFrame(metrics).T.rename(columns={\"f1_score\": \"DA (F1)\"})\n",
"print(\"Per-behavior metrics — Recording 1\")\n",
"print(metrics_df.round(4).to_string())"
]
@@ -136,12 +136,12 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "f202751f",
+ "id": "8d710a0b",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"Shows Python/Matlab parity -- should be SAME
Total: 300 | SAME: 300 | +VALUE->NULL: 0 | NULL->VALUE: 0 | -CHANGED: 0 | -VALUE->NULL: 0
-SAME = match NULL->VALUE = Unexpected divergence (should not occur in v0.2.0) CHANGED = Unexpected numeric mismatch VALUE->NULL = unexpected
+CHANGED: 0 +SAME = match VALUE->NULL = Unexpected divergence (should not occur in v0.2.0) NULL->VALUE = Unexpected divergence (should not occur in v0.2.0) CHANGED = Unexpected numeric mismatch
| Location | Metric | Python v0.2.0 | Matlab v0.2.0 | Status |
|---|---|---|---|---|
| Recording_1/overall | precision | 0.928261 | 0.928261 | SAME |
| Recording_1/overall | recall | 0.781293 | 0.781293 | SAME |
| Recording_1/overall | f1_score | 0.836455 | 0.836455 | SAME |
| Recording_1/overall | so | 0.761228 | 0.761228 | SAME |
| Recording_1/overall | tp | 0.306230 | 0.306230 | SAME |
| Recording_1/overall | ic | 0.991718 | 0.991718 | SAME |
| Recording_1/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_1/group/attack | recall | 1.000000 | 1.000000 | SAME |
| Recording_1/group/attack | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_1/group/attack | so | 1.000000 | 1.000000 | SAME |
| Recording_1/group/attack | tp | 1.000000 | 1.000000 | SAME |
| Recording_1/group/attack | ic | 1.000000 | 1.000000 | SAME |
| Recording_1/group/investigation | precision | 0.800000 | 0.800000 | SAME |
| Recording_1/group/investigation | recall | 0.734694 | 0.734694 | SAME |
| Recording_1/group/investigation | f1_score | 0.765957 | 0.765957 | SAME |
| Recording_1/group/investigation | so | 0.544410 | 0.544410 | SAME |
| Recording_1/group/investigation | tp | 0.084636 | 0.084636 | SAME |
| Recording_1/group/investigation | ic | 0.982941 | 0.982941 | SAME |
| Recording_1/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_1/group/mount | recall | 0.533333 | 0.533333 | SAME |
| Recording_1/group/mount | f1_score | 0.695652 | 0.695652 | SAME |
| Recording_1/group/mount | so | 0.733277 | 0.733277 | SAME |
| Recording_1/group/mount | tp | 0.078471 | 0.078471 | SAME |
| Recording_1/group/mount | ic | 0.990861 | 0.990861 | SAME |
| Recording_1/group/other | precision | 0.913043 | 0.913043 | SAME |
| Recording_1/group/other | recall | 0.857143 | 0.857143 | SAME |
| Recording_1/group/other | f1_score | 0.884211 | 0.884211 | SAME |
| Recording_1/group/other | so | 0.767227 | 0.767227 | SAME |
| Recording_1/group/other | tp | 0.061814 | 0.061814 | SAME |
| Recording_1/group/other | ic | 0.993069 | 0.993069 | SAME |
| Recording_2/overall | precision | 0.967105 | 0.967105 | SAME |
| Recording_2/overall | recall | 0.839924 | 0.839924 | SAME |
| Recording_2/overall | f1_score | 0.897301 | 0.897301 | SAME |
| Recording_2/overall | so | 0.825946 | 0.825946 | SAME |
| Recording_2/overall | tp | 0.309082 | 0.309082 | SAME |
| Recording_2/overall | ic | 0.993907 | 0.993907 | SAME |
| Recording_2/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_2/group/attack | recall | 1.000000 | 1.000000 | SAME |
| Recording_2/group/attack | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_2/group/attack | so | 1.000000 | 1.000000 | SAME |
| Recording_2/group/attack | tp | 1.000000 | 1.000000 | SAME |
| Recording_2/group/attack | ic | 1.000000 | 1.000000 | SAME |
| Recording_2/group/investigation | precision | 0.894737 | 0.894737 | SAME |
| Recording_2/group/investigation | recall | 0.739130 | 0.739130 | SAME |
| Recording_2/group/investigation | f1_score | 0.809524 | 0.809524 | SAME |
| Recording_2/group/investigation | so | 0.660942 | 0.660942 | SAME |
| Recording_2/group/investigation | tp | 0.046151 | 0.046151 | SAME |
| Recording_2/group/investigation | ic | 0.989051 | 0.989051 | SAME |
| Recording_2/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_2/group/mount | recall | 0.833333 | 0.833333 | SAME |
| Recording_2/group/mount | f1_score | 0.909091 | 0.909091 | SAME |
| Recording_2/group/mount | so | 0.931754 | 0.931754 | SAME |
| Recording_2/group/mount | tp | 0.145170 | 0.145170 | SAME |
| Recording_2/group/mount | ic | 0.994648 | 0.994648 | SAME |
| Recording_2/group/other | precision | 0.973684 | 0.973684 | SAME |
| Recording_2/group/other | recall | 0.787234 | 0.787234 | SAME |
| Recording_2/group/other | f1_score | 0.870588 | 0.870588 | SAME |
| Recording_2/group/other | so | 0.711086 | 0.711086 | SAME |
| Recording_2/group/other | tp | 0.045008 | 0.045008 | SAME |
| Recording_2/group/other | ic | 0.991930 | 0.991930 | SAME |
| Recording_3/overall | precision | 0.959481 | 0.959481 | SAME |
| Recording_3/overall | recall | 0.850967 | 0.850967 | SAME |
| Recording_3/overall | f1_score | 0.898627 | 0.898627 | SAME |
| Recording_3/overall | so | 0.723716 | 0.723716 | SAME |
| Recording_3/overall | tp | 0.268876 | 0.268876 | SAME |
| Recording_3/overall | ic | 0.993223 | 0.993223 | SAME |
| Recording_3/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_3/group/attack | recall | 1.000000 | 1.000000 | SAME |
| Recording_3/group/attack | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_3/group/attack | so | 1.000000 | 1.000000 | SAME |
| Recording_3/group/attack | tp | 1.000000 | 1.000000 | SAME |
| Recording_3/group/attack | ic | 1.000000 | 1.000000 | SAME |
| Recording_3/group/investigation | precision | 0.935484 | 0.935484 | SAME |
| Recording_3/group/investigation | recall | 0.783784 | 0.783784 | SAME |
| Recording_3/group/investigation | f1_score | 0.852941 | 0.852941 | SAME |
| Recording_3/group/investigation | so | 0.456310 | 0.456310 | SAME |
| Recording_3/group/investigation | tp | 0.022322 | 0.022322 | SAME |
| Recording_3/group/investigation | ic | 0.984966 | 0.984966 | SAME |
| Recording_3/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_3/group/mount | recall | 0.739130 | 0.739130 | SAME |
| Recording_3/group/mount | f1_score | 0.850000 | 0.850000 | SAME |
| Recording_3/group/mount | so | 0.674504 | 0.674504 | SAME |
| Recording_3/group/mount | tp | 0.028795 | 0.028795 | SAME |
| Recording_3/group/mount | ic | 0.991713 | 0.991713 | SAME |
| Recording_3/group/other | precision | 0.902439 | 0.902439 | SAME |
| Recording_3/group/other | recall | 0.880952 | 0.880952 | SAME |
| Recording_3/group/other | f1_score | 0.891566 | 0.891566 | SAME |
| Recording_3/group/other | so | 0.764048 | 0.764048 | SAME |
| Recording_3/group/other | tp | 0.024386 | 0.024386 | SAME |
| Recording_3/group/other | ic | 0.996212 | 0.996212 | SAME |
| Recording_4/overall | precision | 0.975000 | 0.975000 | SAME |
| Recording_4/overall | recall | 0.615686 | 0.615686 | SAME |
| Recording_4/overall | f1_score | 0.729167 | 0.729167 | SAME |
| Recording_4/overall | so | 0.707087 | 0.707087 | SAME |
| Recording_4/overall | tp | 0.275715 | 0.275715 | SAME |
| Recording_4/overall | ic | 0.996530 | 0.996530 | SAME |
| Recording_4/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_4/group/attack | recall | 1.000000 | 1.000000 | SAME |
| Recording_4/group/attack | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_4/group/attack | so | 1.000000 | 1.000000 | SAME |
| Recording_4/group/attack | tp | 1.000000 | 1.000000 | SAME |
| Recording_4/group/attack | ic | 1.000000 | 1.000000 | SAME |
| Recording_4/group/investigation | precision | 0.900000 | 0.900000 | SAME |
| Recording_4/group/investigation | recall | 0.529412 | 0.529412 | SAME |
| Recording_4/group/investigation | f1_score | 0.666667 | 0.666667 | SAME |
| Recording_4/group/investigation | so | 0.391471 | 0.391471 | SAME |
| Recording_4/group/investigation | tp | 0.014025 | 0.014025 | SAME |
| Recording_4/group/investigation | ic | 0.996882 | 0.996882 | SAME |
| Recording_4/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_4/group/mount | recall | 0.333333 | 0.333333 | SAME |
| Recording_4/group/mount | f1_score | 0.500000 | 0.500000 | SAME |
| Recording_4/group/mount | so | 0.701119 | 0.701119 | SAME |
| Recording_4/group/mount | tp | 0.040087 | 0.040087 | SAME |
| Recording_4/group/mount | ic | 0.995289 | 0.995289 | SAME |
| Recording_4/group/other | precision | 1.000000 | 1.000000 | SAME |
| Recording_4/group/other | recall | 0.600000 | 0.600000 | SAME |
| Recording_4/group/other | f1_score | 0.750000 | 0.750000 | SAME |
| Recording_4/group/other | so | 0.735760 | 0.735760 | SAME |
| Recording_4/group/other | tp | 0.048749 | 0.048749 | SAME |
| Recording_4/group/other | ic | 0.993949 | 0.993949 | SAME |
| Recording_5/overall | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/overall | recall | 0.892105 | 0.892105 | SAME |
| Recording_5/overall | f1_score | 0.941459 | 0.941459 | SAME |
| Recording_5/overall | so | 0.836730 | 0.836730 | SAME |
| Recording_5/overall | tp | 0.279289 | 0.279289 | SAME |
| Recording_5/overall | ic | 0.995120 | 0.995120 | SAME |
| Recording_5/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/group/attack | recall | 1.000000 | 1.000000 | SAME |
| Recording_5/group/attack | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_5/group/attack | so | 1.000000 | 1.000000 | SAME |
| Recording_5/group/attack | tp | 1.000000 | 1.000000 | SAME |
| Recording_5/group/attack | ic | 1.000000 | 1.000000 | SAME |
| Recording_5/group/investigation | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/group/investigation | recall | 0.800000 | 0.800000 | SAME |
| Recording_5/group/investigation | f1_score | 0.888889 | 0.888889 | SAME |
| Recording_5/group/investigation | so | 0.658959 | 0.658959 | SAME |
| Recording_5/group/investigation | tp | 0.035333 | 0.035333 | SAME |
| Recording_5/group/investigation | ic | 0.989914 | 0.989914 | SAME |
| Recording_5/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/group/mount | recall | 0.900000 | 0.900000 | SAME |
| Recording_5/group/mount | f1_score | 0.947368 | 0.947368 | SAME |
| Recording_5/group/mount | so | 0.910024 | 0.910024 | SAME |
| Recording_5/group/mount | tp | 0.035960 | 0.035960 | SAME |
| Recording_5/group/mount | ic | 0.996541 | 0.996541 | SAME |
| Recording_5/group/other | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/group/other | recall | 0.868421 | 0.868421 | SAME |
| Recording_5/group/other | f1_score | 0.929577 | 0.929577 | SAME |
| Recording_5/group/other | so | 0.777939 | 0.777939 | SAME |
| Recording_5/group/other | tp | 0.045862 | 0.045862 | SAME |
| Recording_5/group/other | ic | 0.994024 | 0.994024 | SAME |
| Recording_6/overall | precision | 0.914083 | 0.914083 | SAME |
| Recording_6/overall | recall | 0.745671 | 0.745671 | SAME |
| Recording_6/overall | f1_score | 0.792505 | 0.792505 | SAME |
| Recording_6/overall | so | 0.624464 | 0.624464 | SAME |
| Recording_6/overall | tp | 0.264068 | 0.264068 | SAME |
| Recording_6/overall | ic | 0.993987 | 0.993987 | SAME |
| Recording_6/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_6/group/attack | recall | 0.363636 | 0.363636 | SAME |
| Recording_6/group/attack | f1_score | 0.533333 | 0.533333 | SAME |
| Recording_6/group/attack | so | 0.355488 | 0.355488 | SAME |
| Recording_6/group/attack | tp | 0.014143 | 0.014143 | SAME |
| Recording_6/group/attack | ic | 0.993666 | 0.993666 | SAME |
| Recording_6/group/investigation | precision | 0.767442 | 0.767442 | SAME |
| Recording_6/group/investigation | recall | 0.785714 | 0.785714 | SAME |
| Recording_6/group/investigation | f1_score | 0.776471 | 0.776471 | SAME |
| Recording_6/group/investigation | so | 0.539580 | 0.539580 | SAME |
| Recording_6/group/investigation | tp | 0.020317 | 0.020317 | SAME |
| Recording_6/group/investigation | ic | 0.990392 | 0.990392 | SAME |
| Recording_6/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_6/group/mount | recall | 1.000000 | 1.000000 | SAME |
| Recording_6/group/mount | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_6/group/mount | so | 1.000000 | 1.000000 | SAME |
| Recording_6/group/mount | tp | 1.000000 | 1.000000 | SAME |
| Recording_6/group/mount | ic | 1.000000 | 1.000000 | SAME |
| Recording_6/group/other | precision | 0.888889 | 0.888889 | SAME |
| Recording_6/group/other | recall | 0.833333 | 0.833333 | SAME |
| Recording_6/group/other | f1_score | 0.860215 | 0.860215 | SAME |
| Recording_6/group/other | so | 0.602789 | 0.602789 | SAME |
| Recording_6/group/other | tp | 0.021811 | 0.021811 | SAME |
| Recording_6/group/other | ic | 0.991888 | 0.991888 | SAME |
| Recording_7/overall | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/overall | recall | 0.583910 | 0.583910 | SAME |
| Recording_7/overall | f1_score | 0.706530 | 0.706530 | SAME |
| Recording_7/overall | so | 0.664495 | 0.664495 | SAME |
| Recording_7/overall | tp | 0.276530 | 0.276530 | SAME |
| Recording_7/overall | ic | 0.994944 | 0.994944 | SAME |
| Recording_7/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/group/attack | recall | 0.525000 | 0.525000 | SAME |
| Recording_7/group/attack | f1_score | 0.688525 | 0.688525 | SAME |
| Recording_7/group/attack | so | 0.390695 | 0.390695 | SAME |
| Recording_7/group/attack | tp | 0.030229 | 0.030229 | SAME |
| Recording_7/group/attack | ic | 0.989471 | 0.989471 | SAME |
| Recording_7/group/investigation | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/group/investigation | recall | 0.300000 | 0.300000 | SAME |
| Recording_7/group/investigation | f1_score | 0.461538 | 0.461538 | SAME |
| Recording_7/group/investigation | so | 0.536495 | 0.536495 | SAME |
| Recording_7/group/investigation | tp | 0.031331 | 0.031331 | SAME |
| Recording_7/group/investigation | ic | 0.994544 | 0.994544 | SAME |
| Recording_7/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/group/mount | recall | 1.000000 | 1.000000 | SAME |
| Recording_7/group/mount | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_7/group/mount | so | 1.000000 | 1.000000 | SAME |
| Recording_7/group/mount | tp | 1.000000 | 1.000000 | SAME |
| Recording_7/group/mount | ic | 1.000000 | 1.000000 | SAME |
| Recording_7/group/other | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/group/other | recall | 0.510638 | 0.510638 | SAME |
| Recording_7/group/other | f1_score | 0.676056 | 0.676056 | SAME |
| Recording_7/group/other | so | 0.730790 | 0.730790 | SAME |
| Recording_7/group/other | tp | 0.044561 | 0.044561 | SAME |
| Recording_7/group/other | ic | 0.995760 | 0.995760 | SAME |
| Recording_8/overall | precision | 0.891667 | 0.891667 | SAME |
| Recording_8/overall | recall | 0.346721 | 0.346721 | SAME |
| Recording_8/overall | f1_score | 0.606173 | 0.606173 | SAME |
| Recording_8/overall | so | 0.530562 | 0.530562 | SAME |
| Recording_8/overall | tp | 0.038138 | 0.038138 | SAME |
| Recording_8/overall | ic | 0.995577 | 0.995577 | SAME |
| Recording_8/group/attack | precision | 0.875000 | 0.875000 | SAME |
| Recording_8/group/attack | recall | 0.368421 | 0.368421 | SAME |
| Recording_8/group/attack | f1_score | 0.518519 | 0.518519 | SAME |
| Recording_8/group/attack | so | 0.268364 | 0.268364 | SAME |
| Recording_8/group/attack | tp | 0.020423 | 0.020423 | SAME |
| Recording_8/group/attack | ic | 0.991919 | 0.991919 | SAME |
| Recording_8/group/investigation | precision | 1.000000 | 1.000000 | SAME |
| Recording_8/group/investigation | recall | 0.538462 | 0.538462 | SAME |
| Recording_8/group/investigation | f1_score | 0.700000 | 0.700000 | SAME |
| Recording_8/group/investigation | so | 0.591051 | 0.591051 | SAME |
| Recording_8/group/investigation | tp | 0.025271 | 0.025271 | SAME |
| Recording_8/group/investigation | ic | 0.995282 | 0.995282 | SAME |
| Recording_8/group/mount | precision | NaN | NaN | SAME |
| Recording_8/group/mount | recall | 0.000000 | 0.000000 | SAME |
| Recording_8/group/mount | f1_score | NaN | NaN | SAME |
| Recording_8/group/mount | so | NaN | NaN | SAME |
| Recording_8/group/mount | tp | NaN | NaN | SAME |
| Recording_8/group/mount | ic | 1.000000 | 1.000000 | SAME |
| Recording_8/group/other | precision | 0.800000 | 0.800000 | SAME |
| Recording_8/group/other | recall | 0.480000 | 0.480000 | SAME |
| Recording_8/group/other | f1_score | 0.600000 | 0.600000 | SAME |
| Recording_8/group/other | so | 0.732271 | 0.732271 | SAME |
| Recording_8/group/other | tp | 0.068721 | 0.068721 | SAME |
| Recording_8/group/other | ic | 0.995105 | 0.995105 | SAME |
| Recording_9/overall | precision | 0.798398 | 0.798398 | SAME |
| Recording_9/overall | recall | 0.653819 | 0.653819 | SAME |
| Recording_9/overall | f1_score | 0.712355 | 0.712355 | SAME |
| Recording_9/overall | so | 0.682730 | 0.682730 | SAME |
| Recording_9/overall | tp | 0.277809 | 0.277809 | SAME |
| Recording_9/overall | ic | 0.995671 | 0.995671 | SAME |
| Recording_9/group/attack | precision | 0.956522 | 0.956522 | SAME |
| Recording_9/group/attack | recall | 0.733333 | 0.733333 | SAME |
| Recording_9/group/attack | f1_score | 0.830189 | 0.830189 | SAME |
| Recording_9/group/attack | so | 0.424100 | 0.424100 | SAME |
| Recording_9/group/attack | tp | 0.020590 | 0.020590 | SAME |
| Recording_9/group/attack | ic | 0.989386 | 0.989386 | SAME |
| Recording_9/group/investigation | precision | 0.375000 | 0.375000 | SAME |
| Recording_9/group/investigation | recall | 0.187500 | 0.187500 | SAME |
| Recording_9/group/investigation | f1_score | 0.250000 | 0.250000 | SAME |
| Recording_9/group/investigation | so | 0.631678 | 0.631678 | SAME |
| Recording_9/group/investigation | tp | 0.034810 | 0.034810 | SAME |
| Recording_9/group/investigation | ic | 0.998630 | 0.998630 | SAME |
| Recording_9/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_9/group/mount | recall | 1.000000 | 1.000000 | SAME |
| Recording_9/group/mount | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_9/group/mount | so | 1.000000 | 1.000000 | SAME |
| Recording_9/group/mount | tp | 1.000000 | 1.000000 | SAME |
| Recording_9/group/mount | ic | 1.000000 | 1.000000 | SAME |
| Recording_9/group/other | precision | 0.862069 | 0.862069 | SAME |
| Recording_9/group/other | recall | 0.694444 | 0.694444 | SAME |
| Recording_9/group/other | f1_score | 0.769231 | 0.769231 | SAME |
| Recording_9/group/other | so | 0.675140 | 0.675140 | SAME |
| Recording_9/group/other | tp | 0.055838 | 0.055838 | SAME |
| Recording_9/group/other | ic | 0.994669 | 0.994669 | SAME |
| Recording_10/overall | precision | 0.899107 | 0.899107 | SAME |
| Recording_10/overall | recall | 0.475760 | 0.475760 | SAME |
| Recording_10/overall | f1_score | 0.583475 | 0.583475 | SAME |
| Recording_10/overall | so | 0.666806 | 0.666806 | SAME |
| Recording_10/overall | tp | 0.285923 | 0.285923 | SAME |
| Recording_10/overall | ic | 0.996191 | 0.996191 | SAME |
| Recording_10/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_10/group/attack | recall | 0.285714 | 0.285714 | SAME |
| Recording_10/group/attack | f1_score | 0.444444 | 0.444444 | SAME |
| Recording_10/group/attack | so | 0.184903 | 0.184903 | SAME |
| Recording_10/group/attack | tp | 0.011189 | 0.011189 | SAME |
| Recording_10/group/attack | ic | 0.991412 | 0.991412 | SAME |
| Recording_10/group/investigation | precision | 0.625000 | 0.625000 | SAME |
| Recording_10/group/investigation | recall | 0.263158 | 0.263158 | SAME |
| Recording_10/group/investigation | f1_score | 0.370370 | 0.370370 | SAME |
| Recording_10/group/investigation | so | 0.657207 | 0.657207 | SAME |
| Recording_10/group/investigation | tp | 0.064683 | 0.064683 | SAME |
| Recording_10/group/investigation | ic | 0.995061 | 0.995061 | SAME |
| Recording_10/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_10/group/mount | recall | 1.000000 | 1.000000 | SAME |
| Recording_10/group/mount | f1_score | 1.000000 | 1.000000 | SAME |
| Recording_10/group/mount | so | 1.000000 | 1.000000 | SAME |
| Recording_10/group/mount | tp | 1.000000 | 1.000000 | SAME |
| Recording_10/group/mount | ic | 1.000000 | 1.000000 | SAME |
| Recording_10/group/other | precision | 0.971429 | 0.971429 | SAME |
| Recording_10/group/other | recall | 0.354167 | 0.354167 | SAME |
| Recording_10/group/other | f1_score | 0.519084 | 0.519084 | SAME |
| Recording_10/group/other | so | 0.825115 | 0.825115 | SAME |
| Recording_10/group/other | tp | 0.067820 | 0.067820 | SAME |
| Recording_10/group/other | ic | 0.998293 | 0.998293 | SAME |
Shows ALL changes: absent-behavior fix (0→1 correct absence) + IC bug fix
+Shows the changes of v0.2.0: undefined metrics NaN instead of 0, correct absence 1.0 instead of 0, IC fixes
Total: 300 | SAME: 169 | -NULL->VALUE: 4 | -CHANGED: 127 | -VALUE->NULL: 0
-SAME = match NULL->VALUE = v0.1.5=0 → v0.2.0=1.0 (correct absence now scores 1) CHANGED = IC bug fix VALUE->NULL = unexpected
-| Location | Metric | Python v0.1.5 | Python v0.2.0 | Status |
|---|---|---|---|---|
| Recording_1/overall | precision | 0.678261 | 0.928261 | CHANGED |
| Recording_1/overall | recall | 0.531293 | 0.781293 | CHANGED |
| Recording_1/overall | f1_score | 0.586455 | 0.836455 | CHANGED |
| Recording_1/overall | so | 0.511228 | 0.761228 | CHANGED |
| Recording_1/overall | tp | 0.056230 | 0.306230 | CHANGED |
| Recording_1/overall | ic | 0.737994 | 0.991718 | CHANGED |
| Recording_1/group/attack | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_1/group/attack | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_1/group/attack | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_1/group/attack | so | 0.000000 | 1.000000 | CHANGED |
| Recording_1/group/attack | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_1/group/attack | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_1/group/investigation | precision | 0.800000 | 0.800000 | SAME |
| Recording_1/group/investigation | recall | 0.734694 | 0.734694 | SAME |
| Recording_1/group/investigation | f1_score | 0.765957 | 0.765957 | SAME |
| Recording_1/group/investigation | so | 0.544410 | 0.544410 | SAME |
| Recording_1/group/investigation | tp | 0.084636 | 0.084636 | SAME |
| Recording_1/group/investigation | ic | 0.987641 | 0.982941 | CHANGED |
| Recording_1/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_1/group/mount | recall | 0.533333 | 0.533333 | SAME |
| Recording_1/group/mount | f1_score | 0.695652 | 0.695652 | SAME |
| Recording_1/group/mount | so | 0.733277 | 0.733277 | SAME |
| Recording_1/group/mount | tp | 0.078471 | 0.078471 | SAME |
| Recording_1/group/mount | ic | 0.990861 | 0.990861 | SAME |
| Recording_1/group/other | precision | 0.913043 | 0.913043 | SAME |
| Recording_1/group/other | recall | 0.857143 | 0.857143 | SAME |
| Recording_1/group/other | f1_score | 0.884211 | 0.884211 | SAME |
| Recording_1/group/other | so | 0.767227 | 0.767227 | SAME |
| Recording_1/group/other | tp | 0.061814 | 0.061814 | SAME |
| Recording_1/group/other | ic | 0.973473 | 0.993069 | CHANGED |
| Recording_2/overall | precision | 0.717105 | 0.967105 | CHANGED |
| Recording_2/overall | recall | 0.589924 | 0.839924 | CHANGED |
| Recording_2/overall | f1_score | 0.647301 | 0.897301 | CHANGED |
| Recording_2/overall | so | 0.575946 | 0.825946 | CHANGED |
| Recording_2/overall | tp | 0.059082 | 0.309082 | CHANGED |
| Recording_2/overall | ic | 0.744498 | 0.993907 | CHANGED |
| Recording_2/group/attack | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_2/group/attack | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_2/group/attack | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_2/group/attack | so | 0.000000 | 1.000000 | CHANGED |
| Recording_2/group/attack | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_2/group/attack | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_2/group/investigation | precision | 0.894737 | 0.894737 | SAME |
| Recording_2/group/investigation | recall | 0.739130 | 0.739130 | SAME |
| Recording_2/group/investigation | f1_score | 0.809524 | 0.809524 | SAME |
| Recording_2/group/investigation | so | 0.660942 | 0.660942 | SAME |
| Recording_2/group/investigation | tp | 0.046151 | 0.046151 | SAME |
| Recording_2/group/investigation | ic | 0.991219 | 0.989051 | CHANGED |
| Recording_2/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_2/group/mount | recall | 0.833333 | 0.833333 | SAME |
| Recording_2/group/mount | f1_score | 0.909091 | 0.909091 | SAME |
| Recording_2/group/mount | so | 0.931754 | 0.931754 | SAME |
| Recording_2/group/mount | tp | 0.145170 | 0.145170 | SAME |
| Recording_2/group/mount | ic | 0.994648 | 0.994648 | SAME |
| Recording_2/group/other | precision | 0.973684 | 0.973684 | SAME |
| Recording_2/group/other | recall | 0.787234 | 0.787234 | SAME |
| Recording_2/group/other | f1_score | 0.870588 | 0.870588 | SAME |
| Recording_2/group/other | so | 0.711086 | 0.711086 | SAME |
| Recording_2/group/other | tp | 0.045008 | 0.045008 | SAME |
| Recording_2/group/other | ic | 0.992125 | 0.991930 | CHANGED |
| Recording_3/overall | precision | 0.709481 | 0.959481 | CHANGED |
| Recording_3/overall | recall | 0.600967 | 0.850967 | CHANGED |
| Recording_3/overall | f1_score | 0.648627 | 0.898627 | CHANGED |
| Recording_3/overall | so | 0.473716 | 0.723716 | CHANGED |
| Recording_3/overall | tp | 0.018876 | 0.268876 | CHANGED |
| Recording_3/overall | ic | 0.737293 | 0.993223 | CHANGED |
| Recording_3/group/attack | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_3/group/attack | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_3/group/attack | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_3/group/attack | so | 0.000000 | 1.000000 | CHANGED |
| Recording_3/group/attack | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_3/group/attack | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_3/group/investigation | precision | 0.935484 | 0.935484 | SAME |
| Recording_3/group/investigation | recall | 0.783784 | 0.783784 | SAME |
| Recording_3/group/investigation | f1_score | 0.852941 | 0.852941 | SAME |
| Recording_3/group/investigation | so | 0.456310 | 0.456310 | SAME |
| Recording_3/group/investigation | tp | 0.022322 | 0.022322 | SAME |
| Recording_3/group/investigation | ic | 0.984966 | 0.984966 | SAME |
| Recording_3/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_3/group/mount | recall | 0.739130 | 0.739130 | SAME |
| Recording_3/group/mount | f1_score | 0.850000 | 0.850000 | SAME |
| Recording_3/group/mount | so | 0.674504 | 0.674504 | SAME |
| Recording_3/group/mount | tp | 0.028795 | 0.028795 | SAME |
| Recording_3/group/mount | ic | 0.991713 | 0.991713 | SAME |
| Recording_3/group/other | precision | 0.902439 | 0.902439 | SAME |
| Recording_3/group/other | recall | 0.880952 | 0.880952 | SAME |
| Recording_3/group/other | f1_score | 0.891566 | 0.891566 | SAME |
| Recording_3/group/other | so | 0.764048 | 0.764048 | SAME |
| Recording_3/group/other | tp | 0.024386 | 0.024386 | SAME |
| Recording_3/group/other | ic | 0.972492 | 0.996212 | CHANGED |
| Recording_4/overall | precision | 0.725000 | 0.975000 | CHANGED |
| Recording_4/overall | recall | 0.365686 | 0.615686 | CHANGED |
| Recording_4/overall | f1_score | 0.479167 | 0.729167 | CHANGED |
| Recording_4/overall | so | 0.457087 | 0.707087 | CHANGED |
| Recording_4/overall | tp | 0.025715 | 0.275715 | CHANGED |
| Recording_4/overall | ic | 0.736591 | 0.996530 | CHANGED |
| Recording_4/group/attack | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_4/group/attack | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_4/group/attack | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_4/group/attack | so | 0.000000 | 1.000000 | CHANGED |
| Recording_4/group/attack | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_4/group/attack | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_4/group/investigation | precision | 0.900000 | 0.900000 | SAME |
| Recording_4/group/investigation | recall | 0.529412 | 0.529412 | SAME |
| Recording_4/group/investigation | f1_score | 0.666667 | 0.666667 | SAME |
| Recording_4/group/investigation | so | 0.391471 | 0.391471 | SAME |
| Recording_4/group/investigation | tp | 0.014025 | 0.014025 | SAME |
| Recording_4/group/investigation | ic | 0.996882 | 0.996882 | SAME |
| Recording_4/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_4/group/mount | recall | 0.333333 | 0.333333 | SAME |
| Recording_4/group/mount | f1_score | 0.500000 | 0.500000 | SAME |
| Recording_4/group/mount | so | 0.701119 | 0.701119 | SAME |
| Recording_4/group/mount | tp | 0.040087 | 0.040087 | SAME |
| Recording_4/group/mount | ic | 0.995289 | 0.995289 | SAME |
| Recording_4/group/other | precision | 1.000000 | 1.000000 | SAME |
| Recording_4/group/other | recall | 0.600000 | 0.600000 | SAME |
| Recording_4/group/other | f1_score | 0.750000 | 0.750000 | SAME |
| Recording_4/group/other | so | 0.735760 | 0.735760 | SAME |
| Recording_4/group/other | tp | 0.048749 | 0.048749 | SAME |
| Recording_4/group/other | ic | 0.954191 | 0.993949 | CHANGED |
| Recording_5/overall | precision | 0.750000 | 1.000000 | CHANGED |
| Recording_5/overall | recall | 0.642105 | 0.892105 | CHANGED |
| Recording_5/overall | f1_score | 0.691459 | 0.941459 | CHANGED |
| Recording_5/overall | so | 0.586730 | 0.836730 | CHANGED |
| Recording_5/overall | tp | 0.029289 | 0.279289 | CHANGED |
| Recording_5/overall | ic | 0.732165 | 0.995120 | CHANGED |
| Recording_5/group/attack | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_5/group/attack | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_5/group/attack | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_5/group/attack | so | 0.000000 | 1.000000 | CHANGED |
| Recording_5/group/attack | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_5/group/attack | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_5/group/investigation | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/group/investigation | recall | 0.800000 | 0.800000 | SAME |
| Recording_5/group/investigation | f1_score | 0.888889 | 0.888889 | SAME |
| Recording_5/group/investigation | so | 0.658959 | 0.658959 | SAME |
| Recording_5/group/investigation | tp | 0.035333 | 0.035333 | SAME |
| Recording_5/group/investigation | ic | 0.990205 | 0.989914 | CHANGED |
| Recording_5/group/mount | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/group/mount | recall | 0.900000 | 0.900000 | SAME |
| Recording_5/group/mount | f1_score | 0.947368 | 0.947368 | SAME |
| Recording_5/group/mount | so | 0.910024 | 0.910024 | SAME |
| Recording_5/group/mount | tp | 0.035960 | 0.035960 | SAME |
| Recording_5/group/mount | ic | 0.996541 | 0.996541 | SAME |
| Recording_5/group/other | precision | 1.000000 | 1.000000 | SAME |
| Recording_5/group/other | recall | 0.868421 | 0.868421 | SAME |
| Recording_5/group/other | f1_score | 0.929577 | 0.929577 | SAME |
| Recording_5/group/other | so | 0.777939 | 0.777939 | SAME |
| Recording_5/group/other | tp | 0.045862 | 0.045862 | SAME |
| Recording_5/group/other | ic | 0.941915 | 0.994024 | CHANGED |
| Recording_6/overall | precision | 0.664083 | 0.914083 | CHANGED |
| Recording_6/overall | recall | 0.495671 | 0.745671 | CHANGED |
| Recording_6/overall | f1_score | 0.542505 | 0.792505 | CHANGED |
| Recording_6/overall | so | 0.374464 | 0.624464 | CHANGED |
| Recording_6/overall | tp | 0.014068 | 0.264068 | CHANGED |
| Recording_6/overall | ic | 0.738821 | 0.993987 | CHANGED |
| Recording_6/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_6/group/attack | recall | 0.363636 | 0.363636 | SAME |
| Recording_6/group/attack | f1_score | 0.533333 | 0.533333 | SAME |
| Recording_6/group/attack | so | 0.355488 | 0.355488 | SAME |
| Recording_6/group/attack | tp | 0.014143 | 0.014143 | SAME |
| Recording_6/group/attack | ic | 0.993666 | 0.993666 | SAME |
| Recording_6/group/investigation | precision | 0.767442 | 0.767442 | SAME |
| Recording_6/group/investigation | recall | 0.785714 | 0.785714 | SAME |
| Recording_6/group/investigation | f1_score | 0.776471 | 0.776471 | SAME |
| Recording_6/group/investigation | so | 0.539580 | 0.539580 | SAME |
| Recording_6/group/investigation | tp | 0.020317 | 0.020317 | SAME |
| Recording_6/group/investigation | ic | 0.990392 | 0.990392 | SAME |
| Recording_6/group/mount | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_6/group/mount | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_6/group/mount | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_6/group/mount | so | 0.000000 | 1.000000 | CHANGED |
| Recording_6/group/mount | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_6/group/mount | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_6/group/other | precision | 0.888889 | 0.888889 | SAME |
| Recording_6/group/other | recall | 0.833333 | 0.833333 | SAME |
| Recording_6/group/other | f1_score | 0.860215 | 0.860215 | SAME |
| Recording_6/group/other | so | 0.602789 | 0.602789 | SAME |
| Recording_6/group/other | tp | 0.021811 | 0.021811 | SAME |
| Recording_6/group/other | ic | 0.971224 | 0.991888 | CHANGED |
| Recording_7/overall | precision | 0.750000 | 1.000000 | CHANGED |
| Recording_7/overall | recall | 0.333910 | 0.583910 | CHANGED |
| Recording_7/overall | f1_score | 0.456530 | 0.706530 | CHANGED |
| Recording_7/overall | so | 0.414495 | 0.664495 | CHANGED |
| Recording_7/overall | tp | 0.026530 | 0.276530 | CHANGED |
| Recording_7/overall | ic | 0.734494 | 0.994944 | CHANGED |
| Recording_7/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/group/attack | recall | 0.525000 | 0.525000 | SAME |
| Recording_7/group/attack | f1_score | 0.688525 | 0.688525 | SAME |
| Recording_7/group/attack | so | 0.390695 | 0.390695 | SAME |
| Recording_7/group/attack | tp | 0.030229 | 0.030229 | SAME |
| Recording_7/group/attack | ic | 0.989471 | 0.989471 | SAME |
| Recording_7/group/investigation | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/group/investigation | recall | 0.300000 | 0.300000 | SAME |
| Recording_7/group/investigation | f1_score | 0.461538 | 0.461538 | SAME |
| Recording_7/group/investigation | so | 0.536495 | 0.536495 | SAME |
| Recording_7/group/investigation | tp | 0.031331 | 0.031331 | SAME |
| Recording_7/group/investigation | ic | 0.994544 | 0.994544 | SAME |
| Recording_7/group/mount | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_7/group/mount | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_7/group/mount | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_7/group/mount | so | 0.000000 | 1.000000 | CHANGED |
| Recording_7/group/mount | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_7/group/mount | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_7/group/other | precision | 1.000000 | 1.000000 | SAME |
| Recording_7/group/other | recall | 0.510638 | 0.510638 | SAME |
| Recording_7/group/other | f1_score | 0.676056 | 0.676056 | SAME |
| Recording_7/group/other | so | 0.730790 | 0.730790 | SAME |
| Recording_7/group/other | tp | 0.044561 | 0.044561 | SAME |
| Recording_7/group/other | ic | 0.953960 | 0.995760 | CHANGED |
| Recording_8/overall | precision | 0.668750 | 0.891667 | CHANGED |
| Recording_8/overall | recall | 0.346721 | 0.346721 | SAME |
| Recording_8/overall | f1_score | 0.454630 | 0.606173 | CHANGED |
| Recording_8/overall | so | 0.397921 | 0.530562 | CHANGED |
| Recording_8/overall | tp | 0.028604 | 0.038138 | CHANGED |
| Recording_8/overall | ic | 0.965943 | 0.995577 | CHANGED |
| Recording_8/group/attack | precision | 0.875000 | 0.875000 | SAME |
| Recording_8/group/attack | recall | 0.368421 | 0.368421 | SAME |
| Recording_8/group/attack | f1_score | 0.518519 | 0.518519 | SAME |
| Recording_8/group/attack | so | 0.268364 | 0.268364 | SAME |
| Recording_8/group/attack | tp | 0.020423 | 0.020423 | SAME |
| Recording_8/group/attack | ic | 0.992796 | 0.991919 | CHANGED |
| Recording_8/group/investigation | precision | 1.000000 | 1.000000 | SAME |
| Recording_8/group/investigation | recall | 0.538462 | 0.538462 | SAME |
| Recording_8/group/investigation | f1_score | 0.700000 | 0.700000 | SAME |
| Recording_8/group/investigation | so | 0.591051 | 0.591051 | SAME |
| Recording_8/group/investigation | tp | 0.025271 | 0.025271 | SAME |
| Recording_8/group/investigation | ic | 0.995282 | 0.995282 | SAME |
| Recording_8/group/mount | precision | 0.000000 | NaN | NULL->VALUE |
| Recording_8/group/mount | recall | 0.000000 | 0.000000 | SAME |
| Recording_8/group/mount | f1_score | 0.000000 | NaN | NULL->VALUE |
| Recording_8/group/mount | so | 0.000000 | NaN | NULL->VALUE |
| Recording_8/group/mount | tp | 0.000000 | NaN | NULL->VALUE |
| Recording_8/group/mount | ic | 1.000000 | 1.000000 | SAME |
| Recording_8/group/other | precision | 0.800000 | 0.800000 | SAME |
| Recording_8/group/other | recall | 0.480000 | 0.480000 | SAME |
| Recording_8/group/other | f1_score | 0.600000 | 0.600000 | SAME |
| Recording_8/group/other | so | 0.732271 | 0.732271 | SAME |
| Recording_8/group/other | tp | 0.068721 | 0.068721 | SAME |
| Recording_8/group/other | ic | 0.875693 | 0.995105 | CHANGED |
| Recording_9/overall | precision | 0.548398 | 0.798398 | CHANGED |
| Recording_9/overall | recall | 0.403819 | 0.653819 | CHANGED |
| Recording_9/overall | f1_score | 0.462355 | 0.712355 | CHANGED |
| Recording_9/overall | so | 0.432730 | 0.682730 | CHANGED |
| Recording_9/overall | tp | 0.027809 | 0.277809 | CHANGED |
| Recording_9/overall | ic | 0.738825 | 0.995671 | CHANGED |
| Recording_9/group/attack | precision | 0.956522 | 0.956522 | SAME |
| Recording_9/group/attack | recall | 0.733333 | 0.733333 | SAME |
| Recording_9/group/attack | f1_score | 0.830189 | 0.830189 | SAME |
| Recording_9/group/attack | so | 0.424100 | 0.424100 | SAME |
| Recording_9/group/attack | tp | 0.020590 | 0.020590 | SAME |
| Recording_9/group/attack | ic | 0.989386 | 0.989386 | SAME |
| Recording_9/group/investigation | precision | 0.375000 | 0.375000 | SAME |
| Recording_9/group/investigation | recall | 0.187500 | 0.187500 | SAME |
| Recording_9/group/investigation | f1_score | 0.250000 | 0.250000 | SAME |
| Recording_9/group/investigation | so | 0.631678 | 0.631678 | SAME |
| Recording_9/group/investigation | tp | 0.034810 | 0.034810 | SAME |
| Recording_9/group/investigation | ic | 0.998630 | 0.998630 | SAME |
| Recording_9/group/mount | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_9/group/mount | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_9/group/mount | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_9/group/mount | so | 0.000000 | 1.000000 | CHANGED |
| Recording_9/group/mount | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_9/group/mount | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_9/group/other | precision | 0.862069 | 0.862069 | SAME |
| Recording_9/group/other | recall | 0.694444 | 0.694444 | SAME |
| Recording_9/group/other | f1_score | 0.769231 | 0.769231 | SAME |
| Recording_9/group/other | so | 0.675140 | 0.675140 | SAME |
| Recording_9/group/other | tp | 0.055838 | 0.055838 | SAME |
| Recording_9/group/other | ic | 0.967283 | 0.994669 | CHANGED |
| Recording_10/overall | precision | 0.649107 | 0.899107 | CHANGED |
| Recording_10/overall | recall | 0.225760 | 0.475760 | CHANGED |
| Recording_10/overall | f1_score | 0.333475 | 0.583475 | CHANGED |
| Recording_10/overall | so | 0.416806 | 0.666806 | CHANGED |
| Recording_10/overall | tp | 0.035923 | 0.285923 | CHANGED |
| Recording_10/overall | ic | 0.741064 | 0.996191 | CHANGED |
| Recording_10/group/attack | precision | 1.000000 | 1.000000 | SAME |
| Recording_10/group/attack | recall | 0.285714 | 0.285714 | SAME |
| Recording_10/group/attack | f1_score | 0.444444 | 0.444444 | SAME |
| Recording_10/group/attack | so | 0.184903 | 0.184903 | SAME |
| Recording_10/group/attack | tp | 0.011189 | 0.011189 | SAME |
| Recording_10/group/attack | ic | 0.991412 | 0.991412 | SAME |
| Recording_10/group/investigation | precision | 0.625000 | 0.625000 | SAME |
| Recording_10/group/investigation | recall | 0.263158 | 0.263158 | SAME |
| Recording_10/group/investigation | f1_score | 0.370370 | 0.370370 | SAME |
| Recording_10/group/investigation | so | 0.657207 | 0.657207 | SAME |
| Recording_10/group/investigation | tp | 0.064683 | 0.064683 | SAME |
| Recording_10/group/investigation | ic | 0.995061 | 0.995061 | SAME |
| Recording_10/group/mount | precision | 0.000000 | 1.000000 | CHANGED |
| Recording_10/group/mount | recall | 0.000000 | 1.000000 | CHANGED |
| Recording_10/group/mount | f1_score | 0.000000 | 1.000000 | CHANGED |
| Recording_10/group/mount | so | 0.000000 | 1.000000 | CHANGED |
| Recording_10/group/mount | tp | 0.000000 | 1.000000 | CHANGED |
| Recording_10/group/mount | ic | 0.000000 | 1.000000 | CHANGED |
| Recording_10/group/other | precision | 0.971429 | 0.971429 | SAME |
| Recording_10/group/other | recall | 0.354167 | 0.354167 | SAME |
| Recording_10/group/other | f1_score | 0.519084 | 0.519084 | SAME |
| Recording_10/group/other | so | 0.825115 | 0.825115 | SAME |
| Recording_10/group/other | tp | 0.067820 | 0.067820 | SAME |
| Recording_10/group/other | ic | 0.977784 | 0.998293 | CHANGED |