diff options
Diffstat (limited to 'analysis/scripts/metrics.py')
| -rw-r--r-- | analysis/scripts/metrics.py | 51 |
1 files changed, 48 insertions, 3 deletions
diff --git a/analysis/scripts/metrics.py b/analysis/scripts/metrics.py index 932fbb7c..a504227b 100644 --- a/analysis/scripts/metrics.py +++ b/analysis/scripts/metrics.py @@ -12,6 +12,17 @@ License: See the LICENSE file. def true_positives(T, X, margin=5): """Compute true positives without double counting + + >>> true_positives({1, 10, 20, 23}, {3, 8, 20}) + {1, 10, 20} + >>> true_positives({1, 10, 20, 23}, {1, 3, 8, 20}) + {1, 10, 20} + >>> true_positives({1, 10, 20, 23}, {1, 3, 5, 8, 20}) + {1, 10, 20} + >>> true_positives(set(), {1, 2, 3}) + set() + >>> true_positives({1, 2, 3}, set()) + set() """ # make a copy so we don't affect the caller X = set(list(X)) @@ -37,6 +48,12 @@ def f_measure(annotations, predictions, margin=5, alpha=0.5, return_PR=False): Remember that all CP locations are 0-based! + >>> f_measure({1: [10, 20], 2: [11, 20], 3: [10], 4: [0, 5]}, [10, 20]) + 1.0 + >>> f_measure({1: [], 2: [10], 3: [50]}, [10]) + 0.9090909090909091 + >>> f_measure({1: [], 2: [10], 3: [50]}, []) + 0.8 """ # ensure 0 is in all the sets Tks = {k + 1: set(annotations[uid]) for k, uid in enumerate(annotations)} @@ -46,8 +63,10 @@ def f_measure(annotations, predictions, margin=5, alpha=0.5, return_PR=False): X = set(predictions) X.add(0) - Tstar = [tau for tau in Tk for Tk in Tks.values()] - Tstar = set(Tstar) + Tstar = set() + for Tk in Tks.values(): + for tau in Tk: + Tstar.add(tau) K = len(Tks) @@ -63,7 +82,17 @@ def f_measure(annotations, predictions, margin=5, alpha=0.5, return_PR=False): def overlap(A, B): - """ Return the overlap (i.e. Jaccard index) of two sets """ + """ Return the overlap (i.e. Jaccard index) of two sets + + >>> overlap({1, 2, 3}, set()) + 0.0 + >>> overlap({1, 2, 3}, {2, 5}) + 0.25 + >>> overlap(set(), {1, 2, 3}) + 0.0 + >>> overlap({1, 2, 3}, {1, 2, 3}) + 1.0 + """ return len(A.intersection(B)) / len(A.union(B)) @@ -101,6 +130,15 @@ def cover_single(Sprime, S): """Compute the covering of a segmentation S by a segmentation Sprime. This follows equation (8) in Arbaleaz, 2010. + + >>> cover_single([{1, 2, 3}, {4, 5}, {6}], [{1, 2, 3}, {4, 5, 6}]) + 0.8333333333333334 + >>> cover_single([{1, 2, 3, 4}, {5, 6}], [{1, 2, 3, 4, 5, 6}]) + 0.6666666666666666 + >>> cover_single([{1, 2}, {3, 4}, {5, 6}], [{1, 2, 3}, {4, 5, 6}]) + 0.6666666666666666 + >>> cover_single([{1, 2, 3, 4, 5, 6}], [{1}, {2}, {3}, {4, 5, 6}]) + 0.3333333333333333 """ T = sum(map(len, Sprime)) assert T == sum(map(len, S)) @@ -118,6 +156,13 @@ def covering(annotations, predictions, n_obs): predictions : iterable of predicted Cp locations n_obs : number of observations in the series + >>> covering({1: [10, 20], 2: [10], 3: [0, 5]}, [10, 20], 45) + 0.7962962962962963 + >>> covering({1: [], 2: [10], 3: [40]}, [10], 45) + 0.7954144620811286 + >>> covering({1: [], 2: [10], 3: [40]}, [], 45) + 0.8189300411522634 + """ Ak = { k + 1: partition_from_cps(annotations[uid], n_obs) |