diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml index f3d4fca..c17b8a9 100644 --- a/.github/workflows/python-app.yml +++ b/.github/workflows/python-app.yml @@ -26,14 +26,12 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install flake8 pytest - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. 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-import tabulate - -import re import spacy -import numpy as np +import tabulate +from sklearn.metrics import confusion_matrix NLP = None + def get_spacy(): global NLP if NLP is None: - NLP = spacy.load("en_core_web_sm", disable=["parser", "tagger", "ner", "textcat"]) + NLP = spacy.load( + "en_core_web_sm", disable=["parser", "tagger", "ner", "textcat"] + ) NLP.max_length = ( 800000000 # approximately one volume of the encyclopedia britannica. ) return NLP + def _get_unique_classes(true, predicted): true_and_pred = list(true) + list(predicted) return list(set([seq["label"] for seqs in true_and_pred for seq in seqs])) @@ -112,7 +113,6 @@ def sequence_labeling_token_counts(true, predicted): pred_token["start"] == true_token["start"] and pred_token["end"] == true_token["end"] ): - if pred_token["label"] == true_token["label"]: d[true_token["label"]]["true_positives"].append(true_token) else: @@ -253,7 +253,6 @@ def fuzzy_compare(x: dict, y: dict) -> bool: return _norm_text(x["text"]) == _norm_text(y["text"]) - def sequence_labeling_token_precision(true, predicted): """ Token level precision @@ -345,7 +344,6 @@ def get_seq_count_fn(span_type="token"): "exact": partial(sequence_labeling_counts, equality_fn=sequence_exact_match), "superset": partial(sequence_labeling_counts, equality_fn=sequence_superset), "value": partial(sequence_labeling_counts, equality_fn=fuzzy_compare), - } return span_type_fn_mapping[span_type] @@ -398,7 +396,7 @@ def annotation_report( dict(count_dict), ] labels = set(token_precision.keys()) | set(token_recall.keys()) - target_names = [u"%s" % l for l in labels] + target_names = ["%s" % l for l in labels] counts = [count_dict.get(target_name, 0) for target_name in target_names] last_line_heading = "Weighted Summary" @@ -409,16 +407,16 @@ def annotation_report( "overlap_recall", "support", ] - head_fmt = u"{:>{width}s} " + u" {:>{width}}" * len(headers) - report = head_fmt.format(u"", *headers, width=width) - report += u"\n\n" - row_fmt = u"{:>{width}s} " + u" {:>{width}.{digits}f}" * 4 + u" {:>{width}}" "\n" + head_fmt = "{:>{width}s} " + " {:>{width}}" * len(headers) + report = head_fmt.format("", *headers, width=width) + report += "\n\n" + row_fmt = "{:>{width}s} " + " {:>{width}.{digits}f}" * 4 + " {:>{width}}" "\n" seqs = [[seq.get(target_name, 0.0) for target_name in target_names] for seq in seqs] rows = zip(target_names, *seqs) for row in rows: report += row_fmt.format(*row, width=width, digits=digits) - report += u"\n" + report += "\n" averages = [np.average(seq, weights=counts) for seq in seqs[:-1]] + [ np.sum(seqs[-1]) ] diff --git a/tests/test_metrics.py b/tests/test_metrics.py index be48286..f03d9ea 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,48 +1,59 @@ -import unittest import pytest + from sequence_metrics.metrics import ( - seq_recall, - seq_precision, get_seq_count_fn, + seq_precision, + seq_recall, sequence_f1, sequences_overlap, ) + def insert_text(docs, labels): if len(docs) != len(labels): - raise ValueError( - "Number of documents must be equal to the number of labels" - ) + raise ValueError("Number of documents must be equal to the number of labels") for doc, label in zip(docs, labels): for l in label: l["text"] = doc[l["start"] : l["end"]] return labels + def extend_label(text, label, amt): - return insert_text(text, [label for _ in range(amt)]) + return insert_text([text for _ in range(amt)], [label for _ in range(amt)]) def remove_label(recs, label): - return [ - [pred for pred in rec if not pred.get("label") == label] for rec in recs - ] + return [[pred for pred in rec if not pred.get("label") == label] for rec in recs] + @pytest.mark.parametrize( "a,b,expected", [ - ({"start": 0, "end": 1}, {"start": 2, "end": 3}, False,), # Non-overlapping + ( + {"start": 0, "end": 1}, + {"start": 2, "end": 3}, + False, + ), # Non-overlapping ( {"start": 0, "end": 1}, {"start": 1, "end": 2}, False, ), # Flush against each other, True expected rather than false for frontend - ({"start": 0, "end": 2}, {"start": 1, "end": 3}, True,), # Overlapping + ( + {"start": 0, "end": 2}, + {"start": 1, "end": 3}, + True, + ), # Overlapping ( {"start": 0, "end": 2}, {"start": 1, "end": 2}, True, ), # Contained but flush against end - ({"start": 0, "end": 3}, {"start": 1, "end": 2}, True,), # Full contained + ( + {"start": 0, "end": 3}, + {"start": 1, "end": 2}, + True, + ), # Full contained ( {"start": 0, "end": 1}, {"start": 0, "end": 2}, @@ -72,13 +83,18 @@ def check_metrics(Y, Y_pred, expected, span_type=None): for metric in counts[cls_]: assert len(counts[cls_][metric]) == expected[cls_][metric] assert recalls[cls_] == pytest.approx(expected[cls_]["recall"], abs=1e-3) - assert per_class_f1s[cls_] == pytest.approx(expected[cls_]["f1-score"], abs=1e-3) - assert precisions[cls_] == pytest.approx(expected[cls_]["precision"], abs=1e-3) - + assert per_class_f1s[cls_]["f1-score"] == pytest.approx( + expected[cls_]["f1-score"], abs=1e-3 + ) + assert precisions[cls_] == pytest.approx( + expected[cls_]["precision"], abs=1e-3 + ) + assert micro_f1_score == pytest.approx(expected["micro-f1"], abs=1e-3) assert weighted_f1 == pytest.approx(expected["weighted-f1"], abs=1e-3) assert macro_f1 == pytest.approx(expected["macro-f1"], abs=1e-3) + def test_token_incorrect(): text = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." expected = { @@ -103,24 +119,25 @@ def test_token_incorrect(): "weighted-f1": 0.0, } y_true = extend_label( - text, - [ + text, + [ {"start": 7, "end": 20, "label": "entity"}, {"start": 41, "end": 54, "label": "date"}, ], - 10 + 10, ) y_false_pos = extend_label( - text, + text, [ {"start": 21, "end": 28, "label": "entity"}, {"start": 62, "end": 65, "label": "date"}, ], - 10 + 10, ) check_metrics(y_true, y_false_pos, expected, span_type="token") -def test_token_correct(self): + +def test_token_correct(): text = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." expected = { "entity": { @@ -144,41 +161,42 @@ def test_token_correct(self): "weighted-f1": 1.0, } y_true = extend_label( - text, - [ + text, + [ {"start": 7, "end": 20, "label": "entity"}, {"start": 41, "end": 54, "label": "date"}, ], - 10 + 10, ) - self.check_metrics(y_true, y_true, expected, span_type="token") + check_metrics(y_true, y_true, expected, span_type="token") + def test_token_mixed(): text = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." Y_mixed = extend_label( - text, + text, [ {"start": 21, "end": 28, "label": "entity"}, {"start": 62, "end": 65, "label": "date"}, ], - 5 + 5, ) + extend_label( - text, - [ + text, + [ {"start": 7, "end": 20, "label": "entity"}, {"start": 41, "end": 54, "label": "date"}, ], - 5 + 5, ) y_true = extend_label( - text, - [ + text, + [ {"start": 7, "end": 20, "label": "entity"}, {"start": 41, "end": 54, "label": "date"}, ], - 10 + 10, ) expected = { "entity": { @@ -202,37 +220,41 @@ def test_token_mixed(): "weighted-f1": 0.601, } check_metrics( - y_true, Y_mixed, expected, span_type="token", + y_true, + Y_mixed, + expected, + span_type="token", ) + def test_token_mixed_2(): text = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." - y_mixed = extend_label( - text, - [ - {"start": 21, "end": 28, "label": "entity"}, - {"start": 62, "end": 65, "label": "date"}, - ], - 5 - ) + extend_label( - text, + y_mixed = ( + extend_label( + text, [ - {"start": 7, "end": 20, "label": "entity"}, - {"start": 41, "end": 54, "label": "date"}, - ], - 2 - ) + extend_label( - text, - [{"start": 7, "end": 20, "label": "entity"}] - , 3 + {"start": 21, "end": 28, "label": "entity"}, + {"start": 62, "end": 65, "label": "date"}, + ], + 5, + ) + + extend_label( + text, + [ + {"start": 7, "end": 20, "label": "entity"}, + {"start": 41, "end": 54, "label": "date"}, + ], + 2, + ) + + extend_label(text, [{"start": 7, "end": 20, "label": "entity"}], 3) ) y_true = extend_label( - text, - [ + text, + [ {"start": 7, "end": 20, "label": "entity"}, {"start": 41, "end": 54, "label": "date"}, ], - 10 + 10, ) expected = { "entity": { @@ -256,56 +278,98 @@ def test_token_mixed_2(): "weighted-f1": 0.392, } check_metrics( - y_true, y_mixed, expected, span_type="token", + y_true, + y_mixed, + expected, + span_type="token", ) -def test_seq_correct(self): - # Overlaps - for y_set in [self.Y_true, self.Y_overlap, self.Y_extra_overlap]: - self.check_metrics( - self.Y_true, y_set, self.seq_expected_correct, span_type="overlap" - ) - - # Exact - self.check_metrics( - self.Y_true, self.Y_true, self.seq_expected_correct, span_type="exact" +def test_seq_incorrect(): + text = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." + y_true = extend_label( + text, + [ + {"start": 7, "end": 20, "label": "entity"}, + {"start": 41, "end": 54, "label": "date"}, + ], + 10, ) - - # Superset - self.check_metrics( - self.Y_true, - self.Y_superset, - self.seq_expected_correct, - span_type="superset", + y_false_pos = extend_label( + text, + [ + {"start": 21, "end": 28, "label": "entity"}, + {"start": 62, "end": 65, "label": "date"}, + ], + 10, ) + seq_expected_incorrect = { + "entity": { + "false_positives": 10, + "false_negatives": 10, + "true_positives": 0, + "precision": 0.0, + "recall": 0.0, + "f1-score": 0.0, + }, + "date": { + "false_positives": 10, + "false_negatives": 10, + "true_positives": 0, + "precision": 0.0, + "recall": 0.0, + "f1-score": 0.0, + }, + "micro-f1": 0.0, + "macro-f1": 0.0, + "weighted-f1": 0.0, + } -def test_seq_incorrect(self): # Overlap - self.check_metrics( - self.Y_true, - self.Y_false_pos, - self.seq_expected_incorrect, + check_metrics( + y_true, + y_false_pos, + seq_expected_incorrect, span_type="overlap", ) # Exact - self.check_metrics( - self.Y_true, - self.Y_false_pos, - self.seq_expected_incorrect, + check_metrics( + y_true, + y_false_pos, + seq_expected_incorrect, span_type="exact", ) # Superset - self.check_metrics( - self.Y_true, - self.Y_false_pos, - self.seq_expected_incorrect, + check_metrics( + y_true, + y_false_pos, + seq_expected_incorrect, span_type="superset", ) -def test_seq_mixed(self): + +@pytest.mark.parametrize( + "overlapping", + [ + [ + {"start": 5, "end": 16, "label": "entity"}, + {"start": 34, "end": 50, "label": "date"}, + ], + [ + {"start": 6, "end": 10, "label": "entity"}, + {"start": 15, "end": 23, "label": "entity"}, + {"start": 34, "end": 50, "label": "date"}, + ], + [ + {"start": 6, "end": 21, "label": "entity"}, + {"start": 38, "end": 60, "label": "date"}, + ], + ], +) +def test_seq_mixed(overlapping): + text = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." expected = { "entity": { "false_positives": 4, @@ -327,52 +391,100 @@ def test_seq_mixed(self): "macro-f1": 0.6, "weighted-f1": 0.6, } - Y_mixed_overlap = self.Y_false_pos[:4] + self.Y_overlap[:6] - Y_mixed_extra_overlap = self.Y_false_pos[:4] + self.Y_extra_overlap[:6] - Y_mixed_exact = self.Y_false_pos[:4] + self.Y_true[:6] - for y_set in [Y_mixed_overlap, Y_mixed_extra_overlap, Y_mixed_exact]: - self.check_metrics( - self.Y_true, y_set, expected=expected, span_type="overlap" - ) + y_true = extend_label( + text, + [ + {"start": 7, "end": 20, "label": "entity"}, + {"start": 41, "end": 54, "label": "date"}, + ], + 10, + ) + y_mixed = extend_label( + text, + [ + {"start": 21, "end": 28, "label": "entity"}, + {"start": 62, "end": 65, "label": "date"}, + ], + 4, + ) + extend_label(text, overlapping, 6) + check_metrics(y_true, y_mixed, expected=expected, span_type="overlap") + + +@pytest.mark.parametrize( + "span_type,true_positive_non_exact", + [ + ( + "overlap", + [ + {"start": 5, "end": 16, "label": "entity"}, + {"start": 34, "end": 50, "label": "date"}, + ], + ), + ( + "exact", + [ + {"start": 7, "end": 20, "label": "entity"}, + {"start": 41, "end": 54, "label": "date"}, + ], + ), + ( + "superset", + [ + {"start": 6, "end": 21, "label": "entity"}, + {"start": 38, "end": 60, "label": "date"}, + ], + ), + ], +) +def test_mixed_overlap(span_type, true_positive_non_exact): + text = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." + y_true = [ + {"start": 7, "end": 20, "label": "entity"}, + {"start": 41, "end": 54, "label": "date"}, + ] + y_false_pos = [ + {"start": 21, "end": 28, "label": "entity"}, + {"start": 62, "end": 65, "label": "date"}, + ] + y_false_neg = [ + {"start": 7, "end": 20, "label": "entity"}, + ] - # Move 3 predictions from correct to false negative - Y_mixed_overlap_false_negs = ( - self.Y_false_pos[:4] + self.Y_overlap[:3] + self.Y_false_neg[:3] + mixed_false_negs = ( + extend_label(text, y_false_pos, 4) + + extend_label(text, true_positive_non_exact, 3) + + extend_label(text, y_false_neg, 3) ) - expected["date"] = { - "false_positives": 4, - "false_negatives": 7, - "true_positives": 3, - "precision": 0.42857, - "recall": 0.3, - "f1-score": 0.353, + expected = { + "entity": { + "false_positives": 4, + "false_negatives": 4, + "true_positives": 6, + "precision": 0.6, + "recall": 0.6, + "f1-score": 0.6, + }, + "date": { + "false_positives": 4, + "false_negatives": 7, + "true_positives": 3, + "precision": 0.42857, + "recall": 0.3, + "f1-score": 0.353, + }, + "micro-f1": 0.4864, + "macro-f1": 0.476, + "weighted-f1": 0.476, } - expected["micro-f1"] = 0.4864 - expected["weighted-f1"] = 0.476 - expected["macro-f1"] = 0.476 - self.check_metrics( - self.Y_true, - Y_mixed_overlap_false_negs, - expected=expected, - span_type="overlap", - ) - Y_mixed_exact_false_negs = ( - self.Y_false_pos[:4] + self.Y_true[:3] + self.Y_false_neg[:3] - ) - self.check_metrics( - self.Y_true, Y_mixed_exact_false_negs, expected=expected, span_type="exact" - ) - Y_mixed_superset_false_negs = ( - self.Y_false_pos[:4] + self.Y_superset[:3] + self.Y_false_neg[:3] - ) - self.check_metrics( - self.Y_true, - Y_mixed_superset_false_negs, + check_metrics( + extend_label(text, y_true, 10), + mixed_false_negs, expected=expected, - span_type="superset", + span_type=span_type, ) -def test_overlapping_2_class(self): + +def test_overlapping_2_class(): x = "a and b" y_true = [{"start": 0, "end": 7, "text": x, "label": "class1"}] y_pred = [ @@ -400,11 +512,15 @@ def test_overlapping_2_class(self): "macro-f1": 0.5, "weighted-f1": 1.0, # because there is no support for class2 } - self.check_metrics( - [y_true], [y_pred], expected=expected, span_type="overlap", + check_metrics( + [y_true], + [y_pred], + expected=expected, + span_type="overlap", ) -def test_overlapping_2_class_swapped(self): + +def test_overlapping_2_class_swapped(): x = "a and b" y_true = [{"start": 0, "end": 7, "text": x, "label": "class1"}] y_pred = [ @@ -432,11 +548,15 @@ def test_overlapping_2_class_swapped(self): "macro-f1": 0.5, "weighted-f1": 1.0, # because there is no support for class2 } - self.check_metrics( - [y_true], [y_pred], expected=expected, span_type="overlap", + check_metrics( + [y_true], + [y_pred], + expected=expected, + span_type="overlap", ) -def test_overlapping_1_class(self): + +def test_overlapping_1_class(): x = "a and b" y_true = [{"start": 0, "end": 7, "text": x, "label": "class1"}] y_pred = [ @@ -456,11 +576,15 @@ def test_overlapping_1_class(self): "macro-f1": 1.0, "weighted-f1": 1.0, } - self.check_metrics( - [y_true], [y_pred], expected=expected, span_type="overlap", + check_metrics( + [y_true], + [y_pred], + expected=expected, + span_type="overlap", ) -def test_2_class(self): + +def test_2_class(): x = "a and b" y_true = [ {"start": 0, "end": 1, "text": "a", "label": "class1"}, @@ -483,11 +607,15 @@ def test_2_class(self): "weighted-f1": 1.0, } for span_type in ["overlap", "superset"]: - self.check_metrics( - [y_true], [y_pred], expected=expected, span_type=span_type, + check_metrics( + [y_true], + [y_pred], + expected=expected, + span_type=span_type, ) -def test_whitespace(self): + +def test_whitespace(): x = "a and b" y_true = [{"start": 0, "end": 7, "text": x, "label": "class1"}] y_pred = [ @@ -507,93 +635,9 @@ def test_whitespace(self): "weighted-f1": 1.0, } for span_type in ["superset", "overlap", "exact"]: - self.check_metrics( - [y_true], [y_pred], expected=expected, span_type=span_type, + check_metrics( + [y_true], + [y_pred], + expected=expected, + span_type=span_type, ) - - - -class TestMetrics(unittest.TestCase): - def setUp(self): - x = "Alert: Pepsi Company stocks are up today April 5, 2010 and no one profited." - y_true = [ - {"start": 7, "end": 20, "label": "entity"}, - {"start": 41, "end": 54, "label": "date"}, - ] - y_overlap = [ - {"start": 5, "end": 16, "label": "entity"}, - {"start": 34, "end": 50, "label": "date"}, - ] - y_extra_overlap = [ - {"start": 6, "end": 10, "label": "entity"}, - {"start": 15, "end": 23, "label": "entity"}, - {"start": 34, "end": 50, "label": "date"}, - ] - y_superset = [ - {"start": 6, "end": 21, "label": "entity"}, - {"start": 38, "end": 60, "label": "date"}, - ] - y_false_pos = [ - {"start": 21, "end": 28, "label": "entity"}, - {"start": 62, "end": 65, "label": "date"}, - ] - y_false_neg = [ - {"start": 7, "end": 20, "label": "entity"}, - ] - - self.X = [x for i in range(10)] - self.Y_false_pos = extend_label(self.X, y_false_pos, 10) - self.Y_true = extend_label(self.X, y_true, 10) - self.Y_overlap = extend_label(self.X, y_overlap, 10) - self.Y_extra_overlap = extend_label(self.X, y_extra_overlap, 10) - self.Y_superset = extend_label(self.X, y_superset, 10) - self.Y_false_neg = extend_label(self.X, y_false_neg, 10) - - self.seq_expected_correct = { - "entity": { - "false_positives": 0, - "false_negatives": 0, - "true_positives": 10, - "precision": 1.0, - "recall": 1.0, - "f1-score": 1.0, - }, - "date": { - "false_positives": 0, - "false_negatives": 0, - "true_positives": 10, - "precision": 1.0, - "recall": 1.0, - "f1-score": 1.0, - }, - "micro-f1": 1.0, - "weighted-f1": 1.0, - "macro-f1": 1.0, - } - self.seq_expected_incorrect = { - "entity": { - "false_positives": 10, - "false_negatives": 10, - "true_positives": 0, - "precision": 0.0, - "recall": 0.0, - "f1-score": 0.0, - }, - "date": { - "false_positives": 10, - "false_negatives": 10, - "true_positives": 0, - "precision": 0.0, - "recall": 0.0, - "f1-score": 0.0, - }, - "micro-f1": 0.0, - "macro-f1": 0.0, - "weighted-f1": 0.0, - } - - - - - - \ No newline at end of file