def test_vectorizer_dependencyPathEdgesNearEntities_noTFIDF(): corpus1, _ = generateTestData(positiveCount=5,negativeCount=5) corpus2, _ = generateTestData(positiveCount=10,negativeCount=10) candidateBuilder = kindred.CandidateBuilder() candidateBuilder.fit_transform(corpus1) candidateBuilder.transform(corpus2) chosenFeatures = ["dependencyPathEdgesNearEntities"] vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures,tfidf=False) matrix1 = vectorizer.fit_transform(corpus1) matrix2 = vectorizer.transform(corpus2) assert matrix1.shape == (8,6) assert matrix2.shape == (18,6) colnames = vectorizer.getFeatureNames() expectedNames = [u'dependencypathnearselectedtoken_0_dobj', u'dependencypathnearselectedtoken_0_nsubj', u'dependencypathnearselectedtoken_0_nsubjpass', u'dependencypathnearselectedtoken_1_dobj', u'dependencypathnearselectedtoken_1_nsubj', u'dependencypathnearselectedtoken_1_nsubjpass'] assert colnames == expectedNames # As a quick check, we'll confirm that the column means are as expected colmeans1 = np.sum(matrix1,axis=0) assert colmeans1.tolist() == [[1.0, 2.0, 1.0, 1.0, 2.0, 1.0]] # As a quick check, we'll confirm that the column means are as expected colmeans2 = np.sum(matrix2,axis=0) assert colmeans2.tolist() == [[0.0, 5.0, 1.0, 0.0, 5.0, 1.0]]
def test_vectorizer_defaults():
corpus1, _ = generateTestData(positiveCount=5,negativeCount=5)
corpus2, _ = generateTestData(positiveCount=10,negativeCount=10)
parser = kindred.Parser()
parser.parse(corpus1)
parser.parse(corpus2)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations1 = candidateBuilder.build(corpus1)
candidateRelations2 = candidateBuilder.build(corpus2)
vectorizer = kindred.Vectorizer()
matrix1 = vectorizer.fit_transform(candidateRelations1)
matrix2 = vectorizer.transform(candidateRelations2)
colnames = vectorizer.getFeatureNames()
# As a quick check, we'll confirm that the column means are as expected
colmeans1 = np.sum(matrix1,axis=0).tolist()[0]
namedCols1 = { col:round(v,8) for col,v in zip(colnames,colmeans1) }
check('test_vectorizer_defaults_1',namedCols1)
colmeans2 = np.sum(matrix2,axis=0).tolist()[0]
namedCols2 = { col:round(v,8) for col,v in zip(colnames,colmeans2) }
check('test_vectorizer_defaults_2',namedCols2)
def test_vectorizer_dependencyPathEdgesNearEntities_noTFIDF():
corpus1, _ = generateTestData(positiveCount=5,negativeCount=5)
corpus2, _ = generateTestData(positiveCount=10,negativeCount=10)
parser = kindred.Parser()
parser.parse(corpus1)
parser.parse(corpus2)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations1 = candidateBuilder.build(corpus1)
candidateRelations2 = candidateBuilder.build(corpus2)
chosenFeatures = ["dependencyPathEdgesNearEntities"]
vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures,tfidf=False)
matrix1 = vectorizer.fit_transform(candidateRelations1)
matrix2 = vectorizer.transform(candidateRelations2)
colnames = vectorizer.getFeatureNames()
# As a quick check, we'll confirm that the column means are as expected
colmeans1 = np.sum(matrix1,axis=0).tolist()[0]
namedCols1 = { col:round(v,8) for col,v in zip(colnames,colmeans1) }
check('test_vectorizer_dependencyPathEdgesNearEntities_noTFIDF_1',namedCols1)
colmeans2 = np.sum(matrix1,axis=0).tolist()[0]
namedCols2 = { col:round(v,8) for col,v in zip(colnames,colmeans2) }
check('test_vectorizer_dependencyPathEdgesNearEntities_noTFIDF_2',namedCols2)
def test_simpleVectorizer_triple():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer> which targets <gene id="3">EGFR</gene>. <relation type="druginfo" drug="1" disease="2" gene="3" />'
corpus = kindred.Corpus(text, loadFromSimpleTag=True)
parser = kindred.Parser()
parser.parse(corpus)
candidateBuilder = kindred.CandidateBuilder(entityCount=3)
candidateRelations = candidateBuilder.build(corpus)
# We'll just get the vectors for the entityTypes
vectorizer = kindred.Vectorizer(entityCount=3,
featureChoice=["entityTypes"])
vectors = vectorizer.fit_transform(candidateRelations)
assert vectors.shape == (6, 9)
expected = [(0, 1), (0, 3), (0, 8), (1, 1), (1, 5), (1, 6), (2, 0), (2, 4),
(2, 8), (3, 0), (3, 5), (3, 7), (4, 2), (4, 4), (4, 6), (5, 2),
(5, 3), (5, 7)]
rows, cols = vectors.nonzero()
rowsWithCols = list(zip(rows.tolist(), cols.tolist()))
assert sorted(expected) == sorted(rowsWithCols)
vectorsCSR = vectors.tocsr()
for r, c in expected:
assert vectorsCSR[r, c] == 1.0
def test_vectorizer_bigrams_noTFIDF(): corpus1, _ = generateTestData(positiveCount=5,negativeCount=5) corpus2, _ = generateTestData(positiveCount=10,negativeCount=10) candidateBuilder = kindred.CandidateBuilder() candidateBuilder.fit_transform(corpus1) candidateBuilder.transform(corpus2) chosenFeatures = ["bigrams"] vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures,tfidf=False) matrix1 = vectorizer.fit_transform(corpus1) matrix2 = vectorizer.transform(corpus2) assert matrix1.shape == (8,27) assert matrix2.shape == (18,27) colnames = vectorizer.getFeatureNames() expectedNames = [u'bigrams_ _gnorcyvmer', u'bigrams_a_common', u'bigrams_a_known', u'bigrams_be_treated', u'bigrams_bmzvpvwbpw_failed', u'bigrams_can_be', u'bigrams_clinical_trials', u'bigrams_common_treatment', u'bigrams_effect_of', u'bigrams_failed_clinical', u'bigrams_for_kyekjnkrfo', u'bigrams_for_zgwivlcmly', u'bigrams_gnorcyvmer_is', u'bigrams_is_a', u'bigrams_known_side', u'bigrams_kyekjnkrfo_.', u'bigrams_of_ruswdgzajr', u'bigrams_ootopaoxbg_can', u'bigrams_pehhjnlvvewbjccovflf_is', u'bigrams_ruswdgzajr_.', u'bigrams_side_effect', u'bigrams_treated_with', u'bigrams_treatment_for', u'bigrams_trials_for', u'bigrams_vgypkemhjr_.', u'bigrams_with_vgypkemhjr', u'bigrams_zgwivlcmly_.'] assert colnames == expectedNames # As a quick check, we'll confirm that the column means are as expected expected1 = [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 8.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0] colmeans1 = np.sum(matrix1,axis=0).tolist()[0] assert len(expected1) == len(colmeans1) for gotVal,expectedVal in zip(colmeans1,expected1): assert round(gotVal,8) == round(expectedVal,8) # Check rounded values (for floating point comparison issue) # As a quick check, we'll confirm that the column means are as expected expected2 = [0.0, 4.0, 0.0, 4.0, 4.0, 4.0, 8.0, 4.0, 0.0, 8.0, 0.0, 0.0, 0.0, 4.0, 0.0, 4.0, 0.0, 4.0, 4.0, 0.0, 0.0, 4.0, 4.0, 8.0, 4.0, 4.0, 0.0] colmeans2 = np.sum(matrix2,axis=0).tolist()[0] assert len(expected2) == len(colmeans2) for gotVal,expectedVal in zip(colmeans2,expected2): assert round(gotVal,8) == round(expectedVal,8) # Check rounded values (for floating point comparison issue)
def test_vectorizer_defaults(): corpus1, _ = generateTestData(positiveCount=5,negativeCount=5) corpus2, _ = generateTestData(positiveCount=10,negativeCount=10) candidateBuilder = kindred.CandidateBuilder() candidateBuilder.fit_transform(corpus1) candidateBuilder.transform(corpus2) candidates = corpus1.getCandidateRelations() vectorizer = kindred.Vectorizer() matrix1 = vectorizer.fit_transform(corpus1) matrix2 = vectorizer.transform(corpus2) assert matrix1.shape == (8,61) assert matrix2.shape == (18,61) colnames = vectorizer.getFeatureNames() expectedNames = [u'selectedtokentypes_0_disease', u'selectedtokentypes_0_disease2', u'selectedtokentypes_0_drug', u'selectedtokentypes_1_disease', u'selectedtokentypes_1_disease2', u'selectedtokentypes_1_drug', u'ngrams_betweenentities_a', u'ngrams_betweenentities_be', u'ngrams_betweenentities_can', u'ngrams_betweenentities_clinical', u'ngrams_betweenentities_common', u'ngrams_betweenentities_effect', u'ngrams_betweenentities_failed', u'ngrams_betweenentities_for', u'ngrams_betweenentities_is', u'ngrams_betweenentities_known', u'ngrams_betweenentities_of', u'ngrams_betweenentities_side', u'ngrams_betweenentities_treated', u'ngrams_betweenentities_treatment', u'ngrams_betweenentities_trials', u'ngrams_betweenentities_with', u'bigrams_ _gnorcyvmer', u'bigrams_a_common', u'bigrams_a_known', u'bigrams_be_treated', u'bigrams_bmzvpvwbpw_failed', u'bigrams_can_be', u'bigrams_clinical_trials', u'bigrams_common_treatment', u'bigrams_effect_of', u'bigrams_failed_clinical', u'bigrams_for_kyekjnkrfo', u'bigrams_for_zgwivlcmly', u'bigrams_gnorcyvmer_is', u'bigrams_is_a', u'bigrams_known_side', u'bigrams_kyekjnkrfo_.', u'bigrams_of_ruswdgzajr', u'bigrams_ootopaoxbg_can', u'bigrams_pehhjnlvvewbjccovflf_is', u'bigrams_ruswdgzajr_.', u'bigrams_side_effect', u'bigrams_treated_with', u'bigrams_treatment_for', u'bigrams_trials_for', u'bigrams_vgypkemhjr_.', u'bigrams_with_vgypkemhjr', u'bigrams_zgwivlcmly_.', u'dependencypathelements_attr', u'dependencypathelements_dobj', u'dependencypathelements_nsubj', u'dependencypathelements_nsubjpass', u'dependencypathelements_pobj', u'dependencypathelements_prep', u'dependencypathnearselectedtoken_0_dobj', u'dependencypathnearselectedtoken_0_nsubj', u'dependencypathnearselectedtoken_0_nsubjpass', u'dependencypathnearselectedtoken_1_dobj', u'dependencypathnearselectedtoken_1_nsubj', u'dependencypathnearselectedtoken_1_nsubjpass'] assert colnames == expectedNames # As a quick check, we'll confirm that the column means are as expected expected1 = [2.0, 2.0, 4.0, 2.0, 2.0, 4.0, 1.4519522547520485, 1.0, 1.0, 1.0581526716744893, 1.037330992404908, 0.8817459917627732, 1.0581526716744893, 1.5854195824122916, 1.4519522547520485, 0.8817459917627732, 0.8817459917627732, 0.8817459917627732, 1.0, 1.037330992404908, 1.0581526716744893, 1.0, 0.6830902801437798, 0.7801302536256829, 0.6830902801437798, 0.8164965809277259, 0.8164965809277259, 0.8164965809277259, 0.8164965809277259, 0.7801302536256829, 0.6830902801437798, 0.8164965809277259, 0.8164965809277259, 0.7801302536256829, 0.6830902801437798, 1.1070563456981333, 0.6830902801437798, 0.8164965809277259, 0.6830902801437798, 0.8164965809277259, 0.7801302536256829, 0.6830902801437798, 0.6830902801437798, 0.8164965809277259, 0.7801302536256829, 0.8164965809277259, 0.8164965809277259, 0.8164965809277259, 0.7801302536256829, 4.0, 2.0, 4.0, 2.0, 8.0, 8.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0] colmeans1 = np.sum(matrix1,axis=0).tolist()[0] assert len(expected1) == len(colmeans1) for gotVal,expectedVal in zip(colmeans1,expected1): assert round(gotVal,8) == round(expectedVal,8) # Check rounded values (for floating point comparison issue) # As a quick check, we'll confirm that the column means are as expected expected2 = [5.0, 4.0, 9.0, 5.0, 4.0, 9.0, 0.7848330659854781, 1.0, 1.0, 2.1163053433489787, 1.037330992404908, 0.0, 2.1163053433489787, 2.386006098839105, 1.99154811934689, 0.0, 1.594941622753311, 0.0, 1.0, 1.037330992404908, 2.1163053433489787, 1.0, 0.0, 1.0581526716744893, 0.0, 0.8164965809277259, 1.0, 0.8164965809277259, 2.1547005383792515, 1.0581526716744893, 0.0, 2.1547005383792515, 0.0, 0.0, 0.0, 0.8005865164268136, 0.0, 2.0, 0.0, 0.8164965809277259, 2.0, 0.0, 0.0, 0.8164965809277259, 1.0581526716744893, 2.1547005383792515, 0.8164965809277259, 0.8164965809277259, 0.0, 4.0, 4.0, 16.0, 2.0, 10.0, 10.0, 0.0, 5.0, 1.0, 0.0, 5.0, 1.0] colmeans2 = np.sum(matrix2,axis=0).tolist()[0] assert len(expected2) == len(colmeans2) for gotVal,expectedVal in zip(colmeans2,expected2): assert round(gotVal,8) == round(expectedVal,8) # Check rounded values (for floating point comparison issue)
def test_candidatebuilder_simple():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer>. <drug id="3">Aspirin</drug> is the main cause of <disease id="4">boneitis</disease>. <relation type="treats" subj="1" obj="2" />'
corpus = kindred.Corpus(text, loadFromSimpleTag=True)
candidateBuilder = kindred.CandidateBuilder()
candidateBuilder.fit_transform(corpus)
assert corpus.relationTypes == [('treats', 'obj', 'subj')]
candidateRelations = corpus.getCandidateRelations(2)
candidateClasses = corpus.getCandidateClasses(2)
assert candidateClasses == [[0], [1], [0], [0]]
assert len(candidateRelations) == 4
sourceEntityIDsToEntityIDs = corpus.documents[
0].getSourceEntityIDsToEntityIDs()
assert candidateRelations[0].entityIDs == [
sourceEntityIDsToEntityIDs['1'], sourceEntityIDsToEntityIDs['2']
]
assert candidateRelations[1].entityIDs == [
sourceEntityIDsToEntityIDs['2'], sourceEntityIDsToEntityIDs['1']
]
assert candidateRelations[2].entityIDs == [
sourceEntityIDsToEntityIDs['3'], sourceEntityIDsToEntityIDs['4']
]
assert candidateRelations[3].entityIDs == [
sourceEntityIDsToEntityIDs['4'], sourceEntityIDsToEntityIDs['3']
]
def test_vectorizer_unigramsBetweenEntities_noTFIDF(): corpus1, _ = generateTestData(positiveCount=5,negativeCount=5) corpus2, _ = generateTestData(positiveCount=10,negativeCount=10) candidateBuilder = kindred.CandidateBuilder() candidateBuilder.fit_transform(corpus1) candidateBuilder.transform(corpus2) chosenFeatures = ["unigramsBetweenEntities"] vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures,tfidf=False) matrix1 = vectorizer.fit_transform(corpus1) matrix2 = vectorizer.transform(corpus2) assert matrix1.shape == (8,16) assert matrix2.shape == (18,16) colnames = vectorizer.getFeatureNames() expectedNames = [u'ngrams_betweenentities_a', u'ngrams_betweenentities_be', u'ngrams_betweenentities_can', u'ngrams_betweenentities_clinical', u'ngrams_betweenentities_common', u'ngrams_betweenentities_effect', u'ngrams_betweenentities_failed', u'ngrams_betweenentities_for', u'ngrams_betweenentities_is', u'ngrams_betweenentities_known', u'ngrams_betweenentities_of', u'ngrams_betweenentities_side', u'ngrams_betweenentities_treated', u'ngrams_betweenentities_treatment', u'ngrams_betweenentities_trials', u'ngrams_betweenentities_with'] assert colnames == expectedNames # As a quick check, we'll confirm that the column means are as expected expected1 = [4.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0] colmeans1 = np.sum(matrix1,axis=0).tolist()[0] assert len(expected1) == len(colmeans1) for gotVal,expectedVal in zip(colmeans1,expected1): assert round(gotVal,8) == round(expectedVal,8) # Check rounded values (for floating point comparison issue) expected2 = [4.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0] colmeans2 = np.sum(matrix1,axis=0).tolist()[0] assert len(expected2) == len(colmeans2) for gotVal,expectedVal in zip(colmeans2,expected2): assert round(gotVal,8) == round(expectedVal,8) # Check rounded values (for floating point comparison issue)
def test_vectorizer_entityTypes_noTFIDF():
corpus1, _ = generateTestData(positiveCount=5, negativeCount=5)
corpus2, _ = generateTestData(positiveCount=10, negativeCount=10)
parser = kindred.Parser()
parser.parse(corpus1)
parser.parse(corpus2)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations1 = candidateBuilder.build(corpus1)
candidateRelations2 = candidateBuilder.build(corpus2)
chosenFeatures = ["entityTypes"]
vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures, tfidf=False)
matrix1 = vectorizer.fit_transform(candidateRelations1)
matrix2 = vectorizer.transform(candidateRelations2)
assert matrix1.shape == (8, 6)
assert matrix2.shape == (18, 6)
colnames = vectorizer.getFeatureNames()
expectedNames = [
'selectedtokentypes_0_disease', 'selectedtokentypes_0_disease2',
'selectedtokentypes_0_drug', 'selectedtokentypes_1_disease',
'selectedtokentypes_1_disease2', 'selectedtokentypes_1_drug'
]
assert colnames == expectedNames
# As a quick check, we'll confirm that the column means are as expected
colmeans1 = np.sum(matrix1, axis=0)
assert colmeans1.tolist() == [[2, 2, 4, 2, 2, 4]]
colmeans2 = np.sum(matrix2, axis=0)
assert colmeans2.tolist() == [[5, 4, 9, 5, 4, 9]]
def test_simpleVectorizer_binary():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer>. <drug id="3">Aspirin</drug> is the main cause of <disease id="4">boneitis</disease> . <relation type="treats" subj="1" obj="2" />'
corpus = kindred.Corpus(text, loadFromSimpleTag=True)
parser = kindred.Parser()
parser.parse(corpus)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations = candidateBuilder.build(corpus)
# We'll just get the vectors for the entityTypes
vectorizer = kindred.Vectorizer(featureChoice=["entityTypes"])
vectors = vectorizer.fit_transform(candidateRelations)
assert vectors.shape == (4, 6)
expected = [(0, 2), (1, 0), (2, 2), (3, 1), (0, 3), (1, 5), (2, 4), (3, 5)]
rows, cols = vectors.nonzero()
rowsWithCols = list(zip(rows.tolist(), cols.tolist()))
assert sorted(expected) == sorted(rowsWithCols)
vectorsCSR = vectors.tocsr()
for r, c in expected:
assert vectorsCSR[r, c] == 1.0
def test_vectorizer_unigramsBetweenEntities():
corpus1, _ = generateTestData(positiveCount=5, negativeCount=5)
corpus2, _ = generateTestData(positiveCount=10, negativeCount=10)
parser = kindred.Parser()
parser.parse(corpus1)
parser.parse(corpus2)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations1 = candidateBuilder.build(corpus1)
candidateRelations2 = candidateBuilder.build(corpus2)
chosenFeatures = ["unigramsBetweenEntities"]
vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures, tfidf=True)
matrix1 = vectorizer.fit_transform(candidateRelations1)
matrix2 = vectorizer.transform(candidateRelations2)
assert matrix1.shape == (8, 16)
assert matrix2.shape == (18, 16)
colnames = vectorizer.getFeatureNames()
expectedNames = [
u'ngrams_betweenentities_a', u'ngrams_betweenentities_be',
u'ngrams_betweenentities_can', u'ngrams_betweenentities_clinical',
u'ngrams_betweenentities_common', u'ngrams_betweenentities_effect',
u'ngrams_betweenentities_failed', u'ngrams_betweenentities_for',
u'ngrams_betweenentities_is', u'ngrams_betweenentities_known',
u'ngrams_betweenentities_of', u'ngrams_betweenentities_side',
u'ngrams_betweenentities_treated', u'ngrams_betweenentities_treatment',
u'ngrams_betweenentities_trials', u'ngrams_betweenentities_with'
]
assert colnames == expectedNames
# As a quick check, we'll confirm that the column means are as expected
expected1 = [
1.4519522547520485, 1.0, 1.0, 1.0581526716744893, 1.037330992404908,
0.8817459917627732, 1.0581526716744893, 1.5854195824122916,
1.4519522547520485, 0.8817459917627732, 0.8817459917627732,
0.8817459917627732, 1.0, 1.037330992404908, 1.0581526716744893, 1.0
]
colmeans1 = np.sum(matrix1, axis=0).tolist()[0]
assert len(expected1) == len(colmeans1)
for gotVal, expectedVal in zip(colmeans1, expected1):
assert round(gotVal, 8) == round(
expectedVal,
8) # Check rounded values (for floating point comparison issue)
expected2 = [
1.4519522547520485, 1.0, 1.0, 1.0581526716744893, 1.037330992404908,
0.8817459917627732, 1.0581526716744893, 1.5854195824122916,
1.4519522547520485, 0.8817459917627732, 0.8817459917627732,
0.8817459917627732, 1.0, 1.037330992404908, 1.0581526716744893, 1.0
]
colmeans2 = np.sum(matrix1, axis=0).tolist()[0]
assert len(expected2) == len(colmeans2)
for gotVal, expectedVal in zip(colmeans2, expected2):
assert round(gotVal, 8) == round(
expectedVal,
8) # Check rounded values (for floating point comparison issue)
def test_candidatebuilder_simple():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer>. <drug id="3">Aspirin</drug> is the main cause of <disease id="4">boneitis</disease>. <relation type="treats" subj="1" obj="2" />'
corpus = kindred.Corpus(text,loadFromSimpleTag=True)
parser = kindred.Parser()
parser.parse(corpus)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations = candidateBuilder.build(corpus)
assert len(candidateRelations) == 4
for cr in candidateRelations:
assert isinstance(cr, kindred.CandidateRelation)
assert len(cr.entities) == 2
assert candidateRelations[0].entities[0].sourceEntityID == '1'
assert candidateRelations[0].entities[1].sourceEntityID == '2'
assert candidateRelations[1].entities[0].sourceEntityID == '2'
assert candidateRelations[1].entities[1].sourceEntityID == '1'
assert candidateRelations[2].entities[0].sourceEntityID == '3'
assert candidateRelations[2].entities[1].sourceEntityID == '4'
assert candidateRelations[3].entities[0].sourceEntityID == '4'
assert candidateRelations[3].entities[1].sourceEntityID == '3'
assert candidateRelations[0].knownTypesAndArgNames == []
assert candidateRelations[1].knownTypesAndArgNames == [('treats',['obj','subj'])]
assert candidateRelations[2].knownTypesAndArgNames == []
assert candidateRelations[3].knownTypesAndArgNames == []
def test_candidatebuilder_triple():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer> which targets <gene id="3">EGFR</gene>. <relation type="druginfo" drug="1" disease="2" gene="3" />'
corpus = kindred.Corpus(text, loadFromSimpleTag=True)
candidateBuilder = kindred.CandidateBuilder(entityCount=3)
candidateBuilder.fit_transform(corpus)
assert corpus.relationTypes == [('druginfo', 'disease', 'drug', 'gene')]
candidateRelations = corpus.getCandidateRelations(3)
candidateClasses = corpus.getCandidateClasses(3)
assert candidateClasses == [[0], [0], [1], [0], [0], [0]]
assert len(candidateRelations) == 6
sourceEntityIDsToEntityIDs = corpus.documents[
0].getSourceEntityIDsToEntityIDs()
assert candidateRelations[0].entityIDs == [
sourceEntityIDsToEntityIDs['1'], sourceEntityIDsToEntityIDs['2'],
sourceEntityIDsToEntityIDs['3']
]
assert candidateRelations[1].entityIDs == [
sourceEntityIDsToEntityIDs['1'], sourceEntityIDsToEntityIDs['3'],
sourceEntityIDsToEntityIDs['2']
]
assert candidateRelations[2].entityIDs == [
sourceEntityIDsToEntityIDs['2'], sourceEntityIDsToEntityIDs['1'],
sourceEntityIDsToEntityIDs['3']
]
assert candidateRelations[3].entityIDs == [
sourceEntityIDsToEntityIDs['2'], sourceEntityIDsToEntityIDs['3'],
sourceEntityIDsToEntityIDs['1']
]
assert candidateRelations[4].entityIDs == [
sourceEntityIDsToEntityIDs['3'], sourceEntityIDsToEntityIDs['1'],
sourceEntityIDsToEntityIDs['2']
]
assert candidateRelations[5].entityIDs == [
sourceEntityIDsToEntityIDs['3'], sourceEntityIDsToEntityIDs['2'],
sourceEntityIDsToEntityIDs['1']
]
def test_candidatebuilder_triple():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer> which targets <gene id="3">EGFR</gene>. <relation type="druginfo" drug="1" disease="2" gene="3" />'
corpus = kindred.Corpus(text,loadFromSimpleTag=True)
parser = kindred.Parser()
parser.parse(corpus)
candidateBuilder = kindred.CandidateBuilder(entityCount=3)
candidateRelations = candidateBuilder.build(corpus)
#assert corpus.relationTypes == [('druginfo', 'disease', 'drug', 'gene')]
for cr in candidateRelations:
assert isinstance(cr, kindred.CandidateRelation)
assert len(cr.entities) == 3
assert candidateRelations[0].entities[0].sourceEntityID == '1'
assert candidateRelations[0].entities[1].sourceEntityID == '2'
assert candidateRelations[0].entities[2].sourceEntityID == '3'
assert candidateRelations[1].entities[0].sourceEntityID == '1'
assert candidateRelations[1].entities[1].sourceEntityID == '3'
assert candidateRelations[1].entities[2].sourceEntityID == '2'
assert candidateRelations[2].entities[0].sourceEntityID == '2'
assert candidateRelations[2].entities[1].sourceEntityID == '1'
assert candidateRelations[2].entities[2].sourceEntityID == '3'
assert candidateRelations[3].entities[0].sourceEntityID == '2'
assert candidateRelations[3].entities[1].sourceEntityID == '3'
assert candidateRelations[3].entities[2].sourceEntityID == '1'
assert candidateRelations[4].entities[0].sourceEntityID == '3'
assert candidateRelations[4].entities[1].sourceEntityID == '1'
assert candidateRelations[4].entities[2].sourceEntityID == '2'
assert candidateRelations[5].entities[0].sourceEntityID == '3'
assert candidateRelations[5].entities[1].sourceEntityID == '2'
assert candidateRelations[5].entities[2].sourceEntityID == '1'
assert candidateRelations[0].knownTypesAndArgNames == []
assert candidateRelations[1].knownTypesAndArgNames == []
assert candidateRelations[2].knownTypesAndArgNames == [('druginfo',['disease', 'drug', 'gene'])]
assert candidateRelations[3].knownTypesAndArgNames == []
assert candidateRelations[4].knownTypesAndArgNames == []
assert candidateRelations[5].knownTypesAndArgNames == []
def test_simpleVectorizer_triple():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer> which targets <gene id="3">EGFR</gene>. <relation type="druginfo" drug="1" disease="2" gene="3" />'
corpus = kindred.Corpus(text,loadFromSimpleTag=True)
parser = kindred.Parser()
parser.parse(corpus)
candidateBuilder = kindred.CandidateBuilder(entityCount=3)
candidateRelations = candidateBuilder.build(corpus)
# We'll just get the vectors for the entityTypes
vectorizer = kindred.Vectorizer(entityCount=3,featureChoice=["entityTypes"])
vectors = vectorizer.fit_transform(candidateRelations)
vectorsCSR = vectors.tocsr()
rows,cols = vectors.nonzero()
expected = {(0, 1): 1.0, (0, 3): 1.0, (0, 8): 1.0, (1, 1): 1.0, (1, 5): 1.0, (1, 6): 1.0, (2, 0): 1.0, (2, 4): 1.0, (2, 8): 1.0, (3, 0): 1.0, (3, 5): 1.0, (3, 7): 1.0, (4, 2): 1.0, (4, 4): 1.0, (4, 6): 1.0, (5, 2): 1.0, (5, 3): 1.0, (5, 7): 1.0}
namedCols = { str((r,c)):vectorsCSR[r,c] for r,c in zip(rows.tolist(),cols.tolist()) }
check('test_simpleVectorizer_triple',namedCols)
def test_simpleVectorizer_binary():
text = '<drug id="1">Erlotinib</drug> is a common treatment for <cancer id="2">NSCLC</cancer>. <drug id="3">Aspirin</drug> is the main cause of <disease id="4">boneitis</disease> . <relation type="treats" subj="1" obj="2" />'
corpus = kindred.Corpus(text,loadFromSimpleTag=True)
parser = kindred.Parser()
parser.parse(corpus)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations = candidateBuilder.build(corpus)
# We'll just get the vectors for the entityTypes
vectorizer = kindred.Vectorizer(featureChoice=["entityTypes"])
vectors = vectorizer.fit_transform(candidateRelations)
vectorsCSR = vectors.tocsr()
rows,cols = vectors.nonzero()
expected = {(0, 2): 1.0, (0, 3): 1.0, (1, 0): 1.0, (1, 5): 1.0, (2, 2): 1.0, (2, 4): 1.0, (3, 1): 1.0, (3, 5): 1.0}
namedCols = { str((r,c)):vectorsCSR[r,c] for r,c in zip(rows.tolist(),cols.tolist()) }
check('test_simpleVectorizer_binary',namedCols)
def test_vectorizer_dependencyPathEdges_noTFIDF():
corpus1, _ = generateTestData(positiveCount=5, negativeCount=5)
corpus2, _ = generateTestData(positiveCount=10, negativeCount=10)
parser = kindred.Parser()
parser.parse(corpus1)
parser.parse(corpus2)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations1 = candidateBuilder.build(corpus1)
candidateRelations2 = candidateBuilder.build(corpus2)
chosenFeatures = ["dependencyPathEdges"]
vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures, tfidf=False)
matrix1 = vectorizer.fit_transform(candidateRelations1)
matrix2 = vectorizer.transform(candidateRelations2)
assert matrix1.shape == (8, 5)
assert matrix2.shape == (18, 5)
colnames = vectorizer.getFeatureNames()
expectedNames = [
'dependencypathelements_attr', 'dependencypathelements_nsubj',
'dependencypathelements_nsubjpass', 'dependencypathelements_pobj',
'dependencypathelements_prep'
]
assert colnames == expectedNames
# As a quick check, we'll confirm that the column means are as expected
colmeans1 = np.sum(matrix1, axis=0)
assert colmeans1.tolist() == [[4.0, 4.0, 2.0, 10.0, 8.0]]
# As a quick check, we'll confirm that the column means are as expected
colmeans2 = np.sum(matrix2, axis=0)
assert colmeans2.tolist() == [[4.0, 10.0, 2.0, 10.0, 10.0]]
def test_vectorizer_defaults_triple():
corpus1, _ = generateTestData(entityCount=3,
positiveCount=5,
negativeCount=5)
corpus2, _ = generateTestData(entityCount=3,
positiveCount=10,
negativeCount=10)
parser = kindred.Parser()
parser.parse(corpus1)
parser.parse(corpus2)
candidateBuilder = kindred.CandidateBuilder(entityCount=3)
candidateRelations1 = candidateBuilder.build(corpus1)
candidateRelations2 = candidateBuilder.build(corpus2)
vectorizer = kindred.Vectorizer(entityCount=3)
matrix1 = vectorizer.fit_transform(candidateRelations1)
matrix2 = vectorizer.transform(candidateRelations2)
assert matrix1.shape == (18, 101)
assert matrix2.shape == (60, 101)
colnames = vectorizer.getFeatureNames()
expectedNames = [
'selectedtokentypes_0_disease', 'selectedtokentypes_0_drug',
'selectedtokentypes_0_gene', 'selectedtokentypes_1_disease',
'selectedtokentypes_1_drug', 'selectedtokentypes_1_gene',
'selectedtokentypes_2_disease', 'selectedtokentypes_2_drug',
'selectedtokentypes_2_gene', 'ngrams_betweenentities_0_1_and',
'ngrams_betweenentities_0_1_be', 'ngrams_betweenentities_0_1_by',
'ngrams_betweenentities_0_1_can',
'ngrams_betweenentities_0_1_fvdxdietdx',
'ngrams_betweenentities_0_1_inhibition',
'ngrams_betweenentities_0_1_knetvjnjun',
'ngrams_betweenentities_0_1_targets',
'ngrams_betweenentities_0_1_treated',
'ngrams_betweenentities_0_1_treats',
'ngrams_betweenentities_0_1_using',
'ngrams_betweenentities_0_1_zkrkzlyfef',
'ngrams_betweenentities_0_2_and', 'ngrams_betweenentities_0_2_be',
'ngrams_betweenentities_0_2_by', 'ngrams_betweenentities_0_2_can',
'ngrams_betweenentities_0_2_fvdxdietdx',
'ngrams_betweenentities_0_2_inhibition',
'ngrams_betweenentities_0_2_knetvjnjun',
'ngrams_betweenentities_0_2_targets',
'ngrams_betweenentities_0_2_treated',
'ngrams_betweenentities_0_2_treats',
'ngrams_betweenentities_0_2_using',
'ngrams_betweenentities_0_2_zkrkzlyfef',
'ngrams_betweenentities_1_2_and', 'ngrams_betweenentities_1_2_be',
'ngrams_betweenentities_1_2_by', 'ngrams_betweenentities_1_2_can',
'ngrams_betweenentities_1_2_fvdxdietdx',
'ngrams_betweenentities_1_2_inhibition',
'ngrams_betweenentities_1_2_knetvjnjun',
'ngrams_betweenentities_1_2_targets',
'ngrams_betweenentities_1_2_treated',
'ngrams_betweenentities_1_2_treats',
'ngrams_betweenentities_1_2_using',
'ngrams_betweenentities_1_2_zkrkzlyfef', 'bigrams_and_targets',
'bigrams_be_treated', 'bigrams_by_fvdxdietdx', 'bigrams_by_zkrkzlyfef',
'bigrams_can_be', 'bigrams_elvptnpvyc_.',
'bigrams_fvdxdietdx_inhibition', 'bigrams_hxlfssirgk_.',
'bigrams_inhibition_using', 'bigrams_knetvjnjun_and',
'bigrams_kyekjnkrfo_can', 'bigrams_oxzbaapqct_treats',
'bigrams_targets_hxlfssirgk', 'bigrams_treated_by',
'bigrams_treats_knetvjnjun', 'bigrams_usckfljzxu_.',
'bigrams_using_elvptnpvyc', 'bigrams_using_usckfljzxu',
'bigrams_zgwivlcmly_can', 'bigrams_zkrkzlyfef_inhibition',
'dependencypathelements_0_1_acl', 'dependencypathelements_0_1_advmod',
'dependencypathelements_0_1_agent',
'dependencypathelements_0_1_compound',
'dependencypathelements_0_1_conj', 'dependencypathelements_0_1_dobj',
'dependencypathelements_0_1_nsubjpass',
'dependencypathelements_0_1_pobj', 'dependencypathelements_0_2_acl',
'dependencypathelements_0_2_advmod',
'dependencypathelements_0_2_agent',
'dependencypathelements_0_2_compound',
'dependencypathelements_0_2_conj', 'dependencypathelements_0_2_dobj',
'dependencypathelements_0_2_nsubjpass',
'dependencypathelements_0_2_pobj', 'dependencypathelements_1_2_acl',
'dependencypathelements_1_2_advmod',
'dependencypathelements_1_2_agent',
'dependencypathelements_1_2_compound',
'dependencypathelements_1_2_conj', 'dependencypathelements_1_2_dobj',
'dependencypathelements_1_2_nsubjpass',
'dependencypathelements_1_2_pobj',
'dependencypathnearselectedtoken_0_compound',
'dependencypathnearselectedtoken_0_conj',
'dependencypathnearselectedtoken_0_dobj',
'dependencypathnearselectedtoken_0_nsubjpass',
'dependencypathnearselectedtoken_1_compound',
'dependencypathnearselectedtoken_1_conj',
'dependencypathnearselectedtoken_1_dobj',
'dependencypathnearselectedtoken_1_nsubjpass',
'dependencypathnearselectedtoken_2_compound',
'dependencypathnearselectedtoken_2_conj',
'dependencypathnearselectedtoken_2_dobj',
'dependencypathnearselectedtoken_2_nsubjpass'
]
assert colnames == expectedNames
# As a quick check, we'll confirm that the column means are as expected
expected1 = [
6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 1.4620174403662662,
2.089911361057128, 2.089911361057128, 2.089911361057128,
0.8510011029330441, 2.089911361057128, 0.8909306965737043,
1.4620174403662662, 2.089911361057128, 1.4620174403662662,
2.089911361057128, 0.8510011029330441, 1.4620174403662662,
2.089911361057128, 2.089911361057128, 2.089911361057128,
0.8510011029330442, 2.089911361057128, 0.8909306965737043,
1.4620174403662662, 2.089911361057128, 1.4620174403662662,
2.089911361057128, 0.8510011029330441, 1.4620174403662662,
2.089911361057128, 2.089911361057128, 2.089911361057128,
0.8510011029330442, 2.089911361057128, 0.8909306965737043,
1.4620174403662662, 2.089911361057128, 1.4620174403662662,
2.089911361057128, 0.8510011029330441, 2.4494897427831783,
3.151972689633972, 2.283202494909358, 2.283202494909358,
3.151972689633972, 2.283202494909358, 2.283202494909358,
2.4494897427831783, 3.151972689633972, 2.4494897427831783,
2.283202494909358, 2.4494897427831783, 2.4494897427831783,
3.151972689633972, 2.4494897427831783, 2.283202494909358,
2.283202494909358, 2.283202494909358, 2.283202494909358,
2.283202494909358, 8.0, 4.0, 8.0, 8.0, 4.0, 12.0, 8.0, 8.0, 8.0, 4.0,
8.0, 8.0, 4.0, 12.0, 8.0, 8.0, 8.0, 4.0, 8.0, 8.0, 4.0, 12.0, 8.0, 8.0,
4.0, 2.0, 2.0, 4.0, 4.0, 2.0, 2.0, 4.0, 4.0, 2.0, 2.0, 4.0
]
colmeans1 = np.sum(matrix1, axis=0).tolist()[0]
assert len(expected1) == len(colmeans1)
for gotVal, expectedVal in zip(colmeans1, expected1):
assert round(gotVal, 8) == round(
expectedVal,
8) # Check rounded values (for floating point comparison issue)
# As a quick check, we'll confirm that the column means are as expected
expected2 = [
8.0, 20.0, 20.0, 8.0, 20.0, 20.0, 8.0, 20.0, 20.0, 5.237574707711817,
0.0, 0.0, 0.0, 3.9169357592886755, 0.0, 0.8909306965737043,
18.69384731218667, 0.0, 3.095010602221718, 0.0, 0.0, 5.237574707711817,
0.0, 0.0, 0.0, 3.9169357592886755, 0.0, 0.8909306965737043,
18.69384731218667, 0.0, 3.095010602221718, 0.0, 0.0, 5.237574707711817,
0.0, 0.0, 0.0, 3.9169357592886755, 0.0, 0.8909306965737043,
18.69384731218667, 0.0, 3.095010602221718, 0.0, 0.0, 8.449489742783179,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.4494897427831783, 0.0,
2.4494897427831783, 0.0, 2.4494897427831783, 2.4494897427831783, 0.0,
2.4494897427831783, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 12.0, 20.0,
28.0, 8.0, 28.0, 0.0, 4.0, 0.0, 12.0, 20.0, 28.0, 8.0, 28.0, 0.0, 4.0,
0.0, 12.0, 20.0, 28.0, 8.0, 28.0, 4.0, 10.0, 2.0, 4.0, 4.0, 10.0, 2.0,
4.0, 4.0, 10.0, 2.0, 4.0
]
colmeans2 = np.sum(matrix2, axis=0).tolist()[0]
assert len(expected2) == len(colmeans2)
for gotVal, expectedVal in zip(colmeans2, expected2):
assert round(gotVal, 8) == round(
expectedVal,
8) # Check rounded values (for floating point comparison issue)
doc = kindred.Document(sentence["sentence"],metadata=metadata)
corpus.addDocument(doc)
#if i > 100:
# break
print("%s : corpus loaded" % now())
parser = kindred.Parser()
parser.parse(corpus)
print("%s : parsed" % now())
ner = kindred.EntityRecognizer(lookup=termLookup,detectVariants=False,detectFusionGenes=False,detectMicroRNA=False,acronymDetectionForAmbiguity=True,mergeTerms=True,removePathways=True)
ner.annotate(corpus)
print("%s : ner" % now())
candidateBuilder = kindred.CandidateBuilder(entityCount=2,acceptedEntityTypes=[('keyword','geneOrProtein')])
unfilteredCandidateRelations = candidateBuilder.build(corpus)
print("%s : candidateBuilder (%d)" % (now(),len(unfilteredCandidateRelations)))
entityIDToDoc = {}
for doc in corpus.documents:
for entity in doc.entities:
entityIDToDoc[entity.entityID] = doc
candidateRelations,metadata = [],[]
for cr in unfilteredCandidateRelations:
entityIDToEntity = { entity.entityID:entity for entity,tokenIndices in cr.sentence.entityAnnotations }
entityIDToTokenLocs = { entity.entityID:tokenIndices for entity,tokenIndices in cr.sentence.entityAnnotations }
keyword,geneOrProtein = [ entityIDToEntity[entityID] for entityID in cr.entityIDs ]
keywordLoc,geneOrProteinLoc = [ entityIDToTokenLocs[entityID] for entityID in cr.entityIDs ]
sentenceCorpus = corpus.splitIntoSentences()
print("Looking for measurement words, e.g. voltage")
wordlist = {
('voltage', ): {('measurement', 'voltage')},
('current', ): {('measurement', 'current')}
}
entityRecognizer = kindred.EntityRecognizer(wordlist)
entityRecognizer.annotate(sentenceCorpus)
print("Looking for numeric values")
quantityRecognizer = QuantityRecognizer()
quantityRecognizer.annotate(sentenceCorpus)
print("Find every pair of a measurement word and a value")
candidateBuilder = kindred.CandidateBuilder(
acceptedEntityTypes=[('measurement', 'quantity')])
candidateRelations = candidateBuilder.build(sentenceCorpus)
print("Let's annotate a few")
withRelations, noRelations = kindred.manuallyAnnotate(
sentenceCorpus, candidateRelations)
outDir = 'numericalAnnotations'
if not os.path.isdir(outDir):
os.makedirs(outDir)
print("Saving results to directory...")
kindred.save(withRelations, 'standoff', outDir)
assert len(
wordlistDict
) == 2, "This annotation tool currently only handles two entity relations of different types"
wordlistLookup = kindred.EntityRecognizer.loadWordlists(wordlistDict,
idColumn=0,
termsColumn=0)
print("Annotating entities in corpus with wordlists")
entityRecognizer = kindred.EntityRecognizer(wordlistLookup)
entityRecognizer.annotate(sentenceCorpus)
print("Finding all candidate relations")
acceptedEntityTypes = wordlistDict
candidateBuilder = kindred.CandidateBuilder(
entityCount=len(wordlistDict),
acceptedEntityTypes=[tuple(sorted(wordlistDict.keys()))])
candidateRelations = candidateBuilder.build(sentenceCorpus)
print(
"Time to through some of the candidate relations and annotate some...")
annotatedCorpus, unannotatedCorpus = kindred.manuallyAnnotate(
sentenceCorpus, candidateRelations)
print(
"\nSaving annotated corpus of %d sentences (with relations that you have just annotated)"
% len(annotatedCorpus.documents))
kindred.save(annotatedCorpus, 'standoff', annotatedDir)
print(
"Saving unannotated corpus of %d sentences (which you did not review)"
def test_vectorizer_bigrams():
corpus1, _ = generateTestData(positiveCount=5, negativeCount=5)
corpus2, _ = generateTestData(positiveCount=10, negativeCount=10)
parser = kindred.Parser()
parser.parse(corpus1)
parser.parse(corpus2)
candidateBuilder = kindred.CandidateBuilder()
candidateRelations1 = candidateBuilder.build(corpus1)
candidateRelations2 = candidateBuilder.build(corpus2)
chosenFeatures = ["bigrams"]
vectorizer = kindred.Vectorizer(featureChoice=chosenFeatures, tfidf=True)
matrix1 = vectorizer.fit_transform(candidateRelations1)
matrix2 = vectorizer.transform(candidateRelations2)
assert matrix1.shape == (8, 27)
assert matrix2.shape == (18, 27)
colnames = vectorizer.getFeatureNames()
expectedNames = [
u'bigrams_ _gnorcyvmer', u'bigrams_a_common', u'bigrams_a_known',
u'bigrams_be_treated', u'bigrams_bmzvpvwbpw_failed', u'bigrams_can_be',
u'bigrams_clinical_trials', u'bigrams_common_treatment',
u'bigrams_effect_of', u'bigrams_failed_clinical',
u'bigrams_for_kyekjnkrfo', u'bigrams_for_zgwivlcmly',
u'bigrams_gnorcyvmer_is', u'bigrams_is_a', u'bigrams_known_side',
u'bigrams_kyekjnkrfo_.', u'bigrams_of_ruswdgzajr',
u'bigrams_ootopaoxbg_can', u'bigrams_pehhjnlvvewbjccovflf_is',
u'bigrams_ruswdgzajr_.', u'bigrams_side_effect',
u'bigrams_treated_with', u'bigrams_treatment_for',
u'bigrams_trials_for', u'bigrams_vgypkemhjr_.',
u'bigrams_with_vgypkemhjr', u'bigrams_zgwivlcmly_.'
]
assert colnames == expectedNames
# As a quick check, we'll confirm that the column means are as expected
expected1 = [
0.6830902801437798, 0.7801302536256829, 0.6830902801437798,
0.8164965809277259, 0.8164965809277259, 0.8164965809277259,
0.8164965809277259, 0.7801302536256829, 0.6830902801437798,
0.8164965809277259, 0.8164965809277259, 0.7801302536256829,
0.6830902801437798, 1.1070563456981333, 0.6830902801437798,
0.8164965809277259, 0.6830902801437798, 0.8164965809277259,
0.7801302536256829, 0.6830902801437798, 0.6830902801437798,
0.8164965809277259, 0.7801302536256829, 0.8164965809277259,
0.8164965809277259, 0.8164965809277259, 0.7801302536256829
]
colmeans1 = np.sum(matrix1, axis=0).tolist()[0]
assert len(expected1) == len(colmeans1)
for gotVal, expectedVal in zip(colmeans1, expected1):
assert round(gotVal, 8) == round(
expectedVal,
8) # Check rounded values (for floating point comparison issue)
# As a quick check, we'll confirm that the column means are as expected
expected2 = [
0.0, 1.0581526716744893, 0.0, 0.8164965809277259, 1.0,
0.8164965809277259, 2.1547005383792515, 1.0581526716744893, 0.0,
2.1547005383792515, 0.0, 0.0, 0.0, 0.8005865164268136, 0.0, 2.0, 0.0,
0.8164965809277259, 2.0, 0.0, 0.0, 0.8164965809277259,
1.0581526716744893, 2.1547005383792515, 0.8164965809277259,
0.8164965809277259, 0.0
]
colmeans2 = np.sum(matrix2, axis=0).tolist()[0]
assert len(expected2) == len(colmeans2)
for gotVal, expectedVal in zip(colmeans2, expected2):
assert round(gotVal, 8) == round(
expectedVal,
8) # Check rounded values (for floating point comparison issue)
