code stringlengths 3 6.57k |
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logSumExpTensor(a) |
len(a.size() |
denom.view(-1, 1) |
expand(-1, a.size() |
len(a.size() |
denom.view(a.size() |
expand(-1, a.size() |
a.size() |
return (a-denom) |
computeF1(hyps, golds, prefix, labels_to_ix=None, baseline=False, write_results=False) |
unfreeze_dict(h) |
unfreeze_dict(t) |
enumerate(hyps, start=0) |
word_tags.items() |
sum(f1_precision_scores.values() |
sum(f1_precision_total.values() |
f1_precision_scores.keys() |
enumerate(golds, start=0) |
word_tags.items() |
sum(f1_recall_scores.values() |
sum(f1_recall_total.values() |
f1_recall_scores.keys() |
sum(f1_recall_total.values() |
print("Writing F1 scores...") |
open(prefix + '_results_f1.txt', 'ab') |
file.write(pickle.dumps(f1_scores) |
file.write("\nMacro-averaged F1 Score: " + str(f1_average) |
file.write("\nMicro-averaged F1 Score: " + str(f1_micro_score) |
getCorrectCount(golds, hyps) |
enumerate(golds, start=0) |
word_tags.items() |
LinkageError(Exception) |
clusterSubfamilies(similarities, n_clusters=0, linkage='all', method='tsne', cutoff=0.0, **kwargs) |
one(s) |
ImportError('need sklearn module') |
isinstance(similarities, np.ndarray) |
TypeError('similarities should be a numpy ndarray') |
ValueError('similarities must be a square matrix') |
isinstance(n_clusters, int) |
TypeError('clusters must be an instance of int') |
ValueError('clusters must be a positive integer') |
ValueError('clusters can\'t be longer than similarities matrix') |
range(n_clusters,n_clusters+1) |
range(2,10,1) |
isListLike(linkage) |
val.lower() |
ValueError('linkage must be one or more of: \'ward\', \'average\', \'complete\', or \'single\'') |
len(linkage) |
ValueError('linkage must be one or more of: \'ward\', \'average\', \'complete\', or \'single\'') |
x.lower() |
isinstance(linkage, str) |
TypeError('linkage must be an instance of str or list-like of strs') |
ValueError('linkage must one or more of: \'ward\', \'average\', \'complete\', or \'single\'') |
isinstance(method, str) |
TypeError('method must be an instance of str') |
ValueError('method must be either \'tsne\' or \'spectral\'') |
isinstance(cutoff, float) |
TypeError('cutoff must be an instance of float') |
TSNE(n_components=2) |
embedding.fit_transform(similarities) |
np.where(similarities > cutoff, 0, -1) |
SpectralEmbedding(n_components=2) |
embedding.fit_transform(kirchhoff) |
AgglomerativeClustering(linkage=link, n_clusters=x) |
clustering.fit(transform) |
silhouette_score(transform, clustering.labels_) |
getCoords(data) |
data._getCoords() |
hasattr(data, '_getCoords') |
data.getCoords() |
checkCoords(data) |
getLinkage(names, tree) |
tree.get_terminals() |
len(tree_terminals) |
len(names) |
ValueError('inconsistent number of terminals in tree and names') |
len(names) |
index(names, clade.name) |
len(terminals) |
reversed(tree.get_nonterminals() |
len(nonterminals) |
LinkageError('wrong number of terminal clades') |
np.zeros((n-1, 4) |
_indexOfClade(clade) |
clade.is_terminal() |
index(terminals, clade) |
index(nonterminals, clade) |
_height_of(clade) |
clade.is_terminal() |
max(_height_of(c) |
_dfs(clade) |
clade.is_terminal() |
_indexOfClade(clade) |
_indexOfClade(clade_a) |
_indexOfClade(clade_b) |
min(a, b) |
max(a, b) |
_height_of(clade) |
clade.count_terminals() |
_dfs(clade_a) |
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