import numpy as np
from scipy.sparse import dok_matrix
from tqdm import tqdm
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def from_text(path, encoding='utf-8', verbose=1, class2int=True):
"""
Reads a labelled colletion of documents.
File fomart <0 or 1>\t<document>\n
:param path: path to the labelled collection
:param encoding: the text encoding used to open the file
:param verbose: if >0 (default) shows some progress information in standard output
:return: a list of sentences, and a list of labels
"""
all_sentences, all_labels = [], []
if verbose>0:
file = tqdm(open(path, 'rt', encoding=encoding).readlines(), f'loading {path}')
else:
file = open(path, 'rt', encoding=encoding).readlines()
for line in file:
line = line.strip()
if line:
try:
label, sentence = line.split('\t')
sentence = sentence.strip()
if class2int:
label = int(label)
if sentence:
all_sentences.append(sentence)
all_labels.append(label)
except ValueError:
print(f'format error in {line}')
return all_sentences, all_labels
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def from_sparse(path):
"""
Reads a labelled collection of real-valued instances expressed in sparse format
File format <-1 or 0 or 1>[\s col(int):val(float)]\n
:param path: path to the labelled collection
:return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels
"""
def split_col_val(col_val):
col, val = col_val.split(':')
col, val = int(col) - 1, float(val)
return col, val
all_documents, all_labels = [], []
max_col = 0
for line in tqdm(open(path, 'rt').readlines(), f'loading {path}'):
parts = line.strip().split()
if parts:
all_labels.append(int(parts[0]))
cols, vals = zip(*[split_col_val(col_val) for col_val in parts[1:]])
cols, vals = np.asarray(cols), np.asarray(vals)
max_col = max(max_col, cols.max())
all_documents.append((cols, vals))
n_docs = len(all_labels)
X = dok_matrix((n_docs, max_col + 1), dtype=float)
for i, (cols, vals) in tqdm(enumerate(all_documents), total=len(all_documents),
desc=f'\-- filling matrix of shape {X.shape}'):
X[i, cols] = vals
X = X.tocsr()
y = np.asarray(all_labels) + 1
return X, y
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def from_csv(path, encoding='utf-8'):
"""
Reads a csv file in which columns are separated by ','.
File format <label>,<feat1>,<feat2>,...,<featn>\n
:param path: path to the csv file
:param encoding: the text encoding used to open the file
:return: a np.ndarray for the labels and a ndarray (float) for the covariates
"""
X, y = [], []
for instance in tqdm(open(path, 'rt', encoding=encoding).readlines(), desc=f'reading {path}'):
yi, *xi = instance.strip().split(',')
X.append(list(map(float,xi)))
y.append(yi)
X = np.asarray(X)
y = np.asarray(y)
return X, y
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def reindex_labels(y):
"""
Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.
E.g.:
>>> reindex_labels(['B', 'B', 'A', 'C'])
>>> (array([1, 1, 0, 2]), array(['A', 'B', 'C'], dtype='<U1'))
:param y: the list or array of original labels
:return: a ndarray (int) of class indexes, and a ndarray of classnames corresponding to the indexes.
"""
y = np.asarray(y)
classnames = np.asarray(sorted(np.unique(y)))
label2index = {label: index for index, label in enumerate(classnames)}
indexed = np.empty(y.shape, dtype=int)
for label in classnames:
indexed[y==label] = label2index[label]
return indexed, classnames
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def binarize(y, pos_class):
"""
Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:
>>> binarize([1, 2, 3, 1, 1, 0], pos_class=2)
>>> array([0, 1, 0, 0, 0, 0])
:param y: array-like of labels
:param pos_class: integer, the positive class
:return: a binary np.ndarray, in which values 1 corresponds to positions in whcih `y` had `pos_class` labels, and
0 otherwise
"""
y = np.asarray(y)
ybin = np.zeros(y.shape, dtype=int)
ybin[y == pos_class] = 1
return ybin