1
0
Fork 0
QuaPy/quapy/data/reader.py

57 lines
1.9 KiB
Python

import numpy as np
from scipy.sparse import dok_matrix
from tqdm import tqdm
def from_text(path):
"""
Reas a labelled colletion of documents.
File fomart <0 or 1>\t<document>\n
:param path: path to the labelled collection
:return: a list of sentences, and a list of labels
"""
all_sentences, all_labels = [], []
for line in tqdm(open(path, 'rt').readlines(), f'loading {path}'):
line = line.strip()
if line:
label, sentence = line.split('\t')
sentence = sentence.strip()
label = int(label)
if sentence:
all_sentences.append(sentence)
all_labels.append(label)
return all_sentences, all_labels
def from_sparse(path):
"""
Reas a labelled colletion of real-valued instances expressed in sparse format
File fomart <-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