forked from moreo/QuaPy
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import numpy as np
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from scipy.sparse import dok_matrix
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from tqdm import tqdm
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def from_text(path):
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"""
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Reas a labelled colletion of documents.
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File fomart <0 or 1>\t<document>\n
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:param path: path to the labelled collection
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:return: a list of sentences, and a list of labels
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"""
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all_sentences, all_labels = [], []
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for line in tqdm(open(path, 'rt').readlines(), f'loading {path}'):
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line = line.strip()
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if line:
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label, sentence = line.split('\t')
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sentence = sentence.strip()
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label = int(label)
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if sentence:
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all_sentences.append(sentence)
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all_labels.append(label)
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return all_sentences, all_labels
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def from_sparse(path):
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"""
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Reas a labelled colletion of real-valued instances expressed in sparse format
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File fomart <-1 or 0 or 1>[\s col(int):val(float)]\n
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:param path: path to the labelled collection
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:return: a csr_matrix containing the instances (rows), and a ndarray containing the labels
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"""
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def split_col_val(col_val):
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col, val = col_val.split(':')
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col, val = int(col) - 1, float(val)
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return col, val
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all_documents, all_labels = [], []
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max_col = 0
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for line in tqdm(open(path, 'rt').readlines(), f'loading {path}'):
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parts = line.strip().split()
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if parts:
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all_labels.append(int(parts[0]))
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cols, vals = zip(*[split_col_val(col_val) for col_val in parts[1:]])
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cols, vals = np.asarray(cols), np.asarray(vals)
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max_col = max(max_col, cols.max())
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all_documents.append((cols, vals))
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n_docs = len(all_labels)
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X = dok_matrix((n_docs, max_col + 1), dtype=float)
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for i, (cols, vals) in tqdm(enumerate(all_documents), total=len(all_documents),
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desc=f'\-- filling matrix of shape {X.shape}'):
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X[i, cols] = vals
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X = X.tocsr()
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y = np.asarray(all_labels) + 1
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return X, y
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