Source code for datasets.yeast

# Copyright 2011 Hugo Larochelle. All rights reserved.
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"""
Module ``datasets.yeast`` gives access to the Yeast dataset.

| **Reference:** 
| A kernel method for multi-labelled classification
| Elisseeff and Weston
| http://books.nips.cc/papers/files/nips14/AA45.pdf

"""

import mlpython.misc.io as mlio
import numpy as np
import os

[docs]def load(dir_path,load_to_memory=False): """ Loads the Yeast dataset. The data is given by a dictionary mapping from strings ``'train'``, ``'valid'`` and ``'test'`` to the associated pair of data and metadata. **Defined metadata:** * ``'input_size'`` * ``'target_size'`` * ``'length'`` """ input_size=103 target_size=14 dir_path = os.path.expanduser(dir_path) def convert_target(target_str): targets = np.zeros((target_size)) for l in target_str.split(','): id = int(l) targets[id] = 1 return targets def load_line(line): return mlio.libsvm_load_line(line,convert_target=convert_target,sparse=False,input_size=input_size) train_file,valid_file,test_file = [os.path.join(dir_path, 'yeast_' + ds + '.libsvm') for ds in ['train','valid','test']] # Get data train,valid,test = [mlio.load_from_file(f,load_line) for f in [train_file,valid_file,test_file]] lengths = [1250,250,917] if load_to_memory: train,valid,test = [mlio.MemoryDataset(d,[(input_size,),(target_size,)],[np.float64,bool],l) for d,l in zip([train,valid,test],lengths)] # Get metadata train_meta,valid_meta,test_meta = [{'input_size':input_size,'target_size':target_size, 'length':l} for l in lengths] return {'train':(train,train_meta),'valid':(valid,valid_meta),'test':(test,test_meta)}
[docs]def obtain(dir_path): """ Downloads the dataset to ``dir_path``. """ dir_path = os.path.expanduser(dir_path) print 'Downloading the dataset' import urllib urllib.urlretrieve('http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel/yeast_train.svm.bz2',os.path.join(dir_path,'yeast_train.svm.bz2')) urllib.urlretrieve('http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel/yeast_test.svm.bz2',os.path.join(dir_path,'yeast_test.svm.bz2')) import bz2 train_valid_bz2_file = bz2.BZ2File(os.path.join(dir_path,'yeast_train.svm.bz2')) test_bz2_file = bz2.BZ2File(os.path.join(dir_path,'yeast_test.svm.bz2')) print 'Splitting training set into smaller training/validation sets' train_file,valid_file,test_file = [open(os.path.join(dir_path, 'yeast_' + ds + '.libsvm'),'w') for ds in ['train','valid','test']] # Putting train/valid data in memory train_valid_data = [ line for line in train_valid_bz2_file ] # Shuffle data import random random.seed(12345) perm = range(len(train_valid_data)) random.shuffle(perm) line_id = 0 train_valid_split = 1250 for i in perm: s = train_valid_data[i] if line_id < train_valid_split: train_file.write(s) else: valid_file.write(s) line_id += 1 train_file.close() valid_file.close() train_valid_bz2_file.close() for line in test_bz2_file: test_file.write(line) test_file.close() test_bz2_file.close() print 'Done '