Source code for datasets.heart

# Copyright 2011 Guillaume Roy-Fontaine and David Brouillard. All rights reserved.
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"""
Module ``datasets.heart`` gives access to the Heart (SPECT) dataset.

The Heart dataset is obtained here: http://archive.ics.uci.edu/ml/machine-learning-databases/spect.

"""

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

[docs]def load(dir_path,load_to_memory=False): """ Loads the Heart 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'`` * ``'targets'`` * ``'length'`` """ input_size=22 targets = set(range(2)) dir_path = os.path.expanduser(dir_path) def load_line(line): tokens = line.split() return (np.array([float(i) for i in tokens[:-1]]),int(tokens[-1])) #return mlio.libsvm_load_line(line,float,int,sparse,input_size) train_file,valid_file,test_file = [os.path.join(dir_path, 'heart_' + ds + '.txt') 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 = [50,30,187] if load_to_memory: train,valid,test = [mlio.MemoryDataset(d,[(input_size,),(1,)],[np.float64,int],l) for d,l in zip([train,valid,test],lengths)] # Get metadata train_meta,valid_meta,test_meta = [{'input_size':input_size, 'length':l,'targets':targets} 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://archive.ics.uci.edu/ml/machine-learning-databases/spect/SPECT.train',os.path.join(dir_path,'heart.train')) urllib.urlretrieve('http://archive.ics.uci.edu/ml/machine-learning-databases/spect/SPECT.test',os.path.join(dir_path,'heart.test')) print 'Splitting dataset into training/validation/test sets' file_train_and_valid = open(os.path.join(dir_path,'heart.train')) file_test = open(os.path.join(dir_path,'heart.test')) train_file,valid_file,test_file = [open(os.path.join(dir_path, 'heart_' + ds + '.txt'),'w') for ds in ['train','valid','test']] # Putting all data in memory train_and_valid_data = [] for line in file_train_and_valid: tokens = line.strip('\n').strip(',').split(',') s = '' for t in range(1,len(tokens)): s = s + tokens[t] + ' ' target = tokens[0] s = s + str(target) + '\n' train_and_valid_data += [s] for line in file_test: tokens = line.strip('\n').strip(',').split(',') s = '' for t in range(1,len(tokens)): s = s + tokens[t] + ' ' target = tokens[0] s = s + str(target) + '\n' test_file.write(s) test_file.close() # Shuffle data import random random.seed(25) perm = range(len(train_and_valid_data)) random.shuffle(perm) line_id = 0 train_valid_split = 50 for i in perm: s = train_and_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() print 'Done '