Source code for datasets.housing

# Copyright 2011 Guillaume Roy-Fontaine and David Brouillard. All rights reserved.
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"""
Module ``datasets.housing`` gives access to the Housing dataset.

The Housing dataset is obtained here: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html#housing.

"""

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

[docs]def load(dir_path,load_to_memory=False): """ Loads the Housing 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'`` * ``'length'`` """ input_size = 13 #targets = set(range(2)) #targets = set([0,1]) #target_mapping = {'-1':0,'+1':1} dir_path = os.path.expanduser(dir_path) def load_line(line): return mlio.libsvm_load_line(line, float, float, sparse=False, input_size=input_size) #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, 'housing_' + 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 = [404,51,51] if load_to_memory: train,valid,test = [mlio.MemoryDataset(d,[(input_size,),(1,)],[np.float64,np.float64],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] train_meta,valid_meta,test_meta = [{'input_size':input_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 # Get the main file, will be used to create train, valid and test file. urllib.urlretrieve('http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/housing', os.path.join(dir_path, 'housing_temp.libsvm')) # Create files train_file = open(os.path.join(dir_path, 'housing_train.libsvm'), "w") valid_file = open(os.path.join(dir_path, 'housing_valid.libsvm'), "w") test_file = open(os.path.join(dir_path, 'housing_test.libsvm'), "w") # Split 80%, 10%, 10% (train,valid,test) fp = open(os.path.join(dir_path, 'housing_temp.libsvm')) # Add the lines of the file into a list lineList = [] for line in fp: lineList.append(line) # Shuffle import random random.seed(25) random.shuffle(lineList) # Write lines into each file for i, line in enumerate(lineList): if i < 404: train_file.write(line) elif i < 455: valid_file.write(line) else: test_file.write(line) fp.close() train_file.close() valid_file.close() test_file.close() # Delete Temp file os.remove(os.path.join(dir_path,'housing_temp.libsvm')) print 'Done'