Source code for datasets.abalone

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

The Abalone dataset is obtained here: http://www.csie.ntu.edu.tw/%7Ecjlin/libsvmtools/datasets/regression.html#abalone.

"""

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

[docs]def load(dir_path,load_to_memory=False): """ Loads the Abalone 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 = 8 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) train_file,valid_file,test_file = [os.path.join(dir_path, 'abalone_' + 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 = [3341, 418, 418] 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} 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/%7Ecjlin/libsvmtools/datasets/regression/abalone', os.path.join(dir_path, 'abalone_temp.libsvm')) # Create files train_file = open(os.path.join(dir_path, 'abalone_train.libsvm'), "w") valid_file = open(os.path.join(dir_path, 'abalone_valid.libsvm'), "w") test_file = open(os.path.join(dir_path, 'abalone_test.libsvm'), "w") # Split 80%, 10%, 10% (train,valid,test) fp = open(os.path.join(dir_path, 'abalone_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 < 3341: train_file.write(line) elif i < 3759: 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,'abalone_temp.libsvm')) print 'Done'