123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
# -*- coding: utf-8 -*-
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"""Data providers."""
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import cPickle
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import gzip
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import numpy as np
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import os
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from mlp import DEFAULT_SEED
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class DataProvider(object):
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"""Interface for generic data-independent readers."""
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def __init__(self, inputs, targets, batch_size, max_num_batches=-1,
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shuffle_order=True, rng=None):
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"""
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"""
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self.inputs = inputs
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self.targets = targets
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self.batch_size = batch_size
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assert max_num_batches != 0 and not max_num_batches < -1, (
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'max_num_batches should be -1 or > 0')
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self.max_num_batches = max_num_batches
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possible_num_batches = self.inputs.shape[0] // batch_size
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if self.max_num_batches == -1:
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self.num_batches = possible_num_batches
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else:
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self.num_batches = min(self.max_num_batches, possible_num_batches)
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self.shuffle_order = shuffle_order
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if rng is None:
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rng = np.random.RandomState(DEFAULT_SEED)
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self.rng = rng
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self.reset()
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def __iter__(self):
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return self
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def reset(self):
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"""Resets the provider to the initial state to use in a new epoch."""
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self._curr_batch = 0
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if self.shuffle_order:
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self.shuffle()
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def shuffle(self):
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"""Shuffles order of data."""
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new_order = self.rng.permutation(self.inputs.shape[0])
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self.inputs = self.inputs[new_order]
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self.targets = self.targets[new_order]
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def next(self):
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if self._curr_batch + 1 > self.num_batches:
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self.reset()
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raise StopIteration()
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batch_slice = slice(self._curr_batch * self.batch_size,
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(self._curr_batch + 1) * self.batch_size)
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inputs_batch = self.inputs[batch_slice]
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targets_batch = self.targets[batch_slice]
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self._curr_batch += 1
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return inputs_batch, targets_batch
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class MNISTDataProvider(DataProvider):
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"""Data provider for MNIST handwritten digit images."""
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def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
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shuffle_order=True, rng=None):
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assert which_set in ['train', 'valid', 'eval'], (
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'Expected which_set to be either train, valid or eval. '
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'Got {0}'.format(which_set)
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)
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self.which_set = which_set
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self.num_classes = 10
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data_path = os.path.join(
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os.environ['MLP_DATA_DIR'], 'mnist_{0}.pkl.gz'.format(which_set))
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assert os.path.isfile(data_path), (
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'Data file does not exist at expected path: ' + data_path
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)
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with gzip.open(data_path) as f:
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inputs, targets = cPickle.load(f)
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super(MNISTDataProvider, self).__init__(
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inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
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def next(self):
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inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
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return inputs_batch, self.to_one_of_k(targets_batch)
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def to_one_of_k(self, int_targets):
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one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
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one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
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return one_of_k_targets
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class MetOfficeDataProvider(DataProvider):
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"""South Scotland Met Office weather data provider."""
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def __init__(self, window_size, batch_size=10, max_num_batches=-1,
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shuffle_order=True, rng=None):
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data_path = os.path.join(
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os.environ['MLP_DATA_DIR'], 'HadSSP_daily_qc.txt')
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assert os.path.isfile(data_path), (
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'Data file does not exist at expected path: ' + data_path
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)
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raw = np.loadtxt(data_path, skiprows=3, usecols=range(2, 32))
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assert window_size > 1, 'window_size must be at least 2.'
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self.window_size = window_size
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#filter out all missing datapoints and flatten to a vector
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filtered = raw[raw >= 0].flatten()
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#normalise data to zero mean, unit standard deviation
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mean = np.mean(filtered)
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std = np.std(filtered)
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normalised = (filtered - mean) / std
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# create a view on to array corresponding to a rolling window
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shape = (normalised.shape[-1] - self.window_size + 1, self.window_size)
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strides = normalised.strides + (normalised.strides[-1],)
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windowed = np.lib.stride_tricks.as_strided(
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normalised, shape=shape, strides=strides)
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# inputs are first (window_size - 1) entries in windows
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inputs = windowed[:, :-1]
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# targets are last entry in windows
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targets = windowed[:, -1]
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super(MetOfficeDataProvider, self).__init__(
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inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
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