<commit_before>#!/usr/bin/env python
__authors__ = "<NAME>"
__copyright__ = "Copyright 2013, Universite de Montreal"
__credits__ = ["<NAME>", "<NAME>"]
__license__ = "3-clause BSD"
__maintainer__ = "<NAME>"

"""
This script computes both an estimate of the partition function of the provided
DBM model and an estimate of the log-likelihood on the given training and test
sets.

This is garanteed to work only for DBMs with a BinaryVector visible layer and
BinaryVectorMaxPool hidden layers with pool sizes of 1.

It uses annealed importance sampling (AIS) to estimate Z, the partition
function.

TODO: add more details, cite paper


usage: dbm_metrics.py [-h] {ais} model_path

positional arguments:
    {ais}       the desired metric
    model_path  path to the pickled DBM model

optional arguments:
    -h, --help  show the help message and exit
"""

import argparse
import warnings
import numpy
import logging
from collections import OrderedDict

import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano.sandbox.scan import scan

import pylearn2
from pylearn2.utils import serial
from pylearn2 import utils
from pylearn2.datasets.mnist import MNIST

floatX = theano.config.floatX
logging.basicConfig(level=logging.INFO)
rng = numpy.random.RandomState(9873242)
theano_rng = RandomStreams(rng.randint(2**30))


def _sample_even_odd(W_list, b_list, samples, beta, odd=True):
    """
    Sample from the even (or odd) layers given a list of previous states.
<commit_msg>Import OrderedDict from theano<commit_after>#!/usr/bin/env python
__authors__ = "<NAME>"
__copyright__ = "Copyright 2013, Universite de Montreal"
__credits__ = ["<NAME>", "<NAME>"]
__license__ = "3-clause BSD"
__maintainer__ = "<NAME>"

"""
This script computes both an estimate of the partition function of the provided
DBM model and an estimate of the log-likelihood on the given training and test
sets.

This is garanteed to work only for DBMs with a BinaryVector visible layer and
BinaryVectorMaxPool hidden layers with pool sizes of 1.

It uses annealed importance sampling (AIS) to estimate Z, the partition
function.

TODO: add more details, cite paper


usage: dbm_metrics.py [-h] {ais} model_path

positional arguments:
    {ais}       the desired metric
    model_path  path to the pickled DBM model

optional arguments:
    -h, --help  show the help message and exit
"""

import argparse
import warnings
import numpy
import logging

import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano.sandbox.scan import scan
from theano.compat.python2x import OrderedDict

import pylearn2
from pylearn2.utils import serial
from pylearn2 import utils

floatX = theano.config.floatX
logging.basicConfig(level=logging.INFO)
rng = numpy.random.RandomState(9873242)
theano_rng = RandomStreams(rng.randint(2**30))


def _sample_even_odd(W_list, b_list, samples, beta, odd=True):
    """
    Sample from the even (or odd) layers given a list of previous states.

{'fraction_non_alphanumeric': 0.07264397905759162, 'fraction_numerical': 0.013089005235602094, 'mean_word_length': 4.7897727272727275, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7131895', 'n_tokens_mistral': 968, 'n_tokens_neox': 926, 'n_words': 363}