__author__ = 'chunk'

import os, sys
import time

import numpy as np
from sklearn import cross_validation

import theano
import theano.tensor as T
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv

import gzip
import cPickle




class LogisticRegression(object):
    """
    Multi-class Logistic Regression Class
    """

    def __init__(self, input, n_in, n_out):
        """
        Initialize the parameters of the logistic regression
        """
        self.W = theano.shared(
            value=np.zeros(
                (n_in, n_out),
                dtype=theano.config.floatX
            ),
            name='W',
            borrow=True
        )
        self.b = theano.shared(
            value=np.zeros(
                (n_out,),
                dtype=theano.config.floatX
            ),
            name='b',
            borrow=True
        )
        self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
        self.y_pred = T.argmax(self.p_y_given_x, axis=1)
        self.params = [self.W, self.b]

    def negative_log_likelihood(self, y):
        """
        Return the mean of the negative log-likelihood of the prediction
        of this model under a given target distribution.
        """
        return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])

    def errors(self, y):
        """
        Return a float representing the number of errors in the minibatch
        over the total number of examples of the minibatch ; zero one
        loss over the size of the minibatch
        """
        if y.ndim != self.y_pred.ndim:
            raise TypeError(
                'y should have the same shape as self.y_pred',
                ('y', y.type, 'y_pred', self.y_pred.type)
            )
        if y.dtype.startswith('int'):
            return T.mean(T.neq(self.y_pred, y))
        else:
            raise NotImplementedError()


class HiddenLayer(object):
    def __init__(self, rng, input, n_in, n_out, W=None, b=None,
                 activation=T.tanh):
        self.input = input
        if W is None:
            W_values = np.asarray(
                rng.uniform(
                    low=-np.sqrt(6. / (n_in + n_out)),
                    high=np.sqrt(6. / (n_in + n_out)),
                    size=(n_in, n_out)
                ),
                dtype=theano.config.floatX
            )
            if activation == theano.tensor.nnet.sigmoid:
                W_values *= 4

            W = theano.shared(value=W_values, name='W', borrow=True)

        if b is None:
            b_values = np.zeros((n_out,), dtype=theano.config.floatX)
            b = theano.shared(value=b_values, name='b', borrow=True)

        self.W = W
        self.b = b

        lin_output = T.dot(input, self.W) + self.b
        self.output = (
            lin_output if activation is None
            else activation(lin_output)
        )
        # parameters of the model
        self.params = [self.W, self.b]


class MLP(object):
    def __init__(self, rng, input, n_in, n_hidden, n_out):
        self.hiddenLayer = HiddenLayer(
            rng=rng,
            input=input,
            n_in=n_in,
            n_out=n_hidden,
            activation=T.tanh
        )
        self.logRegressionLayer = LogisticRegression(
            input=self.hiddenLayer.output,
            n_in=n_hidden,
            n_out=n_out
        )

        self.L1 = (
            abs(self.hiddenLayer.W).sum()
            + abs(self.logRegressionLayer.W).sum()
        )
        self.L2_sqr = (
            (self.hiddenLayer.W ** 2).sum()
            + (self.logRegressionLayer.W ** 2).sum()
        )

        self.negative_log_likelihood = (
            self.logRegressionLayer.negative_log_likelihood
        )
        self.errors = self.logRegressionLayer.errors
        self.params = self.hiddenLayer.params + self.logRegressionLayer.params


class ConvPoolLayer(object):
    """Pool Layer of a convolutional network """

    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
        assert image_shape[1] == filter_shape[1]
        self.input = input
        fan_in = np.prod(filter_shape[1:])
        fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) /
                   np.prod(poolsize))

        W_bound = np.sqrt(6. / (fan_in + fan_out))
        self.W = theano.shared(
            np.asarray(
                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
                dtype=theano.config.floatX
            ),
            borrow=True
        )
        b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, borrow=True)

        conv_out = conv.conv2d(
            input=input,
            filters=self.W,
            filter_shape=filter_shape,
            image_shape=image_shape
        )

        pooled_out = downsample.max_pool_2d(
            input=conv_out,
            ds=poolsize,
            ignore_border=True
        )

        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
        self.params = [self.W, self.b]


def shared_dataset(data_xy, borrow=True):
    """ Function that loads the dataset into shared variables

    The reason we store our dataset in shared variables is to allow
    Theano to copy it into the GPU memory (when code is run on GPU).
    Since copying data into the GPU is slow, copying a minibatch everytime
    is needed (the default behaviour if the data is not in a shared
    variable) would lead to a large decrease in performance.
    """
    data_x, data_y = data_xy
    shared_x = theano.shared(np.asarray(data_x,
                                        dtype=theano.config.floatX),
                             borrow=borrow)
    shared_y = theano.shared(np.asarray(data_y,
                                        dtype=theano.config.floatX),
                             borrow=borrow)
    # When storing data on the GPU it has to be stored as floats
    # therefore we will store the labels as ``floatX`` as well
    # (``shared_y`` does exactly that). But during our computations
    # we need them as ints (we use labels as index, and if they are
    # floats it doesn't make sense) therefore instead of returning
    # ``shared_y`` we will have to cast it to int. This little hack
    # lets ous get around this issue
    return shared_x, T.cast(shared_y, 'int32')


def example_cnn_ilscrop(X=None, Y=None, dataset=os.path.join('', '../../res/', 'ils_crop.pkl'),
                        learning_rate=0.1, n_epochs=200,
                        nkerns=[20, 50, 50],
                        batch_size=400):
    if X == None:
        assert dataset != None
        with open(dataset, 'rb') as f:
            train_set, test_set = cPickle.load(f)

        X_train, Y_train = train_set
        X_test, Y_test = test_set
    else:
        X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.2, random_state=0)

    X_train, Y_train = shared_dataset((X_train, Y_train))
    X_test, Y_test = shared_dataset((X_test, Y_test))

    # X_train = theano.shared(np.asarray(X_train, dtype=theano.config.floatX), borrow=True)
    # Y_train = theano.shared(np.asarray(Y_train, dtype=theano.config.floatX), borrow=True)
    # X_test = theano.shared(np.asarray(X_test, dtype=theano.config.floatX), borrow=True)
    # Y_test = theano.shared(np.asarray(Y_test, dtype=theano.config.floatX), borrow=True)

    n_train_batches = X_train.get_value(borrow=True).shape[0] / batch_size
    n_test_batches = X_test.get_value(borrow=True).shape[0] / batch_size

    print X_train.get_value(borrow=True).shape, Y_train.shape

    rng = np.random.RandomState(12306)
    index = T.lscalar()
    x = T.matrix('x')
    y = T.ivector('y')

    ######################
    # BUILD ACTUAL MODEL #
    ######################
    print '... building the model'

    layer0_input = x.reshape((batch_size, 1, 304, 304))

    # Construct the first convolutional pooling layer:
    # filtering reduces the image size to (304-8+1 , 304-8+1) = (297, 297)
    # maxpooling reduces this further to (297/4, 297/4) = (74, 74)
    # 4D output tensor is thus of shape (batch_size, nkerns[0], 74, 74)
    layer0 = ConvPoolLayer(
        rng,
        input=layer0_input,
        image_shape=(batch_size, 1, 304, 304),
        filter_shape=(nkerns[0], 1, 8, 8),
        poolsize=(4, 4)
    )

    # Construct the second convolutional pooling layer
    # filtering reduces the image size to (74-8+1, 74-8+1) = (67, 67)
    # maxpooling reduces this further to (67/4, 67/4) = (16, 16)
    # 4D output tensor is thus of shape (batch_size, nkerns[1], 16, 16)
    layer1 = ConvPoolLayer(
        rng,
        input=layer0.output,
        image_shape=(batch_size, nkerns[0], 74, 74),
        filter_shape=(nkerns[1], nkerns[0], 8, 8),
        poolsize=(4, 4)
    )

    # Construct the third convolutional pooling layer
    # filtering reduces the image size to (16-5+1, 16-5+1) = (12, 12)
    # maxpooling reduces this further to (12/3, 12/3) = (4, 4)
    # 4D output tensor is thus of shape (batch_size, nkerns[2], 4, 4)
    layer2 = ConvPoolLayer(
        rng,
        input=layer1.output,
        image_shape=(batch_size, nkerns[1], 16, 16),
        filter_shape=(nkerns[2], nkerns[1], 5, 5),
        poolsize=(3, 3)
    )

    # the HiddenLayer being fully-connected, it operates on 2D matrices of
    # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
    # This will generate a matrix of shape (batch_size, nkerns[2] * 4 * 4),
    # or (500, 50 * 4 * 4) = (500, 800) with the default values.
    layer3_input = layer2.output.flatten(2)
    # construct a fully-connected sigmoidal layer
    layer3 = HiddenLayer(
        rng,
        input=layer3_input,
        n_in=nkerns[2] * 4 * 4,
        n_out=500,
        activation=T.tanh
    )
    # classify the values of the fully-connected sigmoidal layer
    layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=2)

    # the cost we minimize during training is the NLL of the model
    cost = layer4.negative_log_likelihood(y)
    params = layer4.params + layer3.params + layer2.params + layer1.params + layer0.params
    grads = T.grad(cost, params)
    updates = [
        (param_i, param_i - learning_rate * grad_i)
        for param_i, grad_i in zip(params, grads)
    ]

    """
    Total Parameters:
    >>> 20 * 64 + 1000 * 64 + 2500 * 25 + 50 * 16 * 500 + 500 * 2
    528780
    """
    train_model = theano.function(
        [index],
        cost,
        updates=updates,
        givens={
            x: X_train[index * batch_size: (index + 1) * batch_size],
            y: Y_train[index * batch_size: (index + 1) * batch_size]
        }
    )

    test_model = theano.function(
        [index],
        layer4.errors(y),
        givens={
            x: X_test[index * batch_size: (index + 1) * batch_size],
            y: Y_test[index * batch_size: (index + 1) * batch_size]
        }
    )

    ###############
    # TRAIN MODEL #
    ###############
    print '... training'
    # early-stopping parameters
    patience = 10000  # look as this many examples regardless
    patience_increase = 2  # wait this much longer when a new best is found
    improvement_threshold = 0.995  # a relative improvement of this much is
    # considered significant
    validation_frequency = min(n_train_batches, patience / 2)
    # go through this many
    # minibatche before checking the network
    # on the validation set; in this case we
    # check every epoch

    best_validation_loss = np.inf
    best_iter = 0
    test_score = 0.
    start_time = time.clock()

    epoch = 0
    done_looping = False

    while (epoch < n_epochs) and (not done_looping):
        epoch = epoch + 1
        for minibatch_index in xrange(n_train_batches):

            iter = (epoch - 1) * n_train_batches + minibatch_index

            # if iter % 100 == 0:
            # print 'training @ iter = ', iter
            print 'training @ iter = ', iter
            cost_ij = train_model(minibatch_index)

            if (iter + 1) % validation_frequency == 0:

                # compute zero-one loss on validation set
                validation_losses = [test_model(i) for i in xrange(n_test_batches)]
                this_validation_loss = np.mean(validation_losses)
                print('epoch %i, minibatch %i/%i, validation error %f %%' %
                      (epoch, minibatch_index + 1, n_train_batches,
                       this_validation_loss * 100.))

                # if we got the best validation score until now
                if this_validation_loss < best_validation_loss:

                    # improve patience if loss improvement is good enough
                    if this_validation_loss < best_validation_loss * \
                            improvement_threshold:
                        patience = max(patience, iter * patience_increase)

                    # save best validation score and iteration number
                    best_validation_loss = this_validation_loss
                    best_iter = iter

            if patience <= iter:
                done_looping = True
                break

    end_time = time.clock()
    print('Optimization complete.')
    print('Best validation score of %f %% obtained at iteration %i, '
          'with test performance %f %%' %
          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
    print >> sys.stderr, ('The code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % ((end_time - start_time) / 60.))


def example_cnn_mnist(self, X=None, Y=None, dataset=os.path.join('', '../../res/', 'mnist.pkl.gz'),
                      learning_rate=0.1, n_epochs=200,
                      nkerns=[20, 50],
                      batch_size=500):
    # return example_cnn_ilscrop(X, Y, dataset=dataset, learning_rate=learning_rate, n_epochs=n_epochs, nkerns=nkerns,
    # batch_size=batch_size)

    with gzip.open(dataset, 'rb') as f:
        train_set, valid_set, test_set = cPickle.load(f)

    train_set_x, train_set_y = shared_dataset(train_set)
    valid_set_x, valid_set_y = shared_dataset(valid_set)
    test_set_x, test_set_y = shared_dataset(test_set)

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.get_value(borrow=True).shape[0]
    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
    n_test_batches = test_set_x.get_value(borrow=True).shape[0]
    n_train_batches /= batch_size
    n_valid_batches /= batch_size
    n_test_batches /= batch_size

    print train_set_x.get_value(borrow=True).shape, train_set_y.shape

    rng = np.random.RandomState(12306)
    index = T.lscalar()  # index to a [mini]batch
    # start-snippet-1
    x = T.matrix('x')  # the data is presented as rasterized images
    y = T.ivector('y')  # the labels are presented as 1D vector of
    # [int] labels

    ######################
    # BUILD ACTUAL MODEL #
    ######################
    print '... building the model'

    layer0_input = x.reshape((batch_size, 1, 28, 28))

    # Construct the first convolutional pooling layer:
    # filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
    # maxpooling reduces this further to (24/2, 24/2) = (12, 12)
    # 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
    layer0 = ConvPoolLayer(
        rng,
        input=layer0_input,
        image_shape=(batch_size, 1, 28, 28),
        filter_shape=(nkerns[0], 1, 5, 5),
        poolsize=(2, 2)
    )

    # Construct the second convolutional pooling layer
    # filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
    # maxpooling reduces this further to (8/2, 8/2) = (4, 4)
    # 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
    layer1 = ConvPoolLayer(
        rng,
        input=layer0.output,
        image_shape=(batch_size, nkerns[0], 12, 12),
        filter_shape=(nkerns[1], nkerns[0], 5, 5),
        poolsize=(2, 2)
    )

    # the HiddenLayer being fully-connected, it operates on 2D matrices of
    # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
    # This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
    # or (500, 50 * 4 * 4) = (500, 800) with the default values.
    layer2_input = layer1.output.flatten(2)

    # construct a fully-connected sigmoidal layer
    layer2 = HiddenLayer(
        rng,
        input=layer2_input,
        n_in=nkerns[1] * 4 * 4,
        n_out=500,
        activation=T.tanh
    )

    # classify the values of the fully-connected sigmoidal layer
    layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)

    # the cost we minimize during training is the NLL of the model
    cost = layer3.negative_log_likelihood(y)

    # create a function to compute the mistakes that are made by the model
    test_model = theano.function(
        [index],
        layer3.errors(y),
        givens={
            x: test_set_x[index * batch_size: (index + 1) * batch_size],
            y: test_set_y[index * batch_size: (index + 1) * batch_size]
        }
    )

    validate_model = theano.function(
        [index],
        layer3.errors(y),
        givens={
            x: valid_set_x[index * batch_size: (index + 1) * batch_size],
            y: valid_set_y[index * batch_size: (index + 1) * batch_size]
        }
    )

    # create a list of all model parameters to be fit by gradient descent
    params = layer3.params + layer2.params + layer1.params + layer0.params

    # create a list of gradients for all model parameters
    grads = T.grad(cost, params)

    # train_model is a function that updates the model parameters by
    # SGD Since this model has many parameters, it would be tedious to
    # manually create an update rule for each model parameter. We thus
    # create the updates list by automatically looping over all
    # (params[i], grads[i]) pairs.
    updates = [
        (param_i, param_i - learning_rate * grad_i)
        for param_i, grad_i in zip(params, grads)
    ]

    train_model = theano.function(
        [index],
        cost,
        updates=updates,
        givens={
            x: train_set_x[index * batch_size: (index + 1) * batch_size],
            y: train_set_y[index * batch_size: (index + 1) * batch_size]
        }
    )
    # end-snippet-1

    ###############
    # TRAIN MODEL #
    ###############
    print '... training'
    # early-stopping parameters
    patience = 10000  # look as this many examples regardless
    patience_increase = 2  # wait this much longer when a new best is
    # found
    improvement_threshold = 0.995  # a relative improvement of this much is
    # considered significant
    validation_frequency = min(n_train_batches, patience / 2)
    # go through this many
    # minibatche before checking the network
    # on the validation set; in this case we
    # check every epoch

    best_validation_loss = np.inf
    best_iter = 0
    test_score = 0.
    start_time = time.clock()

    epoch = 0
    done_looping = False

    while (epoch < n_epochs) and (not done_looping):
        epoch = epoch + 1
        for minibatch_index in xrange(n_train_batches):

            iter = (epoch - 1) * n_train_batches + minibatch_index

            if iter % 100 == 0:
                print 'training @ iter = ', iter
            cost_ij = train_model(minibatch_index)

            if (iter + 1) % validation_frequency == 0:

                # compute zero-one loss on validation set
                validation_losses = [validate_model(i) for i
                                     in xrange(n_valid_batches)]
                this_validation_loss = np.mean(validation_losses)
                print('epoch %i, minibatch %i/%i, validation error %f %%' %
                      (epoch, minibatch_index + 1, n_train_batches,
                       this_validation_loss * 100.))

                # if we got the best validation score until now
                if this_validation_loss < best_validation_loss:

                    #improve patience if loss improvement is good enough
                    if this_validation_loss < best_validation_loss * \
                            improvement_threshold:
                        patience = max(patience, iter * patience_increase)

                    # save best validation score and iteration number
                    best_validation_loss = this_validation_loss
                    best_iter = iter

                    # test it on the test set
                    test_losses = [
                        test_model(i)
                        for i in xrange(n_test_batches)
                    ]
                    test_score = np.mean(test_losses)
                    print(('     epoch %i, minibatch %i/%i, test error of '
                           'best model %f %%') %
                          (epoch, minibatch_index + 1, n_train_batches,
                           test_score * 100.))

            if patience <= iter:
                done_looping = True
                break

    end_time = time.clock()
    print('Optimization complete.')
    print('Best validation score of %f %% obtained at iteration %i, '
          'with test performance %f %%' %
          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
    print >> sys.stderr, ('The code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % ((end_time - start_time) / 60.))