#!/bin/python
#
# Hacking together tutorials from deeplearning.net to make
# a MLP model

import numpy
import theano
import theano.tensor as T
import sklearn.cross_validation

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

    The logistic regression is fully described by a weight matrix :math:`W`
    and bias vector :math:`b`. Classification is done by projecting data
    points onto a set of hyperplanes, the distance to which is used to
    determine a class membership probability.
    """

    def __init__(self, input, n_in, n_out):
        """ Initialize the parameters of the logistic regression

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
                      architecture (one minibatch)

        :type n_in: int
        :param n_in: number of input units, the dimension of the space in
                     which the datapoints lie

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
                      which the labels lie

        """
        # start-snippet-1
        # initialize with 0 the weights W as a matrix of shape (n_in, n_out)
        self.W = theano.shared(
            value=numpy.zeros(
                (n_in, n_out),
                dtype=theano.config.floatX
            ),
            name='W',
            borrow=True
        )
        # initialize the baises b as a vector of n_out 0s
        self.b = theano.shared(
            value=numpy.zeros(
                (n_out,),
                dtype=theano.config.floatX
            ),
            name='b',
            borrow=True
        )

        # symbolic expression for computing the matrix of class-membership
        # probabilities
        # Where:
        # W is a matrix where column-k represent the separation hyper plain for
        # class-k
        # x is a matrix where row-j  represents input training sample-j
        # b is a vector where element-k represent the free parameter of hyper
        # plain-k
        self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)

        # symbolic description of how to compute prediction as class whose
        # probability is maximal
        self.y_pred = T.argmax(self.p_y_given_x, axis=1)
        # end-snippet-1

        # parameters of the model
        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.

        .. math::

            \frac{1}{|\mathcal{D}|} \mathcal{L} (\theta=\{W,b\}, \mathcal{D}) =
            \frac{1}{|\mathcal{D}|} \sum_{i=0}^{|\mathcal{D}|}
                \log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\
            \ell (\theta=\{W,b\}, \mathcal{D})

        :type y: theano.tensor.TensorType
        :param y: corresponds to a vector that gives for each example the
                  correct label

        Note: we use the mean instead of the sum so that
              the learning rate is less dependent on the batch size
        """
        # start-snippet-2
        # y.shape[0] is (symbolically) the number of rows in y, i.e.,
        # number of examples (call it n) in the minibatch
        # T.arange(y.shape[0]) is a symbolic vector which will contain
        # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of
        # Log-Probabilities (call it LP) with one row per example and
        # one column per class LP[T.arange(y.shape[0]),y] is a vector
        # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,
        # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is
        # the mean (across minibatch examples) of the elements in v,
        # i.e., the mean log-likelihood across the minibatch.
        return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
        # end-snippet-2

    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

        :type y: theano.tensor.TensorType
        :param y: corresponds to a vector that gives for each example the
                  correct label
        """

        # check if y has same dimension of y_pred
        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)
            )
        # check if y is of the correct datatype
        if y.dtype.startswith('int'):
            # the T.neq operator returns a vector of 0s and 1s, where 1
            # represents a mistake in prediction
            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):
        """
        Typical hidden layer of a MLP: units are fully-connected and have
        sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
        and the bias vector b is of shape (n_out,).

        NOTE : The nonlinearity used here is tanh

        Hidden unit activation is given by: tanh(dot(input,W) + b)

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.dmatrix
        :param input: a symbolic tensor of shape (n_examples, n_in)

        :type n_in: int
        :param n_in: dimensionality of input

        :type n_out: int
        :param n_out: number of hidden units

        :type activation: theano.Op or function
        :param activation: Non linearity to be applied in the hidden
                           layer
        """
        self.input = input
        # end-snippet-1

        # `W` is initialized with `W_values` which is uniformely sampled
        # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))
        # for tanh activation function
        # the output of uniform if converted using asarray to dtype
        # theano.config.floatX so that the code is runable on GPU
        # Note : optimal initialization of weights is dependent on the
        #        activation function used (among other things).
        #        For example, results presented in [Xavier10] suggest that you
        #        should use 4 times larger initial weights for sigmoid
        #        compared to tanh
        #        We have no info for other function, so we use the same as
        #        tanh.
        if W is None:
            W_values = numpy.asarray(
                rng.uniform(
                    low=-numpy.sqrt(6. / (n_in + n_out)),
                    high=numpy.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 = numpy.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]

# start-snippet-2
class MLP(object):
    """Multi-Layer Perceptron Class

    A multilayer perceptron is a feedforward artificial neural network model
    that has one layer or more of hidden units and nonlinear activations.
    Intermediate layers usually have as activation function tanh or the
    sigmoid function (defined here by a ``HiddenLayer`` class)  while the
    top layer is a softamx layer (defined here by a ``LogisticRegression``
    class).
    """

    def __init__(self, rng, input, n_in, n_hidden, n_out):
        """Initialize the parameters for the multilayer perceptron

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
        architecture (one minibatch)

        :type n_in: int
        :param n_in: number of input units, the dimension of the space in
        which the datapoints lie

        :type n_hidden: int
        :param n_hidden: number of hidden units

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
        which the labels lie

        """

        # Since we are dealing with a one hidden layer MLP, this will translate
        # into a HiddenLayer with a tanh activation function connected to the
        # LogisticRegression layer; the activation function can be replaced by
        # sigmoid or any other nonlinear function
        self.hiddenLayer = HiddenLayer(
            rng=rng,
            input=input,
            n_in=n_in,
            n_out=n_hidden,
            activation=T.tanh
        )

        # The logistic regression layer gets as input the hidden units
        # of the hidden layer
        self.logRegressionLayer = LogisticRegression(
            input=self.hiddenLayer.output,
            n_in=n_hidden,
            n_out=n_out
        )
        # end-snippet-2 start-snippet-3
        # L1 norm ; one regularization option is to enforce L1 norm to
        # be small
        self.L1 = (
            abs(self.hiddenLayer.W).sum()
            + abs(self.logRegressionLayer.W).sum()
        )

        # square of L2 norm ; one regularization option is to enforce
        # square of L2 norm to be small
        self.L2_sqr = (
            (self.hiddenLayer.W ** 2).sum()
            + (self.logRegressionLayer.W ** 2).sum()
        )

        # negative log likelihood of the MLP is given by the negative
        # log likelihood of the output of the model, computed in the
        # logistic regression layer
        self.negative_log_likelihood = (
            self.logRegressionLayer.negative_log_likelihood
        )
        # same holds for the function computing the number of errors
        self.errors = self.logRegressionLayer.errors

        # the parameters of the model are the parameters of the two layer it is
        # made out of
        self.params = self.hiddenLayer.params + self.logRegressionLayer.params
        # end-snippet-3

class MLP_sk_interface(object):

    def __init__(self, learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
             batch_size=20, n_hidden=500, verbose=False):
        # store the many initialisation parameters
        self.learning_rate=learning_rate 
        self.L1_reg=L1_reg 
        self.L2_reg=L2_reg
        self.n_epochs=n_epochs
        self.batch_size=batch_size
        self.n_hidden=n_hidden
        self.verbose=verbose

    def fit(self,X,y,random_state=1234):
        """
        Trying to make this class conform to sklearn API conventions.
        input:
            * X - feature vectors, in an array, each row is a data point
            * y - target vector of labels
        """
        # get dimensionality of input
        d_in = int(numpy.sqrt(X.shape[1]))
        d_out = len(set(y))
        
        # Shuffle X and y
        X,y = sklearn.utils.shuffle(X,y,random_state=random_state)
        split = int(X.shape[0]/3)

        # take stratified splits for test, train and validation
        train_set_x,train_set_y = self.shared_dataset(X[:split],y[:split])
        valid_set_x,valid_set_y = self.shared_dataset(X[split:2*split],
                                                        y[split:2*split])
        test_set_x,test_set_y = self.shared_dataset(X[2*split:],y[2*split:])

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

        ##########################################
        # initialise the parameters of the model #
        ##########################################

        # allocate symbolic variables for the data
        index = T.lscalar()  # index to a [mini]batch
        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

        rng = numpy.random.RandomState(random_state)
    
        # construct the MLP class
        classifier = MLP(rng=rng,
                        input=x,
                        n_in=d_in * d_in,
                        n_hidden=self.n_hidden,
                        n_out=d_out)
        # start-snippet-4
        # the cost we minimize during training is the negative log likelihood of
        # the model plus the regularization terms (L1 and L2); cost is expressed
        # here symbolically
        cost = (classifier.negative_log_likelihood(y)
            + self.L1_reg * classifier.L1
            + self.L2_reg * classifier.L2_sqr)

        # compiling a Theano function that computes the mistakes that are made
        # by the model on a minibatch
        test_model = theano.function(
            inputs=[index],
            outputs=classifier.errors(y),
            givens={
                x: test_set_x[index * self.batch_size:(index + 1) * self.batch_size],
                y: test_set_y[index * self.batch_size:(index + 1) * self.batch_size]
            })
        # start-snippet-5
        # compute the gradient of cost with respect to theta (sotred in params)
        # the resulting gradients will be stored in a list gparams
        gparams = [T.grad(cost, param) for param in classifier.params]

        # given two list the zip A = [a1, a2, a3, a4] and B = [b1, b2, b3, b4] of
        # same length, zip generates a list C of same size, where each element
        # is a pair formed from the two lists :
        #    C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
        updates = [
            (param, param - self.learning_rate * gparam)
            for param, gparam in zip(classifier.params, gparams)
        ]

        # compiling a Theano function `train_model` that returns the cost, but
        # in the same time updates the parameter of the model based on the rules
        # defined in `updates`
        train_model = theano.function(
            inputs=[index],
            outputs=cost,
            updates=updates,
            givens={
                x: train_set_x[index * self.batch_size: (index + 1) * self.batch_size],
                y: train_set_y[index * self.batch_size: (index + 1) * self.batch_size]
            }
        )

        ###############
        # TRAIN MODEL #
        ###############
        if self.verbose:
            print('... training')

        # early-stopping parameters
        patience = 50000  # 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 = numpy.inf
        best_i = 0
        test_score = 0.

        epoch = 0
        done_looping = False

        while (epoch < self.n_epochs) and (not done_looping):
            epoch = epoch + 1
            for minibatch_index in range(int(n_train_batches)):

                minibatch_avg_cost = train_model(minibatch_index)
                # iteration number
                i = (epoch - 1) * n_train_batches + minibatch_index

                if (i + 1) % validation_frequency == 0:
                    # compute zero-one loss on validation set
                    validation_losses = [validate_model(i) for i
                                         in range(int(n_valid_batches))]
                    this_validation_loss = numpy.mean(validation_losses)
                    if self.verbose:
                        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, i * patience_increase)

                        best_validation_loss = this_validation_loss
                        best_i = i

                        # test it on the test set
                        test_losses = [test_model(i) for i
                                       in range(int(n_test_batches))]
                        test_score = numpy.mean(test_losses)
                        if self.verbose: