# pylint: skip-file
import sys
import os
# code to automatically download dataset
curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
sys.path = [os.path.join(curr_path, "../autoencoder")] + sys.path
import mxnet as mx
import numpy as np
import data
from scipy.spatial.distance import cdist
from sklearn.cluster import KMeans
import model
from autoencoder import AutoEncoderModel
from solver import Solver, Monitor
import logging

def cluster_acc(Y_pred, Y):
  from sklearn.utils.linear_assignment_ import linear_assignment
  assert Y_pred.size == Y.size
  D = max(Y_pred.max(), Y.max())+1
  w = np.zeros((D,D), dtype=np.int64)
  for i in range(Y_pred.size):
    w[Y_pred[i], Y[i]] += 1
  ind = linear_assignment(w.max() - w)
  return sum([w[i,j] for i,j in ind])*1.0/Y_pred.size, w

class DECModel(model.MXModel):
    class DECLoss(mx.operator.NumpyOp):
        def __init__(self, num_centers, alpha):
            super(DECModel.DECLoss, self).__init__(need_top_grad=False)
            self.num_centers = num_centers
            self.alpha = alpha

        def forward(self, in_data, out_data):
            z = in_data[0]
            mu = in_data[1]
            q = out_data[0]
            self.mask = 1.0/(1.0+cdist(z, mu)**2/self.alpha)
            q[:] = self.mask**((self.alpha+1.0)/2.0)
            q[:] = (q.T/q.sum(axis=1)).T

        def backward(self, out_grad, in_data, out_data, in_grad):
            q = out_data[0]
            z = in_data[0]
            mu = in_data[1]
            p = in_data[2]
            dz = in_grad[0]
            dmu = in_grad[1]
            self.mask *= (self.alpha+1.0)/self.alpha*(p-q)
            dz[:] = (z.T*self.mask.sum(axis=1)).T - self.mask.dot(mu)
            dmu[:] = (mu.T*self.mask.sum(axis=0)).T - self.mask.T.dot(z)

        def infer_shape(self, in_shape):
            assert len(in_shape) == 3
            assert len(in_shape[0]) == 2
            input_shape = in_shape[0]
            label_shape = (input_shape[0], self.num_centers)
            mu_shape = (self.num_centers, input_shape[1])
            out_shape = (input_shape[0], self.num_centers)
            return [input_shape, mu_shape, label_shape], [out_shape]

        def list_arguments(self):
            return ['data', 'mu', 'label']

    def setup(self, X, num_centers, alpha, save_to='dec_model'):
        sep = X.shape[0]*9/10
        X_train = X[:sep]
        X_val = X[sep:]
        ae_model = AutoEncoderModel(self.xpu, [X.shape[1],500,500,2000,10], pt_dropout=0.2)
        if not os.path.exists(save_to+'_pt.arg'):
            ae_model.layerwise_pretrain(X_train, 256, 50000, 'sgd', l_rate=0.1, decay=0.0,
                                        lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
            ae_model.finetune(X_train, 256, 100000, 'sgd', l_rate=0.1, decay=0.0,
                              lr_scheduler=mx.misc.FactorScheduler(20000,0.1))
            ae_model.save(save_to+'_pt.arg')
            logging.log(logging.INFO, "Autoencoder Training error: %f"%ae_model.eval(X_train))
            logging.log(logging.INFO, "Autoencoder Validation error: %f"%ae_model.eval(X_val))
        else:
            ae_model.load(save_to+'_pt.arg')
        self.ae_model = ae_model

        self.dec_op = DECModel.DECLoss(num_centers, alpha)
        label = mx.sym.Variable('label')
        self.feature = self.ae_model.encoder
        self.loss = self.dec_op(data=self.ae_model.encoder, label=label, name='dec')
        self.args.update({k:v for k,v in self.ae_model.args.items() if k in self.ae_model.encoder.list_arguments()})
        self.args['dec_mu'] = mx.nd.empty((num_centers, self.ae_model.dims[-1]), ctx=self.xpu)
        self.args_grad.update({k: mx.nd.empty(v.shape, ctx=self.xpu) for k,v in self.args.items()})
        self.args_mult.update({k: k.endswith('bias') and 2.0 or 1.0 for k in self.args})
        self.num_centers = num_centers

    def cluster(self, X, y=None, update_interval=None):
        N = X.shape[0]
        if not update_interval:
            update_interval = N
        batch_size = 256
        test_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=False,
                                      last_batch_handle='pad')
        args = {k: mx.nd.array(v.asnumpy(), ctx=self.xpu) for k, v in self.args.items()}
        z = model.extract_feature(self.feature, args, test_iter, N, self.xpu).values()[0]
        kmeans = KMeans(self.num_centers, n_init=20)
        kmeans.fit(z)
        args['dec_mu'][:] = kmeans.cluster_centers_
        solver = Solver('sgd', momentum=0.9, wd=0.0, learning_rate=0.01)
        def ce(label, pred):
            return np.sum(label*np.log(label/(pred+0.000001)))/label.shape[0]
        solver.set_metric(mx.metric.CustomMetric(ce))

        label_buff = np.zeros((X.shape[0], self.num_centers))
        train_iter = mx.io.NDArrayIter({'data': X}, {'label': label_buff}, batch_size=batch_size,
                                       shuffle=False, last_batch_handle='roll_over')
        self.y_pred = np.zeros((X.shape[0]))
        def refresh(i):
            if i%update_interval == 0:
                z = model.extract_feature(self.feature, args, test_iter, N, self.xpu).values()[0]
                p = np.zeros((z.shape[0], self.num_centers))
                self.dec_op.forward([z, args['dec_mu'].asnumpy()], [p])
                y_pred = p.argmax(axis=1)
                print np.std(np.bincount(y_pred)), np.bincount(y_pred)
                print np.std(np.bincount(y.astype(np.int))), np.bincount(y.astype(np.int))
                if y is not None:
                    print(cluster_acc(y_pred, y)[0])
                weight = 1.0/p.sum(axis=0)
                weight *= self.num_centers/weight.sum()
                p = (p**2)*weight
                train_iter.data_list[1][:] = (p.T/p.sum(axis=1)).T
                print np.sum(y_pred != self.y_pred), 0.001*y_pred.shape[0]
                if np.sum(y_pred != self.y_pred) < 0.001*y_pred.shape[0]:
                    self.y_pred = y_pred
                    return True
                self.y_pred = y_pred
        solver.set_iter_start_callback(refresh)
        solver.set_monitor(Monitor(50))

        solver.solve(self.xpu, self.loss, args, self.args_grad,
                     train_iter, 0, 1000000000, {}, False)
        self.end_args = args
        if y is not None:
            return cluster_acc(self.y_pred, y)[0]
        else:
            return -1

def mnist_exp(xpu):
    X, Y = data.get_mnist()
    dec_model = DECModel(xpu, X, 10, 1.0, 'data/mnist')
    acc = []
    for i in [10*(2**j) for j in range(9)]:
        acc.append(dec_model.cluster(X, Y, i))
        logging.log(logging.INFO, 'Clustering Acc: %f at update interval: %d'%(acc[-1], i))
    logging.info(str(acc))
    logging.info('Best Clustering ACC: %f at update_interval: %d'%(np.max(acc), 10*(2**np.argmax(acc))))

if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    mnist_exp(mx.gpu(0))
    
[]