"""
===========================================
Deep Poisson Factor Analysis Demo
Scalable Deep Poisson Factor Analysis for Topic Modeling
Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin
Publised in International Conference on Machine Learning 2015

===========================================

"""

# Author: Chaojie Wang <xd_silly@163.com>; Jiawen Wu <wjw19960807@163.com>; Wei Zhao <13279389260@163.com>
# License: BSD-3-Clause

import numpy as np
import argparse
import scipy.io as sio

from torchvision import datasets, transforms

from pydpm.model import DPFA
from pydpm.metric import ACC
from pydpm.dataloader.image_data import tensor_transforms

# =========================================== ArgumentParser ===================================================================== #
parser = argparse.ArgumentParser()

# device
parser.add_argument("--device", type=str, default='gpu')

# dataset
parser.add_argument("--data_path", type=str, default='../../../dataset/mnist/', help="the path of loading data")

# model
parser.add_argument("--save_path", type=str, default='../../save_models', help="the path of saving model")
parser.add_argument("--load_path", type=str, default='../../save_models/DPFA.npy', help="the path of loading model")

parser.add_argument("--z_dims", type=list, default=[128, 64, 32], help="numbers of topics of 3 layers in DPFA(PFA+DSBN+Gibbs)")

# optim
parser.add_argument("--burnin", type=int, default=100, help="the iterations of burnin stage")
parser.add_argument("--collection", type=int, default=80, help="the iterations of collection stage")

args = parser.parse_args()

# =========================================== Dataset ===================================================================== #
# define transform for dataset and load orginal dataset
# load dataset
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root=args.data_path, train=True, download=True)
test_dataset = datasets.MNIST(root=args.data_path, train=False, download=False)

# transform dataset and reshape the dataset into [batch_size, feature_num]
train_data = tensor_transforms(train_dataset.data, transform)
train_data = train_data.permute([1, 2, 0]).reshape(len(train_dataset), -1)  # len(train_dataset, 28*28)
test_data = tensor_transforms(test_dataset.data, transform)
test_data = test_data.permute([1, 2, 0]).reshape(len(test_dataset), -1)
train_label = train_dataset.train_labels
test_label = test_dataset.test_labels

# transpose the dataset to fit the model and convert a tensor to numpy array
train_data = np.array(np.ceil(train_data[:999, :].T.numpy() * 5), order='C')
test_data = np.array(np.ceil(test_data[:999, :].T.numpy() * 5), order='C')
train_label = train_label.numpy()[:999]
test_label = test_label.numpy()[:999]

# =========================================== Model ===================================================================== #
# create the model and deploy it on gpu or cpu
model = DPFA(K=args.z_dims, device=args.device)
model.initial(train_data)  # use the shape of train_data to initialize the params of model

# train and evaluation
train_local_params = model.train(train_data, burnin=args.burnin, collection=args.collection)
train_local_params = model.test(train_data, burnin=args.burnin, collection=args.collection)
test_local_params = model.test(test_data, burnin=args.burnin, collection=args.collection)

# save the model after training
model.save(args.save_path)
# load the model
model.load(args.load_path)

# evaluate the model with classification accuracy
# the demo accuracy can achieve 0.9099
results = ACC(train_local_params.Theta, test_local_params.Theta, train_label, test_label, 'SVM')




