# -*- coding: utf-8 -*-

# @Author: Jiahong Zhou
# @Date: 2018-10-20
# @Email: JoeZJiahong@gmail.com
# implement of L-LDA Model(Labeled Latent Dirichlet Allocation Model)
# References:
#   i.      Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora, Daniel Ramage...
#   ii.     Parameter estimation for text analysis, Gregor Heinrich.
#   iii.    Latent Dirichlet Allocation, David M. Blei, Andrew Y. Ng...

import numpy as np
import os
import json
from concurrent import futures
import copy_reg
import types


class LldaModel:
    """
    L-LDA(Labeled Latent Dirichlet Allocation Model)

    @field K: the number of topics
    @field alpha_vector: the prior distribution of theta_m
                         str("50_div_K"): means [K/50, K/50, ...],
                                this value come from Parameter estimation for text analysis, Gregor Heinrich.
                         int or float: means [alpha_vector, alpha_vector, ...]
                         None: means [0.001, 0.001, ...]
    @field eta_vector: the prior distribution of beta_k
                       int or float: means [eta_vector, eta_vector, ...]
                       None: means [0.001, 0.001, ...]
    @field terms: a list of the all terms
    @field vocabulary: a dict of <term, term_id>, vocabulary[terms[id]] == id
    @field topics: a list of the all topics
    @field topic_vocabulary: a dict of <topic, topic_id>, topic_vocabulary[topics[id]] == id
    @field W: the corpus, a list of terms list,
              W[m] is the document vector, W[m][n] is the id of the term
    @field Lambda: a matrix, shape is M * K,
                   Lambda[m][k] is 1 means topic k is a label of document m
    @field Z: the topic corpus, just same as W,
              except Z[m][n] is the id of the topic of the term
    @field M: the number of documents
    @field T: the number of terms
    @field WN: the number of all words in W
    @field LN: the number of all original labels
    @field iteration: the times of iteration

    # derivative fields
    @field Doc2TopicCount: a matrix, shape is M * K,
                           Doc2TopicCount[m][k] is the times of topic k sampled in document m
    @field Topic2TermCount: a matrix, shape is K * T,
                            Topic2TermCount[k][t] is the times of term t generated from topic k
    @field Doc2TopicCountSum: a vector, shape is M, self.Doc2TopicCount.sum(axis=1)
                              Doc2TopicCountSum[m] is the count of all topic,
                              i.e., Doc2TopicCountSum[m] is the number of words in document m
    @field alpha_vector_Lambda: a matrix, self.alpha_vector * self.Lambda
    @alpha_vector_Lambda_sum: a vector, self.alpha_vector_Lambda.sum(axis=1)
    @eta_vector_sum: float value, sum(self.eta_vector)
    @Topic2TermCountSum: a vector, self.Topic2TermCount.sum(axis=1)

    """
    def __init__(self, alpha_vector="50_div_K", eta_vector=None, labeled_documents=None):
        """

        :param alpha_vector: the prior distribution of theta_m
        :param eta_vector: the prior distribution of beta_k
        :param labeled_documents: a iterable of tuple(doc, iterable of label), contains all doc and their labels
        """
        self.alpha_vector = alpha_vector
        self.eta_vector = eta_vector
        self.terms = []
        self.vocabulary = {}
        self.topics = []
        self.topic_vocabulary = {}
        self.W = []
        self.Z = []
        self.K = 0
        self.M = 0
        self.T = 0
        self.WN = 0
        self.LN = 0
        self.iteration = 0
        self.all_perplexities = []

        self.Lambda = None

        # derivative fields:
        # the following fields could reduce operations in training and inference
        # it is not necessary to save them to file, we can recover them by other fields

        self.Doc2TopicCount = None
        self.Topic2TermCount = None
        # self.Doc2TopicCountSum = None
        self.alpha_vector_Lambda = None
        # self.alpha_vector_Lambda_sum = None
        self.eta_vector_sum = 0.0
        self.Topic2TermCountSum = None

        if labeled_documents is not None:
            self._load_labeled_documents(labeled_documents)

        pass

    def _initialize_derivative_fields(self):
        """
        initialize derivative fields
        :return: None
        """
        # TODO: Doc2TopicCount could be reduced to a smaller matrix,
        # TODO: because some vector in Doc2TopicCount will always been 0
        self.Doc2TopicCount = np.zeros((self.M, self.K), dtype=int)
        self.Topic2TermCount = np.zeros((self.K, self.T), dtype=int)
        for m in range(self.M):
            # print self.Z[m]
            for t, z in zip(self.W[m], self.Z[m]):
                k = z
                # print "[m=%s, k=%s]" % (m, k)
                # print "[k=%s, t=%s]" % (k, t)
                self.Doc2TopicCount[m, k] += 1
                self.Topic2TermCount[k, t] += 1

        # self.Doc2TopicCountSum = self.Doc2TopicCount.sum(axis=1)
        self.alpha_vector_Lambda = self.alpha_vector * self.Lambda
        # self.alpha_vector_Lambda_sum = self.alpha_vector_Lambda.sum(axis=1)
        self.eta_vector_sum = sum(self.eta_vector)
        self.Topic2TermCountSum = self.Topic2TermCount.sum(axis=1)

    def _load_labeled_documents(self, labeled_documents):
        """
        input labeled corpus, which contains all documents and their corresponding labels
        :param labeled_documents: a iterable of tuple(doc, iterable of label), contains all doc and their labels
        :return:
        """
        # self.documents = []
        all_labels = []
        all_words = []
        doc_corpus = []
        labels_corpus = []
        for document, labels in labeled_documents:
            doc_words = document.split()
            # self.documents.append(doc_words)
            doc_corpus.append(doc_words)
            if labels is None:
                labels = []
            labels.append("common_topic")
            labels_corpus.append(labels)
            all_words.extend(doc_words)
            all_labels.extend(labels)
        self.terms = list(set(all_words))
        self.vocabulary = {term: index for index, term in enumerate(self.terms)}
        self.topics = list(set(all_labels))
        self.topic_vocabulary = {topic: index for index, topic in enumerate(self.topics)}
        self.K = len(self.topics)
        self.T = len(self.terms)
        self.W = [[self.vocabulary[term] for term in doc_words] for doc_words in doc_corpus]
        self.M = len(self.W)
        self.WN = len(all_words)
        # we appended topic "common_topic" to each doc at the beginning
        # so we need minus the number of "common_topic"
        # LN is the number of original labels
        self.LN = len(all_labels) - self.M

        self.Lambda = np.zeros((self.M, self.K), dtype=float)
        for m in range(self.M):
            if len(labels_corpus[m]) == 1:
                labels_corpus[m] = self.topics
            for label in labels_corpus[m]:
                k = self.topic_vocabulary[label]
                self.Lambda[m, k] = 1.0

        if self.alpha_vector is None:
            self.alpha_vector = [0.001 for _ in range(self.K)]
        elif type(self.alpha_vector) is str and self.alpha_vector == "50_div_K":
            self.alpha_vector = [50.0/self.K for _ in range(self.K)]
        elif type(self.alpha_vector) is float or type(self.alpha_vector) is int:
            self.alpha_vector = [self.alpha_vector for _ in range(self.K)]
        else:
            message = "error alpha_vector: %s" % self.alpha_vector
            raise Exception(message)

        if self.eta_vector is None:
            self.eta_vector = [0.001 for _ in range(self.T)]
        elif type(self.eta_vector) is float or type(self.eta_vector) is int:
            self.eta_vector = [self.eta_vector for _ in range(self.T)]
        else:
            message = "error eta_vector: %s" % self.eta_vector
            raise Exception(message)

        self.Z = []
        for m in range(self.M):
            # print "self.Lambda[m]: ", self.Lambda[m]
            numerator_vector = self.Lambda[m] * self.alpha_vector
            p_vector = 1.0 * numerator_vector / sum(numerator_vector)
            # print p_vector
            # print "p_vector: ", p_vector
            # z_vector is a vector of a document,
            # just like [2, 3, 6, 0], which means this doc have 4 word and them generated
            # from the 2nd, 3rd, 6th, 0th topic, respectively
            z_vector = [LldaModel._multinomial_sample(p_vector) for _ in range(len(self.W[m]))]
            self.Z.append(z_vector)

        self._initialize_derivative_fields()
        pass

    @staticmethod
    def _multinomial_sample(p_vector, random_state=None):
        """
        sample a number from multinomial distribution
        :param p_vector: the probabilities
        :return: a int value
        """
        if random_state is not None:
            return random_state.multinomial(1, p_vector).argmax()
        return np.random.multinomial(1, p_vector).argmax()

    def _gibbs_sample_training(self):
        """
        sample a topic(k) for each word(t) of all documents, Generate a new matrix Z
        :return: None
        """
        # TODO: the operations of addition and multiplication could be reduced, because some
        count = 0
        for m in range(self.M):

            # doc_m_eta_vector = self.eta_vector
            # doc_m_alpha_vector = self.alpha_vector * self.Lambda[m]
            doc_m_alpha_vector = self.alpha_vector_Lambda[m]
            # assert (doc_m_alpha_vector == self.alpha_vector_Lambda[m]).all()

            # sum_doc_m_alpha_vector = sum(doc_m_alpha_vector)
            # sum_doc_m_alpha_vector = self.alpha_vector_Lambda_sum[m]
            # assert sum_doc_m_alpha_vector == self.alpha_vector_Lambda_sum[m]

            for t, z, n in zip(self.W[m], self.Z[m], range(len(self.W[m]))):
                k = z
                self.Doc2TopicCount[m, k] -= 1
                self.Topic2TermCount[k, t] -= 1
                self.Topic2TermCountSum[k] -= 1

                numerator_theta_vector = self.Doc2TopicCount[m] + doc_m_alpha_vector
                # denominator_theta = sum(self.Doc2TopicCount[m]) + sum_doc_m_alpha_vector
                # denominator_theta = self.Doc2TopicCountSum[m]-1 + sum_doc_m_alpha_vector
                # assert sum(self.Doc2TopicCount[m]) == self.Doc2TopicCountSum[m]-1

                numerator_beta_vector = self.Topic2TermCount[:, t] + self.eta_vector[t]
                # denominator_beta = self.Topic2TermCount.sum(axis=1) + sum(self.eta_vector)
                # denominator_beta = self.Topic2TermCount.sum(axis=1) + self.eta_vector_sum
                denominator_beta = self.Topic2TermCountSum + self.eta_vector_sum
                # assert (self.Topic2TermCount.sum(axis=1) == self.Topic2TermCountSum).all()
                # assert sum(self.eta_vector) == self.eta_vector_sum

                beta_vector = 1.0 * numerator_beta_vector / denominator_beta
                # theta_vector = 1.0 * numerator_theta_vector / denominator_theta
                # denominator_theta is independent with t and k, so denominator could be any value except 0
                # will set denominator_theta as 1.0
                theta_vector = numerator_theta_vector

                p_vector = beta_vector * theta_vector
                # print p_vector
                """
                for some special document m (only have one word) p_vector may be zero here, sum(p_vector) will be zero too
                1.0 * p_vector / sum(p_vector) will be [...nan...]
                so we should avoid inputting the special document 
                """
                p_vector = 1.0 * p_vector / sum(p_vector)
                # print p_vector
                sample_z = LldaModel._multinomial_sample(p_vector)
                self.Z[m][n] = sample_z

                k = sample_z
                self.Doc2TopicCount[m, k] += 1
                self.Topic2TermCount[k, t] += 1
                self.Topic2TermCountSum[k] += 1
                count += 1
        assert count == self.WN
        print "gibbs sample count: ", self.WN
        self.iteration += 1
        self.all_perplexities.append(self.perplexity)
        pass

    def _gibbs_sample_inference(self, term_vector, iteration=30):
        """
        inference with gibbs sampling
        :param term_vector: the term vector of document
        :param iteration: the times of iteration
        :return: theta_new, a vector, theta_new[k] is the probability of doc(term_vector) to be generated from topic k
                 theta_new, a theta_vector, the doc-topic distribution
        """
        doc_topic_count = np.zeros(self.K, dtype=int)
        p_vector = np.ones(self.K, dtype=int)
        p_vector = p_vector * 1.0 / sum(p_vector)
        z_vector = [LldaModel._multinomial_sample(p_vector) for _ in term_vector]
        for n, t in enumerate(term_vector):
            k = z_vector[n]
            doc_topic_count[k] += 1
            self.Topic2TermCount[k, t] += 1
            self.Topic2TermCountSum[k] += 1

        # sum_doc_topic_count = sum(doc_topic_count)
        doc_m_alpha_vector = self.alpha_vector
        # sum_doc_m_alpha_vector = sum(doc_m_alpha_vector)
        for i in range(iteration):
            for n, t in enumerate(term_vector):
                k = z_vector[n]
                doc_topic_count[k] -= 1
                self.Topic2TermCount[k, t] -= 1
                self.Topic2TermCountSum[k] -= 1

                numerator_theta_vector = doc_topic_count + doc_m_alpha_vector
                # denominator_theta = sum_doc_topic_count - 1 + sum_doc_m_alpha_vector

                numerator_beta_vector = self.Topic2TermCount[:, t] + self.eta_vector[t]
                # denominator_beta = self.Topic2TermCount.sum(axis=1) + sum(self.eta_vector)
                denominator_beta = self.Topic2TermCountSum + self.eta_vector_sum

                beta_vector = 1.0 * numerator_beta_vector / denominator_beta
                # theta_vector = 1.0 numerator_theta_vector / denominator_theta
                # denominator_theta is independent with t and k, so denominator could be any value except 0
                # will set denominator_theta as 1.0
                theta_vector = numerator_theta_vector

                p_vector = beta_vector * theta_vector
                # print p_vector
                p_vector = 1.0 * p_vector / sum(p_vector)
                # print p_vector
                sample_z = LldaModel._multinomial_sample(p_vector)
                z_vector[n] = sample_z

                k = sample_z
                doc_topic_count[k] += 1
                self.Topic2TermCount[k, t] += 1
                self.Topic2TermCountSum[k] += 1
        # reset self.Topic2TermCount
        for n, t in enumerate(term_vector):
            k = z_vector[n]
            self.Topic2TermCount[k, t] -= 1
            self.Topic2TermCountSum[k] -= 1

        numerator_theta_vector = doc_topic_count + doc_m_alpha_vector
        # denominator_theta = sum(doc_topic_count) + sum(doc_m_alpha_vector)
        denominator_theta = sum(numerator_theta_vector)
        theta_new = 1.0 * numerator_theta_vector / denominator_theta
        return theta_new

    def _gibbs_sample_inference_multi_processors(self, term_vector, iteration=30):
        """
        inference with gibbs sampling
        :param term_vector: the term vector of document
        :param iteration: the times of iteration
        :return: theta_new, a vector, theta_new[k] is the probability of doc(term_vector) to be generated from topic k
                 theta_new, a theta_vector, the doc-topic distribution
        """
        # print("gibbs sample inference iteration: %s" % iteration)
        # TODO: complete multi-processors code here
        # we copy all the shared variables may be modified on runtime
        random_state = np.random.RandomState()
        topic2term_count = self.Topic2TermCount.copy()
        topic2term_count_sum = self.Topic2TermCountSum.copy()

        doc_topic_count = np.zeros(self.K, dtype=int)
        p_vector = np.ones(self.K, dtype=int)
        p_vector = p_vector * 1.0 / sum(p_vector)
        z_vector = [LldaModel._multinomial_sample(p_vector, random_state=random_state) for _ in term_vector]
        for n, t in enumerate(term_vector):
            k = z_vector[n]
            doc_topic_count[k] += 1
            topic2term_count[k, t] += 1
            topic2term_count_sum[k] += 1

        # sum_doc_topic_count = sum(doc_topic_count)
        doc_m_alpha_vector = self.alpha_vector
        # sum_doc_m_alpha_vector = sum(doc_m_alpha_vector)
        for i in range(iteration):
            for n, t in enumerate(term_vector):
                k = z_vector[n]
                doc_topic_count[k] -= 1
                topic2term_count[k, t] -= 1
                topic2term_count_sum[k] -= 1

                numerator_theta_vector = doc_topic_count + doc_m_alpha_vector
                # denominator_theta = sum_doc_topic_count - 1 + sum_doc_m_alpha_vector

                numerator_beta_vector = topic2term_count[:, t] + self.eta_vector[t]
                # denominator_beta = self.Topic2TermCount.sum(axis=1) + sum(self.eta_vector)
                denominator_beta = topic2term_count_sum + self.eta_vector_sum

                beta_vector = 1.0 * numerator_beta_vector / denominator_beta
                # theta_vector = 1.0 numerator_theta_vector / denominator_theta
                # denominator_theta is independent with t and k, so denominator could be any value except 0
                # will set denominator_theta as 1.0
                theta_vector = numerator_theta_vector

                p_vector = beta_vector * theta_vector
                # print p_vector
                p_vector = 1.0 * p_vector / sum(p_vector)
                # print p_vector
                sample_z = LldaModel._multinomial_sample(p_vector, random_state)
                z_vector[n] = sample_z

                k = sample_z
                doc_topic_count[k] += 1
                topic2term_count[k, t] += 1
                topic2term_count_sum[k] += 1
        # reset self.Topic2TermCount
        # for n, t in enumerate(term_vector):
        #     k = z_vector[n]
        #     self.Topic2TermCount[k, t] -= 1
        #     self.Topic2TermCountSum[k] -= 1

        numerator_theta_vector = doc_topic_count + doc_m_alpha_vector
        # denominator_theta = sum(doc_topic_count) + sum(doc_m_alpha_vector)
        denominator_theta = sum(numerator_theta_vector)
        theta_new = 1.0 * numerator_theta_vector / denominator_theta
        return theta_new

    def training(self, iteration=10, log=False):
        """
        training this model with gibbs sampling
        :param log: print perplexity after every gibbs sampling if True
        :param iteration: the times of iteration
        :return: None
        """
        for i in range(iteration):
            if log:
                print "after iteration: %s, perplexity: %s" % (self.iteration, self.perplexity)
            self._gibbs_sample_training()
        pass

    def inference(self, document, iteration=30, times=10):
        # TODO: inference of a document
        """
        inference for one document
        :param times: the times of gibbs sampling, the result is the average value of all times(gibbs sampling)
        :param iteration: the times of iteration
        :param document: some sentence like "this is a method for inference"
        :return: theta_new, a vector, theta_new[k] is the probability of doc(term_vector) to be generated from topic k
                 theta_new, a theta_vector, the doc-topic distribution
        """

        words = document.split()
        term_vector = [self.vocabulary[word] for word in words if word in self.vocabulary]
        theta_new_accumulation = np.zeros(self.K, float)
        for time in range(times):
            theta_new = self._gibbs_sample_inference(term_vector, iteration=iteration)
            # print theta_new
            theta_new_accumulation += theta_new
        theta_new = 1.0 * theta_new_accumulation / times
        # print "avg: \n", theta_new
        doc_topic_new = [(self.topics[k], probability) for k, probability in enumerate(theta_new)]
        sorted_doc_topic_new = sorted(doc_topic_new,
                                      key=lambda topic_probability: topic_probability[1],
                                      reverse=True)
        return sorted_doc_topic_new
        pass

    def inference_multi_processors(self, document, iteration=30, times=8, max_workers=8):
        # TODO: inference of a document with multi processors
        """
        inference for one document
        :param times: the times of gibbs sampling, the result is the average value of all times(gibbs sampling)
        :param iteration: the times of iteration
        :param document: some sentence like "this is a method for inference"
        :param max_workers: the max number of processors(workers)
        :return: theta_new, a vector, theta_new[k] is the probability of doc(term_vector) to be generated from topic k
                 theta_new, a theta_vector, the doc-topic distribution
        """

        def _pickle_method(m):
            if m.im_self is None:
                return getattr, (m.im_class, m.im_func.func_name)
            else:
                return getattr, (m.im_self, m.im_func.func_name)
        copy_reg.pickle(types.MethodType, _pickle_method)

        words = document.split()
        term_vector = [self.vocabulary[word] for word in words if word in self.vocabulary]
        term_vectors = [term_vector for _ in range(times)]
        iterations = [iteration for _ in range(times)]

        with futures.ProcessPoolExecutor(max_workers) as executor:
            # print "executor.map"
            res = executor.map(self._gibbs_sample_inference_multi_processors, term_vectors, iterations)
        theta_new_accumulation = np.zeros(self.K, float)
        for theta_new in res:
            theta_new_accumulation += theta_new
        theta_new = 1.0 * theta_new_accumulation / times
        # print "avg: \n", theta_new
        doc_topic_new = [(self.topics[k], probability) for k, probability in enumerate(theta_new)]
        sorted_doc_topic_new = sorted(doc_topic_new,
                                      key=lambda topic_probability: topic_probability[1],
                                      reverse=True)
        return sorted_doc_topic_new
        pass

    def beta_k(self, k):
        """
        topic-term distribution
        beta_k[t] is the probability of term t(word) to be generated from topic k
        :return: a vector, shape is T
        """
        numerator_vector = self.Topic2TermCount[k] + self.eta_vector
        # denominator = sum(self.Topic2TermCount[k]) + sum(self.eta_vector)
        denominator = sum(numerator_vector)
        return 1.0 * numerator_vector / denominator

    def theta_m(self, m):
        """
        doc-topic distribution
        theta_m[k] is the probability of doc m to be generated from topic k
        :return: a vector, shape is K
        """
        numerator_vector = self.Doc2TopicCount[m] + self.alpha_vector * self.Lambda[m]
        # denominator = sum(self.Doc2TopicCount[m]) + sum(self.alpha_vector * self.Lambda[m])
        denominator = sum(numerator_vector)
        return 1.0 * numerator_vector / denominator

    @property
    def beta(self):
        """
        This name "beta" comes from
            "Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora, Daniel Ramage..."
        topic-term distribution
        beta[k, t] is the probability of term t(word) to be generated from topic k
        :return: a matrix, shape is K * T
        """
        numerator_matrix = self.Topic2TermCount + self.eta_vector
        # column vector
        # denominator_vector = self.Topic2TermCount.sum(axis=1).reshape(self.K, 1) + sum(self.eta_vector)
        denominator_vector = numerator_matrix.sum(axis=1).reshape(self.K, 1)
        return 1.0 * numerator_matrix / denominator_vector

        pass

    @property
    def theta(self):
        """
        doc-topic distribution
        theta[m, k] is the probability of doc m to be generated from topic k
        :return: a matrix, shape is M * K
        """
        numerator_matrix = self.Doc2TopicCount + self.alpha_vector * self.Lambda
        denominator_vector = numerator_matrix.sum(axis=1).reshape(self.M, 1)
        # column vector
        return 1.0 * numerator_matrix / denominator_vector
        pass

    @property
    def log_perplexity(self):
        """
        log perplexity of LDA topic model
        Reference:  Parameter estimation for text analysis, Gregor Heinrich.
        :return: a float value
        """
        beta = self.beta
        # theta = self.theta
        log_likelihood = 0
        # word_count = 0
        for m, theta_m in enumerate(self.theta):
            for t in self.W[m]:
                likelihood_t = np.inner(beta[:, t], theta_m)
                # print likelihood_t
                log_likelihood += -np.log(likelihood_t)
                # word_count += 1
        # assert word_count == self.WN, "word_count: %s\tself.WN: %s" % (word_count, self.WN)
        return 1.0 * log_likelihood / self.WN

    @property
    def perplexity(self):
        """
        perplexity of LDA topic model
        Reference:  Parameter estimation for text analysis, Gregor Heinrich.
        :return: a float value, perplexity = exp{log_perplexity}
        """
        return np.exp(self.log_perplexity)

    def __repr__(self):
        return "\nLabeled-LDA Model:\n" \
               "\tK = %s\n" \
               "\tM = %s\n" \
               "\tT = %s\n" \
               "\tWN = %s\n" \
               "\tLN = %s\n" \
               "\talpha = %s\n" \
               "\teta = %s\n" \
               "\tperplexity = %s\n" \
               "\t" % (self.K, self.M, self.T, self.WN, self.LN, self.alpha_vector[0], self.eta_vector[0], self.perplexity)
        pass

    class SaveModel:
        def __init__(self, save_model_dict=None):
            self.alpha_vector = []
            self.eta_vector = []
            self.terms = []
            self.vocabulary = {}
            self.topics = []
            self.topic_vocabulary = {}
            self.W = []
            self.Z = []
            self.K = 0
            self.M = 0
            self.T = 0
            self.WN = 0
            self.LN = 0
            self.iteration = 0

            # the following fields cannot be dumped into json file
            # we need write them with np.save() and read them with np.load()
            # self.Doc2TopicCount = None
            # self.Topic2TermCount = None
            self.Lambda = None

            if save_model_dict is not None:
                self.__dict__ = save_model_dict
        pass

    @staticmethod
    def _read_object_from_file(file_name):
        """
        read an object from json file
        :param file_name: json file name
        :return: None if file doesn't exist or can not convert to an object by json, else return the object
        """
        if os.path.exists(file_name) is False:
            print ("Error read path: [%s]" % file_name)
            return None
        with open(file_name, 'r') as f:
            try:
                obj = json.load(f)
            except Exception:
                print ("Error json: [%s]" % f.read()[0:10])
                return None
        return obj

    @staticmethod
    def _write_object_to_file(file_name, target_object):
        """
        write the object to file with json(if the file exists, this function will overwrite it)
        :param file_name: the name of new file
        :param target_object: the target object for writing
        :return: True if success else False
        """
        dirname = os.path.dirname(file_name)
        LldaModel._find_and_create_dirs(dirname)
        try:
            with open(file_name, "w") as f:
                json.dump(target_object, f, skipkeys=False, ensure_ascii=False, check_circular=True, allow_nan=True, cls=None, indent=True, separators=None, encoding="utf-8", default=None, sort_keys=False)
        except Exception, e:
            message = "Write [%s...] to file [%s] error: json.dump error" % (str(target_object)[0:10], file_name)
            print ("%s\n\t%s" % (message, e.message))
            print "e.message: ", e.message
            return False
        else:
            # print ("Write %s" % file_name)
            return True

    @staticmethod
    def _find_and_create_dirs(dir_name):
        """
        find dir, create it if it doesn't exist
        :param dir_name: the name of dir
        :return: the name of dir
        """
        if os.path.exists(dir_name) is False:
            os.makedirs(dir_name)
        return dir_name

    def save_model_to_dir(self, dir_name, save_derivative_properties=False):
        """
        save model to directory dir_name
        :param save_derivative_properties: save derivative properties if True
            some properties are not necessary save to disk, they could be derived from some basic properties,
            we call they derivative properties.
            to save derivative properties to disk:
                it will reduce the time of loading model from disk (read properties directly but do not compute them)
                but, meanwhile, it will take up more disk space
        :param dir_name: the target directory name
        :return: None
        """
        save_model = LldaModel.SaveModel()
        save_model.alpha_vector = self.alpha_vector
        save_model.eta_vector = self.eta_vector
        save_model.terms = self.terms
        save_model.vocabulary = self.vocabulary
        save_model.topics = self.topics
        save_model.topic_vocabulary = self.topic_vocabulary
        save_model.W = self.W
        save_model.Z = self.Z
        save_model.K = self.K
        save_model.M = self.M
        save_model.T = self.T
        save_model.WN = self.WN
        save_model.LN = self.LN
        save_model.iteration = self.iteration

        save_model_path = os.path.join(dir_name, "llda_model.json")
        LldaModel._write_object_to_file(save_model_path, save_model.__dict__)

        np.save(os.path.join(dir_name, "Lambda.npy"), self.Lambda)
        # save derivative properties
        if save_derivative_properties:
            np.save(os.path.join(dir_name, "Doc2TopicCount.npy"), self.Doc2TopicCount)
            np.save(os.path.join(dir_name, "Topic2TermCount.npy"), self.Topic2TermCount)
            np.save(os.path.join(dir_name, "alpha_vector_Lambda.npy"), self.alpha_vector_Lambda)
            np.save(os.path.join(dir_name, "eta_vector_sum.npy"), self.eta_vector_sum)
            np.save(os.path.join(dir_name, "Topic2TermCountSum.npy"), self.Topic2TermCountSum)
        pass

    def load_model_from_dir(self, dir_name, load_derivative_properties=True):
        """
        load model from directory dir_name
        :param load_derivative_properties: load derivative properties from disk if True
        :param dir_name: the target directory name
        :return: None
        """
        save_model_path = os.path.join(dir_name, "llda_model.json")
        save_model_dict = LldaModel._read_object_from_file(save_model_path)
        save_model = LldaModel.SaveModel(save_model_dict=save_model_dict)
        self.alpha_vector = save_model.alpha_vector
        self.eta_vector = save_model.eta_vector
        self.terms = save_model.terms
        self.vocabulary = save_model.vocabulary
        self.topics = save_model.topics
        self.topic_vocabulary = save_model.topic_vocabulary
        self.W = save_model.W
        self.Z = save_model.Z
        self.K = save_model.K
        self.M = save_model.M
        self.T = save_model.T
        self.WN = save_model.WN
        self.LN = save_model.LN
        self.iteration = save_model.iteration

        self.Lambda = np.load(os.path.join(dir_name, "Lambda.npy"))

        # load load_derivative properties
        if load_derivative_properties:
            try:
                self.Doc2TopicCount = np.load(os.path.join(dir_name, "Doc2TopicCount.npy"))
                self.Topic2TermCount = np.load(os.path.join(dir_name, "Topic2TermCount.npy"))
                self.alpha_vector_Lambda = np.load(os.path.join(dir_name, "alpha_vector_Lambda.npy"))
                self.eta_vector_sum = np.load(os.path.join(dir_name, "eta_vector_sum.npy"))
                self.Topic2TermCountSum = np.load(os.path.join(dir_name, "Topic2TermCountSum.npy"))
            except IOError or ValueError, e:
                print("%s: load derivative properties fail, initialize them with basic properties" % e)
                self._initialize_derivative_fields()
        else:
            self._initialize_derivative_fields()
        pass

    def update(self, labeled_documents=None):
        """
        update model with labeled documents, incremental update
        :return: None
        """
        self.all_perplexities = []
        if labeled_documents is None:
            pass

        new_labels = []
        new_words = []
        new_doc_corpus = []
        new_labels_corpus = []
        for document, labels in labeled_documents:
            doc_words = document.split()
            new_doc_corpus.append(doc_words)
            if labels is None:
                labels = []
            labels.append("common_topic")
            new_labels_corpus.append(labels)
            new_words.extend(doc_words)
            new_labels.extend(labels)
        # self.terms = list(set(new_words))
        new_terms = set(new_words) - set(self.terms)
        self.terms.extend(new_terms)
        self.vocabulary = {term: index for index, term in enumerate(self.terms)}

        # self.topics = list(set(new_labels))
        new_topics = set(new_labels) - set(self.topics)
        self.topics.extend(new_topics)
        self.topic_vocabulary = {topic: index for index, topic in enumerate(self.topics)}

        old_K = self.K
        old_T = self.T
        self.K = len(self.topics)
        self.T = len(self.terms)

        # self.W = [[self.vocabulary[term] for term in doc_words] for doc_words in new_doc_corpus]
        new_w_vectors = [[self.vocabulary[term] for term in doc_words] for doc_words in new_doc_corpus]
        for new_w_vector in new_w_vectors:
            self.W.append(new_w_vector)

        old_M = self.M
        old_WN = self.WN
        self.M = len(self.W)
        self.WN += len(new_words)
        # we appended topic "common_topic" to each doc at the beginning
        # so we need minus the number of "common_topic"
        # LN is the number of original labels
        old_LN = self.LN

        self.LN += len(new_labels) + len(new_labels_corpus)

        old_Lambda = self.Lambda
        self.Lambda = np.zeros((self.M, self.K), dtype=float)
        for m in range(self.M):
            if m < old_M:
                # if the old document has no topic, we also init it to all topics here
                if sum(old_Lambda[m]) == old_K:
                    # set all value of self.Lambda[m] to 1.0
                    self.Lambda[m] += 1.0
                continue
            # print m, old_M
            if len(new_labels_corpus[m-old_M]) == 1:
                new_labels_corpus[m-old_M] = self.topics
            for label in new_labels_corpus[m-old_M]:
                k = self.topic_vocabulary[label]
                self.Lambda[m, k] = 1.0

        # TODO: the following 2 fields should be modified again if alpha_vector is not constant vector
        self.alpha_vector = [self.alpha_vector[0] for _ in range(self.K)]
        self.eta_vector = [self.eta_vector[0] for _ in range(self.T)]

        # self.Z = []
        for m in range(old_M, self.M):
            # print "self.Lambda[m]: ", self.Lambda[m]
            numerator_vector = self.Lambda[m] * self.alpha_vector
            p_vector = numerator_vector / sum(numerator_vector)
            # print p_vector
            # print "p_vector: ", p_vector
            # z_vector is a vector of a document,
            # just like [2, 3, 6, 0], which means this doc have 4 word and them generated
            # from the 2nd, 3rd, 6th, 0th topic, respectively
            z_vector = [LldaModel._multinomial_sample(p_vector) for _ in range(len(self.W[m]))]
            self.Z.append(z_vector)

        self._initialize_derivative_fields()
        pass

    @staticmethod
    def _extend_matrix(origin=None, shape=None, padding_value=0):
        """
        for quickly extend the matrices when update
        extend origin matrix with shape, padding with padding_value
        :type shape: the shape of new matrix
        :param origin: np.ndarray, the original matrix
        :return: np.ndarray, a matrix with new shape
        """
        new_matrix = np.zeros(shape, dtype=origin.dtype)

        for row in range(new_matrix.shape[0]):
            for col in range(new_matrix.shape[1]):
                if row < origin.shape[0] and col < origin.shape[0]:
                    new_matrix[row, col] = origin[row, col]
                else:
                    new_matrix[row, col] = padding_value

        return new_matrix
        pass

    @property
    def is_convergent(self):
        """
        is this model convergent?
        :return: True if model is convergent
        """
        if len(self.all_perplexities) < 10:
            return False
        perplexities = self.all_perplexities[-10:]
        if max(perplexities) - min(perplexities) > 0.5:
            return False
        return True


if __name__ == "__main__":
    pass