[
  {
    "path": ".gitignore",
    "content": "# Apple\n.DS_Store\n\n# Logs\nlogs\n*.log\nnpm-debug.log*\n\n# Runtime data\npids\n*.pid\n*.seed\n*.pid.lock\n\n# Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files)\n.grunt\n\n# node-waf configuration\n.lock-wscript\n\n# Compiled binary addons (http://nodejs.org/api/addons.html)\nbuild/Release\n\n# Dependency directories\nnode_modules\njspm_packages\n\n# Optional npm cache directory\n.npm\n\n# Optional eslint cache\n.eslintcache\n\n# Optional REPL history\n.node_repl_history\n\n# Output of 'npm pack'\n*.tgz\n\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n# ipython/\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\n.venv/\nvenv/\nENV/\n\nv# Misc data files\n*.h5\n*.omx\n\n# Auxilarly files\n*.pyc\n\n# Visual Studio\n*.vscode\n# used for vscode nosetest debugging\nnose_stub.py\n.noseids\n\n# Emacs\n*~\n\n\n# metastore\nmetastore_db/*\nexamples/notebooks/metastore_db/*\n\n\n\n"
  },
  {
    "path": ".travis.yml",
    "content": "language: python\npython:\n  - \"3.7\"\n  - \"3.8\"\n  - \"3.9\"\n\n# command to install dependencies\ninstall:\n - pip install -r requirements.txt --upgrade\n - pip install flake8\n - pip install coveralls\n\n# command to execute test suite\nscript:\n - flake8 .\n - nosetests --with-coverage --cover-package=IOHMM\n\n# Send results of tests to coveralls\nafter_success:\n - coveralls\n"
  },
  {
    "path": "IOHMM/IOHMM.py",
    "content": "'''\nThis module implements IOHMM models:\n\n(1) UnSupervisedIOHMM:\n    Standard IOHMM with no ground truth label of hidden states.\n\n(2) SemiSupervisedIOHMM:\n    With a little ground truth labels of hidden states, use these labels to\n    direct the learning process during EM.\n\n(3) SupervisedIOHMM:\n    With some ground truth labels of hidden states,\n    use only ground truth labels to train. There are no iterations of EM.\n\nThe structure of the code is inspired by\ndepmixS4: An R Package for Hidden Markov Models:\nhttps://cran.r-project.org/web/packages/depmixS4/vignettes/depmixS4.pdf\n\nFeatures:\n1. Can take a list of dataframes each representing a sequence.\n2. Forward Backward algorithm fully vectorized.\n3. Support json-serialization of the model so that model can be saved and loaded easily.\n'''\n\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom builtins import range\nfrom builtins import object\nfrom copy import deepcopy\nimport logging\nimport os\nimport warnings\n\n\nimport numpy as np\n\n\nfrom .forward_backward import forward_backward\nfrom .linear_models import (GLM, OLS, DiscreteMNL, CrossEntropyMNL)\n\n\nwarnings.simplefilter(\"ignore\")\nnp.random.seed(0)\nEPS = np.finfo(float).eps\n\n\nclass LinearModelLoader(object):\n    \"\"\"\n    The map from data_type of a linear model\n    ('GLM', 'OLS', 'DiscreteMNL', 'CrossEntropyMNL')\n    to the correct class.\n    \"\"\"\n    GLM = GLM\n    OLS = OLS\n    DiscreteMNL = DiscreteMNL\n    CrossEntropyMNL = CrossEntropyMNL\n\n\nclass BaseIOHMM(object):\n    \"\"\"\n    Base class for IOHMM models. Should not be directly called.\n    Intended for subclassing.\n    \"\"\"\n\n    def __init__(self, num_states=2):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        num_states: the number of hidden states\n        \"\"\"\n        self.num_states = num_states\n        self.trained = False\n\n    def set_models(self, model_emissions,\n                   model_initial=CrossEntropyMNL(),\n                   model_transition=CrossEntropyMNL(), trained=False):\n        \"\"\"\n        Set the initial probability model, transition probability models,\n        and emission models.\n        (1) model_initial: a linear model\n        (2) model_transitions: a list of linear models, one for each hidden state.\n        (3) model_emissions: a list of list of linear models,\n                             the outer list is for each hidden state,\n                             the inner list is for each emission model.\n        Parameters\n        ----------\n        trained: a boolean indicating whether the models are already trained.\n                 If the models are already trained, set the models directly,\n                 otherwise initialize from empty linear models.\n        if trained models, then the parameters are:\n            model_initial: a linear model\n            model_transitions: a list of linear models, one for each hidden state.\n            model_emissions: a list of list of linear models,\n                             the outer list is for each hidden state,\n                             the inner list is for each emission model.\n        otherwise:\n            model_initial: the initial probability model (simply indicates its type)\n            model_transition: the transition probability model (simply indicates its type)\n            model_emissions: list of linear models, one for each emission.\n\n        Notes\n        -------\n        Initial model and transition model must be CrossEntropyMNL models\n        \"\"\"\n        if trained:\n            self.model_initial = model_initial\n            self.model_transition = model_transition\n            self.model_emissions = model_emissions\n            self.trained = True\n        else:\n            self.model_initial = model_initial\n            self.model_transition = [deepcopy(model_initial) for _ in range(self.num_states)]\n            self.model_emissions = [deepcopy(model_emissions) for _ in range(self.num_states)]\n\n    def set_inputs(self, covariates_initial, covariates_transition, covariates_emissions):\n        \"\"\"\n        Set input covariates for initial, transition and emission models\n        Parameters\n        ----------\n        covariates_initial: list of strings,\n                            indicates the field names in the dataframe\n                            to use as the independent variables.\n        covariates_transition: list of strings,\n                               indicates the field names in the dataframe\n                               to use as the independent variables.\n        covariates_emissions: list of list of strings, each outer list is for one emission model\n                              and each inner list of strings\n                              indicates the field names in the dataframe\n                              to use as the independent variables.\n        \"\"\"\n        self.covariates_initial = covariates_initial\n        self.covariates_transition = covariates_transition\n        self.covariates_emissions = covariates_emissions\n\n    def set_outputs(self, responses_emissions):\n        \"\"\"\n        Set output covariates for emission models\n        Parameters\n        ----------\n        responses_emissions: list of list of strings, each outer list is for one emission\n                             and each inner list of strings\n                             indicates the field names in the dataframe\n                             to use as the dependent variables.\n        Notes\n        ----------\n        Emission model such as Multivariate OLS, CrossEntropyMNL\n        will have multiple strings (columns) in the inner list.\n        \"\"\"\n        self.responses_emissions = responses_emissions\n        self.num_emissions = len(responses_emissions)\n\n    def set_data(self, dfs):\n        \"\"\"\n        Set data for the model\n        Parameters\n        ----------\n        dfs: a list of dataframes, each df represents a sequence.\n        Notes\n        ----------\n        The column names of each df must contains the covariates and response fields\n        specified above.\n        \"\"\"\n        raise NotImplementedError\n\n    def _initialize(self, with_randomness=True):\n        \"\"\"\n        Initialize\n        (1) log_gammas: list of arrays, the state posterior probability for each sequence\n        (2) log_epsilons: list of arrays, the state posterior 'transition' probability\n            (joint probability of two consecutive points) for each sequence\n        based on the ground truth labels supplied.\n        (3) log likelihood as negative inifinity\n        (4) inp_initials: list of arrays of shape (1, len(covariates_initial)),\n                          the independent variables for the initial activity of each sequence.\n        (5) inp_initials_all_sequences: array of shape(len(sequences), len(covariates_initial)),\n                                        the concatenation of inp_initials.\n        (6) inp_transitions: list of arrays of shape (df.shape[0]-1, len(covariates_transition)),\n                             the independent variables for the transition activity of each sequence.\n                             (Note that the -1 is for the first activity,\n                             there is no transition, it is used for initial independent variable.)\n        (7) inp_transitions_all_sequences: array of shape\n                                           (sum(df.shape[0]-1 for df in dfs),\n                                           len(covariates_transition)),\n                                           the concatenation of inp_transitions.\n        (8) inp_emissions: list of list of arrays of shape\n                           (df.shape[0], len(covariates_emission[i])).\n                           The outer list is for each df (sequence).\n                           The inner list is for each emission model.\n        (9) inp_emissions_all_sequences: list of array of shape\n                                         (sum(df.shape[0] for df in dfs),\n                                         len(covariates_emission[i])).\n                                         The list is for each emission model.\n                                         This is the concatenation of all sequences\n                                         for each emission model.\n        (10) out_emissions: list of list of arrays of shape\n                            (df.shape[0], len(response_emission[i])).\n                            The outer list is for each df (sequence).\n                            The inner list is for each emission model.\n        (11) out_emissions_all_sequences: list of array of shape\n                                          (sum(df.shape[0] for df in dfs),\n                                          len(response_emission[i])).\n                                          The list is for each emission model.\n                                          This is the concatenation of all sequences\n                                          for each emission model.\n\n\n        Parameters\n        ----------\n        with_randomness: After initializing log_gammas and log_epsilons,\n                         there might be some states that no sample is associated with it.\n                         In this case, should we add some random posterior probability to it,\n                         so as to start the EM iterations?\n\n                         For UnsupervisedIOHMM and SemiSupervisedIOHMM this is set to True,\n                         since we want to use EM iterations to figure out the true posterior.\n\n                         For SupervisedIOHMM, this is set to False,\n                         since we only want to use labeled data for training.\n        \"\"\"\n        def _initialize_log_gamma(df, log_state):\n            \"\"\"\n            Initialize posterior probability for a dataframe and the log_state provided.\n            Parameters\n            ----------\n            df: The dataframe for a sequence, actually we only need its length.\n            log_state: a dictionary (int -> array of shape (num_states, )).\n                       The log_state[t] is the ground truth hidden state array of time stamp t.\n                       log_state[t][k] is 0 and log_state[t][~k] is -np.Infinity\n                       if the hidden state of timestamp t is k.\n            Returns:\n            ----------\n            log_gamma: array of shape (df.shape[0], num_states).\n                       The posterior probability of each timestamp.\n                       log_gamma[t][k] is 0 and log_gamma[t][k] is -np.Infinity\n                       if the hidden state of timestamp t is k.\n                       If at time stamp t there is no ground truth,\n                       log_gamma[t] will be all -np.Infinity.\n            \"\"\"\n            log_gamma = np.log(np.zeros((df.shape[0], self.num_states)))\n            for time_stamp in log_state:\n                log_gamma[time_stamp, :] = log_state[time_stamp]\n            return log_gamma\n\n        def _initialize_log_epsilon(df, log_state):\n            \"\"\"\n            Initialize posterior joint probability of two consecutive timestamp\n            for a dataframe and the log_state provided.\n            Parameters\n            ----------\n            df: The dataframe for a sequence, actually we only need its length.\n            log_state: a dictionary (int -> array of shape (num_states, )).\n                       The log_state[i] is the ground truth hidden state array of time stamp i.\n                       log_state[i][k] is 0 and log_state[i][~k] is -np.Infinity\n                       if the hidden state of timestamp i is k.\n            Returns:\n            ----------\n            log_epsilon: array of shape (df.shape[0] - 1, num_states, num_states).\n                         The posterior joint probability of two consecutive points.\n                         log_epsilon[t][k][j] is 0 and log_epsilon[t][~k][~j] is -np.Infinity\n                         if the hidden state of timestamp t is k and\n                         hidden state of timestamp t+1 is j.\n                         If at time stamp t or t+1 there is no ground truth,\n                         log_epsilon[t] will be all -np.Infinity.\n\n            \"\"\"\n            log_epsilon = np.log(np.zeros((df.shape[0] - 1, self.num_states, self.num_states)))\n            for time_stamp in log_state:\n                if time_stamp + 1 in log_state:\n                    # actually should find the index of 1\n                    st = int(np.argmax(log_state[time_stamp]))\n                    log_epsilon[time_stamp, st, :] = log_state[time_stamp + 1]\n            return log_epsilon\n\n        # initialize log_gammas\n        self.log_gammas = [_initialize_log_gamma(df, log_state)\n                           for df, log_state in self.dfs_logStates]\n        # initialize log_epsilons\n        self.log_epsilons = [_initialize_log_epsilon(df, log_state)\n                             for df, log_state in self.dfs_logStates]\n        if with_randomness:\n            for st in range(self.num_states):\n                if np.exp(np.hstack([lg[:, st] for lg in self.log_gammas])).sum() < EPS:\n                    # there is no any sample associated with this state\n                    for lg in self.log_gammas:\n                        lg[:, st] = np.random.rand(lg.shape[0])\n            for st in range(self.num_states):\n                if np.exp(np.vstack([le[:, st, :] for le in self.log_epsilons])).sum() < EPS:\n                    # there is no any sample associated with this state\n                    for le in self.log_epsilons:\n                        le[:, st, :] = np.random.rand(le.shape[0], self.num_states)\n\n        # initialize log_likelihood\n        self.log_likelihoods = [-np.Infinity for _ in range(self.num_seqs)]\n        self.log_likelihood = -np.Infinity\n\n        # initialize input/output covariates\n        self.inp_initials = [np.array(df[self.covariates_initial].iloc[0]).reshape(\n            1, -1).astype('float64') for df, log_state in self.dfs_logStates]\n        self.inp_initials_all_sequences = np.vstack(self.inp_initials)\n\n        self.inp_transitions = [np.array(df[self.covariates_transition].iloc[1:]).astype(\n            'float64') for df, log_state in self.dfs_logStates]\n        self.inp_transitions_all_sequences = np.vstack(self.inp_transitions)\n\n        self.inp_emissions = [[np.array(df[cov]).astype('float64') for\n                               cov in self.covariates_emissions]\n                              for df, log_state in self.dfs_logStates]\n        self.inp_emissions_all_sequences = [np.vstack([seq[emis] for\n                                                       seq in self.inp_emissions]) for\n                                            emis in range(self.num_emissions)]\n        self.out_emissions = [[np.array(df[res]) for\n                               res in self.responses_emissions]\n                              for df, log_state in self.dfs_logStates]\n\n        self.out_emissions_all_sequences = [np.vstack([seq[emis] for\n                                                       seq in self.out_emissions]) for\n                                            emis in range(self.num_emissions)]\n\n    def E_step(self):\n        \"\"\"\n        The Expectation step, Update\n        (1) log_gammas: list of arrays, state posterior probability for each sequence\n        (2) log_epsilons: list of arrays, state posterior 'transition' probability\n            (joint probability of two consecutive points) for each sequence\n        (3) log likelihood\n        based on the model coefficients from last iteration,\n        with respect to the ground truth hidden states if any.\n        \"\"\"\n        self.log_gammas = []\n        self.log_epsilons = []\n        self.log_likelihoods = []\n        for seq in range(self.num_seqs):\n            n_records = self.dfs_logStates[seq][0].shape[0]\n            # initial probability\n            log_prob_initial = self.model_initial.predict_log_proba(\n                self.inp_initials[seq]).reshape(self.num_states,)\n            # transition probability\n            log_prob_transition = np.zeros((n_records - 1, self.num_states, self.num_states))\n            for st in range(self.num_states):\n                log_prob_transition[:, st, :] = self.model_transition[st].predict_log_proba(\n                    self.inp_transitions[seq])\n            assert log_prob_transition.shape == (n_records - 1, self.num_states, self.num_states)\n            # emission probability\n            log_Ey = np.zeros((n_records, self.num_states))\n            for emis in range(self.num_emissions):\n                model_collection = [models[emis] for models in self.model_emissions]\n                log_Ey += np.vstack([model.loglike_per_sample(\n                    np.array(self.inp_emissions[seq][emis]).astype('float64'),\n                    np.array(self.out_emissions[seq][emis])) for model in model_collection]).T\n            # forward backward to calculate posterior\n            log_gamma, log_epsilon, log_likelihood = forward_backward(\n                log_prob_initial, log_prob_transition, log_Ey, self.dfs_logStates[seq][1])\n            self.log_gammas.append(log_gamma)\n            self.log_epsilons.append(log_epsilon)\n            self.log_likelihoods.append(log_likelihood)\n        self.log_likelihood = sum(self.log_likelihoods)\n\n    def M_step(self):\n        \"\"\"\n        The Maximization step, Update\n        (1) model_initial: a linear model\n        (2) model_transitions: a list of linear models, one for each hidden state.\n        (3) model_emissions: a list of list of linear models,\n                             the outer list is for each hidden state,\n                             the inner list is for each emission model.\n        based on the posteriors, and dependent/independent covariates.\n        Notes:\n        ----------\n        In the emission models, if the sum of sample weight is zero,\n        the linear model will raise ValueError.\n        \"\"\"\n\n        # optimize initial model\n        X = self.inp_initials_all_sequences\n        Y = np.exp(np.vstack([lg[0, :].reshape(1, -1) for lg in self.log_gammas]))\n        self.model_initial.fit(X, Y)\n\n        # optimize transition models\n        X = self.inp_transitions_all_sequences\n        for st in range(self.num_states):\n            Y = np.exp(np.vstack([eps[:, st, :] for eps in self.log_epsilons]))\n            self.model_transition[st].fit(X, Y)\n\n        # optimize emission models\n        for emis in range(self.num_emissions):\n            X = self.inp_emissions_all_sequences[emis]\n            Y = self.out_emissions_all_sequences[emis]\n            for st in range(self.num_states):\n                sample_weight = np.exp(np.hstack([lg[:, st] for lg in self.log_gammas]))\n                self.model_emissions[st][emis].fit(X, Y, sample_weight=sample_weight)\n\n    def train(self):\n        \"\"\"\n        The ieratioin of EM step,\n        Notes:\n        ----------\n        For SupervisedIOHMM, max_EM_iter is 1, thus will only go through one iteration of EM step,\n        which means that it will only use the ground truth hidden states to train.\n        \"\"\"\n        for it in range(self.max_EM_iter):\n            log_likelihood_prev = self.log_likelihood\n            self.M_step()\n            self.E_step()\n            logging.info('log likelihood of iteration {0}: {1:.4f}'.format(it, self.log_likelihood))\n            if abs(self.log_likelihood - log_likelihood_prev) < self.EM_tol:\n                break\n        self.trained = True\n\n    def to_json(self, path):\n        \"\"\"\n        Generate json object of the IOHMM model\n        Parameters\n        ----------\n        path : the path to save the model\n        Returns\n        -------\n        json_dict: a dictionary containing the attributes of the model\n        \"\"\"\n        json_dict = {\n            'data_type': self.__class__.__name__,\n            'properties': {\n                'num_states': self.num_states,\n                'covariates_initial': self.covariates_initial,\n                'covariates_transition': self.covariates_transition,\n                'covariates_emissions': self.covariates_emissions,\n                'responses_emissions': self.responses_emissions,\n                'model_initial': self.model_initial.to_json(\n                    path=os.path.join(path, 'model_initial')),\n                'model_transition': [self.model_transition[st].to_json(\n                    path=os.path.join(path, 'model_transition', 'state_{}'.format(st))) for\n                    st in range(self.num_states)],\n                'model_emissions': [[self.model_emissions[st][emis].to_json(\n                    path=os.path.join(\n                        path, 'model_emissions', 'state_{}'.format(st), 'emission_{}'.format(emis))\n                ) for emis in range(self.num_emissions)] for st in range(self.num_states)]\n            }\n        }\n        return json_dict\n\n    @classmethod\n    def _from_setup(\n            cls, json_dict, num_states,\n            model_initial, model_transition, model_emissions,\n            covariates_initial, covariates_transition, covariates_emissions,\n            responses_emissions, trained):\n        \"\"\"\n        Helper function to construct the IOHMM model used by from_json and from_config.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        num_states: number of hidden states\n        trained: a boolean indicating whether the models are already trained.\n                 If the models are already trained, set the models directly,\n                 otherwise initialize from empty linear models.\n        if trained models, then the parameters are:\n            model_initial: a linear model\n            model_transitions: a list of linear models, one for each hidden state.\n            model_emissions: a list of list of linear models,\n                             the outer list is for each hidden state,\n                             the inner list is for each emission model.\n        otherwise:\n            model_initial: the initial probability model (simply indicates its type)\n            model_transition: the transition probability model (simply indicates its type)\n            model_emissions: list of linear models, one for each emission.\n        covariates_initial: list of strings,\n                            each indicates the field name in the dataframe\n                            to use as the independent variables.\n        covariates_transition: list of strings,\n                               each indicates the field name in the dataframe\n                               to use as the independent variables.\n        covariates_emissions: list of list of strings, each outer list is for one emission model\n                              and each inner list of strings\n                              indicates the field names in the dataframe\n                              to use as the independent variables.\n        responses_emissions: list of list of strings, each outer list is for one emission\n                             and each inner list of strings\n                             indicates the field names in the dataframe\n                             to use as the dependent variables.\n        Returns\n        -------\n        IOHMM object: an IOHMM object specified by the json_dict and other arguments\n        \"\"\"\n        model = cls(num_states=num_states)\n        model.set_models(\n            model_initial=model_initial,\n            model_transition=model_transition,\n            model_emissions=model_emissions,\n            trained=trained)\n        model.set_inputs(covariates_initial=covariates_initial,\n                         covariates_transition=covariates_transition,\n                         covariates_emissions=covariates_emissions)\n        model.set_outputs(responses_emissions=responses_emissions)\n        return model\n\n    @classmethod\n    def from_config(cls, json_dict):\n        \"\"\"\n        Construct an IOHMM object from a json dictionary which specifies the structure of the model.\n        Parameters\n        ----------\n        json_dict: a json dictionary containing the config/structure of the IOHMM.\n        Returns\n        -------\n        IOHMM: an IOHMM object specified by the json_dict\n        \"\"\"\n        return cls._from_setup(\n            json_dict,\n            num_states=json_dict['properties']['num_states'],\n            model_initial=getattr(\n                LinearModelLoader, json_dict['properties']['model_initial']['data_type'])(\n                    **json_dict['properties']['model_initial']['properties']),\n            model_transition=getattr(\n                LinearModelLoader, json_dict['properties']['model_transition']['data_type'])(\n                    **json_dict['properties']['model_transition']['properties']),\n            model_emissions=[getattr(\n                LinearModelLoader, model_emission['data_type'])(**model_emission['properties'])\n                for model_emission in json_dict['properties']['model_emissions']],\n            covariates_initial=json_dict['properties']['covariates_initial'],\n            covariates_transition=json_dict['properties']['covariates_transition'],\n            covariates_emissions=json_dict['properties']['covariates_emissions'],\n            responses_emissions=json_dict['properties']['responses_emissions'],\n            trained=False)\n\n    @classmethod\n    def from_json(cls, json_dict):\n        \"\"\"\n        Construct an IOHMM object from a saved json dictionary.\n        Parameters\n        ----------\n        json_dict: a json dictionary containing the attributes of the IOHMM.\n        Returns\n        -------\n        IOHMM: an IOHMM object specified by the json_dict\n        \"\"\"\n        return cls._from_setup(\n            json_dict,\n            num_states=json_dict['properties']['num_states'],\n            model_initial=getattr(\n                LinearModelLoader, json_dict['properties']['model_initial']['data_type']).from_json(\n                json_dict['properties']['model_initial']),\n            model_transition=[getattr(\n                LinearModelLoader, model_transition_json['data_type']\n            ).from_json(model_transition_json) for\n                model_transition_json in json_dict['properties']['model_transition']],\n            model_emissions=[[getattr(\n                LinearModelLoader, model_emission_json['data_type']\n            ).from_json(model_emission_json) for model_emission_json in model_emissions_json] for\n                model_emissions_json in json_dict['properties']['model_emissions']],\n            covariates_initial=json_dict['properties']['covariates_initial'],\n            covariates_transition=json_dict['properties']['covariates_transition'],\n            covariates_emissions=json_dict['properties']['covariates_emissions'],\n            responses_emissions=json_dict['properties']['responses_emissions'],\n            trained=True)\n\n\nclass UnSupervisedIOHMM(BaseIOHMM):\n    \"\"\"\n    Unsupervised IOHMM models.\n    This model is intended to be used when no ground truth hidden states are available.\n    \"\"\"\n\n    def __init__(self, num_states=2, EM_tol=1e-4, max_EM_iter=100):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        num_states: the number of hidden states\n        EM_tol: the tolerance of the EM iteration convergence\n        max_EM_iter: the maximum number of EM iterations\n        -------\n        \"\"\"\n        super(UnSupervisedIOHMM, self).__init__(num_states=num_states)\n        self.EM_tol = EM_tol\n        self.max_EM_iter = max_EM_iter\n\n    def set_data(self, dfs):\n        \"\"\"\n        Set data for the model\n        Constructs:\n        ----------\n        (1) num_seqs: number of seqences\n        (2) dfs_logStates: list of (dataframe, log_state)\n        (3) posteriors with randomness and input/output covariates\n        Parameters\n        ----------\n        dfs: a list of dataframes, each df represents a sequence.\n        Notes\n        ----------\n        The column names of each df must contains the covariates and response fields\n        specified above.\n\n        Since there are no ground truth hidden states, all log_state should be empty {}.\n        \"\"\"\n        assert all([df.shape[0] > 0 for df in dfs])\n        self.num_seqs = len(dfs)\n        self.dfs_logStates = [[x, {}] for x in dfs]\n        self._initialize(with_randomness=True)\n\n    def to_json(self, path):\n        \"\"\"\n        Generate json object of the UnSupervisedIOHMM/SemiSupervisedIOHMM model\n        Parameters\n        ----------\n        path : the path to save the model\n        Returns\n        -------\n        json_dict: a dictionary containing the attributes of the model\n        \"\"\"\n        json_dict = super(UnSupervisedIOHMM, self).to_json(path)\n        json_dict['properties'].update(\n            {\n                'EM_tol': self.EM_tol,\n                'max_EM_iter': self.max_EM_iter,\n            }\n        )\n        return json_dict\n\n    @classmethod\n    def _from_setup(\n            cls, json_dict, num_states,\n            model_initial, model_transition, model_emissions,\n            covariates_initial, covariates_transition, covariates_emissions,\n            responses_emissions, trained):\n        \"\"\"\n        Helper function to construct the UnSupervisedIOHMM/SemiSupervisedIOHMM model\n        used by from_json and from_config.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        num_states: number of hidden states\n        trained: a boolean indicating whether the models are already trained.\n                 If the models are already trained, set the models directly,\n                 otherwise initialize from empty linear models.\n        if trained models, then the parameters are:\n            model_initial: a linear model\n            model_transitions: a list of linear models, one for each hidden state.\n            model_emissions: a list of list of linear models,\n                             the outer list is for each hidden state,\n                             the inner list is for each emission model.\n        otherwise:\n            model_initial: the initial probability model (simply indicates its type)\n            model_transition: the transition probability model (simply indicates its type)\n            model_emissions: list of linear models, one for each emission.\n        covariates_initial: list of strings,\n                            each indicates the field name in the dataframe\n                            to use as the independent variables.\n        covariates_transition: list of strings,\n                               each indicates the field name in the dataframe\n                               to use as the independent variables.\n        covariates_emissions: list of list of strings, each outer list is for one emission model\n                              and each inner list of strings\n                              indicates the field names in the dataframe\n                              to use as the independent variables.\n        responses_emissions: list of list of strings, each outer list is for one emission\n                             and each inner list of strings\n                             indicates the field names in the dataframe\n                             to use as the dependent variables.\n        Returns\n        -------\n        IOHMM object: an IOHMM object specified by the json_dict and other arguments\n        \"\"\"\n        model = cls(num_states=num_states,\n                    EM_tol=json_dict['properties']['EM_tol'],\n                    max_EM_iter=json_dict['properties']['max_EM_iter'])\n        model.set_models(\n            model_initial=model_initial,\n            model_transition=model_transition,\n            model_emissions=model_emissions,\n            trained=trained)\n        model.set_inputs(covariates_initial=covariates_initial,\n                         covariates_transition=covariates_transition,\n                         covariates_emissions=covariates_emissions)\n        model.set_outputs(responses_emissions=responses_emissions)\n        return model\n\n\nclass SemiSupervisedIOHMM(UnSupervisedIOHMM):\n    \"\"\"\n    SemiSupervised IOHMM models.\n    This model is intended to be used when there are some ground truth hidden states,\n    but the user don't want to solely use these labeled data to train.\n    \"\"\"\n\n    def set_data(self, dfs_states):\n        \"\"\"\n        Set data for the model\n        Constructs:\n        ----------\n        (1) num_seqs: number of seqences\n        (2) dfs_logStates: list of (dataframe, log_state)\n        (3) posteriors with randomness and input/output covariates\n        Parameters\n        ----------\n        dfs_states: a list of (dataframes, states), each dataframe represents a sequence.\n                    and states is a dictionary of (timestamp -> array of shape (num_states, ))\n                    states[t][k] is 1 and states[t][~k] is 0 if the hidden state is k at\n                    timestamp t.\n        Notes\n        ----------\n        The column names of each df must contain the covariates and response fields\n        specified above.\n        \"\"\"\n        self.num_seqs = len(dfs_states)\n        self.dfs_logStates = [[x[0], {k: np.log(x[1][k]) for k in x[1]}] for x in dfs_states]\n        self._initialize(with_randomness=True)\n\n\nclass SupervisedIOHMM(BaseIOHMM):\n    \"\"\"\n    SemiSupervised IOHMM models.\n    This model is intended to be used when the user\n    simply want to use ground truth hidden states to train the model\n    \"\"\"\n\n    def __init__(self, num_states=2):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        num_states: the number of hidden states\n        -------\n        \"\"\"\n        super(SupervisedIOHMM, self).__init__(num_states=num_states)\n        self.max_EM_iter = 1\n        self.EM_tol = 0\n\n    def set_data(self, dfs_states):\n        \"\"\"\n        Set data for the model\n        Constructs:\n        ----------\n        (1) num_seqs: number of seqences\n        (2) dfs_logStates: list of (dataframe, log_state)\n        (3) posteriors withOUT randomness and input/output covariates\n        Parameters\n        ----------\n        dfs_states: a list of (dataframes, states), each dataframe represents a sequence.\n                    and states if a dictionary of (timestamp -> array of shape (num_states, ))\n                    states[t][k] is 1 and states[t][~k] is 0 if the hidden state is k at\n                    timestamp t.\n        Notes\n        ----------\n        The column names of each df must contains the covariates and response fields\n        specified above.\n        \"\"\"\n        self.num_seqs = len(dfs_states)\n        self.dfs_logStates = [[x[0], {k: np.log(x[1][k]) for k in x[1]}] for x in dfs_states]\n        self._initialize(with_randomness=False)\n"
  },
  {
    "path": "IOHMM/__init__.py",
    "content": "from .IOHMM import (UnSupervisedIOHMM,\n                    SemiSupervisedIOHMM,\n                    SupervisedIOHMM)\nfrom .forward_backward import (forward_backward,\n                               forward,\n                               backward,\n                               cal_log_gamma,\n                               cal_log_epsilon,\n                               cal_log_likelihood)\nfrom .linear_models import (GLM,\n                            OLS,\n                            DiscreteMNL,\n                            CrossEntropyMNL)\n\n__all__ = [\n    UnSupervisedIOHMM, SemiSupervisedIOHMM, SupervisedIOHMM,\n    forward_backward, forward, backward,\n    cal_log_gamma, cal_log_epsilon,\n    cal_log_likelihood,\n    GLM, OLS, DiscreteMNL, CrossEntropyMNL\n]\n"
  },
  {
    "path": "IOHMM/forward_backward.py",
    "content": "'''\nThe forward backward algorithm of hidden markov model (HMM) .\nMainly used in the E-step of IOHMM given the\n(1) initial probabilities, (2) transition probabilities, and (3) emission probabilities.\n\nA feature of this implementation is that it is vectorized to the greatest extent\nthat we use numpy matrix operation as much as possible.\nWe have only one for loop in forward/backward calculation,\nwhich is necessary due to dynamic programming (DP).\n\nAnother feature of this implementation is that it is calculated at the log level,\nso that it is more robust to long sequences.\n'''\nfrom __future__ import division\n\nfrom builtins import range\nimport warnings\n\n\nimport numpy as np\nfrom scipy.special import logsumexp\n\nwarnings.simplefilter(\"ignore\")\n\n\ndef forward_backward(log_prob_initial, log_prob_transition, log_Ey, log_state={}):\n    \"\"\"\n    The forward_backward algorithm.\n    Parameters\n    ----------\n    log_prob_initial : array-like of shape (k, )\n        where k is the number of states of the HMM\n        The log of the probability of initial state at timestamp 0.\n        log_prob_initial_{i} is the log of the probability of being in state i\n        at timestamp 0.\n    log_prob_transition : array-like of shape (t-1, k, k)\n        where t is the number of timestamps (length) of the sequence.\n        log_prob_transition_{t, i, j} is the log of the probability of transferring\n        to state j from state i at timestamp t.\n    log_Ey : array-like of shape (t, k)\n        log_Ey_{t, i} is the log of the probability of observing emission variables\n        from state i at timestamp t.\n    log_state: dict(int -> array-like of shape (k, ))\n        timestamp i is a key of log_state if we know the state of that timestamp.\n        Mostly used in semi-supervised and supervised IOHMM.\n        log_state[t][i] is 0 and log_state[t][~i] is -np.Infinity\n        if we know the state is i at timestamp t.\n    Returns\n    -------\n    (1) posterior state log probability of each timestamp.\n    (2) posterior \"transition\" log probability of each timestamp.\n    (3) log likelihood of the sequence.\n    see https://en.wikipedia.org/wiki/Forward-backward_algorithm for details.\n    \"\"\"\n    log_alpha = forward(log_prob_initial, log_prob_transition, log_Ey, log_state)\n    log_beta = backward(log_prob_transition, log_Ey, log_state)\n    log_likelihood = cal_log_likelihood(log_alpha)\n    log_gamma = cal_log_gamma(log_alpha, log_beta, log_likelihood, log_state)\n    log_epsilon = cal_log_epsilon(log_prob_transition, log_Ey, log_alpha,\n                                  log_beta, log_likelihood, log_state)\n    return log_gamma, log_epsilon, log_likelihood\n\n\ndef forward(log_prob_initial, log_prob_transition, log_Ey, log_state={}):\n    \"\"\"\n    The forward function to calculate log of forward variable alpha.\n    Parameters\n    ----------\n    log_prob_initial : array-like of shape (k, )\n        where k is the number of states of the HMM\n        The log of the probability of initial state at timestamp 0.\n        log_prob_initial_{i} is the log of the probability of being in state i\n        at timestamp 0.\n    log_prob_transition : array-like of shape (t-1, k, k)\n        where t is the number of timestamps (length) of the sequence.\n        log_prob_transition_{t, i, j} is the log of the probability of transferring\n        to state j from state i at timestamp t.\n    log_Ey : array-like of shape (t, k)\n        log_Ey_{t, i} is the log of the probability of observing emission variables\n        from state i at timestamp t.\n    log_state: dict(int -> array-like of shape (k, ))\n        timestamp i is a key of log_state if we know the state of that timestamp.\n        Mostly used in semi-supervised and supervised IOHMM.\n        log_state[t][i] is 0 and log_state[t][~i] is -np.Infinity\n        if we know the state is i at timestamp t.\n    Returns\n    -------\n    log_alpha : array-like of shape (t, k)\n        log of forward variable alpha.\n        see https://en.wikipedia.org/wiki/Forward-backward_algorithm for details.\n    \"\"\"\n    assert log_prob_initial.ndim == 1\n    assert log_prob_transition.ndim == 3\n    assert log_Ey.ndim == 2\n    t = log_Ey.shape[0]\n    k = log_Ey.shape[1]\n    log_alpha = np.zeros((t, k))\n    if 0 in log_state:\n        log_alpha[0, :] = log_state[0] + log_Ey[0, :]\n    else:\n        log_alpha[0, :] = log_prob_initial + log_Ey[0, :]\n    for i in range(1, t):\n        if i in log_state:\n            log_alpha[i, :] = logsumexp(log_alpha[i - 1, :]) + log_state[i] + log_Ey[i, :]\n        else:\n            log_alpha[i, :] = logsumexp(log_prob_transition[i - 1, :, :].T +\n                                        log_alpha[i - 1, :], axis=1) + log_Ey[i, :]\n    assert log_alpha.shape == (t, k)\n    return log_alpha\n\n\ndef backward(log_prob_transition, log_Ey, log_state={}):\n    \"\"\"\n    The function to calculate log of backward variable beta.\n    Parameters\n    ----------\n    log_prob_transition : array-like of shape (t-1, k, k)\n        where t is the number of timestamps (length) of the sequence.\n        log_prob_transition_{t, i, j} is the log of the probability of transferring\n        to state j from state i at timestamp t.\n    log_Ey : array-like of shape (t, k)\n        log_Ey_{t, i} is the log of the probability of observing emission variables\n        from state i at timestamp t.\n    log_state: dict(int -> array-like of shape (k, ))\n        timestamp i is a key of log_state if we know the state of that timestamp.\n        Mostly used in semi-supervised and supervised IOHMM.\n        log_state[t][i] is 0 and log_state[t][~i] is -np.Infinity\n        if we know the state is i at timestamp t.\n    Returns\n    -------\n    log_beta : array-like of shape (t, k)\n        log of backward variable beta.\n        see https://en.wikipedia.org/wiki/Forward-backward_algorithm for details.\n    \"\"\"\n    assert log_prob_transition.ndim == 3\n    assert log_Ey.ndim == 2\n    t = log_Ey.shape[0]\n    k = log_Ey.shape[1]\n    log_beta = np.zeros((t, k))\n    for i in range(t - 2, -1, -1):\n        if i + 1 in log_state:\n            log_beta[i, :] = logsumexp(log_state[i + 1] + log_beta[i + 1, :] + log_Ey[i + 1, :])\n        else:\n            log_beta[i, :] = logsumexp(log_prob_transition[i, :, :] +\n                                       (log_beta[i + 1, :] + log_Ey[i + 1, :]), axis=1)\n    assert log_beta.shape == (t, k)\n    return log_beta\n\n\ndef cal_log_likelihood(log_alpha):\n    \"\"\"\n    The function to calculate the log likelihood of the sequence.\n    Parameters\n    ----------\n    log_alpha : array-like of shape (t, k)\n        log of forward variable alpha.\n    Returns\n    -------\n    log_likelihood : float\n        The log likelihood of the sequence.\n        see https://en.wikipedia.org/wiki/Forward-backward_algorithm for details.\n    \"\"\"\n    return logsumexp(log_alpha[-1, :])\n\n\ndef cal_log_gamma(log_alpha, log_beta, log_likelihood, log_state={}):\n    \"\"\"\n    The function to calculate the log of the posterior probability of each state\n    at each timestamp.\n    Parameters\n    ----------\n    log_alpha : array-like of shape (t, k)\n        log of forward variable alpha.\n    log_alpha : array-like of shape (t, k)\n        log of backward variable beta.\n    log_likelihood : float\n        log likelihood of the sequence\n    log_state: dict(int -> array-like of shape (k, ))\n        timestamp i is a key of log_state if we know the state of that timestamp.\n        Mostly used in semi-supervised and supervised IOHMM.\n        log_state[t][i] is 0 and log_state[t][~i] is -np.Infinity\n        if we know the state is i at timestamp t.\n    Returns\n    -------\n    log_gamma : array-like of shape (t, k)\n        the log of the posterior probability of each state.\n        log_gamma_{t, i} is the posterior log of the probability of\n        being in state i at stimestamp t.\n        see https://en.wikipedia.org/wiki/Forward-backward_algorithm for details.\n    \"\"\"\n    log_gamma = log_alpha + log_beta - log_likelihood\n    for i in log_state:\n        log_gamma[i, :] = log_state[i]\n    return log_gamma\n\n\ndef cal_log_epsilon(log_prob_transition, log_Ey, log_alpha, log_beta, log_likelihood, log_state={}):\n    \"\"\"\n    The function to calculate the log of the posterior joint probability\n    of two consecutive timestamps\n    Parameters\n    ----------\n    log_prob_transition : array-like of shape (t-1, k, k)\n        where t is the number of timestamps (length) of the sequence.\n        log_prob_transition_{t, i, j} is the log of the probability of transferring\n        to state j from state i at timestamp t.\n    log_Ey : array-like of shape (t, k)\n        log_Ey_{t, i} is the log of the probability of observing emission variables\n        from state i at timestamp t.\n    log_alpha : array-like of shape (t, k)\n        log of forward variable alpha.\n    log_alpha : array-like of shape (t, k)\n        log of backward variable beta.\n    log_likelihood : float\n        log likelihood of the sequence\n    log_state: dict(int -> array-like of shape (k, ))\n        timestamp i is a key of log_state if we know the state of that timestamp.\n        Mostly used in semi-supervised and supervised IOHMM.\n        log_state[t][i] is 0 and log_state[t][~i] is -np.Infinity\n        if we know the state is i at timestamp t.\n    Returns\n    -------\n    log_epsilon : array-like of shape (t-1, k, k)\n        the log of the posterior probability of two consecutive timestamps.\n        log_gamma_{t, i, j} is the posterior log of the probability of\n        being in state i at timestamp t and\n        being in state j at timestamp t+1.\n        see https://en.wikipedia.org/wiki/Forward-backward_algorithm for details.\n    \"\"\"\n    k = log_Ey.shape[1]\n    if log_prob_transition.shape[0] == 0:\n        return np.zeros((0, k, k))\n    else:\n        log_p = log_prob_transition\n        for i in log_state:\n            log_p[i - 1, :, :] = log_state[i]\n        log_epsilon = np.tile((log_Ey + log_beta)[1:, np.newaxis, :], [1, k, 1]) + \\\n            np.tile(log_alpha[:-1, :, np.newaxis], [1, 1, k]) + log_p - log_likelihood\n        for i in log_state:\n            if i + 1 in log_state:\n                log_epsilon[i, :, :] = np.add.outer(log_state[i], log_state[i + 1])\n        return log_epsilon\n"
  },
  {
    "path": "IOHMM/linear_models.py",
    "content": "'''\nThis is a unified interface/wrapper of general/generalized linear models from\nsklearn/statsmodels packages.\n\nProblems with sklearn:\n1. No Generalized linear models available.\n2. Does not estimate standard error of coefficients.\n3. Logistic regression does not handle 1 class case.\n4. For 2 class logistic regression, the 'ovr' result is not same as 'multinomial' result.\n\nProblems with statsmodels:\n1. No working version of multivariate OLS with sample weights.\n2. MNLogit does not support sample weights.\n\nProblem with both:\n1. No interface to calculate loglike_per_sample,\n   which is need to calculate emission probability in IOHMM.\n2. No json-serialization.\n\n\nIn this implementations,\nwe will mainly use statsmodels for\n1. Generalized linear models with simple response\n\nwe will mainly use sklearn for\n1. Univariate/Multivariate Ordinary least square (OLS) models,\n2. Multinomial Logistic Regression with discrete output/probability outputs\n\nNote:\n1. If using customized arguments for constructor, you may encounter compalints\n   from the statsmodels/sklearn on imcompatible arguments.\n   This maybe especially true for the compatibility between solver and regularization method.\n\n2. For the GLM, statsmodels is not great when fitting with regularizations\n   (espicially l1, and elstic_net). In this case the coefficients might be np.nan.\n   Try not using regularizations if you select GLM until statsmodels is stable on this.\n'''\n\n# //TODO in future add arguments compatibility check\n\nfrom __future__ import division\n\nfrom future import standard_library\n\nfrom builtins import range\nfrom builtins import object\nimport pickle as pickle\nimport logging\nimport numbers\nimport os\n\n\nimport numpy as np\nfrom scipy.stats import multivariate_normal\nfrom sklearn import linear_model\nfrom sklearn.linear_model._base import _rescale_data\nfrom sklearn.preprocessing import label_binarize\nimport statsmodels.api as sm\nfrom statsmodels.genmod.families import Poisson, Binomial\nfrom statsmodels.tools import add_constant\nstandard_library.install_aliases()\nEPS = np.finfo(float).eps\n\n\nclass BaseModel(object):\n    \"\"\"\n    A generic supervised model for data with input and output.\n    BaseModel does nothing, but lays out the methods expected of any subclass.\n    \"\"\"\n\n    def __init__(self,\n                 solver,\n                 fit_intercept=True,\n                 est_stderr=False,\n                 tol=1e-4,\n                 max_iter=100,\n                 reg_method=None,\n                 alpha=0,\n                 l1_ratio=0,\n                 coef=None,\n                 stderr=None):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        solver: specific solver for each linear model\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (None, l1, l2, elstic_net).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: if elastic_net, the l1 alpha ratio\n        coef: the coefficients if loading from trained model\n        stderr: the std.err of coefficients if loading from trained model\n        -------\n        \"\"\"\n        self.solver = solver\n        self.fit_intercept = fit_intercept\n        self.est_stderr = est_stderr\n        self.tol = tol\n        self.max_iter = max_iter\n        self.reg_method = reg_method\n        self.alpha = alpha\n        self.l1_ratio = l1_ratio\n        self.coef = coef\n        self.stderr = stderr\n\n    def fit(self, X, Y, sample_weight=None):\n        \"\"\"\n        Fit the weighted model\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : observed response matrix of shape\n            (n_samples, ) or (n_samples, k) based on specific model\n        sample_weight: sample weight vector of shape (n_samples, ), or float, or None\n        \"\"\"\n        raise NotImplementedError\n\n    def _raise_error_if_model_not_trained(self):\n        \"\"\"\n        Raise error if the model is not trained (thus has coef)\n        ----------\n        \"\"\"\n        if self.coef is None:\n            raise ValueError('Model is not trained.')\n\n    def _raise_error_if_sample_weight_sum_zero(self, sample_weight):\n        \"\"\"\n        Raise error if the sum of sample_weight is 0\n        ----------\n        sample_weight: array of (n_samples, )\n        \"\"\"\n        if np.sum(sample_weight) < EPS:\n            raise ValueError('Sum of sample weight is 0.')\n\n    def _transform_X(self, X):\n        \"\"\"\n        Transform the design matrix X\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Returns\n        -------\n        X : design matrix of shape (n_samples, n_features + 1) if fit intercept\n        \"\"\"\n        if self.fit_intercept:\n            X = add_constant(X, has_constant='add')\n        return X\n\n    def _transform_sample_weight(self, X, sample_weight=None):\n        \"\"\"\n        Transform the sample weight from anyform to array\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        sample_weight: sample weight vector of shape (n_samples, ), or float, or None\n        Returns\n        -------\n        sample_weight: array of (n_samples, )\n        \"\"\"\n        if sample_weight is None:\n            sample_weight = np.ones(X.shape[0])\n        elif isinstance(sample_weight, numbers.Number):\n            sample_weight = np.ones(X.shape[0]) * sample_weight\n        assert X.shape[0] == sample_weight.shape[0]\n        return sample_weight\n\n    def _transform_X_sample_weight(self, X, sample_weight=None):\n        \"\"\"\n        Transform the design matrix X and sample_weight to the form they can be used to fit\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        sample_weight: sample weight vector of shape (n_samples, ), or float, or None\n        Returns\n        -------\n        X : design matrix of shape (n_samples, n_features + 1) if fit intercept\n        sample_weight: array of (n_samples, )\n        \"\"\"\n        X = self._transform_X(X)\n        sample_weight = self._transform_sample_weight(X, sample_weight=sample_weight)\n        return X, sample_weight\n\n    def predict(self, X):\n        \"\"\"\n        Predict the Y value based on the model\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Returns\n        -------\n        predicted value: of shape (n_samples, ) or (n_samples, k) based on specific model\n        \"\"\"\n        raise NotImplementedError\n\n    def loglike_per_sample(self, X, Y):\n        \"\"\"\n        Given a set of X and Y, calculate the log probability of\n        observing each of Y_i value given each X_i value\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : observed response matrix of shape\n            (n_samples, ) or (n_samples, k) based on specific model\n        Returns\n        -------\n        log_p: array of shape (n_samples, )\n        \"\"\"\n        raise NotImplementedError\n\n    def loglike(self, X, Y, sample_weight=None):\n        \"\"\"\n        Given a set of X and Y, calculate the log probability of\n        observing Y, considering the sample weight.\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : observed response matrix of shape\n            (n_samples, ) or (n_samples, k) based on specific model\n        Returns\n        -------\n        log_likelihood: float\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        sample_weight = self._transform_sample_weight(X, sample_weight=sample_weight)\n        return np.sum(sample_weight * self.loglike_per_sample(X, Y))\n\n    def to_json(self, path):\n        \"\"\"\n        Generate json object of the model\n        Parameters\n        ----------\n        path : the path to save the model\n        Returns\n        -------\n        json_dict: a dictionary containing the attributes of the model\n        \"\"\"\n        json_dict = {\n            'data_type': self.__class__.__name__,\n            'properties': {\n                'solver': self.solver,\n                'fit_intercept': self.fit_intercept,\n                'est_stderr': self.est_stderr,\n                'tol': self.tol,\n                'max_iter': self.max_iter,\n                'reg_method': self.reg_method,\n                'alpha': self.alpha,\n                'l1_ratio': self.l1_ratio,\n                'coef': {\n                    'data_type': 'numpy.ndarray',\n                    'path': os.path.join(path, 'coef.npy')\n                },\n                'stderr': {\n                    'data_type': 'numpy.ndarray',\n                    'path': os.path.join(path, 'stderr.npy')\n                }\n            }\n        }\n        if not os.path.exists(os.path.dirname(json_dict['properties']['coef']['path'])):\n            os.makedirs(os.path.dirname(json_dict['properties']['coef']['path']))\n        np.save(json_dict['properties']['coef']['path'], self.coef)\n        if not os.path.exists(os.path.dirname(json_dict['properties']['stderr']['path'])):\n            os.makedirs(os.path.dirname(json_dict['properties']['stderr']['path']))\n        np.save(json_dict['properties']['stderr']['path'], self.stderr)\n        return json_dict\n\n    @classmethod\n    def _from_json(cls, json_dict, solver, fit_intercept, est_stderr,\n                   tol, max_iter, reg_method, alpha, l1_ratio, coef, stderr):\n        \"\"\"\n        Helper function to construct the linear model used by from_json.\n        This function is designed to be override by subclasses.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        solver: specific solver for each linear model\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (None, l1, l2, elstic_net).\n        alpha: regularization strength\n        l1_ratio: if elastic_net, the l1 alpha ratio\n        coef: the coefficients\n        stderr: the std.err of coefficients\n        Returns\n        -------\n        linear model object: a linear model object specified by the json_dict and other arguments\n        \"\"\"\n        return cls(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            tol=tol, max_iter=max_iter,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr)\n\n    @classmethod\n    def from_json(cls, json_dict):\n        \"\"\"\n        Construct a linear model from a saved dictionary.\n        This function is NOT designed to be override by subclasses.\n        Parameters\n        ----------\n        json_dict: a json dictionary containing the attributes of the linear model.\n        Returns\n        -------\n        linear model: a linear model object specified by the json_dict\n        \"\"\"\n        return cls._from_json(\n            json_dict,\n            solver=json_dict['properties']['solver'],\n            fit_intercept=json_dict['properties']['fit_intercept'],\n            est_stderr=json_dict['properties']['est_stderr'],\n            tol=json_dict['properties']['tol'],\n            max_iter=json_dict['properties']['max_iter'],\n            reg_method=json_dict['properties']['reg_method'],\n            alpha=json_dict['properties']['alpha'],\n            l1_ratio=json_dict['properties']['l1_ratio'],\n            coef=np.load(json_dict['properties']['coef']['path'], allow_pickle=True),\n            stderr=np.load(json_dict['properties']['stderr']['path'], allow_pickle=True))\n\n\nclass GLM(BaseModel):\n    \"\"\"\n    A wrapper for Generalized linear models.\n    fit_regularized only support Poisson and Binomial due to statsmodels,\n    and it is not stable. Try not using regularizations in GLM.\n    \"\"\"\n\n    def __init__(self,\n                 family,\n                 solver='IRLS',\n                 fit_intercept=True,\n                 est_stderr=False,\n                 tol=1e-4,\n                 max_iter=100,\n                 reg_method=None,\n                 alpha=0,\n                 l1_ratio=0,\n                 coef=None,\n                 stderr=None,\n                 dispersion=None):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        solver: solver for GLM, default 'IRLS', otherwise will use gradient.\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: TRY NOT USING REGULARIZATIONS FOR GLM.\n                    method to regularize the model, one of (None, elstic_net).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: if elastic_net, the l1 alpha ratio\n        coef: the coefficients if loading from trained model\n        stderr: the std.err of coefficients if loading from trained model\n\n        family: statsmodels.genmod.families.family.Family\n        dispersion: dispersion/scale of the GLM\n        -------\n        \"\"\"\n        super(GLM, self).__init__(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            tol=tol, max_iter=max_iter,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr)\n        self.family = family\n        self.dispersion = dispersion\n        if self.coef is not None:\n            dummy_X = dummy_Y = dummy_weight = np.zeros(1)\n            self._model = sm.GLM(dummy_Y, dummy_X, family=family,\n                                 freq_weights=dummy_weight)\n\n    def fit(self, X, Y, sample_weight=None):\n        \"\"\"\n        Fit the weighted model\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, ) or (n_samples, k) depending on family\n        sample_weight: sample weight vector of shape (n_samples, ), or float, or None\n        \"\"\"\n        def _estimate_dispersion():\n            \"\"\"\n            Estimate dispersion/scale based on the fitted model\n            Returns\n            -------\n            dispersion: float\n            \"\"\"\n            if isinstance(self.family, (Binomial, Poisson)):\n                return 1.\n            return self._model.scale\n\n        def _estimate_stderr():\n            \"\"\"\n            Estimate standard deviation of the coefficients.\n            Returns\n            -------\n            standard deviation of the coefficients: array with the same shape as coef\n            Notes\n            -------\n            I think the stderr of statsmodels is wrong.\n            It uses the WLS stderr as the std err of GLM, which does not make sense,\n            because the variance in WLS is inverse proportional to the weights.\n\n            Anyway I will leave it here, stderr is not important.\n            \"\"\"\n            if self.reg_method is None or self.alpha < EPS:\n                return fit_results.bse * np.sqrt(self.dispersion / self._model.scale)\n            return None\n\n        X, sample_weight = self._transform_X_sample_weight(X, sample_weight=sample_weight)\n        self._raise_error_if_sample_weight_sum_zero(sample_weight)\n        Y = self._transform_Y(Y)\n        self._model = sm.GLM(Y, X, family=self.family, freq_weights=sample_weight)\n        # dof in weighted regression does not make sense, hard code it to the total weights\n        self._model.df_resid = np.sum(sample_weight)\n        if self.reg_method is None or self.alpha < EPS:\n            fit_results = self._model.fit(\n                maxiter=self.max_iter, tol=self.tol, method=self.solver, wls_method='pinv')\n        else:\n            fit_results = self._model.fit_regularized(\n                method=self.reg_method, alpha=self.alpha,\n                L1_wt=self.l1_ratio, maxiter=self.max_iter)\n        self.coef = fit_results.params\n        self.dispersion = _estimate_dispersion()\n        if self.est_stderr:\n            self.stderr = _estimate_stderr()\n\n    def _transform_Y(self, Y):\n        \"\"\"\n        Transform the response Y\n        ----------\n        Y : response matrix of shape (n_samples, ) or (n_samples, k) depending on family\n        Returns\n        -------\n        Y : response matrix of shape (n_samples, ) or (n_samples, k) depending on family\n        \"\"\"\n        if Y.ndim == 2 and Y.shape[1] == 1:\n            Y = Y.reshape(-1,)\n        return Y\n\n    def predict(self, X):\n        \"\"\"\n        Predict the Y value based on the model\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Returns\n        -------\n        predicted value, of shape (n_samples, ), 1d\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        X = self._transform_X(X)\n        return self._model.predict(self.coef, exog=X)\n\n    def loglike_per_sample(self, X, Y):\n        \"\"\"\n        Given a set of X and Y, calculate the log probability of\n        observing each of Y value given each X value\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, ) or (n_samples, k) depending on family\n        Returns\n        -------\n        log_p: array of shape (n_samples, )\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        assert X.shape[0] == Y.shape[0]\n        Y = self._transform_Y(Y)\n        mu = self.predict(X)\n        if isinstance(self.family, Binomial):\n            endog, _ = self.family.initialize(Y, 1.0)\n        else:\n            endog = Y\n        if self.dispersion > EPS:\n            return self.family.loglike_obs(endog, mu, scale=self.dispersion)\n        log_p = np.zeros(endog.shape[0])\n        log_p[~np.isclose(endog, mu)] = - np.Infinity\n        return log_p\n\n    def to_json(self, path):\n        \"\"\"\n        Generate json object of the model\n        Parameters\n        ----------\n        path : the path to save the model\n        Returns\n        -------\n        json_dict: a dictionary containing the attributes of the GLM\n        \"\"\"\n        json_dict = super(GLM, self).to_json(path=path)\n        json_dict['properties'].update(\n            {\n                'family': {\n                    'data_type': self.family.__class__.__name__,\n                    'path': os.path.join(path, 'family.p')\n                },\n                'dispersion': {\n                    'data_type': 'numpy.ndarray',\n                    'path': os.path.join(path, 'dispersion.npy')\n                }\n            })\n        if not os.path.exists(os.path.dirname(json_dict['properties']['family']['path'])):\n            os.makedirs(os.path.dirname(json_dict['properties']['family']['path']))\n        pickle.dump(self.family, open(json_dict['properties']['family']['path'], 'wb'))\n        if not os.path.exists(os.path.dirname(json_dict['properties']['dispersion']['path'])):\n            os.makedirs(os.path.dirname(json_dict['properties']['dispersion']['path']))\n        np.save(json_dict['properties']['dispersion']['path'], self.dispersion)\n        return json_dict\n\n    @classmethod\n    def _from_json(cls, json_dict, solver, fit_intercept, est_stderr,\n                   tol, max_iter, reg_method, alpha, l1_ratio, coef, stderr):\n        \"\"\"\n        Helper function to construct the GLM used by from_json.\n        This function overrides the parent class.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        solver: specific solver for GLM\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (None, elstic_net).\n        alpha: regularization strength\n        l1_ratio: if elastic_net, the l1 alpha ratio\n        coef: the coefficients\n        stderr: the std.err of coefficients\n        Returns\n        -------\n        GLM object: a GLM object specified by the json_dict and other arguments\n        \"\"\"\n        with open(json_dict['properties']['family']['path'], 'rb') as f:\n            return cls(\n                solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n                reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n                coef=coef, stderr=stderr, tol=tol, max_iter=max_iter,\n                family=pickle.load(f),\n                dispersion=np.load(json_dict['properties']['dispersion']['path'],\n                                   allow_pickle=True))\n\n\nclass OLS(BaseModel):\n    \"\"\"\n    A wrapper for Univariate and Multivariate Ordinary Least Squares (OLS).\n    \"\"\"\n\n    def __init__(self, solver='svd', fit_intercept=True, est_stderr=False,\n                 reg_method=None,  alpha=0, l1_ratio=0, tol=1e-4, max_iter=100,\n                 coef=None, stderr=None,  dispersion=None, n_targets=None):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        solver: specific solver for OLS, default 'svd', possible solvers are:\n                {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag'}.\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (None, l1, l2, elstic_net).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: if elastic_net, the l1 alpha ratio\n        coef: the coefficients if loading from trained model\n        stderr: the std.err of coefficients if loading from trained model\n\n        n_targets: the number of dependent variables\n        dispersion: dispersion/scale mareix of the OLS\n        -------\n        \"\"\"\n        super(OLS, self).__init__(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            tol=tol, max_iter=max_iter,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr)\n        self.dispersion = dispersion\n        self.n_targets = n_targets\n        self._pick_model()\n        if self.coef is not None:\n            self._model.coef_ = coef\n            self._model.intercept_ = 0\n\n    def _pick_model(self):\n        \"\"\"\n        Helper function to select a proper sklearn linear regression model\n        based on the regulariztaion specified by the user.\n        \"\"\"\n        if self.reg_method is None or self.alpha < EPS:\n            self._model = linear_model.LinearRegression(\n                fit_intercept=False)\n        if self.reg_method == 'l1':\n            self._model = linear_model.Lasso(\n                fit_intercept=False, alpha=self.alpha,\n                tol=self.tol, max_iter=self.max_iter)\n        if self.reg_method == 'l2':\n            self._model = linear_model.Ridge(\n                fit_intercept=False, alpha=self.alpha, tol=self.tol,\n                max_iter=self.max_iter, solver=self.solver)\n        if self.reg_method == 'elastic_net':\n            self._model = linear_model.ElasticNet(\n                fit_intercept=False, alpha=self.alpha,\n                l1_ratio=self.l1_ratio, tol=self.tol,\n                max_iter=self.max_iter)\n\n    def fit(self, X, Y, sample_weight=None):\n        \"\"\"\n        Fit the weighted model\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, n_targets), 2d\n        sample_weight: sample weight vector of shape (n_samples, ), or float, or None\n        \"\"\"\n        def _estimate_dispersion():\n            \"\"\"\n            Estimate dispersion matrix based on the fitted model\n            Returns\n            -------\n            dispersion matrix: array of shape (n_targets, n_targets), 2d\n            \"\"\"\n            mu, wendog, _ = _rescale_data(self.predict(X), Y, sample_weight)\n            wresid = mu - wendog\n            return np.dot(wresid.T, wresid) / np.sum(sample_weight)\n\n        def _estimate_stderr():\n            \"\"\"\n            Estimate standard deviation of the coefficients.\n            Returns\n            -------\n            standard deviation of the coefficients: array with the same shape as coef\n            Notes\n            -------\n            It is not the same stderr as Weighted Least Squares (WLS).\n            WLS assumes sample weight is inversely proportional to the covariance.\n            Useful links:\n            http://www.public.iastate.edu/~maitra/stat501/lectures/MultivariateRegression.pdf\n            https://stats.stackexchange.com/questions/52704/covariance-of-linear-\n            regression-coefficients-in-weighted-least-squares-method\n            http://pj.freefaculty.org/guides/stat/Regression/GLS/GLS-1-guide.pdf\n            https://stats.stackexchange.com/questions/27033/in-r-given-an-output-from-\n            optim-with-a-hessian-matrix-how-to-calculate-paramet\n            http://msekce.karlin.mff.cuni.cz/~vorisek/Seminar/0910l/jonas.pdf\n            \"\"\"\n            if self.reg_method is None or self.alpha < EPS:\n                wexog, wendog, _ = _rescale_data(X_train, Y, sample_weight)\n                stderr = np.zeros((self.n_targets, X_train.shape[1]))\n                try:\n                    XWX_inverse_XW_sqrt = np.linalg.inv(np.dot(wexog.T, wexog)).dot(wexog.T)\n                except np.linalg.linalg.LinAlgError:\n                    logging.warning('Covariance matrix is singular, cannot estimate stderr.')\n                    return None\n                sqrt_diag_XWX_inverse_XW_sqrt_W_XWX_inverse_XW_sqrt = np.sqrt(np.diag(\n                    XWX_inverse_XW_sqrt.dot(np.diag(sample_weight)).dot(XWX_inverse_XW_sqrt.T)))\n                for target in range(self.n_targets):\n                    stderr[target, :] = (np.sqrt(self.dispersion[target, target]) *\n                                         sqrt_diag_XWX_inverse_XW_sqrt_W_XWX_inverse_XW_sqrt)\n                return stderr.reshape(self.coef.shape)\n            return None\n\n        X_train, sample_weight = self._transform_X_sample_weight(X, sample_weight=sample_weight)\n        self._raise_error_if_sample_weight_sum_zero(sample_weight)\n        Y = self._transform_Y(Y)\n        self.n_targets = Y.shape[1]\n        self._model.fit(X_train, Y, sample_weight)\n        self.coef = self._model.coef_\n        self.dispersion = _estimate_dispersion()\n        if self.est_stderr:\n            self.stderr = _estimate_stderr()\n\n    def _transform_Y(self, Y):\n        \"\"\"\n        Transform the response Y\n        ----------\n        Y : response matrix of shape (n_samples, ) or (n_samples, n_targets) depending on family\n        Returns\n        -------\n        Y : response matrix of shape (n_samples, n_targets)\n        \"\"\"\n        if Y.ndim == 1:\n            Y = Y.reshape(-1, 1)\n        return Y\n\n    def predict(self, X):\n        \"\"\"\n        Predict the Y value based on the model\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Returns\n        -------\n        predicted value, of shape (n_samples, n_targets), 2d\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        X = self._transform_X(X)\n        return self._model.predict(X).reshape(-1, self.n_targets)\n\n    def loglike_per_sample(self, X, Y):\n        \"\"\"\n        Given a set of X and Y, calculate the log probability of\n        observing each of Y value given each X value\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : observed response matrix of shape (n_samples, n_targets), 2d\n        Returns\n        -------\n        log_p: array of shape (n_samples, )\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        assert X.shape[0] == Y.shape[0]\n        mu = self.predict(X)\n        # https://stackoverflow.com/questions/13312498/how-to-find-degenerate-\n        # rows-columns-in-a-covariance-matrix\n        Y = self._transform_Y(Y)\n        zero_inds = np.where(np.diag(self.dispersion) < EPS)[0]\n        log_p = np.zeros(Y.shape[0])\n        log_p[~np.isclose(\n            np.linalg.norm(\n                Y[:, zero_inds] - mu[:, zero_inds], axis=1), 0)] = - np.Infinity\n        non_zero_inds = np.setdiff1d(\n            np.arange(Y.shape[1]), zero_inds, assume_unique=True)\n        dispersion = self.dispersion[np.ix_(non_zero_inds, non_zero_inds)]\n        if dispersion.shape[0] == 0:\n            return log_p\n        if np.linalg.cond(dispersion) < 1 / EPS:\n            # This is a harsh test, if the det is ensured to be > 0\n            # all diagonal of dispersion will be > 0\n            # for the zero parts:\n            rv = multivariate_normal(cov=dispersion)\n            log_p += rv.logpdf(Y[:, non_zero_inds] - mu[:, non_zero_inds])\n            return log_p\n        else:\n            raise ValueError(\n                \"\"\"\n                    Dispersion matrix is singular, cannot calculate likelike_per_sample.\n                    Most like due to perfect correlations among dependent variables.\n                    Try another model specification.\n                \"\"\"\n            )\n\n    def to_json(self, path):\n        \"\"\"\n        Generate json object of the model\n        Parameters\n        ----------\n        path : the path to save the model\n        Returns\n        -------\n        json_dict: a dictionary containing the attributes of the OLS\n        \"\"\"\n        json_dict = super(OLS, self).to_json(path=path)\n        json_dict['properties'].update(\n            {\n                'dispersion': {\n                    'data_type': 'numpy.ndarray',\n                    'path': os.path.join(path, 'dispersion.npy')\n                },\n                'n_targets': self.n_targets\n            })\n        if not os.path.exists(os.path.dirname(json_dict['properties']['dispersion']['path'])):\n            os.makedirs(os.path.dirname(json_dict['properties']['dispersion']['path']))\n        np.save(json_dict['properties']['dispersion']['path'], self.dispersion)\n        return json_dict\n\n    @classmethod\n    def _from_json(cls, json_dict, solver, fit_intercept, est_stderr,\n                   tol, max_iter, reg_method, alpha, l1_ratio, coef, stderr):\n        \"\"\"\n        Helper function to construct the OLS used by from_json.\n        This function overrides the parent class.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        solver: specific solver for OLS\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (None, l1, l2, elstic_net).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: if elastic_net, the l1 alpha ratio\n        coef: the coefficients\n        stderr: the std.err of coefficients\n        Returns\n        -------\n        OLS object: an OLS object specified by the json_dict and other arguments\n        \"\"\"\n        return cls(solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n                   reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n                   coef=coef, stderr=stderr,\n                   tol=tol, max_iter=max_iter,\n                   dispersion=np.load(json_dict['properties']['dispersion']['path'],\n                                      allow_pickle=True),\n                   n_targets=json_dict['properties']['n_targets'])\n\n\nclass BaseMNL(BaseModel):\n    \"\"\"\n    A Base Multinomial Logistic regression model.\n    BaseMNL does nothing, to be extended by\n    (1) MNL with discrete output (DiscreteMNL) and.\n    (2) MNL with probability output (CrossEntropyMNL).\n    \"\"\"\n\n    def __init__(self, solver='lbfgs', fit_intercept=True, est_stderr=False,\n                 reg_method='l2', alpha=0, l1_ratio=0,\n                 tol=1e-4, max_iter=100,\n                 coef=None, stderr=None,\n                 classes=None, n_classes=None):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        solver: specific solver for each linear model, default 'lbfgs',\n                possible solvers are {'newton-cg', 'lbfgs', 'liblinear', 'sag'}.\n                Need to be consistent with the regularization method.\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (l1, l2).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: the l1 alpha ratio\n        coef: the coefficients if loading from trained model\n        stderr: the std.err of coefficients if loading from trained model\n\n        classes: an array of class labels\n        n_classes: the number of classes to be classified\n        -------\n        \"\"\"\n        super(BaseMNL, self).__init__(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            tol=tol, max_iter=max_iter,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr)\n\n        self.classes = classes\n        self.n_classes = n_classes\n        if self.coef is not None:\n            if self.n_classes >= 2:\n                self._pick_model()\n                self._model.coef_ = coef\n                self._model.classes_ = classes\n                self._model.intercept_ = 0\n\n    def _pick_model(self):\n        \"\"\"\n        Helper function to select a proper sklearn logistic regression model\n        based on the regulariztaion specified by the user.\n        \"\"\"\n        C = np.float64(1) / self.alpha\n        if self.n_classes == 2:\n            # perform logistic regression\n            self._model = linear_model.LogisticRegression(\n                fit_intercept=False, penalty=self.reg_method, C=C,\n                solver=self.solver, tol=self.tol, max_iter=self.max_iter)\n\n        else:\n            # perform multinomial logistic regression\n            self._model = linear_model.LogisticRegression(\n                fit_intercept=False, penalty=self.reg_method, C=C,\n                solver=self.solver, tol=self.tol, max_iter=self.max_iter,\n                multi_class='multinomial')\n\n    def fit(self, X, Y, sample_weight=None):\n        \"\"\"\n        Fit the weighted model\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, ) for DiscreteMNL and\n            (n_samples, n_classes) for CrossEntropyMNL\n        sample_weight: sample weight vector of shape (n_samples, ), or float, or None\n        \"\"\"\n        def _estimate_stderr():\n            \"\"\"\n            Estimate standard deviation of the coefficients.\n            Returns\n            -------\n            None for now, since I am not sure if we can estimate the stderr\n            under the case there is sample_weight since there is no likelihood,\n            thus no hessian of the log likelihood.\n            Notes\n            -------\n            http://mplab.ucsd.edu/tutorials/MultivariateLogisticRegression.pdf\n            https://github.com/cran/mlogit/blob/master/R/mlogit.methods.R\n            https://arxiv.org/pdf/1404.3177.pdf\n            https://stats.stackexchange.com/questions/283780/calculate-standard-\n            error-of-weighted-logistic-regression-coefficients\n\n            Two codes to calculate hessian:\n            1. with sample weights:\n            https://github.com/scikit-learn/scikit-learn/\n            blob/ab93d657eb4268ac20c4db01c48065b5a1bfe80d/sklearn/linear_model/logistic.py\n            2. without sample weights\n            http://www.statsmodels.org/dev/_modules/statsmodels/\n            discrete/discrete_model.html#MNLogit\n            \"\"\"\n            return None\n\n        X, sample_weight = self._transform_X_sample_weight(X, sample_weight=sample_weight)\n        self._raise_error_if_sample_weight_sum_zero(sample_weight)\n        X, Y, sample_weight = self._label_encoder(\n            X, Y, sample_weight)\n        assert Y.ndim == 1\n        classes = np.unique(Y)\n        self.n_classes = len(classes)\n\n        if self.n_classes == 1:\n            # no need to perform any model\n            # self.coef is a all zeros array of shape (n_features,1)\n            self.coef = np.zeros((X.shape[1], 1))\n            self.classes = classes\n        else:\n            self._pick_model()\n            self._model.fit(X, Y, sample_weight=sample_weight)\n            # self.coef shape is wierd in sklearn, I will stick with it\n            self.coef = self._model.coef_\n            self.classes = self._model.classes_\n            if self.est_stderr:\n                self.stderr = _estimate_stderr()\n\n    @staticmethod\n    def _label_encoder(X, Y, sample_weight):\n        \"\"\"\n        Convert input to proper format to be used by sklearn logistic regression.\n        Mainly transforms Y to a 1d vector containing the class label for each sample.\n        This function is designed to be override by subclasses.\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, ) for DiscreteMNL and\n            (n_samples, n_classes) for CrossEntropyMNL\n        sample_weight: sample weight vector of shape (n_samples, )\n        Returns\n        -------\n        X_transformed : design matrix of shape (n, n_features), 2d\n        Y_transformed : response matrix of shape (n, )\n        sample_weight_transformed: sample weight vector of shape (n, )\n        where n:\n        is n_samples in the discrete case and\n        is n_samples * n_classes in the cross entropy case\n        \"\"\"\n        raise NotImplementedError\n\n    def _label_decoder(self, Y):\n        \"\"\"\n        Convert the response vector to probability matrix.\n        This function is designed to be override by subclasses.\n        Parameters\n        ----------\n        Y : response matrix of shape (n_samples, ) for DiscreteMNL and\n            (n_samples, n_classes) for CrossEntropyMNL\n        Returns\n        -------\n        Y_transformed : of shape (n_samples, n_classes).\n        \"\"\"\n        raise NotImplementedError\n\n    def predict_log_proba(self, X):\n        \"\"\"\n        Predict the log probability of each class\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Returns\n        -------\n        log probability matrix : of shape (n_samples, n_classes), 2d\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        X = self._transform_X(X)\n        if self.n_classes == 1:\n            return np.zeros((X.shape[0], 1))\n\n        return self._model.predict_log_proba(X)\n\n    def predict(self, X):\n        \"\"\"\n        Predict the most likely class label for each sample\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Returns\n        -------\n        labels : of shape (n_samples, ), 1d\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        X = self._transform_X(X)\n        if self.n_classes == 1:\n            return self.classes[np.zeros(X.shape[0], dtype=np.int)]\n        return self._model.predict(X)\n\n    def loglike_per_sample(self, X, Y):\n        \"\"\"\n        Given a set of X and Y, calculate the log probability of\n        observing each of Y value given each X value\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, ) for DiscreteMNL and\n            (n_samples, n_classes) for CrossEntropyMNL\n        Returns\n        -------\n        log_p: array of shape (n_samples, )\n        \"\"\"\n        self._raise_error_if_model_not_trained()\n        assert X.shape[0] == Y.shape[0]\n        Y = self._label_decoder(Y)\n        assert X.shape[0] == Y.shape[0]\n        assert Y.shape[1] == self.n_classes\n        log_p = np.sum(self.predict_log_proba(X) * Y, axis=1)\n        log_p[np.sum(Y, axis=1) < EPS] = -np.Infinity\n        return log_p\n\n    @classmethod\n    def _from_json_MNL(cls, json_dict, solver, fit_intercept, est_stderr,\n                       tol, max_iter, reg_method, alpha, l1_ratio, coef, stderr):\n        \"\"\"\n        Helper function within the BaseMNL class to construct the specific MNL used by _from_json.\n        This function is designed to be override by subsubclasses.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        solver: specific solver for each MNL\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (l1, l2).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: the l1 alpha ratio\n        coef: the coefficients\n        stderr: the std.err of coefficients\n        Returns\n        -------\n        Discrete/CrossEntropyMNL object: a MNL object specified by the json_dict and other arguments\n        \"\"\"\n        return cls(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr, tol=tol, max_iter=max_iter)\n\n    @classmethod\n    def _from_json(cls, json_dict, solver, fit_intercept, est_stderr,\n                   tol, max_iter, reg_method, alpha, l1_ratio, coef, stderr):\n        \"\"\"\n        Helper function to construct the linear model used by from_json.\n        This function overrides the parent class.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        solver: specific solver for each MNL\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (l1, l2).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: the l1 alpha ratio\n        coef: the coefficients\n        stderr: the std.err of coefficients\n        Returns\n        -------\n        Discrete/CrossEntropyMNL object: a MNL object specified by the json_dict and other arguments\n        \"\"\"\n        return cls._from_json_MNL(\n            json_dict,\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr, tol=tol, max_iter=max_iter)\n\n\nclass DiscreteMNL(BaseMNL):\n    \"\"\"\n    A MNL for the case where responses are discrete labels.\n    \"\"\"\n\n    def __init__(self, solver='lbfgs', fit_intercept=True, est_stderr=False,\n                 reg_method='l2', alpha=0, l1_ratio=0,\n                 tol=1e-4, max_iter=100,\n                 coef=None, stderr=None,\n                 classes=None):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        solver: specific solver for each linear model, default 'lbfgs',\n                possible solvers are {'newton-cg', 'lbfgs', 'liblinear', 'sag'}.\n                Need to be consistent with the regularization method.\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (l1, l2).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: the l1 alpha ratio\n        coef: the coefficients if loading from trained model\n        stderr: the std.err of coefficients if loading from trained model\n\n        classes: class labels if loading from trained model\n        -------\n        \"\"\"\n        n_classes = None if classes is None else classes.shape[0]\n        super(DiscreteMNL, self).__init__(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            tol=tol, max_iter=max_iter,\n            coef=coef, stderr=stderr,\n            classes=classes, n_classes=n_classes)\n\n    @staticmethod\n    def _label_encoder(X, Y, sample_weight):\n        \"\"\"\n        Convert input to proper format to be used by sklearn logistic regression.\n        Basically do nothing for the discrete case.\n        This function overrides parent class.\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, )\n        sample_weight: sample weight vector of shape (n_samples, )\n        Returns\n        -------\n        X_transformed : design matrix of shape (n_samples, n_features), 2d\n        Y_transformed : response matrix of shape (n_samples, )\n        sample_weight_transformed: sample weight vector of shape (n_samples, )\n        \"\"\"\n        if Y.ndim == 2 and Y.shape[1] == 1:\n            Y = Y.reshape(-1,)\n        return X, Y, sample_weight\n\n    def _label_decoder(self, Y):\n        \"\"\"\n        Convert the response vector to probability matrix.\n        This function overrides parent classes.\n        Parameters\n        ----------\n        Y : response matrix of shape (n_samples, )\n        Returns\n        -------\n        Y_transformed : of shape (n_samples, n_classes).\n        \"\"\"\n        # consider the case of outside labels\n        if Y.ndim == 2 and Y.shape[1] == 1:\n            Y = Y.reshape(-1,)\n        assert Y.ndim == 1\n        if self.n_classes == 1:\n            return (Y == self.classes).reshape(-1, 1).astype(float)\n        if self.n_classes == 2:\n            # sklearn is stupid here\n            label = np.zeros((Y.shape[0], self.n_classes))\n            for clas_i, clas in enumerate(self.classes):\n                label[:, clas_i] = (Y == clas).astype(float)\n            return label\n        return label_binarize(Y, self.classes)\n\n    def to_json(self, path):\n        \"\"\"\n        Generate json object of the model\n        Parameters\n        ----------\n        path : the path to save the model\n        Returns\n        -------\n        json_dict: a dictionary containing the attributes of the DiscreteMNL\n        \"\"\"\n        json_dict = super(DiscreteMNL, self).to_json(path=path)\n        json_dict['properties'].update(\n            {\n                'classes': {\n                    'data_type': 'numpy.ndarray',\n                    'path': os.path.join(path, 'classes.npy')\n                }\n            })\n        if not os.path.exists(os.path.dirname(json_dict['properties']['classes']['path'])):\n            os.makedirs(os.path.dirname(json_dict['properties']['classes']['path']))\n        np.save(json_dict['properties']['classes']['path'], self.classes)\n        return json_dict\n\n    @classmethod\n    def _from_json_MNL(cls, json_dict, solver, fit_intercept, est_stderr,\n                       reg_method, alpha, l1_ratio, coef, stderr,\n                       tol, max_iter):\n        \"\"\"\n        Helper function within the construct the DiscreteMNL used by _from_json.\n        This function overrides parent class.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        solver: specific solver for each linear model\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (None, l1, l2, elstic_net).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: the l1 alpha ratio\n        coef: the coefficients\n        stderr: the std.err of coefficients\n        Returns\n        -------\n        DiscreteMNL object: a DiscreteMNL object specified by the json_dict and other arguments\n        \"\"\"\n        return cls(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr,\n            tol=tol, max_iter=max_iter,\n            classes=np.load(json_dict['properties']['classes']['path'], allow_pickle=True))\n\n\nclass CrossEntropyMNL(BaseMNL):\n    \"\"\"\n    A MNL for the case where responses are probabilities sum to one.\n    \"\"\"\n\n    def __init__(self, solver='lbfgs', fit_intercept=True, est_stderr=False,\n                 reg_method='l2', alpha=0, l1_ratio=0,\n                 tol=1e-4, max_iter=100,\n                 coef=None, stderr=None,\n                 n_classes=None):\n        \"\"\"\n        Constructor\n        Parameters\n        ----------\n        solver: specific solver for each linear model, default 'lbfgs',\n                possible solvers are {'newton-cg', 'lbfgs', 'liblinear', 'sag'}.\n                Need to be consistent with the regularization method.\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (l1, l2).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: the l1 alpha ratio\n        coef: the coefficients if loading from trained model\n        stderr: the std.err of coefficients if loading from trained model\n\n        n_classes: number of classes to be classified\n        -------\n        \"\"\"\n        classes = None if n_classes is None else np.arange(n_classes)\n        super(CrossEntropyMNL, self).__init__(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            tol=tol, max_iter=max_iter,\n            coef=coef, stderr=stderr,\n            classes=classes, n_classes=n_classes)\n\n    @staticmethod\n    def _label_encoder(X, Y, sample_weight):\n        \"\"\"\n        Convert input to proper format to be used by sklearn logistic regression.\n        Mainly transforms Y to a 1d vector containing the class label for each sample.\n        This function overrides parent class.\n        Parameters\n        ----------\n        X : design matrix of shape (n_samples, n_features), 2d\n        Y : response matrix of shape (n_samples, n_classes)\n        sample_weight: sample weight vector of shape (n_samples, )\n        Returns\n        -------\n        X_repeated : design matrix of shape (n_samples * n_classes, n_features), 2d\n        Y_repeated : response matrix of shape (n_samples * n_classes, )\n        sample_weight_repeated: sample weight vector of shape (n_samples * n_classes, )\n        Notes\n        ----------\n        idea from https://stats.stackexchange.com/questions/90622/\n        regression-model-where-output-is-a-probability\n        \"\"\"\n        n_samples, n_classes = X.shape[0], Y.shape[1]\n        X_repeated = np.repeat(X, n_classes, axis=0)\n        Y_repeated = np.tile(np.arange(n_classes), n_samples)\n        sample_weight_repeated = Y.reshape(-1, ) * np.repeat(sample_weight, n_classes)\n        return X_repeated, Y_repeated, sample_weight_repeated\n\n    def _label_decoder(self, Y):\n        \"\"\"\n        Convert the response vector to probability matrix.\n        In CrossEntropyMNL, this function basically does nothing.\n        This function overrides parent classes.\n        Parameters\n        ----------\n        Y : response matrix of shape (n_samples, n_classes)\n        Returns\n        -------\n        Y_transformed : of shape (n_samples, n_classes).\n        \"\"\"\n        assert Y.ndim == 2\n        assert Y.shape[1] == self.n_classes\n        return Y\n\n    def to_json(self, path):\n        \"\"\"\n        Generate json object of the model\n        Parameters\n        ----------\n        path : the path to save the model\n        Returns\n        -------\n        json_dict: a dictionary containing the attributes of the CrossEntropyMNL\n        \"\"\"\n        json_dict = super(CrossEntropyMNL, self).to_json(path=path)\n        json_dict['properties'].update(\n            {\n                'n_classes': self.n_classes\n            })\n        return json_dict\n\n    @classmethod\n    def _from_json_MNL(cls, json_dict, solver, fit_intercept, est_stderr,\n                       reg_method, alpha, l1_ratio, coef, stderr,\n                       tol, max_iter):\n        \"\"\"\n        Helper function within the construct the CrossEntropyMNL used by _from_json.\n        This function overrides parent class.\n        Parameters\n        ----------\n        json_dict : the dictionary that specifies the model\n        solver: specific solver for each linear model\n        fit_intercept: boolean indicating fit intercept or not\n        est_stderr: boolean indicating calculte std.err of coefficients (usually expensive) or not\n        tol: tolerence of fitting error\n        max_iter: maximum iteraration of fitting\n        reg_method: method to regularize the model, one of (l1, l2).\n                    Need to be compatible with the solver.\n        alpha: regularization strength\n        l1_ratio: the l1 alpha ratio\n        coef: the coefficients\n        stderr: the std.err of coefficients\n        Returns\n        -------\n        CrossEntropyMNL object:\n            a CrossEntropyMNL object specified by the json_dict and other arguments\n        \"\"\"\n        return cls(\n            solver=solver, fit_intercept=fit_intercept, est_stderr=est_stderr,\n            reg_method=reg_method, alpha=alpha, l1_ratio=l1_ratio,\n            coef=coef, stderr=stderr,\n            tol=tol, max_iter=max_iter,\n            n_classes=json_dict['properties']['n_classes'])\n"
  },
  {
    "path": "LICENCE",
    "content": "Copyright (c) 2017, Mogeng Yin, Alexei Pozdnukhov\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n    * Redistributions of source code must retain the above copyright\n      notice, this list of conditions and the following disclaimer.\n    * Redistributions in binary form must reproduce the above copyright\n      notice, this list of conditions and the following disclaimer in the\n      documentation and/or other materials provided with the distribution.\n    * Neither the name of the copyright holder nor the\n      names of its contributors may be used to endorse or promote products\n      derived from this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY\nDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\nON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
  },
  {
    "path": "README.md",
    "content": "# IOHMM\n\nA Python package of Input-Output Hidden Markov Model (IOHMM).\n\n[![Build Status](https://travis-ci.org/Mogeng/IOHMM.svg?branch=master)](https://travis-ci.org/Mogeng/IOHMM) [![Coverage Status](https://coveralls.io/repos/github/Mogeng/IOHMM/badge.svg)](https://coveralls.io/github/Mogeng/IOHMM)\n\nIOHMM extends standard HMM by allowing (a) initial, (b) transition and (c) emission probabilities to depend on various covariates. A graphical representation of  standard HMM and IOHMM:\n\n| Standard HMM | IOHMM |\n| --- | --- |\n| <img width=\"300\" src=\"./documents/HMM.png\">  |  <img width=\"300\" src=\"./documents/IOHMM.png\">|\n\n\nThe solid nodes represent observed information, while the transparent (white) nodes represent latent random variables. The top layer contains the **observed** input variables *__u<sub>t</sub>__*; the middle layer contains **latent** categorical variable *z<sub>t</sub>*; and the bottom layer contains **observed** output variables *__x<sub>t</sub>__*. The input for (a) initial, (b) transition and (c) emission probabilities does not have to be the same.\n\nFor more theoretical details:\n* [An Input Output HMM Architecture](https://papers.nips.cc/paper/964-an-input-output-hmm-architecture.pdf)\n* [Input-output HMMs for sequence processing](http://ieeexplore.ieee.org/document/536317/)\n\nApplications of IOHMM:\n* [A Generative Model of Urban Activities from Cellular Data](http://ieeexplore.ieee.org/document/7932990/)\n## Installing\n```shell\npip install IOHMM\n```\n\n## Examples\n\nThe `example` directory contains a set of [Jupyter Notebook of examples and demonstrations](https://github.com/Mogeng/IOHMM/tree/master/examples/notebooks) of:\n\n* [UnSupervised IOHMM](https://github.com/Mogeng/IOHMM/blob/master/examples/notebooks/UnSupervisedIOHMM.ipynb)\n\n* [SemiSupervised IOHMM](https://github.com/Mogeng/IOHMM/blob/master/examples/notebooks/SemiSupervisedIOHMM.ipynb)\n\n* [Supervised IOHMM](https://github.com/Mogeng/IOHMM/blob/master/examples/notebooks/SupervisedIOHMM.ipynb)\n\n## Features\n\n* **3-in-1 IOHMM**. IOHMM package supports:\n  - [UnSupervised](https://en.wikipedia.org/wiki/Unsupervised_learning) IOHMM when you have no ground truth hidden states at any timestamp. [Expectation-Maximization (EM) algorithm](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) will be used to estimate parameters (maximization step) and posteriors (expectation step).\n  - [SemiSupervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) IOHMM when you have certain amount of ground truth hidden states and would like to enforce these labeled hidden states during learning and use these labels to help direct the learning process.\n  - [Supervised](https://en.wikipedia.org/wiki/Supervised_learning) IOHMM when you want to purely use labeled ground truth hidden states during the learning. There will be no expectation step and only one shot of maximization step during the learning since all the posteriors are from labeled ground truth.\n\n* __Crystal clear structure__. Know each step you go:\n\t- All sequences are represented by [pandas](http://pandas.pydata.org/) dataframes. It has great interface to load csv, json, etc. files or to pull data from sql databases. It is also easy to visualize.\n\t- Inputs and outputs covariates are specified by the column names (strings) in the dataframes.\n\t- You can pass a list of sequences (dataframes) as data -- there is no more need to tag the start of each sequence in a single stacked sequence.\n\t- You can specify different set of inputs for (a) initial, (b) transition and (c) different emission models.\n\n* __Forward Backward algorithm__. Faster and more robust:\n\t- Fully [vectorized](https://en.wikipedia.org/wiki/Array_programming). Only one 'for loop' (due to [dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)) in the forward/backward pass where most current implementations have more than one 'for loop'.\n\t- All calculations are at *log* level, this is more robust to long sequence for which the probabilities easily vanish to 0.\n\n* __Json-serialization__. Models on the go:\n\t-  Save (`to_json`) and load (`from_json`) a trained model in json format. All the attributes are easily visualizable in the json dictionary/file. See [Jupyter Notebook of examples](https://github.com/Mogeng/IOHMM/tree/master/examples/notebooks) for more details.\n\t- Use a json configuration file to specify the structure of an IOHMM model (`from_config`). This is useful when you have an application that uses IOHMM models and would like to specify the model before hand.\n\n* __Statsmodels and scikit-learn as the backbone__. Take the best of both and better:\n\t- Unified interface/wrapper to statsmodels and scikit-learn linear models/generalized linear models.\n\t- Supports fitting the model with sample frequency weights.\n\t- Supports regularizations in these models.\n\t- Supports estimation of standard error of the coefficients in certain models.\n\t- Json-serialization to save (`to_json`) and load (`from_json`) of trained linear models.\n\n## Credits\n\n* The structure of this implementation is inspired by depmixS4: [depmixS4: An R Package for Hidden Markov Models](https://cran.opencpu.org/web/packages/depmixS4/vignettes/depmixS4.pdf).\n* This IOHMM package uses/wraps [statsmodels](http://www.statsmodels.org/stable/index.html) and [scikit-learn](http://scikit-learn.org/stable/) APIs for linear supervised models.\n\n## Licensing\n\nModified BSD (3-clause)"
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    "content": "{\n    \"data_type\": \"SemiSupervisedIOHMM\",\n    \"properties\": {\n        \"EM_tol\": 1e-10,\n        \"covariates_emissions\": [\n            []\n        ],\n        \"covariates_initial\": [],\n        \"covariates_transition\": [],\n        \"max_EM_iter\": 200,\n        \"model_emissions\": [\n            {\n                \"data_type\": \"OLS\",\n                \"properties\": {}\n            }\n        ],\n        \"model_initial\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {\n                \"solver\": \"lbfgs\"\n            }\n        },\n        \"model_transition\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {\n                \"solver\": \"lbfgs\"\n            }\n        },\n        \"num_states\": 4,\n        \"responses_emissions\": [\n            [\n                \"rt\"\n            ]\n        ]\n    }\n}"
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    "content": "{\n    \"data_type\": \"SemiSupervisedIOHMM\", \n    \"properties\": {\n        \"EM_tol\": 1e-10, \n        \"covariates_emissions\": [\n            []\n        ], \n        \"covariates_initial\": [], \n        \"covariates_transition\": [], \n        \"max_EM_iter\": 200, \n        \"model_emissions\": [\n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy\"\n                        }, \n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy\"\n                        }, \n                        \"est_stderr\": false, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"n_targets\": 1, \n                        \"reg_method\": null, \n                        \"solver\": \"svd\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }\n            ], \n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": 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     }\n                }\n            ], \n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/coef.npy\"\n                        }, \n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/dispersion.npy\"\n                        }, \n                        \"est_stderr\": false, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"n_targets\": 1, \n                        \"reg_method\": null, \n                        \"solver\": \"svd\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }\n            ], \n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SemiSupervisedIOHMM/model_emissions/state_3/emission_0/coef.npy\"\n                        }, \n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": 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\"data_type\": \"numpy.ndarray\", \n                    \"path\": \"../models/SemiSupervisedIOHMM/model_initial/coef.npy\"\n                }, \n                \"est_stderr\": false, \n                \"fit_intercept\": true, \n                \"l1_ratio\": 0, \n                \"max_iter\": 100, \n                \"n_classes\": 4, \n                \"reg_method\": \"l2\", \n                \"solver\": \"lbfgs\", \n                \"stderr\": {\n                    \"data_type\": \"numpy.ndarray\", \n                    \"path\": \"../models/SemiSupervisedIOHMM/model_initial/stderr.npy\"\n                }, \n                \"tol\": 0.0001\n            }\n        }, \n        \"model_transition\": [\n            {\n                \"data_type\": \"CrossEntropyMNL\", \n                \"properties\": {\n                    \"alpha\": 0, \n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": 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  },
  {
    "path": "examples/models/SupervisedIOHMM/config.json",
    "content": "{\n    \"data_type\": \"SupervisedIOHMM\",\n    \"properties\": {\n        \"covariates_emissions\": [\n            []\n        ],\n        \"covariates_initial\": [],\n        \"covariates_transition\": [],\n        \"model_emissions\": [\n            {\n                \"data_type\": \"OLS\",\n                \"properties\": {}\n            }\n        ],\n        \"model_initial\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {\n                \"solver\": \"lbfgs\"\n            }\n        },\n        \"model_transition\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {\n                \"solver\": \"lbfgs\"\n            }\n        },\n        \"num_states\": 2,\n        \"responses_emissions\": [\n            [\n                \"rt\"\n            ]\n        ]\n    }\n}"
  },
  {
    "path": "examples/models/SupervisedIOHMM/model.json",
    "content": "{\n    \"data_type\": \"SupervisedIOHMM\", \n    \"properties\": {\n        \"covariates_emissions\": [\n            []\n        ], \n        \"covariates_initial\": [], \n        \"covariates_transition\": [], \n        \"model_emissions\": [\n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy\"\n                        }, \n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy\"\n                        }, \n                        \"est_stderr\": false, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"n_targets\": 1, \n                        \"reg_method\": null, \n                        \"solver\": \"svd\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }\n            ], \n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy\"\n                        }, \n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy\"\n                        }, \n                        \"est_stderr\": false, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"n_targets\": 1, \n                        \"reg_method\": null, \n                        \"solver\": \"svd\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/SupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }\n            ]\n        ], \n        \"model_initial\": {\n            \"data_type\": 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\"coef\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/SupervisedIOHMM/model_transition/state_0/coef.npy\"\n                    }, \n                    \"est_stderr\": false, \n                    \"fit_intercept\": true, \n                    \"l1_ratio\": 0, \n                    \"max_iter\": 100, \n                    \"n_classes\": 2, \n                    \"reg_method\": \"l2\", \n                    \"solver\": \"lbfgs\", \n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/SupervisedIOHMM/model_transition/state_0/stderr.npy\"\n                    }, \n                    \"tol\": 0.0001\n                }\n            }, \n            {\n                \"data_type\": \"CrossEntropyMNL\", \n                \"properties\": {\n                    \"alpha\": 0, \n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/SupervisedIOHMM/model_transition/state_1/coef.npy\"\n                    }, \n                    \"est_stderr\": false, \n                    \"fit_intercept\": true, \n                    \"l1_ratio\": 0, \n                    \"max_iter\": 100, \n                    \"n_classes\": 2, \n                    \"reg_method\": \"l2\", \n                    \"solver\": \"lbfgs\", \n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/SupervisedIOHMM/model_transition/state_1/stderr.npy\"\n                    }, \n                    \"tol\": 0.0001\n                }\n            }\n        ], \n        \"num_states\": 2, \n        \"responses_emissions\": [\n            [\n                \"rt\"\n            ]\n        ]\n    }\n}"
  },
  {
    "path": "examples/models/UnSupervisedIOHMM/config.json",
    "content": "{\n    \"data_type\": \"UnSupervisedIOHMM\",\n    \"properties\": {\n        \"EM_tol\": 1e-06,\n        \"covariates_emissions\": [\n            [],\n            [\n                \"Pacc\"\n            ]\n        ],\n        \"covariates_initial\": [],\n        \"covariates_transition\": [],\n        \"max_EM_iter\": 200,\n        \"model_emissions\": [\n            {\n                \"data_type\": \"OLS\",\n                \"properties\": {\n                    \"est_stderr\": true\n                }\n            },\n            {\n                \"data_type\": \"DiscreteMNL\",\n                \"properties\": {}\n            }\n        ],\n        \"model_initial\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {}\n        },\n        \"model_transition\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {}\n        },\n        \"num_states\": 2,\n        \"responses_emissions\": [\n            [\n                \"rt\"\n            ],\n            [\n                \"corr\"\n            ]\n        ]\n    }\n}"
  },
  {
    "path": "examples/models/UnSupervisedIOHMM/model.json",
    "content": "{\n    \"data_type\": \"UnSupervisedIOHMM\", \n    \"properties\": {\n        \"EM_tol\": 1e-06, \n        \"covariates_emissions\": [\n            [], \n            [\n                \"Pacc\"\n            ]\n        ], \n        \"covariates_initial\": [], \n        \"covariates_transition\": [], \n        \"max_EM_iter\": 200, \n        \"model_emissions\": [\n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy\"\n                        }, \n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy\"\n                        }, \n                        \"est_stderr\": true, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"n_targets\": 1, \n                        \"reg_method\": null, \n                        \"solver\": \"svd\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }, \n                {\n                    \"data_type\": \"DiscreteMNL\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"classes\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/classes.npy\"\n                        }, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/coef.npy\"\n                        }, \n                        \"est_stderr\": false, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"reg_method\": \"l2\", \n                        \"solver\": \"lbfgs\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }\n            ], \n            [\n                {\n                    \"data_type\": \"OLS\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy\"\n                        }, \n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy\"\n                        }, \n                        \"est_stderr\": true, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"n_targets\": 1, \n                        \"reg_method\": null, \n                        \"solver\": \"svd\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }, \n                {\n                    \"data_type\": \"DiscreteMNL\", \n                    \"properties\": {\n                        \"alpha\": 0, \n                        \"classes\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/classes.npy\"\n                        }, \n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/coef.npy\"\n                        }, \n                        \"est_stderr\": false, \n                        \"fit_intercept\": true, \n                        \"l1_ratio\": 0, \n                        \"max_iter\": 100, \n                        \"reg_method\": \"l2\", \n                        \"solver\": \"lbfgs\", \n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\", \n                            \"path\": \"../models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/stderr.npy\"\n                        }, \n                        \"tol\": 0.0001\n                    }\n                }\n            ]\n        ], \n        \"model_initial\": {\n            \"data_type\": \"CrossEntropyMNL\", \n            \"properties\": {\n                \"alpha\": 0, \n                \"coef\": {\n                    \"data_type\": \"numpy.ndarray\", \n                    \"path\": \"../models/UnSupervisedIOHMM/model_initial/coef.npy\"\n                }, \n                \"est_stderr\": false, \n                \"fit_intercept\": true, \n                \"l1_ratio\": 0, \n                \"max_iter\": 100, \n                \"n_classes\": 2, \n                \"reg_method\": \"l2\", \n                \"solver\": \"lbfgs\", \n                \"stderr\": {\n                    \"data_type\": \"numpy.ndarray\", \n                    \"path\": \"../models/UnSupervisedIOHMM/model_initial/stderr.npy\"\n                }, \n                \"tol\": 0.0001\n            }\n        }, \n        \"model_transition\": [\n            {\n                \"data_type\": \"CrossEntropyMNL\", \n                \"properties\": {\n                    \"alpha\": 0, \n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/UnSupervisedIOHMM/model_transition/state_0/coef.npy\"\n                    }, \n                    \"est_stderr\": false, \n                    \"fit_intercept\": true, \n                    \"l1_ratio\": 0, \n                    \"max_iter\": 100, \n                    \"n_classes\": 2, \n                    \"reg_method\": \"l2\", \n                    \"solver\": \"lbfgs\", \n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/UnSupervisedIOHMM/model_transition/state_0/stderr.npy\"\n                    }, \n                    \"tol\": 0.0001\n                }\n            }, \n            {\n                \"data_type\": \"CrossEntropyMNL\", \n                \"properties\": {\n                    \"alpha\": 0, \n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/UnSupervisedIOHMM/model_transition/state_1/coef.npy\"\n                    }, \n                    \"est_stderr\": false, \n                    \"fit_intercept\": true, \n                    \"l1_ratio\": 0, \n                    \"max_iter\": 100, \n                    \"n_classes\": 2, \n                    \"reg_method\": \"l2\", \n                    \"solver\": \"lbfgs\", \n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\", \n                        \"path\": \"../models/UnSupervisedIOHMM/model_transition/state_1/stderr.npy\"\n                    }, \n                    \"tol\": 0.0001\n                }\n            }\n        ], \n        \"num_states\": 2, \n        \"responses_emissions\": [\n            [\n                \"rt\"\n            ], \n            [\n                \"corr\"\n            ]\n        ]\n    }\n}"
  },
  {
    "path": "examples/notebooks/SemiSupervisedIOHMM.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is the IOHMM model with the parameters learned in a semi-supervised way. By using some labeled data, we force the learning process in a certain direction. The unlabeled data will be estimated using EM algorithm iteratively. See notes in http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# SemiSupervisedIOHMM \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import  division\\n\",\n    \"\\n\",\n    \"import json\\n\",\n    \"import warnings\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from IOHMM import SemiSupervisedIOHMM\\n\",\n    \"from IOHMM import OLS, CrossEntropyMNL\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"warnings.simplefilter(\\\"ignore\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load speed data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Unnamed: 0</th>\\n\",\n       \"      <th>rt</th>\\n\",\n       \"      <th>corr</th>\\n\",\n       \"      <th>Pacc</th>\\n\",\n       \"      <th>prev</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6.456770</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>5.602119</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>6.253829</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>5.451038</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>5.872118</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Unnamed: 0        rt corr  Pacc prev\\n\",\n       \"0           1  6.456770  cor   0.0  inc\\n\",\n       \"1           2  5.602119  cor   0.0  cor\\n\",\n       \"2           3  6.253829  inc   0.0  cor\\n\",\n       \"3           4  5.451038  inc   0.0  inc\\n\",\n       \"4           5  5.872118  inc   0.0  inc\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"speed = pd.read_csv('../data/speed.csv')\\n\",\n    \"speed.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Label some states\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In our structure of the code, the states should be a dictionary, the key is the index in the sequence (e.g. 0, 5) and the value is a one-out-of-n code of array where the kth value is 1 if the hidden state is k. n is the number of states in total.\\n\",\n    \"\\n\",\n    \"In the following example, we assume that the \\\"corr\\\" column gives the correct hidden states. Here we assume only the first half of the sequence is labeled.\\n\",\n    \"\\n\",\n    \"To make sure that the semi supervised model works, we set the value of 'rt' in state 0 as 0 and we set the value of 'rt' in state 1 as 1, the other values are not changed. So after training, we should be able to see 4 states clearly\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"states = {}\\n\",\n    \"corr = np.array(speed['corr'])\\n\",\n    \"for i in range(int(len(corr)/2)):\\n\",\n    \"    state = np.zeros((4,))\\n\",\n    \"    if corr[i] == 'cor':\\n\",\n    \"        states[i] = np.array([0,1,0,0])\\n\",\n    \"        speed.at[i, 'rt'] = 1\\n\",\n    \"    else:\\n\",\n    \"        states[i] = np.array([1,0,0,0])\\n\",\n    \"        speed.at[i, 'rt'] = 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"439\\n\",\n      \"219\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(speed.shape[0])\\n\",\n    \"print(len(states))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 0 0]\\n\",\n      \"[0 1 0 0]\\n\",\n      \"[1 0 0 0]\\n\",\n      \"[1 0 0 0]\\n\",\n      \"[1 0 0 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for t in range(5):\\n\",\n    \"    print(states[t])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up a simple model manully\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# there should be 4 hidden states in this model\\n\",\n    \"SHMM = SemiSupervisedIOHMM(num_states=4, max_EM_iter=200, EM_tol=1e-10)\\n\",\n    \"\\n\",\n    \"# we set only one output 'rt' modeled by a linear regression model\\n\",\n    \"SHMM.set_models(model_emissions = [OLS()], \\n\",\n    \"                model_transition=CrossEntropyMNL(solver='lbfgs'),\\n\",\n    \"                model_initial=CrossEntropyMNL(solver='lbfgs'))\\n\",\n    \"\\n\",\n    \"# we set no covariates associated with initial/transitiojn/emission models\\n\",\n    \"SHMM.set_inputs(covariates_initial = [], covariates_transition = [], covariates_emissions = [[]])\\n\",\n    \"\\n\",\n    \"# set the response of the emission model\\n\",\n    \"SHMM.set_outputs([['rt']])\\n\",\n    \"\\n\",\n    \"# set the data and ground truth states\\n\",\n    \"SHMM.set_data([[speed, states]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Start training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM.train()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See the training results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.]]\\n\",\n      \"[[ 1.]]\\n\",\n      \"[[ 6.38975526]]\\n\",\n      \"[[ 5.47039844]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the coefficients of the output model for each states\\n\",\n    \"# clearly the enforcement worked since the coefficient of the first two states are 0, and 1\\n\",\n    \"print(SHMM.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM.model_emissions[1][0].coef)\\n\",\n    \"print(SHMM.model_emissions[2][0].coef)\\n\",\n    \"print(SHMM.model_emissions[3][0].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.]]\\n\",\n      \"[[  1.66533454e-15]]\\n\",\n      \"[[ 0.22536249]]\\n\",\n      \"[[ 0.17915255]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the scale/dispersion of the output model of each states\\n\",\n    \"# since we know the first two states perfectly, the dispersions are 0.\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[0][0].dispersion))\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[1][0].dispersion))\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[2][0].dispersion))\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[3][0].dispersion))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[  4.03845430e-01   5.96154225e-01   1.72468132e-07   1.72468132e-07]]\\n\",\n      \"[[  1.85628510e-01   8.08383511e-01   8.78709574e-10   5.98797866e-03]]\\n\",\n      \"[[  1.74937041e-07   1.74937041e-07   9.27082453e-01   7.29171969e-02]]\\n\",\n      \"[[  3.41629345e-08   3.41629345e-08   1.11257897e-01   8.88742035e-01]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the transition probability from each state\\n\",\n    \"print(np.exp(SHMM.model_transition[0].predict_log_proba(np.array([[]]))))\\n\",\n    \"print(np.exp(SHMM.model_transition[1].predict_log_proba(np.array([[]]))))\\n\",\n    \"print(np.exp(SHMM.model_transition[2].predict_log_proba(np.array([[]]))))\\n\",\n    \"print(np.exp(SHMM.model_transition[3].predict_log_proba(np.array([[]]))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Save the trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'data_type': 'SemiSupervisedIOHMM',\\n\",\n       \" 'properties': {'EM_tol': 1e-10,\\n\",\n       \"  'covariates_emissions': [[]],\\n\",\n       \"  'covariates_initial': [],\\n\",\n       \"  'covariates_transition': [],\\n\",\n       \"  'max_EM_iter': 200,\\n\",\n       \"  'model_emissions': [[{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}],\\n\",\n       \"   [{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}],\\n\",\n       \"   [{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}],\\n\",\n       \"   [{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_3/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_3/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SemiSupervisedIOHMM/model_emissions/state_3/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}]],\\n\",\n       \"  'model_initial': {'data_type': 'CrossEntropyMNL',\\n\",\n       \"   'properties': {'alpha': 0,\\n\",\n       \"    'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"     'path': '../models/SemiSupervisedIOHMM/model_initial/coef.npy'},\\n\",\n       \"    'est_stderr': False,\\n\",\n       \"    'fit_intercept': True,\\n\",\n       \"    'l1_ratio': 0,\\n\",\n       \"    'max_iter': 100,\\n\",\n       \"    'n_classes': 4,\\n\",\n       \"    'reg_method': 'l2',\\n\",\n       \"    'solver': 'lbfgs',\\n\",\n       \"    'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"     'path': '../models/SemiSupervisedIOHMM/model_initial/stderr.npy'},\\n\",\n       \"    'tol': 0.0001}},\\n\",\n       \"  'model_transition': [{'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_0/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 4,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_0/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}},\\n\",\n       \"   {'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_1/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 4,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_1/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}},\\n\",\n       \"   {'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_2/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 4,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_2/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}},\\n\",\n       \"   {'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_3/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 4,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SemiSupervisedIOHMM/model_transition/state_3/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}}],\\n\",\n       \"  'num_states': 4,\\n\",\n       \"  'responses_emissions': [['rt']]}}\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"json_dict = SHMM.to_json('../models/SemiSupervisedIOHMM/')\\n\",\n    \"json_dict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open('../models/SemiSupervisedIOHMM/model.json', 'w') as outfile:\\n\",\n    \"    json.dump(json_dict, outfile, indent=4, sort_keys=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load back the trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM_from_json = SemiSupervisedIOHMM.from_json(json_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See if the coefficients are any different\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.]]\\n\",\n      \"[[ 1.]]\\n\",\n      \"[[ 6.38975526]]\\n\",\n      \"[[ 5.47039844]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the coefficients of the output model for each states\\n\",\n    \"# clearly the enforcement worked since the coefficient of the first two states are 0, and 1\\n\",\n    \"print(SHMM_from_json.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM_from_json.model_emissions[1][0].coef)\\n\",\n    \"print(SHMM_from_json.model_emissions[2][0].coef)\\n\",\n    \"print(SHMM_from_json.model_emissions[3][0].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up the model using a config file, instead of doing it manully\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open('../models/SemiSupervisedIOHMM/config.json') as json_data:\\n\",\n    \"    json_dict = json.load(json_data)\\n\",\n    \"\\n\",\n    \"SHMM_from_config = SemiSupervisedIOHMM.from_config(json_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set data and start training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM_from_config.set_data([[speed, states]])\\n\",\n    \"SHMM_from_config.train()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See if the training results are any different?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.]]\\n\",\n      \"[[ 1.]]\\n\",\n      \"[[ 6.38975526]]\\n\",\n      \"[[ 5.47039844]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the coefficients of the output model for each states\\n\",\n    \"# clearly the enforcement worked since the coefficient of the first two states are 0, and 1\\n\",\n    \"print(SHMM_from_config.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM_from_config.model_emissions[1][0].coef)\\n\",\n    \"print(SHMM_from_config.model_emissions[2][0].coef)\\n\",\n    \"print(SHMM_from_config.model_emissions[3][0].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "examples/notebooks/SupervisedIOHMM.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is the IOHMM model with the parameters learned in a supervised way. This is corresponding to the counting frequency process as in the supervised HMM. See notes in http://www.cs.columbia.edu/4761/notes07/chapter4.3-HMM.pdf.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# SupervisedIOHMM \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import  division\\n\",\n    \"\\n\",\n    \"import json\\n\",\n    \"import warnings\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from IOHMM import SupervisedIOHMM\\n\",\n    \"from IOHMM import OLS, CrossEntropyMNL\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"warnings.simplefilter(\\\"ignore\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load speed data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Unnamed: 0</th>\\n\",\n       \"      <th>rt</th>\\n\",\n       \"      <th>corr</th>\\n\",\n       \"      <th>Pacc</th>\\n\",\n       \"      <th>prev</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6.456770</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>5.602119</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>6.253829</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>5.451038</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>5.872118</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Unnamed: 0        rt corr  Pacc prev\\n\",\n       \"0           1  6.456770  cor   0.0  inc\\n\",\n       \"1           2  5.602119  cor   0.0  cor\\n\",\n       \"2           3  6.253829  inc   0.0  cor\\n\",\n       \"3           4  5.451038  inc   0.0  inc\\n\",\n       \"4           5  5.872118  inc   0.0  inc\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"speed = pd.read_csv('../data/speed.csv')\\n\",\n    \"speed.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Label some/all states\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In our structure of the code, the states should be a dictionary, the key is the index in the sequence (e.g. 0, 5) and the value is a one-out-of-n code of array where the kth value is 1 if the hidden state is k. n is the number of states in total.\\n\",\n    \"\\n\",\n    \"In the following example, we assume that the \\\"corr\\\" column gives the correct hidden states.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"states = {}\\n\",\n    \"corr = np.array(speed['corr'])\\n\",\n    \"for i in range(len(corr)):\\n\",\n    \"    state = np.zeros((2,))\\n\",\n    \"    if corr[i] == 'cor':\\n\",\n    \"        states[i] = np.array([0,1])\\n\",\n    \"    else:\\n\",\n    \"        states[i] = np.array([1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up a simple model manully\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# we choose 2 hidden states in this model\\n\",\n    \"SHMM = SupervisedIOHMM(num_states=2)\\n\",\n    \"\\n\",\n    \"# we set only one output 'rt' modeled by a linear regression model\\n\",\n    \"SHMM.set_models(model_emissions = [OLS()], \\n\",\n    \"                model_transition=CrossEntropyMNL(solver='lbfgs'),\\n\",\n    \"                model_initial=CrossEntropyMNL(solver='lbfgs'))\\n\",\n    \"\\n\",\n    \"# we set no covariates associated with initial/transitiojn/emission models\\n\",\n    \"SHMM.set_inputs(covariates_initial = [], covariates_transition = [], covariates_emissions = [[]])\\n\",\n    \"\\n\",\n    \"# set the response of the emission model\\n\",\n    \"SHMM.set_outputs([['rt']])\\n\",\n    \"\\n\",\n    \"# set the data and ground truth states\\n\",\n    \"SHMM.set_data([[speed, states]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Start training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"scrolled\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM.train()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See the training results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 5.70451774]]\\n\",\n      \"[[ 6.13678825]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the coefficients of the output model for each states\\n\",\n    \"print(SHMM.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM.model_emissions[1][0].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.35831781]]\\n\",\n      \"[[ 0.47356034]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the scale/dispersion of the output model of each states\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[0][0].dispersion))\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[1][0].dispersion))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.38392857  0.61607143]]\\n\",\n      \"[[ 0.21165647  0.78834353]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the transition probability from each state\\n\",\n    \"print(np.exp(SHMM.model_transition[0].predict_log_proba(np.array([[]]))))\\n\",\n    \"print(np.exp(SHMM.model_transition[1].predict_log_proba(np.array([[]]))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Save the trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'data_type': 'SupervisedIOHMM',\\n\",\n       \" 'properties': {'covariates_emissions': [[]],\\n\",\n       \"  'covariates_initial': [],\\n\",\n       \"  'covariates_transition': [],\\n\",\n       \"  'model_emissions': [[{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}],\\n\",\n       \"   [{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/SupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}]],\\n\",\n       \"  'model_initial': {'data_type': 'CrossEntropyMNL',\\n\",\n       \"   'properties': {'alpha': 0,\\n\",\n       \"    'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"     'path': '../models/SupervisedIOHMM/model_initial/coef.npy'},\\n\",\n       \"    'est_stderr': False,\\n\",\n       \"    'fit_intercept': True,\\n\",\n       \"    'l1_ratio': 0,\\n\",\n       \"    'max_iter': 100,\\n\",\n       \"    'n_classes': 2,\\n\",\n       \"    'reg_method': 'l2',\\n\",\n       \"    'solver': 'lbfgs',\\n\",\n       \"    'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"     'path': '../models/SupervisedIOHMM/model_initial/stderr.npy'},\\n\",\n       \"    'tol': 0.0001}},\\n\",\n       \"  'model_transition': [{'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SupervisedIOHMM/model_transition/state_0/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 2,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SupervisedIOHMM/model_transition/state_0/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}},\\n\",\n       \"   {'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SupervisedIOHMM/model_transition/state_1/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 2,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/SupervisedIOHMM/model_transition/state_1/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}}],\\n\",\n       \"  'num_states': 2,\\n\",\n       \"  'responses_emissions': [['rt']]}}\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"json_dict = SHMM.to_json('../models/SupervisedIOHMM/')\\n\",\n    \"json_dict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open('../models/SupervisedIOHMM/model.json', 'w') as outfile:\\n\",\n    \"    json.dump(json_dict, outfile, indent=4, sort_keys=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load back the trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM_from_json = SupervisedIOHMM.from_json(json_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See if the coefficients are any different\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 5.70451774]]\\n\",\n      \"[[ 6.13678825]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the coefficients of the output model for each states\\n\",\n    \"print(SHMM.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM.model_emissions[1][0].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up the model using a config file, instead of doing it manully\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open('../models/SupervisedIOHMM/config.json') as json_data:\\n\",\n    \"    json_dict = json.load(json_data)\\n\",\n    \"\\n\",\n    \"SHMM_from_config = SupervisedIOHMM.from_config(json_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set data and start training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM_from_config.set_data([[speed, states]])\\n\",\n    \"SHMM_from_config.train()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See if the training results are any different?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 5.70451774]]\\n\",\n      \"[[ 6.13678825]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# the coefficients of the output model for each states\\n\",\n    \"print(SHMM_from_config.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM_from_config.model_emissions[1][0].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "examples/notebooks/UnSupervisedIOHMM.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# UnSupervisedIOHMM\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from __future__ import  division\\n\",\n    \"\\n\",\n    \"import json\\n\",\n    \"import warnings\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from IOHMM import UnSupervisedIOHMM\\n\",\n    \"from IOHMM import OLS, DiscreteMNL, CrossEntropyMNL\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"warnings.simplefilter(\\\"ignore\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load speed data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style>\\n\",\n       \"    .dataframe thead tr:only-child th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Unnamed: 0</th>\\n\",\n       \"      <th>rt</th>\\n\",\n       \"      <th>corr</th>\\n\",\n       \"      <th>Pacc</th>\\n\",\n       \"      <th>prev</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6.456770</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>5.602119</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>6.253829</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>cor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>5.451038</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>5.872118</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>inc</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Unnamed: 0        rt corr  Pacc prev\\n\",\n       \"0           1  6.456770  cor   0.0  inc\\n\",\n       \"1           2  5.602119  cor   0.0  cor\\n\",\n       \"2           3  6.253829  inc   0.0  cor\\n\",\n       \"3           4  5.451038  inc   0.0  inc\\n\",\n       \"4           5  5.872118  inc   0.0  inc\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"speed = pd.read_csv('../data/speed.csv')\\n\",\n    \"speed.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Example 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up a simple model manully\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# set two hidden states\\n\",\n    \"SHMM = UnSupervisedIOHMM(num_states=2, max_EM_iter=200, EM_tol=1e-6)\\n\",\n    \"\\n\",\n    \"# This model has only one output which is modeled by a linear regression model\\n\",\n    \"SHMM.set_models(model_emissions = [OLS()], \\n\",\n    \"                model_transition=CrossEntropyMNL(solver='lbfgs'),\\n\",\n    \"                model_initial=CrossEntropyMNL(solver='lbfgs'))\\n\",\n    \"\\n\",\n    \"# We don't set any covariates to this OLS model\\n\",\n    \"SHMM.set_inputs(covariates_initial = [], covariates_transition = [], covariates_emissions = [[]])\\n\",\n    \"\\n\",\n    \"# This OLS has only one output target, which is 'rt' column in the dataframe\\n\",\n    \"SHMM.set_outputs([['rt']])\\n\",\n    \"\\n\",\n    \"# we only have a list of one sequence.\\n\",\n    \"SHMM.set_data([speed])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Start training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM.train()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See the training results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 5.51036369]]\\n\",\n      \"[[ 6.38505309]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The coefficients of the OLS model for each hidden states\\n\",\n    \"print(SHMM.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM.model_emissions[1][0].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.19175214]]\\n\",\n      \"[[ 0.24415967]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The scale/dispersion of the OLS model for each hidden states\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[0][0].dispersion))\\n\",\n    \"print(np.sqrt(SHMM.model_emissions[1][0].dispersion))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.88351131  0.11648869]]\\n\",\n      \"[[ 0.08433152  0.91566848]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The transition probability between two hidden states\\n\",\n    \"print(np.exp(SHMM.model_transition[0].predict_log_proba(np.array([[]]))))\\n\",\n    \"print(np.exp(SHMM.model_transition[1].predict_log_proba(np.array([[]]))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Example 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up the another model with two outputs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM = UnSupervisedIOHMM(num_states=2, max_EM_iter=200, EM_tol=1e-6)\\n\",\n    \"\\n\",\n    \"# This model has two outputs which is modeled by \\n\",\n    \"# a linear regression model and \\n\",\n    \"# a discrete logistic regression model\\n\",\n    \"SHMM.set_models(model_emissions = [OLS(est_stderr=True), \\n\",\n    \"                                   DiscreteMNL(solver='lbfgs')], \\n\",\n    \"                model_transition=CrossEntropyMNL(solver='lbfgs'),\\n\",\n    \"                model_initial=CrossEntropyMNL(solver='lbfgs'))\\n\",\n    \"\\n\",\n    \"# We set no covariates associated with the first output and\\n\",\n    \"# We set 'Pacc' as the input covariate associate with the second output\\n\",\n    \"SHMM.set_inputs(covariates_initial = [], covariates_transition = [], covariates_emissions = [[],['Pacc']])\\n\",\n    \"\\n\",\n    \"# 'rt' is one output modeled by linear regression and\\n\",\n    \"# 'corr' is the other output modeled by discrete logistic regression model\\n\",\n    \"SHMM.set_outputs([['rt'],['corr']])\\n\",\n    \"\\n\",\n    \"SHMM.set_data([speed])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Start training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM.train()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See the training results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 6.38764009]]\\n\",\n      \"[[ 5.51390328]]\\n\",\n      \"()\\n\",\n      \"[[-1.13690447 -2.17394618]]\\n\",\n      \"[[-0.21848303  0.57762625]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# See the coefficients of the emission model 1 ('rt') of each hidden states\\n\",\n    \"print(SHMM.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM.model_emissions[1][0].coef)\\n\",\n    \"print('')\\n\",\n    \"# See the coefficients of the emission model 2 ('corr') of each hidden states\\n\",\n    \"print(SHMM.model_emissions[0][1].coef)\\n\",\n    \"print(SHMM.model_emissions[1][1].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.01498923]]\\n\",\n      \"[[ 0.0143314]]\\n\",\n      \"()\\n\",\n      \"None\\n\",\n      \"None\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# See the estimated standard error of coefficients of the emission model 1 ('rt') of each hidden states\\n\",\n    \"print(SHMM.model_emissions[0][0].stderr)\\n\",\n    \"print(SHMM.model_emissions[1][0].stderr)\\n\",\n    \"print('')\\n\",\n    \"# See the estimated standard error of coefficients of the emission model 2 ('corr') of each hidden states\\n\",\n    \"# Note that est_stderr is not supported in the MNL model.\\n\",\n    \"print(SHMM.model_emissions[0][1].stderr)\\n\",\n    \"print(SHMM.model_emissions[1][1].stderr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 0.91491024  0.08508976]]\\n\",\n      \"[[ 0.11590608  0.88409392]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The transition probability between two hidden states\\n\",\n    \"print(np.exp(SHMM.model_transition[0].predict_log_proba(np.array([[]]))))\\n\",\n    \"print(np.exp(SHMM.model_transition[1].predict_log_proba(np.array([[]]))))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Save the trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'data_type': 'UnSupervisedIOHMM',\\n\",\n       \" 'properties': {'EM_tol': 1e-06,\\n\",\n       \"  'covariates_emissions': [[], ['Pacc']],\\n\",\n       \"  'covariates_initial': [],\\n\",\n       \"  'covariates_transition': [],\\n\",\n       \"  'max_EM_iter': 200,\\n\",\n       \"  'model_emissions': [[{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': True,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}},\\n\",\n       \"    {'data_type': 'DiscreteMNL',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'classes': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/classes.npy'},\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/coef.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'reg_method': 'l2',\\n\",\n       \"      'solver': 'lbfgs',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}],\\n\",\n       \"   [{'data_type': 'OLS',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy'},\\n\",\n       \"      'dispersion': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy'},\\n\",\n       \"      'est_stderr': True,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'n_targets': 1,\\n\",\n       \"      'reg_method': None,\\n\",\n       \"      'solver': 'svd',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}},\\n\",\n       \"    {'data_type': 'DiscreteMNL',\\n\",\n       \"     'properties': {'alpha': 0,\\n\",\n       \"      'classes': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/classes.npy'},\\n\",\n       \"      'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/coef.npy'},\\n\",\n       \"      'est_stderr': False,\\n\",\n       \"      'fit_intercept': True,\\n\",\n       \"      'l1_ratio': 0,\\n\",\n       \"      'max_iter': 100,\\n\",\n       \"      'reg_method': 'l2',\\n\",\n       \"      'solver': 'lbfgs',\\n\",\n       \"      'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"       'path': '../models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/stderr.npy'},\\n\",\n       \"      'tol': 0.0001}}]],\\n\",\n       \"  'model_initial': {'data_type': 'CrossEntropyMNL',\\n\",\n       \"   'properties': {'alpha': 0,\\n\",\n       \"    'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"     'path': '../models/UnSupervisedIOHMM/model_initial/coef.npy'},\\n\",\n       \"    'est_stderr': False,\\n\",\n       \"    'fit_intercept': True,\\n\",\n       \"    'l1_ratio': 0,\\n\",\n       \"    'max_iter': 100,\\n\",\n       \"    'n_classes': 2,\\n\",\n       \"    'reg_method': 'l2',\\n\",\n       \"    'solver': 'lbfgs',\\n\",\n       \"    'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"     'path': '../models/UnSupervisedIOHMM/model_initial/stderr.npy'},\\n\",\n       \"    'tol': 0.0001}},\\n\",\n       \"  'model_transition': [{'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/UnSupervisedIOHMM/model_transition/state_0/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 2,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/UnSupervisedIOHMM/model_transition/state_0/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}},\\n\",\n       \"   {'data_type': 'CrossEntropyMNL',\\n\",\n       \"    'properties': {'alpha': 0,\\n\",\n       \"     'coef': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/UnSupervisedIOHMM/model_transition/state_1/coef.npy'},\\n\",\n       \"     'est_stderr': False,\\n\",\n       \"     'fit_intercept': True,\\n\",\n       \"     'l1_ratio': 0,\\n\",\n       \"     'max_iter': 100,\\n\",\n       \"     'n_classes': 2,\\n\",\n       \"     'reg_method': 'l2',\\n\",\n       \"     'solver': 'lbfgs',\\n\",\n       \"     'stderr': {'data_type': 'numpy.ndarray',\\n\",\n       \"      'path': '../models/UnSupervisedIOHMM/model_transition/state_1/stderr.npy'},\\n\",\n       \"     'tol': 0.0001}}],\\n\",\n       \"  'num_states': 2,\\n\",\n       \"  'responses_emissions': [['rt'], ['corr']]}}\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"json_dict = SHMM.to_json('../models/UnSupervisedIOHMM/')\\n\",\n    \"json_dict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open('../models/UnSupervisedIOHMM/model.json', 'w') as outfile:\\n\",\n    \"    json.dump(json_dict, outfile, indent=4, sort_keys=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Load back the trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM_from_json = UnSupervisedIOHMM.from_json(json_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See if the coefficients are any different\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 6.38764009]]\\n\",\n      \"[[ 5.51390328]]\\n\",\n      \"()\\n\",\n      \"[[-1.13690447 -2.17394618]]\\n\",\n      \"[[-0.21848303  0.57762625]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# See the coefficients of the emission model 1 ('rt') of each hidden states\\n\",\n    \"print(SHMM_from_json.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM_from_json.model_emissions[1][0].coef)\\n\",\n    \"print('')\\n\",\n    \"# See the coefficients of the emission model 2 ('corr') of each hidden states\\n\",\n    \"print(SHMM_from_json.model_emissions[0][1].coef)\\n\",\n    \"print(SHMM_from_json.model_emissions[1][1].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up the model using a config file, instead of doing it manully\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open('../models/UnSupervisedIOHMM/config.json') as json_data:\\n\",\n    \"    json_dict = json.load(json_data)\\n\",\n    \"\\n\",\n    \"SHMM_from_config = UnSupervisedIOHMM.from_config(json_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set data and start training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"SHMM_from_config.set_data([speed])\\n\",\n    \"SHMM_from_config.train()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## See if the training results are any different?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[ 6.38763985]]\\n\",\n      \"[[ 5.51390293]]\\n\",\n      \"()\\n\",\n      \"[[-1.13689776 -2.17395753]]\\n\",\n      \"[[-0.21848318  0.57762729]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# See the coefficients of the emission model 1 ('rt') of each hidden states\\n\",\n    \"print(SHMM_from_config.model_emissions[0][0].coef)\\n\",\n    \"print(SHMM_from_config.model_emissions[1][0].coef)\\n\",\n    \"print('')\\n\",\n    \"# See the coefficients of the emission model 2 ('corr') of each hidden states\\n\",\n    \"print(SHMM_from_config.model_emissions[0][1].coef)\\n\",\n    \"print(SHMM_from_config.model_emissions[1][1].coef)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "requirements.txt",
    "content": "numpy >= 1.20.0\nfuture >= 0.18.2\npandas >= 1.2.1\nscikit-learn >= 1.2.2\nscipy >= 1.6.0\nstatsmodels >= 0.12.2\n"
  },
  {
    "path": "setup.cfg",
    "content": "[metadata]\ndescription-file = README.md\n[flake8]\nmax-line-length = 100\n"
  },
  {
    "path": "setup.py",
    "content": "from setuptools import setup\n\nsetup(\n    name=\"IOHMM\",\n    version=\"0.0.7\",\n    description='A python library for Input Output Hidden Markov Models',\n    url='https://github.com/Mogeng/IOHMM',\n    author='Mogeng Yin',\n    author_email='mogengyin@berkeley.edu',\n    license='BSD License',\n    packages=['IOHMM'],\n    install_requires=[\n        'numpy >= 1.20.0',\n        'future >= 0.18.2',\n        'pandas >= 1.2.1',\n        'scikit-learn >= 1.2.2',\n        'scipy >= 1.6.0',\n        'statsmodels >= 0.12.2',\n    ],\n    extras_require={\n        'tests': [\n            'flake8>=3.8.4',\n            'mock>=3.9.1',\n            'nose>=1.3.7',\n            'coveralls>=3.0.0',\n            'pytest',\n        ]\n    },\n    zip_safe=True,\n    keywords=' '.join([\n        'python',\n        'hidden-markov-model',\n        'graphical-models',\n        'sequence-to-sequence',\n        'machine-learning',\n        'linear-models',\n        'sequence-labeling',\n        'supervised-learning',\n        'semi-supervised-learning',\n        'unsupervised-learning',\n        'time-series',\n        'scikit-learn',\n        'statsmodels']),\n    classifiers=[\n        'Development Status :: 2 - Pre-Alpha',\n        'Environment :: Console',\n        'Intended Audience :: Science/Research',\n        'Intended Audience :: End Users/Desktop',\n        'Intended Audience :: Developers',\n        'Topic :: Scientific/Engineering',\n        'License :: OSI Approved :: BSD License',\n        'Programming Language :: Python :: 3.7',\n        'Programming Language :: Python :: 3.8',\n        'Programming Language :: Python :: 3.9'\n    ],\n)\n"
  },
  {
    "path": "tests/IOHMM_models/SemiSupervisedIOHMM/model.json",
    "content": "{\n    \"data_type\": \"SemiSupervisedIOHMM\",\n    \"properties\": {\n        \"EM_tol\": 1e-10,\n        \"covariates_emissions\": [\n            []\n        ],\n        \"covariates_initial\": [],\n        \"covariates_transition\": [],\n        \"max_EM_iter\": 100,\n        \"model_emissions\": [\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ],\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ],\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_2/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ],\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_3/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_3/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_emissions/state_3/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ]\n        ],\n        \"model_initial\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {\n                \"alpha\": 0,\n                \"coef\": {\n                    \"data_type\": \"numpy.ndarray\",\n                    \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_initial/coef.npy\"\n                },\n                \"est_stderr\": false,\n                \"fit_intercept\": true,\n                \"l1_ratio\": 0,\n                \"max_iter\": 100,\n                \"n_classes\": 4,\n                \"reg_method\": \"l2\",\n                \"solver\": \"newton-cg\",\n                \"stderr\": {\n                    \"data_type\": \"numpy.ndarray\",\n                    \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_initial/stderr.npy\"\n                },\n                \"tol\": 0.0001\n            }\n        },\n        \"model_transition\": [\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_0/coef.npy\"\n                    },\n                    \"est_stderr\": false,\n                    \"fit_intercept\": true,\n                    \"l1_ratio\": 0,\n                    \"max_iter\": 100,\n                    \"n_classes\": 4,\n                    \"reg_method\": \"l2\",\n                    \"solver\": \"newton-cg\",\n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_0/stderr.npy\"\n                    },\n                    \"tol\": 0.0001\n                }\n            },\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_1/coef.npy\"\n                    },\n                    \"est_stderr\": false,\n                    \"fit_intercept\": true,\n                    \"l1_ratio\": 0,\n                    \"max_iter\": 100,\n                    \"n_classes\": 4,\n                    \"reg_method\": \"l2\",\n                    \"solver\": \"newton-cg\",\n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_1/stderr.npy\"\n                    },\n                    \"tol\": 0.0001\n                }\n            },\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_2/coef.npy\"\n                    },\n                    \"est_stderr\": false,\n                    \"fit_intercept\": true,\n                    \"l1_ratio\": 0,\n                    \"max_iter\": 100,\n                    \"n_classes\": 4,\n                    \"reg_method\": \"l2\",\n                    \"solver\": \"newton-cg\",\n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_2/stderr.npy\"\n                    },\n                    \"tol\": 0.0001\n                }\n            },\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_3/coef.npy\"\n                    },\n                    \"est_stderr\": false,\n                    \"fit_intercept\": true,\n                    \"l1_ratio\": 0,\n                    \"max_iter\": 100,\n                    \"n_classes\": 4,\n                    \"reg_method\": \"l2\",\n                    \"solver\": \"newton-cg\",\n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SemiSupervisedIOHMM/model_transition/state_3/stderr.npy\"\n                    },\n                    \"tol\": 0.0001\n                }\n            }\n        ],\n        \"num_states\": 4,\n        \"responses_emissions\": [\n            [\n                \"rt\"\n            ]\n        ]\n    }\n}"
  },
  {
    "path": "tests/IOHMM_models/SupervisedIOHMM/model.json",
    "content": "{\n    \"data_type\": \"SupervisedIOHMM\",\n    \"properties\": {\n        \"covariates_emissions\": [\n            []\n        ],\n        \"covariates_initial\": [],\n        \"covariates_transition\": [],\n        \"model_emissions\": [\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ],\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ]\n        ],\n        \"model_initial\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {\n                \"alpha\": 0,\n                \"coef\": {\n                    \"data_type\": \"numpy.ndarray\",\n                    \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_initial/coef.npy\"\n                },\n                \"est_stderr\": false,\n                \"fit_intercept\": true,\n                \"l1_ratio\": 0,\n                \"max_iter\": 100,\n                \"n_classes\": 2,\n                \"reg_method\": \"l2\",\n                \"solver\": \"newton-cg\",\n                \"stderr\": {\n                    \"data_type\": \"numpy.ndarray\",\n                    \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_initial/stderr.npy\"\n                },\n                \"tol\": 0.0001\n            }\n        },\n        \"model_transition\": [\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_transition/state_0/coef.npy\"\n                    },\n                    \"est_stderr\": false,\n                    \"fit_intercept\": true,\n                    \"l1_ratio\": 0,\n                    \"max_iter\": 100,\n                    \"n_classes\": 2,\n                    \"reg_method\": \"l2\",\n                    \"solver\": \"newton-cg\",\n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_transition/state_0/stderr.npy\"\n                    },\n                    \"tol\": 0.0001\n                }\n            },\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_transition/state_1/coef.npy\"\n                    },\n                    \"est_stderr\": false,\n                    \"fit_intercept\": true,\n                    \"l1_ratio\": 0,\n                    \"max_iter\": 100,\n                    \"n_classes\": 2,\n                    \"reg_method\": \"l2\",\n                    \"solver\": \"newton-cg\",\n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/SupervisedIOHMM/model_transition/state_1/stderr.npy\"\n                    },\n                    \"tol\": 0.0001\n                }\n            }\n        ],\n        \"num_states\": 2,\n        \"responses_emissions\": [\n            [\n                \"rt\"\n            ]\n        ]\n    }\n}"
  },
  {
    "path": "tests/IOHMM_models/UnSupervisedIOHMM/model.json",
    "content": "{\n    \"data_type\": \"UnSupervisedIOHMM\",\n    \"properties\": {\n        \"EM_tol\": 1e-06,\n        \"covariates_emissions\": [\n            [],\n            [\n                \"Pacc\"\n            ]\n        ],\n        \"covariates_initial\": [],\n        \"covariates_transition\": [],\n        \"max_EM_iter\": 100,\n        \"model_emissions\": [\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_0/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                },\n                {\n                    \"data_type\": \"DiscreteMNL\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"classes\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/classes.npy\"\n                        },\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/coef.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"reg_method\": \"l2\",\n                        \"solver\": \"lbfgs\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_0/emission_1/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ],\n            [\n                {\n                    \"data_type\": \"OLS\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/coef.npy\"\n                        },\n                        \"dispersion\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/dispersion.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"n_targets\": 1,\n                        \"reg_method\": null,\n                        \"solver\": \"svd\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_1/emission_0/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                },\n                {\n                    \"data_type\": \"DiscreteMNL\",\n                    \"properties\": {\n                        \"alpha\": 0,\n                        \"classes\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/classes.npy\"\n                        },\n                        \"coef\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/coef.npy\"\n                        },\n                        \"est_stderr\": false,\n                        \"fit_intercept\": true,\n                        \"l1_ratio\": 0,\n                        \"max_iter\": 100,\n                        \"reg_method\": \"l2\",\n                        \"solver\": \"lbfgs\",\n                        \"stderr\": {\n                            \"data_type\": \"numpy.ndarray\",\n                            \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_emissions/state_1/emission_1/stderr.npy\"\n                        },\n                        \"tol\": 0.0001\n                    }\n                }\n            ]\n        ],\n        \"model_initial\": {\n            \"data_type\": \"CrossEntropyMNL\",\n            \"properties\": {\n                \"alpha\": 0,\n                \"coef\": {\n                    \"data_type\": \"numpy.ndarray\",\n                    \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_initial/coef.npy\"\n                },\n                \"est_stderr\": false,\n                \"fit_intercept\": true,\n                \"l1_ratio\": 0,\n                \"max_iter\": 100,\n                \"n_classes\": 2,\n                \"reg_method\": \"l2\",\n                \"solver\": \"newton-cg\",\n                \"stderr\": {\n                    \"data_type\": \"numpy.ndarray\",\n                    \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_initial/stderr.npy\"\n                },\n                \"tol\": 0.0001\n            }\n        },\n        \"model_transition\": [\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_transition/state_0/coef.npy\"\n                    },\n                    \"est_stderr\": false,\n                    \"fit_intercept\": true,\n                    \"l1_ratio\": 0,\n                    \"max_iter\": 100,\n                    \"n_classes\": 2,\n                    \"reg_method\": \"l2\",\n                    \"solver\": \"newton-cg\",\n                    \"stderr\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": \"tests/IOHMM_models/UnSupervisedIOHMM/model_transition/state_0/stderr.npy\"\n                    },\n                    \"tol\": 0.0001\n                }\n            },\n            {\n                \"data_type\": \"CrossEntropyMNL\",\n                \"properties\": {\n                    \"alpha\": 0,\n                    \"coef\": {\n                        \"data_type\": \"numpy.ndarray\",\n                        \"path\": 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  },
  {
    "path": "tests/test_CrossentropyMNL.py",
    "content": "from __future__ import division\nfrom builtins import range\nfrom past.utils import old_div\nimport unittest\n\n\nimport numpy as np\nfrom sklearn.preprocessing import label_binarize\nimport statsmodels.api as sm\n\n\nfrom IOHMM import DiscreteMNL, CrossEntropyMNL\n\n\nclass CrossEntropyMNLUnaryTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_spector = sm.datasets.spector.load()\n        cls.y = np.ones((cls.data_spector.endog.shape[0], 1))\n\n    def test_label_encoder(self):\n        x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n        y = np.array([[1], [1], [1]])\n        X_repeated, Y_repeated, sample_weight_repeated = \\\n            CrossEntropyMNL._label_encoder(x, y, np.ones(3))\n        np.testing.assert_array_equal(X_repeated, x)\n        np.testing.assert_array_equal(\n            Y_repeated, np.array([0, 0, 0]))\n        np.testing.assert_array_equal(\n            sample_weight_repeated,\n            np.array([1, 1, 1]))\n        # with sample_weight\n        x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n        y = np.array([[1], [1], [1]])\n        sample_weight = np.array([0.25, 0.5, 0.25])\n        X_repeated, Y_repeated, sample_weight_repeated = \\\n            CrossEntropyMNL._label_encoder(x, y, sample_weight)\n        np.testing.assert_array_equal(X_repeated, x)\n        np.testing.assert_array_equal(\n            Y_repeated, np.array([0, 0, 0]))\n        np.testing.assert_array_equal(\n            sample_weight_repeated, sample_weight)\n\n    def test_lr(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog, self.y)\n        # coefficient\n        np.testing.assert_array_equal(\n            self.model.coef,\n            np.zeros((4, 1)))\n\n        # predict\n        np.testing.assert_array_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array([0] * self.data_spector.endog.shape[0]))\n        # loglike/_per_sample\n        np.testing.assert_array_equal(\n            self.model.loglike_per_sample(self.data_spector.exog,\n                                          np.array([1] * 16 + [0] * 16).reshape(-1, 1)),\n            np.array([0] * 16 + [-np.Infinity] * 16))\n\n    def test_lr_sample_weight_all_half(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog, self.y, sample_weight=.5)\n        # coefficient\n        np.testing.assert_array_equal(\n            self.model.coef,\n            np.zeros((4, 1)))\n        # loglike/_per_sample\n        self.assertEqual(\n            self.model.loglike(self.data_spector.exog, self.y, sample_weight=.5), 0)\n\n    # corner cases\n    def test_lr_one_data_point(self):\n        # with regularization\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog[4:5, :],\n                       self.y[4:5, ], sample_weight=0.5)\n        # coef\n        np.testing.assert_array_equal(\n            self.model.coef,\n            np.zeros((4, 1)))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], np.array([1, 0]).reshape(-1, 1)),\n            np.array([0, -np.Infinity]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], np.array([1, 1]).reshape(-1, 1)),\n            np.array([0, 0]), decimal=3)\n\n\nclass CrossEntropyMNLBinaryTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_spector = sm.datasets.spector.load()\n        cls.y = np.array([\n            [1, 0], [1, 0], [1, 0], [1, 0],\n            [0, 1], [1, 0], [1, 0], [1, 0],\n            [1, 0], [0, 1], [1, 0], [1, 0],\n            [1, 0], [0, 1], [1, 0], [1, 0],\n            [1, 0], [1, 0], [1, 0], [0, 1],\n            [1, 0], [0, 1], [1, 0], [1, 0],\n            [0, 1], [0, 1], [0, 1], [1, 0],\n            [0, 1], [0, 1], [1, 0], [0, 1]])\n        cls.y_disturbed = np.array([\n            [0.99, 0.01], [0.99, 0.01], [0.99, 0.01], [0.99, 0.01],\n            [0.01, 0.99], [0.99, 0.01], [0.99, 0.01], [0.99, 0.01],\n            [0.99, 0.01], [0.01, 0.99], [0.99, 0.01], [0.99, 0.01],\n            [0.99, 0.01], [0.01, 0.99], [0.99, 0.01], [0.99, 0.01],\n            [0.99, 0.01], [0.99, 0.01], [0.99, 0.01], [0.01, 0.99],\n            [0.99, 0.01], [0.01, 0.99], [0.99, 0.01], [0.99, 0.01],\n            [0.01, 0.99], [0.01, 0.99], [0.01, 0.99], [0.99, 0.01],\n            [0.01, 0.99], [0.01, 0.99], [0.99, 0.01], [0.01, 0.99]])\n\n    def test_lr(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog, self.y)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-13.021, 2.8261, .09515, 2.378]]),\n            decimal=3)\n\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array((0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n                      0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,\n                      1.,  0.,  1.,  1.,  1.,  0.)),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.y),\n            -12.8896334653335,\n            places=3)\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/CrossentropyMNL/Binary/')\n        self.assertEqual(json_dict['properties']['solver'], 'lbfgs')\n\n        # from_json\n        self.model_from_json = CrossEntropyMNL.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.classes, np.array([0, 1]), decimal=3)\n        self.assertEqual(self.model.n_classes, 2)\n\n    def test_lr_disturbed(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog, self.y_disturbed)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-12.327,  2.686,  0.089,  2.258]]),\n            decimal=3)\n\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array((0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n                      0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,\n                      1.,  0.,  1.,  1.,  1.,  0.)),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.y_disturbed),\n            -13.366314173353134,\n            places=3)\n\n    def test_lr_regularized(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.01, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog, self.y)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-10.66,   2.364,   0.064,   2.142]]),\n            decimal=3)\n\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array((0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n                      0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,\n                      1.,  0.,  1.,  1.,  1.,  0.)),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.y),\n            -13.016861222748515,\n            places=3)\n\n    def test_lr_sample_weight_all_half(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog, self.y, sample_weight=.5)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-13.021, 2.8261, .09515, 2.378]]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.y, sample_weight=.5),\n            old_div(-12.8896334653335, 2.),\n            places=3)\n\n    def test_lr_disturbed_sample_weight_all_half(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog, self.y_disturbed, sample_weight=.5)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-12.327,  2.686,  0.089,  2.258]]),\n            decimal=3)\n\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array((0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n                      0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,\n                      1.,  0.,  1.,  1.,  1.,  0.)),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.y_disturbed, sample_weight=.5),\n            old_div(-13.366314173353134, 2.),\n            places=3)\n\n    def test_lr_sample_weight_all_zero(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.assertRaises(ValueError, self.model.fit,\n                          self.data_spector.exog, self.y, 0)\n\n    def test_lr_sample_weight_half_zero_half_one(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        len_half = 8\n        self.model.fit(self.data_spector.exog, self.y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.y.shape[0] - len_half)))\n        self.model_half = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model_half.fit(self.data_spector.exog[:len_half], self.y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n\n    def test_lr_disturbed_sample_weight_half_zero_half_one(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        len_half = 8\n        self.model.fit(self.data_spector.exog, self.y_disturbed,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.y_disturbed.shape[0] - len_half)))\n        self.model_half = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model_half.fit(self.data_spector.exog[:len_half], self.y_disturbed[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n\n    # corner cases\n    def test_lr_two_data_point(self):\n        # with regularization\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.1, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog[4:6, :],\n                       self.y[4:6, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (1, 4))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], self.y[4:6, ]),\n            np.array([-0.495, -0.661]), decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :],\n            np.array([[0, 0], [1, 0]])),\n            np.array([-np.Infinity, -0.661]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :],\n            np.array([[0, 0], [0, 1]])),\n            np.array([-np.Infinity, -0.726]), decimal=3)\n\n    def test_lr_disturbed_two_data_point(self):\n        # with regularization\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.1, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog[4:6, :],\n                       self.y_disturbed[4:6, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (1, 4))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], self.y_disturbed[4:6, ]),\n            np.array([-0.503, -0.662]), decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :],\n            np.array([[0, 0], [0.99, 0.01]])),\n            np.array([-np.Infinity, -0.662]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :],\n            np.array([[0, 0], [0.01, 0.99]])),\n            np.array([-np.Infinity, -0.725]), decimal=3)\n\n    def test_lr_multicolinearty(self):\n        self.model_col = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        X = np.hstack([self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])\n        self.model_col.fit(X,\n                           self.y, sample_weight=0.5)\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog[:, 0:1],\n                       self.y, sample_weight=0.5)\n\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([[-9.703,  1.42002783,  1.42002783]]), decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.y),\n            self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],\n                                          self.y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.data_spector.exog[:, 0:1]), decimal=3)\n\n    def test_lr_disturbed_multicolinearty(self):\n        self.model_col = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        X = np.hstack([self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])\n        self.model_col.fit(X,\n                           self.y_disturbed, sample_weight=0.5)\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_spector.exog[:, 0:1],\n                       self.y_disturbed, sample_weight=0.5)\n\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([[-9.359,  1.37,  1.37]]), decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.y_disturbed),\n            self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],\n                                          self.y_disturbed), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.data_spector.exog[:, 0:1]), decimal=3)\n\n\nclass CrossEntropyMNLMultinomialTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_anes96 = sm.datasets.anes96.load()\n        cls.y = label_binarize(cls.data_anes96.endog, classes=list(range(7)))\n        cls.y_disturbed = old_div((cls.y + 0.01), 1.07)\n\n    def test_label_encoder(self):\n        x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n        y = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n        X_repeated, Y_repeated, sample_weight_repeated = \\\n            CrossEntropyMNL._label_encoder(x, y, np.ones(3))\n        np.testing.assert_array_equal(\n            X_repeated,\n            np.array([\n                [1, 2, 3], [1, 2, 3], [1, 2, 3],\n                [4, 5, 6], [4, 5, 6], [4, 5, 6],\n                [7, 8, 9], [7, 8, 9], [7, 8, 9]]))\n        np.testing.assert_array_equal(\n            Y_repeated,\n            np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))\n        np.testing.assert_array_equal(\n            sample_weight_repeated,\n            np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]))\n        # with sample_weight\n        x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n        y = np.array([[0.5, 0.25, 0.25], [0.25, 0.5, 0.25], [0.25, 0.25, 0.5]])\n        sample_weight = np.array([0.25, 0.5, 0.25])\n        X_repeated, Y_repeated, sample_weight_repeated = \\\n            CrossEntropyMNL._label_encoder(x, y, sample_weight)\n        np.testing.assert_array_equal(\n            X_repeated,\n            np.array([\n                [1, 2, 3], [1, 2, 3], [1, 2, 3],\n                [4, 5, 6], [4, 5, 6], [4, 5, 6],\n                [7, 8, 9], [7, 8, 9], [7, 8, 9]]))\n        np.testing.assert_array_equal(\n            Y_repeated,\n            np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]))\n        np.testing.assert_array_equal(\n            sample_weight_repeated,\n            np.array([0.125, 0.0625, 0.0625, 0.125, 0.25, 0.125, 0.0625, 0.0625, 0.125]))\n\n    def test_lr(self):\n        self.model = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=10, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog, self.y)\n        # coefficient\n        # predict\n        self.assertEqual(\n            np.sum(self.model.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 333)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.y),\n            -1540.888458338286,\n            places=3)\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/CrossentropyMNL/Multinomial/')\n        self.assertEqual(json_dict['properties']['solver'], 'newton-cg')\n\n        # from_json\n        self.model_from_json = CrossEntropyMNL.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.classes, np.array(list(range(7))), decimal=3)\n        self.assertEqual(self.model.n_classes, 7)\n\n    def test_lr_disturbed(self):\n        self.model = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=10, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog, self.y_disturbed)\n        # coefficient\n        # predict\n        self.assertEqual(\n            np.sum(self.model.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 335)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.y_disturbed),\n            -1580.5280532302786,\n            places=3)\n\n    def test_lr_regularized(self):\n        self.model = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.5, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog, self.y)\n        # predict\n        self.assertEqual(\n            np.sum(self.model.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 369)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.y),\n            -1466.9886103092626,\n            places=3)\n\n    def test_lr_disturbed_regularized(self):\n        self.model = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.5, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog, self.y_disturbed)\n        # predict\n        self.assertEqual(\n            np.sum(self.model.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 366)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.y_disturbed),\n            -1519.9521131193064,\n            places=3)\n\n    def test_lr_sample_weight_all_half(self):\n        self.model_half = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model_half.fit(self.data_anes96.exog, self.y, sample_weight=.5)\n        self.model = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog, self.y)\n        # coefficient\n        np.testing.assert_array_almost_equal(self.model.coef, self.model_half.coef, decimal=3)\n        # predict\n        self.assertEqual(\n            np.sum(self.model_half.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 372)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.y, sample_weight=.5),\n            old_div(-1461.92274725, 2.),\n            places=3)\n\n    def test_lr_disturbed_sample_weight_all_half(self):\n        self.model_half = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model_half.fit(self.data_anes96.exog, self.y_disturbed, sample_weight=.5)\n        self.model = CrossEntropyMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog, self.y_disturbed)\n        # coefficient\n        np.testing.assert_array_almost_equal(self.model.coef, self.model_half.coef, decimal=3)\n        # predict\n        self.assertEqual(\n            np.sum(self.model_half.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 367)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.y_disturbed, sample_weight=.5),\n            old_div(-1516.50148, 2.),\n            places=3)\n\n    def test_lr_sample_weight_all_zero(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.assertRaises(ValueError, self.model.fit,\n                          self.data_anes96.exog, self.y_disturbed, 0)\n\n    def test_lr_sample_weight_half_zero_half_one(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        len_half = 500\n        self.model.fit(self.data_anes96.exog, self.y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.data_anes96.exog.shape[0] - len_half)))\n        self.model_half = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model_half.fit(self.data_anes96.exog[:len_half], self.y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n\n    def test_lr_disturbed_sample_weight_half_zero_half_one(self):\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        len_half = 500\n        self.model.fit(self.data_anes96.exog, self.y_disturbed,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.data_anes96.exog.shape[0] - len_half)))\n        self.model_half = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model_half.fit(self.data_anes96.exog[:len_half], self.y_disturbed[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n\n    # corner cases\n    def test_lr_three_data_point(self):\n        # with regularization\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.1, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog[6:9, :],\n                       self.y[6:9, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (7, 6))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], self.y[6:9, ]),\n            np.array([-0.015, -0.091, -0.095]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], label_binarize([3, 1, 4], list(range(7)))),\n            np.array([-4.201, -5.094, -2.825]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], label_binarize([3, 0, 5], list(range(7)))),\n            np.array([-4.201, -7.352, -8.957]), decimal=3)\n\n    def test_lr_disturbed_three_data_point(self):\n        # with regularization\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.1, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog[6:9, :],\n                       self.y_disturbed[6:9, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (7, 6))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], self.y_disturbed[6:9, ]),\n            np.array([-0.336, -0.389, -0.398]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], label_binarize([3, 1, 4], list(range(7)))),\n            np.array([-3.415, -4.506, -2.367]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], label_binarize([3, 0, 5], list(range(7)))),\n            np.array([-3.415, -4.492, -4.301]), decimal=3)\n\n    def test_lr_multicolinearty(self):\n        self.model_col = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        X = np.hstack([self.data_anes96.exog[:, 0:1], self.data_anes96.exog[:, 0:1]])\n        self.model_col.fit(X,\n                           self.y, sample_weight=0.5)\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog[:, 0:1],\n                       self.y, sample_weight=0.5)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.y),\n            self.model.loglike_per_sample(self.data_anes96.exog[:, 0:1],\n                                          self.y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.data_anes96.exog[:, 0:1]), decimal=3)\n\n    def test_lr_disturbed_multicolinearty(self):\n        self.model_col = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        X = np.hstack([self.data_anes96.exog[:, 0:1], self.data_anes96.exog[:, 0:1]])\n        self.model_col.fit(X,\n                           self.y_disturbed, sample_weight=0.5)\n        self.model = CrossEntropyMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, n_classes=None)\n        self.model.fit(self.data_anes96.exog[:, 0:1],\n                       self.y_disturbed, sample_weight=0.5)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.y_disturbed),\n            self.model.loglike_per_sample(self.data_anes96.exog[:, 0:1],\n                                          self.y_disturbed), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.data_anes96.exog[:, 0:1]), decimal=3)\n"
  },
  {
    "path": "tests/test_DiscreteMNL.py",
    "content": "from __future__ import print_function\nfrom __future__ import division\nfrom builtins import range\nfrom past.utils import old_div\nimport unittest\n\n\nimport numpy as np\nimport statsmodels.api as sm\n\n\nfrom IOHMM import DiscreteMNL\n\n\nclass DiscreteMNLUnaryTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_spector = sm.datasets.spector.load()\n        cls.y = np.array(['foo'] * cls.data_spector.endog.shape[0])\n\n    def test_lr(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog, self.y)\n        # coefficient\n        np.testing.assert_array_equal(\n            self.model.coef,\n            np.zeros((4, 1)))\n\n        # predict\n        np.testing.assert_array_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array(['foo'] * self.data_spector.endog.shape[0]))\n        # loglike/_per_sample\n        np.testing.assert_array_equal(\n            self.model.loglike_per_sample(self.data_spector.exog,\n                                          np.array(['bar'] * 16 + ['foo'] * 16)),\n            np.array([-np.Infinity] * 16 + [0] * 16))\n\n    def test_lr_sample_weight_all_half(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog, self.y, sample_weight=.5)\n        # coefficient\n        np.testing.assert_array_equal(\n            self.model.coef,\n            np.zeros((4, 1)))\n        # loglike/_per_sample\n        self.assertEqual(\n            self.model.loglike(self.data_spector.exog, self.y, sample_weight=.5), 0)\n\n    # corner cases\n    def test_lr_one_data_point(self):\n        # with regularization\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog[4:5, :],\n                       self.y[4:5, ], sample_weight=0.5)\n        # coef\n        np.testing.assert_array_equal(\n            self.model.coef,\n            np.zeros((4, 1)))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], np.array(['foo', 'foo'])),\n            np.array([0, 0]), decimal=3)\n\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], np.array(['foo', 'bar'])),\n            np.array([0, -np.Infinity]), decimal=3)\n\n\nclass DiscreteMNLBinaryTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_spector = sm.datasets.spector.load()\n\n    def test_lr(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog, self.data_spector.endog)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-13.021, 2.8261, .09515, 2.378]]),\n            decimal=3)\n\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array((0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n                      0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,\n                      1.,  0.,  1.,  1.,  1.,  0.)),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.data_spector.endog),\n            -12.8896334653335,\n            places=3)\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/DiscreteMNL/Binary/')\n        self.assertEqual(json_dict['properties']['solver'], 'lbfgs')\n\n        # from_json\n        self.model_from_json = DiscreteMNL.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.classes, np.array([0, 1]), decimal=3)\n        self.assertEqual(self.model.n_classes, 2)\n\n    def test_lr_regularized(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.01, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog, self.data_spector.endog)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-10.66,   2.364,   0.064,   2.142]]),\n            decimal=3)\n\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.data_spector.exog),\n            np.array((0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n                      0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,\n                      1.,  0.,  1.,  1.,  1.,  0.)),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.data_spector.endog),\n            -13.016861222748519,\n            places=3)\n\n    def test_lr_sample_weight_all_half(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog, self.data_spector.endog, sample_weight=.5)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-13.021, 2.8261, .09515, 2.378]]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_spector.exog, self.data_spector.endog, sample_weight=.5),\n            old_div(-12.8896334653335, 2.),\n            places=3)\n\n    def test_lr_sample_weight_all_zero(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.assertRaises(ValueError, self.model.fit,\n                          self.data_spector.exog, self.data_spector.endog, 0)\n\n    def test_lr_sample_weight_half_zero_half_one(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        len_half = 8\n        self.model.fit(self.data_spector.exog, self.data_spector.endog,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.data_spector.exog.shape[0] - len_half)))\n        self.model_half = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model_half.fit(self.data_spector.exog[:len_half], self.data_spector.endog[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n\n    # corner cases\n    def test_lr_two_data_point(self):\n        # with regularization\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.01, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog[4:6, :],\n                       self.data_spector.endog[4:6, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (1, 4))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], self.data_spector.endog[4:6, ]),\n            np.array([-0.226, -0.289]), decimal=3)\n        # with no regularization\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog[4:6, :],\n                       self.data_spector.endog[4:6, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (1, 4))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[4:6, :], self.data_spector.endog[4:6, ]),\n            np.array([0, 0]), decimal=3)\n        # class in reverse\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog[3:5, :],\n                       self.data_spector.endog[3:5, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (1, 4))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[3:5, :], self.data_spector.endog[3:5, ]),\n            np.array([0, 0]), decimal=3)\n        print(self.model.classes, 'class')\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_spector.exog[3:5, :], np.array([0, 2])),\n            np.array([0, -np.Infinity]), decimal=3)\n\n    def test_lr_multicolinearty(self):\n        self.model_col = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        X = np.hstack([self.data_spector.exog[:, 0:1], self.data_spector.exog[:, 0:1]])\n        self.model_col.fit(X,\n                           self.data_spector.endog, sample_weight=0.5)\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_spector.exog[:, 0:1],\n                       self.data_spector.endog, sample_weight=0.5)\n\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([[-9.703,  1.42002783,  1.42002783]]), decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.data_spector.endog),\n            self.model.loglike_per_sample(self.data_spector.exog[:, 0:1],\n                                          self.data_spector.endog), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.data_spector.exog[:, 0:1]), decimal=3)\n\n\nclass DiscreteMNLMultinomialTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_anes96 = sm.datasets.anes96.load()\n\n    def test_lr(self):\n        self.model = DiscreteMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_anes96.exog, self.data_anes96.endog)\n        # coefficient\n        # predict\n        self.assertEqual(\n            np.sum(self.model.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 372)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.data_anes96.endog),\n            -1461.9227472481984,\n            places=3)\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/DiscreteMNL/Multinomial/')\n        self.assertEqual(json_dict['properties']['solver'], 'newton-cg')\n\n        # from_json\n        self.model_from_json = DiscreteMNL.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.classes, np.array(list(range(7))), decimal=3)\n        self.assertEqual(self.model.n_classes, 7)\n\n    def test_lr_regularized(self):\n        self.model = DiscreteMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=10, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_anes96.exog, self.data_anes96.endog)\n        # predict\n        self.assertEqual(\n            np.sum(self.model.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 333)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.data_anes96.endog),\n            -1540.888456277886,\n            places=3)\n\n    def test_lr_sample_weight_all_half(self):\n        self.model_half = DiscreteMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model_half.fit(self.data_anes96.exog, self.data_anes96.endog, sample_weight=.5)\n        self.model = DiscreteMNL(\n            solver='newton-cg', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_anes96.exog, self.data_anes96.endog)\n        # coefficient\n        np.testing.assert_array_almost_equal(self.model.coef, self.model_half.coef, decimal=3)\n        # predict\n        self.assertEqual(\n            np.sum(self.model.predict(self.data_anes96.exog) ==\n                   self.data_anes96.endog), 372)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_anes96.exog, self.data_anes96.endog, sample_weight=.5),\n            old_div(-1461.92274725, 2.),\n            places=3)\n\n    def test_lr_sample_weight_all_zero(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.assertRaises(ValueError, self.model.fit,\n                          self.data_anes96.exog, self.data_anes96.endog, 0)\n\n    def test_lr_sample_weight_half_zero_half_one(self):\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        len_half = 500\n        self.model.fit(self.data_anes96.exog, self.data_anes96.endog,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.data_anes96.exog.shape[0] - len_half)))\n        self.model_half = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model_half.fit(self.data_anes96.exog[:len_half], self.data_anes96.endog[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n\n    # corner cases\n    def test_lr_three_data_point(self):\n        # with regularization\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=.1, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_anes96.exog[6:9, :],\n                       self.data_anes96.endog[6:9, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (3, 6))\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], np.array([1, 4, 3])),\n            np.array([-0.015, -0.089, -0.095]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], np.array([3, 1, 4])),\n            np.array([-4.2, -5.046, -2.827]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.data_anes96.exog[6:9, :], np.array([3, 0, 5])),\n            np.array([-4.2, -np.Infinity,  -np.Infinity]), decimal=3)\n\n    def test_lr_multicolinearty(self):\n        self.model_col = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        X = np.hstack([self.data_anes96.exog[:, 0:1], self.data_anes96.exog[:, 0:1]])\n        self.model_col.fit(X,\n                           self.data_anes96.endog, sample_weight=0.5)\n        self.model = DiscreteMNL(\n            solver='lbfgs', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None, classes=None)\n        self.model.fit(self.data_anes96.exog[:, 0:1],\n                       self.data_anes96.endog, sample_weight=0.5)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.data_anes96.endog),\n            self.model.loglike_per_sample(self.data_anes96.exog[:, 0:1],\n                                          self.data_anes96.endog), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.data_anes96.exog[:, 0:1]), decimal=3)\n"
  },
  {
    "path": "tests/test_GLM.py",
    "content": "from __future__ import print_function\nfrom __future__ import division\nfrom past.utils import old_div\nimport unittest\n\n\nimport numpy as np\nimport statsmodels.api as sm\nfrom statsmodels.genmod.tests.results.results_glm import InvGauss\n\n\nfrom IOHMM import GLM\n\n\nclass PoissonTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data = sm.datasets.cpunish.load()\n        cls.X = cls.data.exog\n        cls.X[:, 3] = np.log(cls.X[:, 3])\n        cls.Y = cls.data.endog\n\n    def test_glm_IRLS(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Poisson(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((-6.801480e+00, 2.611017e-04, 7.781801e-02, -9.493111e-02, 2.969349e-01,\n                      2.301183e+00, -1.872207e+01)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            np.array((4.146850e+00, 5.187132e-05, 7.940193e-02, 2.291926e-02, 4.375164e-01,\n                      4.283826e-01, 4.283961e+00)),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([35.2263655,  8.1965744,  1.3118966,\n                      3.6862982,  2.0823003,  1.0650316,  1.9260424,  2.4171405,\n                      1.8473219,  2.8643241,  3.1211989,  3.3382067,  2.5269969,\n                      0.8972542, 0.9793332,  0.5346209,  1.9790936]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -31.92732869482515,\n            places=3)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -31.92732869482515,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (17,))\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/GLM/Poisson/')\n        self.assertEqual(json_dict['properties']['solver'], 'IRLS')\n\n        # from_json\n        self.model_from_json = GLM.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        self.assertEqual(\n            self.model.dispersion,\n            self.model_from_json.dispersion)\n\n        np.testing.assert_array_almost_equal(\n            self.model_from_json.predict(self.X),\n            np.array([35.2263655,  8.1965744,  1.3118966,\n                      3.6862982,  2.0823003,  1.0650316,  1.9260424,  2.4171405,\n                      1.8473219,  2.8643241,  3.1211989,  3.3382067,  2.5269969,\n                      0.8972542, 0.9793332,  0.5346209,  1.9790936]),\n            decimal=3)\n\n    def test_glm_regularized(self):\n        # there is a bug in sklearn with weights, it can only use list right now\n        self.model = GLM(\n            solver='auto', family=sm.families.Poisson(),\n            fit_intercept=True, est_stderr=True,\n            reg_method='elastic_net',  alpha=0.01, l1_ratio=0.5,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        self.assertEqual(self.model.coef.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((2.104e-01,   8.331e-05,  -2.736e-02,  -1.347e-01,  -4.327e-02,\n                      3.241e+00,  -4.788e+00)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertTrue(self.model.stderr is None)\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([23.949,  10.275,   1.12,   7.302,   2.707,   1.585,   0.776,\n                      1.894,   3.242,   8.968,   2.265,   1.735,   1.152,   0.202,\n                      2.412,   0.952,   3.488]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -42.636883391983268,\n            places=3)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -42.636883391983268,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (17,))\n\n    def test_glm_sample_weight_all_half(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Poisson(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((-6.801480e+00, 2.611017e-04, 7.781801e-02, -9.493111e-02, 2.969349e-01,\n                      2.301183e+00, -1.872207e+01)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            np.array((5.86e+00,   7.33e-05,   1.12e-01,   3.24e-02,   6.19e-01,\n                      6.06e-01,   6.06e+00)),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([35.2263655,  8.1965744,  1.3118966,\n                      3.6862982,  2.0823003,  1.0650316,  1.9260424,  2.4171405,\n                      1.8473219,  2.8643241,  3.1211989,  3.3382067,  2.5269969,\n                      0.8972542, 0.9793332,  0.5346209,  1.9790936]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y, sample_weight=0.5),\n            old_div(-31.92732869482515, 2.),\n            places=3)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -31.92732869482515,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (17,))\n\n    def test_glm_sample_weight_all_zero(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Poisson(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_GLM_sample_weight_half_zero_half_one(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Poisson(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 8\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.X.shape[0] - len_half)))\n        self.model_half = GLM(\n            solver='IRLS', family=sm.families.Poisson(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=2)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n    def test_glm_one_data_point(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Poisson(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[0:1, :],\n                       self.Y[0:1, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (7, ))\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.X[0:1, :], self.Y[0:1, ]), np.array([-2.72665]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            np.array(self.X[0:1, :].tolist() * 6),\n            np.array([31, 32, 33, 34, 35, 36])),\n            np.array([-3.154, -3.009, -2.894, -2.81, -2.754, -2.727]), decimal=3)\n\n    def test_ols_multicolinearty(self):\n        self.model_col = GLM(\n            solver='irls', family=sm.families.Poisson(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], 2 * self.X[:, 0:1]])\n        self.model_col.fit(X,\n                           self.Y, sample_weight=0.5)\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Poisson(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1],\n                       self.Y, sample_weight=0.5)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([8.000e-06, 1.6000e-05]), decimal=3)\n        # stderr\n        np.testing.assert_array_almost_equal(\n            self.model_col.stderr, np.array([9.09531196e-07, 1.81906239e-06]), decimal=3)\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.Y),\n            self.model.loglike_per_sample(self.X[:, 0:1],\n                                          self.Y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=3)\n\n\nclass GammaTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data = sm.datasets.scotland.load()\n        cls.X = cls.data.exog\n        cls.Y = cls.data.endog\n\n    def test_glm_IRLS(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gamma(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (8, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((-1.776527e-02, 4.961768e-05, 2.034423e-03, -7.181429e-05, 1.118520e-04,\n                      -1.467515e-07, -5.186831e-04, -2.42717498e-06)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (8, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr * np.sqrt(old_div(32., 24.)),\n            np.array((1.147922e-02, 1.621577e-05, 5.320802e-04, 2.711664e-05, 4.057691e-05,\n                      1.236569e-07, 2.402534e-04, 7.460253e-07)),\n            decimal=2)\n        # scale\n        self.assertAlmostEqual(self.model.dispersion * 32. / 24., 0.003584283, places=6)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([57.80431482,  53.2733447, 50.56347993, 58.33003783,\n                      70.46562169,  56.88801284,  66.81878401,  66.03410393,\n                      57.92937473,  63.23216907,  53.9914785,  61.28993391,\n                      64.81036393,  63.47546816,  60.69696114,  74.83508176,\n                      56.56991106,  72.01804172,  64.35676519,  52.02445881,\n                      64.24933079,  71.15070332,  45.73479688,  54.93318588,\n                      66.98031261,  52.02479973,  56.18413736,  58.12267471,\n                      67.37947398,  60.49162862,  73.82609217,  69.61515621]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -82.47352,\n            places=2)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -82.47352,\n            places=2)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (32,))\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/GLM/Gamma/')\n        self.assertEqual(json_dict['properties']['solver'], 'IRLS')\n\n        # from_json\n        self.model_from_json = GLM.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        self.assertEqual(\n            self.model.dispersion,\n            self.model_from_json.dispersion)\n\n        np.testing.assert_array_almost_equal(\n            self.model_from_json.predict(self.X),\n            np.array([57.80431482,  53.2733447, 50.56347993, 58.33003783,\n                      70.46562169,  56.88801284,  66.81878401,  66.03410393,\n                      57.92937473,  63.23216907,  53.9914785,  61.28993391,\n                      64.81036393,  63.47546816,  60.69696114,  74.83508176,\n                      56.56991106,  72.01804172,  64.35676519,  52.02445881,\n                      64.24933079,  71.15070332,  45.73479688,  54.93318588,\n                      66.98031261,  52.02479973,  56.18413736,  58.12267471,\n                      67.37947398,  60.49162862,  73.82609217,  69.61515621]),\n            decimal=3)\n\n    def test_glm_regularized(self):\n        pass\n\n    def test_glm_sample_weight_all_half(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gamma(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (8, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((-1.776527e-02, 4.961768e-05, 2.034423e-03, -7.181429e-05, 1.118520e-04,\n                      -1.467515e-07, -5.186831e-04, -2.42717498e-06)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (8, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr * np.sqrt(32. / 24. / 2.),\n            np.array((1.147922e-02, 1.621577e-05, 5.320802e-04, 2.711664e-05, 4.057691e-05,\n                      1.236569e-07, 2.402534e-04, 7.460253e-07)),\n            decimal=3)\n        # scale\n        self.assertAlmostEqual(self.model.dispersion * 32. / 24., 0.003584283, places=6)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([57.80431482,  53.2733447, 50.56347993, 58.33003783,\n                      70.46562169,  56.88801284,  66.81878401,  66.03410393,\n                      57.92937473,  63.23216907,  53.9914785,  61.28993391,\n                      64.81036393,  63.47546816,  60.69696114,  74.83508176,\n                      56.56991106,  72.01804172,  64.35676519,  52.02445881,\n                      64.24933079,  71.15070332,  45.73479688,  54.93318588,\n                      66.98031261,  52.02479973,  56.18413736,  58.12267471,\n                      67.37947398,  60.49162862,  73.82609217,  69.61515621]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y, sample_weight=0.5),\n            old_div(-82.47352, 2.),\n            places=2)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -82.47352,\n            places=2)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (32,))\n\n    def test_glm_sample_weight_all_zero(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gamma(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_GLM_sample_weight_half_zero_half_one(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gamma(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 16\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.X.shape[0] - len_half)))\n        self.model_half = GLM(\n            solver='IRLS', family=sm.families.Gamma(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=3)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n    def test_glm_one_data_point(self):\n        pass\n\n    def test_ols_multicolinearty(self):\n        self.model_col = GLM(\n            solver='irls', family=sm.families.Gamma(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], self.X[:, 0:1]])\n        self.model_col.fit(X, self.Y, sample_weight=0.5)\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gamma(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1], self.Y, sample_weight=0.5)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([1.080e-05,   1.080e-05]), decimal=3)\n        # stderr\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.Y),\n            self.model.loglike_per_sample(self.X[:, 0:1],\n                                          self.Y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=3)\n\n\nclass GaussianTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data = sm.datasets.longley.load()\n        cls.X = cls.data.exog\n        cls.Y = cls.data.endog\n\n    def test_glm_IRLS(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((-3.48225863e+06, 1.50618723e+01,  -3.58191793e-02,\n                      -2.02022980e+00, -1.03322687e+00,  -5.11041057e-02,\n                      1.82915146e+03)),\n            decimal=2)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr * np.sqrt(old_div(16., 9.)),\n            np.array((8.90420384e+05, 8.49149258e+01, 3.34910078e-02, 4.88399682e-01,\n                      2.14274163e-01, 2.26073200e-01, 4.55478499e+02)),\n            decimal=3)\n        # scale\n        self.assertAlmostEqual(self.model.dispersion * 16. / 9., 92936.006167311629, places=6)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([60055.659970240202, 61216.013942398131,\n                      60124.71283224225, 61597.114621930756, 62911.285409240052,\n                      63888.31121532945, 65153.048956395127, 63774.180356866214,\n                      66004.695227399934, 67401.605905447621,\n                      68186.268927114084,  66552.055042522494,\n                      68810.549973595422, 69649.67130804155, 68989.068486039061,\n                      70757.757825193927]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -109.61743480847952,\n            places=3)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -109.61743480847952,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (16,))\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/GLM/Gaussian/')\n        self.assertEqual(json_dict['properties']['solver'], 'IRLS')\n\n        # from_json\n        self.model_from_json = GLM.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        self.assertEqual(\n            self.model.dispersion,\n            self.model_from_json.dispersion)\n\n        np.testing.assert_array_almost_equal(\n            self.model_from_json.predict(self.X),\n            np.array([60055.659970240202, 61216.013942398131,\n                      60124.71283224225, 61597.114621930756, 62911.285409240052,\n                      63888.31121532945, 65153.048956395127, 63774.180356866214,\n                      66004.695227399934, 67401.605905447621,\n                      68186.268927114084,  66552.055042522494,\n                      68810.549973595422, 69649.67130804155, 68989.068486039061,\n                      70757.757825193927]),\n            decimal=3)\n\n    def test_glm_regularized(self):\n        pass\n\n    def test_glm_sample_weight_all_half(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((-3.48225863e+06, 1.50618723e+01,  -3.58191793e-02,\n                      -2.02022980e+00, -1.03322687e+00,  -5.11041057e-02,\n                      1.82915146e+03)),\n            decimal=2)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr * np.sqrt(16. / 9. / 2.),\n            np.array((8.90420384e+05, 8.49149258e+01, 3.34910078e-02, 4.88399682e-01,\n                      2.14274163e-01, 2.26073200e-01, 4.55478499e+02)),\n            decimal=3)\n        # scale\n        self.assertAlmostEqual(self.model.dispersion * 16. / 9., 92936.006167311629, places=6)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([60055.659970240202, 61216.013942398131,\n                      60124.71283224225, 61597.114621930756, 62911.285409240052,\n                      63888.31121532945, 65153.048956395127, 63774.180356866214,\n                      66004.695227399934, 67401.605905447621,\n                      68186.268927114084,  66552.055042522494,\n                      68810.549973595422, 69649.67130804155, 68989.068486039061,\n                      70757.757825193927]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y, sample_weight=0.5),\n            old_div(-109.61743480847952, 2.),\n            places=3)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -109.61743480847952,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (16,))\n\n    def test_glm_sample_weight_all_zero(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_GLM_sample_weight_half_zero_half_one(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 8\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.X.shape[0] - len_half)))\n        self.model_half = GLM(\n            solver='IRLS', family=sm.families.Gaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=3)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n    def test_glm_one_data_point(self):\n        pass\n\n    def test_ols_multicolinearty(self):\n        self.model_col = GLM(\n            solver='irls', family=sm.families.Gaussian(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], self.X[:, 0:1]])\n        self.model_col.fit(X, self.Y, sample_weight=0.5)\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Gaussian(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1], self.Y, sample_weight=0.5)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([319.48,  319.48]), decimal=3)\n        # stderr\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.Y),\n            self.model.loglike_per_sample(self.X[:, 0:1],\n                                          self.Y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=3)\n\n\nclass BinomialTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data = sm.datasets.star98.load()\n        cls.X = cls.data.exog\n        cls.Y = cls.data.endog\n\n    def test_glm_IRLS(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Binomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (21, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((2.9588779262, -0.0168150366,  0.0099254766, -0.0187242148,\n                      -0.0142385609, 0.2544871730,  0.2406936644,  0.0804086739,\n                      -1.9521605027, -0.3340864748, -0.1690221685,  0.0049167021,\n                      -0.0035799644, -0.0140765648, -0.0040049918, -0.0039063958,\n                      0.0917143006,  0.0489898381,  0.0080407389,  0.0002220095,\n                      -0.0022492486)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (21, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            np.array((1.546712e+00, 4.339467e-04, 6.013714e-04, 7.435499e-04, 4.338655e-04,\n                      2.994576e-02, 5.713824e-02, 1.392359e-02, 3.168109e-01,\n                      6.126411e-02, 3.270139e-02, 1.253877e-03, 2.254633e-04,\n                      1.904573e-03, 4.739838e-04, 9.623650e-04, 1.450923e-02,\n                      7.451666e-03, 1.499497e-03, 2.988794e-05, 3.489838e-04)),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # predict\n        pred = np.array([0.5833118,  0.75144661,  0.50058272, 0.68534524,  0.32251021,\n                         0.68693601,  0.33299827,  0.65624766, 0.49851481,  0.506736,\n                         0.23954874,  0.86631452,  0.46432936,  0.44171873,  0.66797935,\n                         0.73988491,  0.51966014,  0.42442446,  0.5649369,  0.59251634,\n                         0.34798337,  0.56415024,  0.49974355,  0.3565539,  0.20752309,\n                         0.18269097,  0.44932642,  0.48025128,  0.59965277,  0.58848671,\n                         0.36264203,  0.33333196,  0.74253352,  0.5081886,  0.53421878,\n                         0.56291445,  0.60205239,  0.29174423,  0.2954348,  0.32220414,\n                         0.47977903,  0.23687535,  0.11776464,  0.1557423,  0.27854799,\n                         0.22699533,  0.1819439,  0.32554433,  0.22681989,  0.15785389,\n                         0.15268609,  0.61094772,  0.20743222,  0.51649059,  0.46502006,\n                         0.41031788,  0.59523288,  0.65733285,  0.27835336,  0.2371213,\n                         0.25137045,  0.23953942,  0.27854519,  0.39652413,  0.27023163,\n                         0.61411863,  0.2212025,  0.42005842,  0.55940397,  0.35413774,\n                         0.45724563,  0.57399437,  0.2168918,  0.58308738,  0.17181104,\n                         0.49873249,  0.22832683,  0.14846056,  0.5028073,  0.24513863,\n                         0.48202096,  0.52823155,  0.5086262,  0.46295993,  0.57869402,\n                         0.78363217,  0.21144435,  0.2298366,  0.17954825,  0.32232586,\n                         0.8343015,  0.56217006,  0.47367315,  0.52535649,  0.60350746,\n                         0.43210701,  0.44712008,  0.35858239,  0.2521347,  0.19787004,\n                         0.63256553,  0.51386532,  0.64997027,  0.13402072,  0.81756174,\n                         0.74543642,  0.30825852,  0.23988707,  0.17273125,  0.27880599,\n                         0.17395893,  0.32052828,  0.80467697,  0.18726218,  0.23842081,\n                         0.19020381,  0.85835388,  0.58703615,  0.72415106,  0.64433695,\n                         0.68766653,  0.32923663,  0.16352185,  0.38868816,  0.44980444,\n                         0.74810044,  0.42973792,  0.53762581,  0.72714996,  0.61229484,\n                         0.30267667,  0.24713253,  0.65086008,  0.48957265,  0.54955545,\n                         0.5697156,  0.36406211,  0.48906545,  0.45919413,  0.4930565,\n                         0.39785555,  0.5078719,  0.30159626,  0.28524393,  0.34687707,\n                         0.22522042,  0.52947159,  0.29277287,  0.8585002,  0.60800389,\n                         0.75830521,  0.35648175,  0.69508796,  0.45518355,  0.21567675,\n                         0.39682985,  0.49042948,  0.47615798,  0.60588234,  0.62910299,\n                         0.46005639,  0.71755165,  0.48852156,  0.47940661,  0.60128813,\n                         0.16589699,  0.68512861,  0.46305199,  0.68832227,  0.7006721,\n                         0.56564937,  0.51753941,  0.54261733,  0.56072214,  0.34545715,\n                         0.30226104,  0.3572956,  0.40996287,  0.33517519,  0.36248407,\n                         0.33937041,  0.34140691,  0.2627528,  0.29955161,  0.38581683,\n                         0.24840026,  0.15414272,  0.40415991,  0.53936252,  0.52111887,\n                         0.28060168,  0.45600958,  0.51110589,  0.43757523,  0.46891953,\n                         0.39425249,  0.5834369,  0.55817308,  0.32051259,  0.43567448,\n                         0.34134195,  0.43016545,  0.4885413,  0.28478325,  0.2650776,\n                         0.46784606,  0.46265983,  0.42655938,  0.18972234,  0.60448491,\n                         0.211896,  0.37886032,  0.50727577,  0.39782309,  0.50427121,\n                         0.35882898,  0.39596807,  0.49160806,  0.35618002,  0.6819922,\n                         0.36871093,  0.43079679,  0.67985516,  0.41270595,  0.68952767,\n                         0.52587734,  0.32042126,  0.39120123,  0.56870985,  0.32962349,\n                         0.32168989,  0.54076251,  0.4592907,  0.48480182,  0.4408386,\n                         0.431178,  0.47078232,  0.55911605,  0.30331618,  0.50310393,\n                         0.65036038,  0.45078895,  0.62354291,  0.56435463,  0.50034281,\n                         0.52693538,  0.57217285,  0.49221472,  0.40707122,  0.44226533,\n                         0.3475959,  0.54746396,  0.86385832,  0.48402233,  0.54313657,\n                         0.61586824,  0.27097185,  0.69717808,  0.52156974,  0.50401189,\n                         0.56724181,  0.6577178,  0.42732047,  0.44808396,  0.65435634,\n                         0.54766225,  0.38160648,  0.49890847,  0.50879037,  0.5875452,\n                         0.45101593,  0.5709704,  0.3175516,  0.39813159,  0.28305688,\n                         0.40521062,  0.30120578,  0.26400428,  0.44205496,  0.40545798,\n                         0.39366599,  0.55288196,  0.14104184,  0.17550155,  0.1949095,\n                         0.40255144,  0.21016822,  0.09712017,  0.63151487,  0.25885514,\n                         0.57323748,  0.61836898,  0.43268601,  0.67008878,  0.75801989,\n                         0.50353406,  0.64222315,  0.29925757,  0.32592036,  0.39634977,\n                         0.39582747,  0.41037006,  0.34174944])\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X), pred, decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -2998.61255899391,\n            places=3)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -2998.61255899391,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (303,))\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X[:5], self.Y[:5]).shape, (5,))\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/GLM/Binomial/')\n        self.assertEqual(json_dict['properties']['solver'], 'IRLS')\n\n        # from_json\n        self.model_from_json = GLM.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        self.assertEqual(\n            self.model.dispersion,\n            self.model_from_json.dispersion)\n\n        np.testing.assert_array_almost_equal(\n            self.model_from_json.predict(self.X), pred, decimal=3)\n\n    def test_glm_regularized(self):\n        # not supported by statsmodels\n        pass\n\n    def test_glm_sample_weight_all_half(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Binomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (21, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((2.9588779262, -0.0168150366,  0.0099254766, -0.0187242148,\n                      -0.0142385609, 0.2544871730,  0.2406936644,  0.0804086739,\n                      -1.9521605027, -0.3340864748, -0.1690221685,  0.0049167021,\n                      -0.0035799644, -0.0140765648, -0.0040049918, -0.0039063958,\n                      0.0917143006,  0.0489898381,  0.0080407389,  0.0002220095,\n                      -0.0022492486)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (21, ))\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.stderr, np.sqrt(2)),\n            np.array((1.546712e+00, 4.339467e-04, 6.013714e-04, 7.435499e-04, 4.338655e-04,\n                      2.994576e-02, 5.713824e-02, 1.392359e-02, 3.168109e-01,\n                      6.126411e-02, 3.270139e-02, 1.253877e-03, 2.254633e-04,\n                      1.904573e-03, 4.739838e-04, 9.623650e-04, 1.450923e-02,\n                      7.451666e-03, 1.499497e-03, 2.988794e-05, 3.489838e-04)),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # predict\n        pred = np.array([0.5833118,  0.75144661,  0.50058272, 0.68534524,  0.32251021,\n                         0.68693601,  0.33299827,  0.65624766, 0.49851481,  0.506736,\n                         0.23954874,  0.86631452,  0.46432936,  0.44171873,  0.66797935,\n                         0.73988491,  0.51966014,  0.42442446,  0.5649369,  0.59251634,\n                         0.34798337,  0.56415024,  0.49974355,  0.3565539,  0.20752309,\n                         0.18269097,  0.44932642,  0.48025128,  0.59965277,  0.58848671,\n                         0.36264203,  0.33333196,  0.74253352,  0.5081886,  0.53421878,\n                         0.56291445,  0.60205239,  0.29174423,  0.2954348,  0.32220414,\n                         0.47977903,  0.23687535,  0.11776464,  0.1557423,  0.27854799,\n                         0.22699533,  0.1819439,  0.32554433,  0.22681989,  0.15785389,\n                         0.15268609,  0.61094772,  0.20743222,  0.51649059,  0.46502006,\n                         0.41031788,  0.59523288,  0.65733285,  0.27835336,  0.2371213,\n                         0.25137045,  0.23953942,  0.27854519,  0.39652413,  0.27023163,\n                         0.61411863,  0.2212025,  0.42005842,  0.55940397,  0.35413774,\n                         0.45724563,  0.57399437,  0.2168918,  0.58308738,  0.17181104,\n                         0.49873249,  0.22832683,  0.14846056,  0.5028073,  0.24513863,\n                         0.48202096,  0.52823155,  0.5086262,  0.46295993,  0.57869402,\n                         0.78363217,  0.21144435,  0.2298366,  0.17954825,  0.32232586,\n                         0.8343015,  0.56217006,  0.47367315,  0.52535649,  0.60350746,\n                         0.43210701,  0.44712008,  0.35858239,  0.2521347,  0.19787004,\n                         0.63256553,  0.51386532,  0.64997027,  0.13402072,  0.81756174,\n                         0.74543642,  0.30825852,  0.23988707,  0.17273125,  0.27880599,\n                         0.17395893,  0.32052828,  0.80467697,  0.18726218,  0.23842081,\n                         0.19020381,  0.85835388,  0.58703615,  0.72415106,  0.64433695,\n                         0.68766653,  0.32923663,  0.16352185,  0.38868816,  0.44980444,\n                         0.74810044,  0.42973792,  0.53762581,  0.72714996,  0.61229484,\n                         0.30267667,  0.24713253,  0.65086008,  0.48957265,  0.54955545,\n                         0.5697156,  0.36406211,  0.48906545,  0.45919413,  0.4930565,\n                         0.39785555,  0.5078719,  0.30159626,  0.28524393,  0.34687707,\n                         0.22522042,  0.52947159,  0.29277287,  0.8585002,  0.60800389,\n                         0.75830521,  0.35648175,  0.69508796,  0.45518355,  0.21567675,\n                         0.39682985,  0.49042948,  0.47615798,  0.60588234,  0.62910299,\n                         0.46005639,  0.71755165,  0.48852156,  0.47940661,  0.60128813,\n                         0.16589699,  0.68512861,  0.46305199,  0.68832227,  0.7006721,\n                         0.56564937,  0.51753941,  0.54261733,  0.56072214,  0.34545715,\n                         0.30226104,  0.3572956,  0.40996287,  0.33517519,  0.36248407,\n                         0.33937041,  0.34140691,  0.2627528,  0.29955161,  0.38581683,\n                         0.24840026,  0.15414272,  0.40415991,  0.53936252,  0.52111887,\n                         0.28060168,  0.45600958,  0.51110589,  0.43757523,  0.46891953,\n                         0.39425249,  0.5834369,  0.55817308,  0.32051259,  0.43567448,\n                         0.34134195,  0.43016545,  0.4885413,  0.28478325,  0.2650776,\n                         0.46784606,  0.46265983,  0.42655938,  0.18972234,  0.60448491,\n                         0.211896,  0.37886032,  0.50727577,  0.39782309,  0.50427121,\n                         0.35882898,  0.39596807,  0.49160806,  0.35618002,  0.6819922,\n                         0.36871093,  0.43079679,  0.67985516,  0.41270595,  0.68952767,\n                         0.52587734,  0.32042126,  0.39120123,  0.56870985,  0.32962349,\n                         0.32168989,  0.54076251,  0.4592907,  0.48480182,  0.4408386,\n                         0.431178,  0.47078232,  0.55911605,  0.30331618,  0.50310393,\n                         0.65036038,  0.45078895,  0.62354291,  0.56435463,  0.50034281,\n                         0.52693538,  0.57217285,  0.49221472,  0.40707122,  0.44226533,\n                         0.3475959,  0.54746396,  0.86385832,  0.48402233,  0.54313657,\n                         0.61586824,  0.27097185,  0.69717808,  0.52156974,  0.50401189,\n                         0.56724181,  0.6577178,  0.42732047,  0.44808396,  0.65435634,\n                         0.54766225,  0.38160648,  0.49890847,  0.50879037,  0.5875452,\n                         0.45101593,  0.5709704,  0.3175516,  0.39813159,  0.28305688,\n                         0.40521062,  0.30120578,  0.26400428,  0.44205496,  0.40545798,\n                         0.39366599,  0.55288196,  0.14104184,  0.17550155,  0.1949095,\n                         0.40255144,  0.21016822,  0.09712017,  0.63151487,  0.25885514,\n                         0.57323748,  0.61836898,  0.43268601,  0.67008878,  0.75801989,\n                         0.50353406,  0.64222315,  0.29925757,  0.32592036,  0.39634977,\n                         0.39582747,  0.41037006,  0.34174944])\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X), pred, decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y, sample_weight=0.5),\n            old_div(-2998.61255899391, 2.),\n            places=3)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -2998.61255899391,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (303,))\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X[:5], self.Y[:5]).shape, (5,))\n\n    def test_glm_sample_weight_all_zero(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Binomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_GLM_sample_weight_half_zero_half_one(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Binomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 160\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.X.shape[0] - len_half)))\n        self.model_half = GLM(\n            solver='IRLS', family=sm.families.Binomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=2)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n\n    def test_glm_one_data_point(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Binomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[0:1, :],\n                       self.Y[0:1, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (21, ))\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            self.X[0:1, :], self.Y[0:1, ]), np.array([-3.565]), decimal=3)\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            np.array(self.X[0:1, :].tolist() * 6),\n            np.array([[452., 355.], [510., 235.], [422., 335.],\n                      [454., 355.], [452., 355.], [422., 355.]])),\n            np.array([-3.565, -27.641,  -3.545,  -3.568,  -3.565,  -4.004]), decimal=3)\n\n    def test_ols_multicolinearty(self):\n        self.model_col = GLM(\n            solver='irls', family=sm.families.Binomial(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], self.X[:, 0:1]])\n        self.model_col.fit(X, self.Y, sample_weight=0.5)\n        self.model = GLM(\n            solver='IRLS', family=sm.families.Binomial(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1], self.Y, sample_weight=0.5)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([-0.006, -0.006]), decimal=3)\n        # stderr\n        np.testing.assert_array_almost_equal(\n            self.model_col.stderr, np.array([5.684e-05, 5.684e-05]), decimal=3)\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.Y),\n            self.model.loglike_per_sample(self.X[:, 0:1],\n                                          self.Y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=3)\n\n\nclass InverseGaussianTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        filename = 'tests/linear_models/GLM/InverseGaussian/inv_gaussian.csv'\n        data = np.genfromtxt(open(filename, 'rb'), delimiter=\",\", dtype=float)[1:]\n        cls.Y = data[:5000, 0]\n        cls.X = data[:5000, 1:]\n        cls.res = InvGauss()\n\n    def test_glm_IRLS(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.InverseGaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (3, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((1.0359574, 0.4519770, -0.2508288)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (3, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr * np.sqrt(old_div(5000., 4997.)),\n            np.array((0.03429943, 0.03148291, 0.02237211)),\n            decimal=3)\n        # scale\n        self.assertAlmostEqual(self.model.dispersion * 5000. / 4997., 0.2867266359127567, places=6)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            self.res.fittedvalues,\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -2525.70955823223,\n            places=1)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -2525.70955823223,\n            places=1)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (5000,))\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/GLM/InverseGaussian/')\n        self.assertEqual(json_dict['properties']['solver'], 'IRLS')\n\n        # from_json\n        self.model_from_json = GLM.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        self.assertEqual(\n            self.model.dispersion,\n            self.model_from_json.dispersion)\n\n        np.testing.assert_array_almost_equal(\n            self.model_from_json.predict(self.X),\n            self.res.fittedvalues,\n            decimal=3)\n\n    def test_glm_regularized(self):\n        pass\n\n    def test_glm_sample_weight_all_half(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.InverseGaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (3, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((1.0359574, 0.4519770, -0.2508288)),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (3, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr * np.sqrt(5000. / 4997. / 2.),\n            np.array((0.03429943, 0.03148291, 0.02237211)),\n            decimal=3)\n        # scale\n        self.assertAlmostEqual(self.model.dispersion * 5000. / 4997., 0.2867266359127567, places=6)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            self.res.fittedvalues,\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y, sample_weight=0.5),\n            old_div(-2525.70955823223, 2.),\n            places=1)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -2525.70955823223,\n            places=1)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (5000,))\n\n    def test_glm_sample_weight_all_zero(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.InverseGaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_GLM_sample_weight_half_zero_half_one(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.InverseGaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 2500\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.X.shape[0] - len_half)))\n        self.model_half = GLM(\n            solver='IRLS', family=sm.families.InverseGaussian(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=3)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n\n    def test_glm_one_data_point(self):\n        pass\n\n    def test_ols_multicolinearty(self):\n        self.model_col = GLM(\n            solver='irls', family=sm.families.InverseGaussian(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], self.X[:, 0:1]])\n        self.model_col.fit(X, self.Y, sample_weight=0.5)\n        self.model = GLM(\n            solver='IRLS', family=sm.families.InverseGaussian(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1], self.Y, sample_weight=0.5)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([0.712,  0.712]), decimal=3)\n        # stderr\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.Y),\n            self.model.loglike_per_sample(self.X[:, 0:1],\n                                          self.Y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=3)\n\n\nclass NegativeBinomialTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        data = sm.datasets.committee.load()\n        data.exog[:, 2] = np.log(data.exog[:, 2])\n        interaction = data.exog[:, 2] * data.exog[:, 1]\n        data.exog = np.column_stack((data.exog, interaction))\n\n        cls.Y = data.endog\n        cls.X = data.exog\n\n    def test_glm_IRLS(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.NegativeBinomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([-6.44847076, -0.0268147,  1.25103364,  2.91070663,\n                      -0.34799563,  0.00659808, -0.31303026]),\n            decimal=2)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            np.array([3.21429775e+00, 3.22130435e-02, 7.68090529e-01,\n                      1.04436390e+00, 6.73309516e-01, 2.27984343e-03, 1.73596557e-01]),\n            decimal=3)\n        # scale\n        self.assertEqual(self.model.dispersion, 1)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([12.62019383,  30.18289514, 21.48377849, 496.74068604,\n                      103.23024673,  219.94693494,  324.4301163,  110.82526477,\n                      112.44244488,  219.86056381,   56.84399998,   61.19840382,\n                      114.09290269,   75.29071944,   61.21994387,   21.05130889,\n                      42.75939828,   55.56133536,    0.72532053,   18.14664665]),\n            decimal=0)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -101.33286676188968,\n            places=1)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -101.33286676188968,\n            places=1)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (20,))\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/GLM/NegativeBinomial/')\n        self.assertEqual(json_dict['properties']['solver'], 'IRLS')\n\n        # from_json\n        self.model_from_json = GLM.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        self.assertEqual(\n            self.model.dispersion,\n            self.model_from_json.dispersion)\n\n        np.testing.assert_array_almost_equal(\n            self.model_from_json.predict(self.X),\n            np.array([12.62019383,  30.18289514, 21.48377849, 496.74068604,\n                      103.23024673,  219.94693494,  324.4301163,  110.82526477,\n                      112.44244488,  219.86056381,   56.84399998,   61.19840382,\n                      114.09290269,   75.29071944,   61.21994387,   21.05130889,\n                      42.75939828,   55.56133536,    0.72532053,   18.14664665]),\n            decimal=0)\n\n    def test_glm_regularized(self):\n        pass\n\n    def test_glm_sample_weight_all_half(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.NegativeBinomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([-6.44847076, -0.0268147,  1.25103364,  2.91070663,\n                      -0.34799563,  0.00659808, -0.31303026]),\n            decimal=2)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (7, ))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            np.array([4.54570348e+00, 4.55561229e-02, 1.08624404e+00,\n                      1.47695359e+00, 9.52203449e-01, 3.22418550e-03, 2.45502605e-01]),\n            decimal=3)\n        # scale\n        self.assertAlmostEqual(self.model.dispersion, 1, places=4)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.model.predict(self.X),\n            np.array([12.62019383,  30.18289514, 21.48377849, 496.74068604,\n                      103.23024673,  219.94693494,  324.4301163,  110.82526477,\n                      112.44244488,  219.86056381,   56.84399998,   61.19840382,\n                      114.09290269,   75.29071944,   61.21994387,   21.05130889,\n                      42.75939828,   55.56133536,    0.72532053,   18.14664665]),\n            decimal=0)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y, sample_weight=0.5),\n            old_div(-101.33286676188968, 2.),\n            places=1)\n\n        self.assertAlmostEqual(\n            self.model.loglike_per_sample(self.X, self.Y).sum(),\n            -101.33286676188968,\n            places=1)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape, (20,))\n\n    def test_glm_sample_weight_all_zero(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.NegativeBinomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_GLM_sample_weight_half_zero_half_one(self):\n        self.model = GLM(\n            solver='IRLS', family=sm.families.NegativeBinomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 10\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.X.shape[0] - len_half)))\n        self.model_half = GLM(\n            solver='IRLS', family=sm.families.NegativeBinomial(),\n            fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=2)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=3)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n    def test_glm_one_data_point(self):\n        pass\n\n    def test_ols_multicolinearty(self):\n        self.model_col = GLM(\n            solver='irls', family=sm.families.NegativeBinomial(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], self.X[:, 0:1]])\n        self.model_col.fit(X, self.Y, sample_weight=0.5)\n        self.model = GLM(\n            solver='IRLS', family=sm.families.NegativeBinomial(),\n            fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1], self.Y, sample_weight=0.5)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([0.059,  0.059]), decimal=3)\n        # stderr\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.Y),\n            self.model.loglike_per_sample(self.X[:, 0:1],\n                                          self.Y), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=3)\n"
  },
  {
    "path": "tests/test_HMM_utils.py",
    "content": "import unittest\n\nimport numpy as np\n\nfrom IOHMM import (forward, backward, forward_backward)\n\n\nclass HMMUtilsTests(unittest.TestCase):\n    # test case from pyhmm\n    @classmethod\n    def setUpClass(cls):\n        cls.log_prob_initial = np.log(\n            np.array([0.4, 0.3, 0.3]))\n        cls.log_prob_transition = np.log(\n            np.array([[[0.6, 0.3, 0.1],\n                       [0.2, 0.5, 0.3],\n                       [0.3, 0.2, 0.5]]] * 11))\n        cls.log_Ey = np.log(\n            np.array([[0.3, 0.7, 0.5],\n                      [0.3, 0.7, 0.5],\n                      [0.7, 0.3, 0.5],\n                      [0.7, 0.3, 0.5],\n                      [0.7, 0.3, 0.5],\n                      [0.7, 0.3, 0.5],\n                      [0.3, 0.7, 0.5],\n                      [0.3, 0.7, 0.5],\n                      [0.3, 0.7, 0.5],\n                      [0.7, 0.3, 0.5],\n                      [0.7, 0.3, 0.5],\n                      [0.3, 0.7, 0.5]]))\n\n    def test_cal_alpha_beta(self):\n        # forward algorithm\n        log_alpha = forward(self.log_prob_initial, self.log_prob_transition, self.log_Ey, {})\n        np.testing.assert_array_almost_equal(\n            np.exp(log_alpha),\n            np.array([[1.20000000e-01, 2.10000000e-01, 1.50000000e-01],\n                      [4.77000000e-02, 1.19700000e-01, 7.50000000e-02],\n                      [5.25420000e-02, 2.67480000e-02, 3.90900000e-02],\n                      [3.40212600e-02, 1.10863800e-02, 1.64118000e-02],\n                      [1.92875004e-02, 5.70957840e-03, 7.46697000e-03],\n                      [1.04681548e-02, 3.04033000e-03, 3.68755428e-03],\n                      [2.39856756e-03, 3.77868562e-03, 1.90134581e-03],\n                      [8.29584420e-04, 2.09242757e-03, 1.16206767e-03],\n                      [3.79456940e-04, 1.06925185e-03, 6.45860274e-04],\n                      [4.44697832e-04, 2.33290519e-04, 3.40825693e-04],\n                      [2.91007157e-04, 9.54659242e-05, 1.42434893e-04],\n                      [7.09283841e-05, 1.14465462e-04, 6.44789697e-05]]),\n            decimal=2)\n        # backward algorithm\n        log_beta = backward(self.log_prob_transition, self.log_Ey, {})\n        np.testing.assert_array_almost_equal(\n            np.exp(log_beta),\n            np.array([[4.66170742e-04, 5.55151324e-04, 5.15670321e-04],\n                      [1.23431939e-03, 8.96027127e-04, 1.11655111e-03],\n                      [2.32427431e-03, 1.69786509e-03, 2.10632641e-03],\n                      [4.35918163e-03, 3.27228685e-03, 3.97824421e-03],\n                      [8.02824268e-03, 6.78021860e-03, 7.54200053e-03],\n                      [1.35088245e-02, 1.81218837e-02, 1.44713375e-02],\n                      [2.63228424e-02, 3.48915192e-02, 2.88698760e-02],\n                      [5.40079200e-02, 6.48328800e-02, 5.97302400e-02],\n                      [1.40748000e-01, 1.05828000e-01, 1.28988000e-01],\n                      [2.59200000e-01, 2.17600000e-01, 2.46000000e-01],\n                      [4.40000000e-01, 5.60000000e-01, 4.80000000e-01],\n                      [1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]),\n            decimal=2)\n\n    def test_forward_backward(self):\n        log_gamma, log_epsilon, log_likelihood = forward_backward(\n            self.log_prob_initial, self.log_prob_transition, self.log_Ey, {})\n        # likelihood\n        self.assertAlmostEqual(np.exp(log_likelihood), 0.000249872815287, places=6)\n\n        # gamma\n        np.testing.assert_array_almost_equal(\n            np.exp(log_gamma),\n            np.array([[0.22387585, 0.46656447, 0.30955968],\n                      [0.23562801, 0.42923616, 0.33513583],\n                      [0.48873672, 0.18175044, 0.32951283],\n                      [0.59352135, 0.14518512, 0.26129352],\n                      [0.6196942, 0.15492758, 0.22537823],\n                      [0.56593778, 0.2204982, 0.21356402],\n                      [0.25267701, 0.52764476, 0.21967823],\n                      [0.17930774, 0.54290862, 0.27778364],\n                      [0.21373996, 0.45285753, 0.33340251],\n                      [0.46129739, 0.20315942, 0.33554319],\n                      [0.51243329, 0.21395252, 0.27361419],\n                      [0.28385795, 0.4580949, 0.25804716]]),\n            decimal=6)\n\n        # epsilon\n        np.testing.assert_array_almost_equal(\n            np.exp(log_epsilon),\n            np.array([[[0.10669948, 0.09036551, 0.02681087],\n                       [0.06224136, 0.26356606, 0.14075705],\n                       [0.06668717, 0.07530459, 0.16756792]],\n\n                      [[0.18635285, 0.02917058, 0.02010458],\n                       [0.15588006, 0.12200274, 0.15135336],\n                       [0.14650381, 0.03057713, 0.15805489]],\n\n                      [[0.38498326, 0.0619272, 0.04182626],\n                       [0.06532889, 0.05254301, 0.06387854],\n                       [0.1432092, 0.03071491, 0.15558872]],\n\n                      [[0.45909352, 0.08308404, 0.05134379],\n                       [0.04986769, 0.0451238, 0.05019363],\n                       [0.11073298, 0.02671974, 0.12384081]],\n\n                      [[0.43794925, 0.12589335, 0.0558516],\n                       [0.04321461, 0.06211259, 0.04960038],\n                       [0.08477392, 0.03249226, 0.10811204]],\n\n                      [[0.19849813, 0.30696602, 0.06047363],\n                       [0.01921701, 0.14859002, 0.05269118],\n                       [0.03496187, 0.07208872, 0.10651342]],\n\n                      [[0.09331746, 0.13069156, 0.02866799],\n                       [0.04900388, 0.34315087, 0.13549001],\n                       [0.0369864, 0.06906618, 0.11362565]],\n\n                      [[0.08411168, 0.07378388, 0.02141218],\n                       [0.07071718, 0.31017019, 0.16202125],\n                       [0.0589111, 0.06890346, 0.14996908]],\n\n                      [[0.16532091, 0.02974027, 0.01867878],\n                       [0.15528304, 0.13967258, 0.1579019],\n                       [0.14069344, 0.03374658, 0.1589625]],\n\n                      [[0.32888796, 0.08969672, 0.04271272],\n                       [0.05751204, 0.07842551, 0.06722187],\n                       [0.12603329, 0.04583029, 0.1636796]],\n\n                      [[0.2096318, 0.24457043, 0.05823106],\n                       [0.02292348, 0.13372032, 0.05730871],\n                       [0.05130266, 0.07980414, 0.14250739]]]),\n            decimal=6)\n"
  },
  {
    "path": "tests/test_OLS.py",
    "content": "from __future__ import print_function\nfrom __future__ import division\n# import json\nfrom past.utils import old_div\nimport unittest\n\n\nimport numpy as np\nimport statsmodels.api as sm\n\n\nfrom IOHMM import OLS\n\n# //TODO sample weight all zero\n\n# Corner cases\n# General\n# 1. sample_weight is all zero\n# 2. sample_weight is all one\n# 3. sample_weight is a scale of all one\n# 4. sample_weight is mixed of 0 and 1\n# 6. when number of data is 1/or very small, less than the number of features\n# 7. standard dataset compare with sklearn/statsmodels\n# 8. output dimensions\n# 9. collinearty in X\n# 10. to/from json\n# MultivariateOLS\n# 1. Y is not column/row independent\n# Discrete/CrossEntropyMNL\n# 1. number of class is 1\n# 2. number of class is 2\n\n\nclass UnivariateOLSTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_longley = sm.datasets.longley.load()\n\n    def test_ols(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.data_longley.exog, self.data_longley.endog)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (1, 7))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([-3482258.63459582, 15.0618722713733, -0.358191792925910E-01,\n                      -2.02022980381683, -1.03322686717359, -0.511041056535807E-01,\n                      1829.15146461355]).reshape(1, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (1, 7))\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.stderr, np.sqrt(old_div(9., self.data_longley.exog.shape[0]))),\n            np.array([890420.383607373, 84.9149257747669, 0.03349,\n                      0.488399681651699, 0.214274163161675, 0.226073200069370,\n                      455.478499142212]).reshape(1, -1),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion.shape, (1, 1))\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.dispersion, (old_div(9., self.data_longley.exog.shape[0]))),\n            np.array([[92936.0061673238]]),\n            decimal=3)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.data_longley.endog.reshape(-1, 1) - self.model.predict(self.data_longley.exog),\n            np.array([267.34003, -94.01394, 46.28717, -410.11462,\n                      309.71459, -249.31122, -164.04896, -13.18036, 14.30477, 455.39409,\n                      -17.26893, -39.05504, -155.54997, -85.67131, 341.93151,\n                      -206.75783]).reshape(-1, 1),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_longley.exog, self.data_longley.endog),\n            -109.61743480849013,\n            places=3)\n\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/OLS/UnivariateOLS/')\n        self.assertEqual(json_dict['properties']['solver'], 'pinv')\n\n        # from_json\n        self.model_from_json = OLS.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        self.assertEqual(\n            self.model.dispersion,\n            self.model_from_json.dispersion)\n\n    def test_ols_l1_regularized(self):\n        # sklearn elastic net and l1 does not take sample_weights, will not test\n        pass\n\n    def test_ols_l2_regularized(self):\n        # there is a bug in sklearn with weights, it can only use list right now\n        self.model = OLS(\n            solver='auto', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0.1, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.data_longley.exog, self.data_longley.endog, sample_weight=0.5)\n\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([-2.0172203, -52.14364269, 0.07089677, -0.42552125,\n                      -0.57305292, -0.41272483, 48.32484052]).reshape(1, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertTrue(self.model.stderr is None)\n        # scale\n        self.assertEqual(self.model.dispersion.shape, (1, 1))\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.dispersion, (old_div(9., self.data_longley.exog.shape[0]))),\n            np.array([[250870.081]]),\n            decimal=3)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.data_longley.endog.reshape(-1, 1) - self.model.predict(self.data_longley.exog),\n            np.array([[280.31871146],\n                      [-131.6981265],\n                      [90.64414685],\n                      [-400.10244445],\n                      [-440.59604167],\n                      [-543.88595187],\n                      [200.70483416],\n                      [215.88629903],\n                      [74.9456573],\n                      [913.85128645],\n                      [424.15996133],\n                      [-9.5797488],\n                      [-360.96841852],\n                      [27.214226],\n                      [150.87705909],\n                      [-492.17489392]]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_longley.exog, self.data_longley.endog),\n            -117.561627187,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.data_longley.exog, self.data_longley.endog).shape,\n            (16, ))\n\n    def test_ols_elastic_net_regularized(self):\n        # sklearn elastic net and l1 does not take sample_weights, will not test\n        pass\n\n    def test_ols_sample_weight_all_half(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.data_longley.exog, self.data_longley.endog, sample_weight=0.5)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array((-3482258.63459582, 15.0618722713733, -0.358191792925910E-01,\n                      -2.02022980381683, -1.03322686717359, -0.511041056535807E-01,\n                      1829.15146461355)).reshape(1, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.stderr, np.sqrt(old_div(9., self.data_longley.exog.shape[0]))),\n            np.array((890420.383607373, 84.9149257747669, 0.334910077722432E-01,\n                      0.488399681651699, 0.214274163161675, 0.226073200069370,\n                      455.478499142212)).reshape(1, -1),\n            decimal=1)\n        # scale\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.dispersion, (old_div(9., self.data_longley.exog.shape[0]))),\n            np.array((92936.0061673238)))\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.data_longley.endog.reshape(-1, 1) - self.model.predict(self.data_longley.exog),\n            np.array((267.34003, -94.01394, 46.28717, -410.11462,\n                      309.71459, -249.31122, -164.04896, -13.18036, 14.30477, 455.39409,\n                      -17.26893, -39.05504, -155.54997, -85.67131, 341.93151,\n                      -206.75783)).reshape(-1, 1),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.data_longley.exog, self.data_longley.endog),\n            -109.61743480849013,\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.data_longley.exog, self.data_longley.endog).shape,\n            (16, ))\n\n    def test_ols_sample_weight_all_zero(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit,\n                          self.data_longley.exog, self.data_longley.endog, 0)\n\n    def test_ols_sample_weight_half_zero_half_one(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 8\n        self.model.fit(self.data_longley.exog, self.data_longley.endog,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.data_longley.exog.shape[0] - len_half)))\n        self.model_half = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.data_longley.exog[:len_half], self.data_longley.endog[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=3)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n    def test_ols_one_data_point(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.data_longley.exog[0:1, :],\n                       self.data_longley.endog[0:1, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (1, 7))\n        # scale\n        np.testing.assert_array_almost_equal(self.model.dispersion, np.array([[0]]))\n        # loglike_per_sample\n        np.testing.assert_array_equal(self.model.loglike_per_sample(\n            self.data_longley.exog[0:1, :], self.data_longley.endog[0:1, ]), np.array([0]))\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            np.array(self.data_longley.exog[0:1, :].tolist() * 6),\n            np.array([60323, 0, 60323, 60322, 60322, 60323])),\n            np.array([0, -np.Infinity, 0, -np.Infinity, -np.Infinity, 0]), decimal=3)\n\n    def test_ols_multicolinearty(self):\n        self.model_col = OLS(\n            solver='pinv', fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.data_longley.exog[:, 0:1], self.data_longley.exog[:, 0:1]])\n        self.model_col.fit(X,\n                           self.data_longley.endog, sample_weight=0.8)\n        self.model = OLS(\n            solver='pinv', fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.data_longley.exog[:, 0:1],\n                       self.data_longley.endog, sample_weight=0.8)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([319.47969664, 319.47969664]).reshape(1, -1), decimal=3)\n        # stderr\n        self.assertEqual(self.model_col.stderr, None)\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.data_longley.endog),\n            self.model.loglike_per_sample(self.data_longley.exog[:, 0:1],\n                                          self.data_longley.endog), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.data_longley.exog[:, 0:1]), decimal=3)\n\n\nclass IndependentMultivariateOLSTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        np.random.seed(0)\n        cls.X = np.random.normal(size=(1000, 1))\n        cls.Y = np.random.normal(size=(cls.X.shape[0], 2))\n\n    def test_ols(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-0.02924966, -0.03484827],\n                      [-0.00978688, 0.00336316]]).reshape(2, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            np.array([[0.03083908, 0.03121143],\n                      [0.03002101, 0.03038348]]).reshape(2, -1),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            np.array([[0.94905363, 0.0164185],\n                      [0.0164185, 0.89937019]]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -2758.54387369,\n            places=3)\n\n        # to_json\n        json_dict = self.model.to_json('./tests/linear_models/OLS/MultivariateOLS/')\n        self.assertEqual(json_dict['properties']['solver'], 'pinv')\n\n        # from_json\n        self.model_from_json = OLS.from_json(json_dict)\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_from_json.coef,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_from_json.stderr,\n            decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_from_json.dispersion,\n            decimal=3)\n\n    def test_ols_l2_regularized(self):\n        self.model = OLS(\n            solver='auto', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0.1, l1_ratio=1,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-0.0292465, -0.03484456],\n                      [-0.00978591, 0.00336286]]).reshape(2, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertTrue(self.model.stderr is None)\n        # scale\n        self.assertEqual(self.model.dispersion.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            np.array([[0.94905363, 0.0164185],\n                      [0.0164185, 0.89937019]]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y),\n            -2758.5438737,\n            places=3)\n\n    def test_ols_l1_regularized(self):\n        # sklearn l1 and elstic net does not support sample weight\n        pass\n\n    def test_ols_sample_weight_all_half(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-0.02924966, -0.03484827],\n                      [-0.00978688, 0.00336316]]).reshape(2, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            np.array([[0.03083908, 0.03121143],\n                      [0.03002101, 0.03038348]]).reshape(2, -1),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            np.array([[0.94905363, 0.0164185],\n                      [0.0164185, 0.89937019]]),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertAlmostEqual(\n            self.model.loglike(self.X, self.Y, 0.5),\n            old_div(-2758.54387369, 2.),\n            places=3)\n\n        self.assertEqual(\n            self.model.loglike_per_sample(self.X, self.Y).shape,\n            (1000, ))\n\n    def test_ols_sample_weight_all_zero(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_ols_sample_weight_half_zero_half_one(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 500\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.X.shape[0] - len_half)))\n        self.model_half = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=3)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n    def test_ols_one_data_point(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[0:1, :],\n                       self.Y[0:1, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (2, 2))\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion, np.array([[0, 0], [0, 0]]), decimal=6)\n        # loglike_per_sample\n        np.testing.assert_array_equal(self.model.loglike_per_sample(\n            self.X[0:1, :], self.Y[0:1, ]), np.array([0]))\n\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            np.array(self.X[0:1, :].tolist() * 6),\n            np.array([self.Y[0, ], self.Y[1, ], self.Y[0, ],\n                      self.Y[1, ], self.Y[1, ], self.Y[0, ]])),\n            np.array([0, -np.Infinity, 0, -np.Infinity, -np.Infinity, 0]), decimal=3)\n\n    def test_ols_multicolinearty(self):\n        self.model_col = OLS(\n            solver='pinv', fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], self.X[:, 0:1]])\n        self.model_col.fit(X,\n                           self.Y, sample_weight=0.5)\n        self.model = OLS(\n            solver='pinv', fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1],\n                       self.Y, sample_weight=0.5)\n        # stderr\n        self.assertEqual(self.model_col.stderr, None)\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        np.testing.assert_array_almost_equal(\n            self.model_col.loglike_per_sample(X, self.Y),\n            self.model.loglike_per_sample(self.X[:, 0:1],\n                                          self.Y), decimal=0)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=1)\n\n\nclass PerfectCorrelationMultivariateOLSTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        np.random.seed(0)\n        cls.data_longley = sm.datasets.longley.load()\n        cls.X = cls.data_longley.exog\n        cls.Y = np.hstack((cls.data_longley.endog.reshape(-1, 1),\n                           cls.data_longley.endog.reshape(-1, 1)))\n\n    def test_ols(self):\n        self.model = OLS(\n            solver='auto', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y)\n        # coefficient\n        self.assertEqual(self.model.coef.shape, (2, 7))\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-3482258.63459582, 15.0618722713733, -0.358191792925910E-01,\n                       -2.02022980381683, -1.03322686717359, -0.511041056535807E-01,\n                       1829.15146461355],\n                      [-3482258.63459582, 15.0618722713733, -0.358191792925910E-01,\n                       -2.02022980381683, -1.03322686717359, -0.511041056535807E-01,\n                       1829.15146461355]]).reshape(2, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertEqual(self.model.stderr.shape, (2, 7))\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.stderr, np.sqrt(old_div(9., self.data_longley.exog.shape[0]))),\n            np.array([[890420.383607373, 84.9149257747669, 0.03349,\n                       0.488399681651699, 0.214274163161675, 0.226073200069370,\n                       455.478499142212],\n                      [890420.383607373, 84.9149257747669, 0.03349,\n                       0.488399681651699, 0.214274163161675, 0.226073200069370,\n                       455.478499142212]]).reshape(2, -1),\n            decimal=2)\n        # scale\n        self.assertEqual(self.model.dispersion.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.dispersion, (old_div(9., self.data_longley.exog.shape[0]))),\n            np.array([[92936.0061673238, 92936.0061673238],\n                      [92936.0061673238, 92936.0061673238]]),\n            decimal=3)\n        # predict\n        np.testing.assert_array_almost_equal(\n            self.Y - self.model.predict(self.X),\n            np.hstack((np.array([267.34003, -94.01394, 46.28717, -410.11462,\n                                 309.71459, -249.31122, -164.04896, -13.18036, 14.30477, 455.39409,\n                                 -17.26893, -39.05504, -155.54997, -85.67131, 341.93151,\n                                 -206.75783]).reshape(-1, 1),\n                       np.array([267.34003, -94.01394, 46.28717, -410.11462,\n                                 309.71459, -249.31122, -164.04896, -13.18036, 14.30477, 455.39409,\n                                 -17.26893, -39.05504, -155.54997, -85.67131, 341.93151,\n                                 -206.75783]).reshape(-1, 1))),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertRaises(ValueError,\n                          self.model.loglike_per_sample, self.X, self.Y)\n\n    def test_ols_l1_regularized(self):\n        # sklearn elastic net and l1 does not take sample_weights, will not test\n        pass\n\n    def test_ols_l2_regularized(self):\n        # there is a bug in sklearn with weights, it can only use list right now\n        self.model = OLS(\n            solver='auto', fit_intercept=True, est_stderr=True,\n            reg_method='l2',  alpha=0.1, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array([[-2.0172203, -52.14364269, 0.07089677, -0.42552125,\n                       -0.57305292, -0.41272483, 48.32484052],\n                      [-2.0172203, -52.14364269, 0.07089677, -0.42552125,\n                       -0.57305292, -0.41272483, 48.32484052]]).reshape(2, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        self.assertTrue(self.model.stderr is None)\n        # scale\n        self.assertEqual(self.model.dispersion.shape, (2, 2))\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.dispersion, (old_div(9., self.data_longley.exog.shape[0]))),\n            np.array([[250870.081, 250870.081],\n                      [250870.081, 250870.081]]),\n            decimal=3)\n        # predict\n        res = np.array([[280.31871146],\n                        [-131.6981265],\n                        [90.64414685],\n                        [-400.10244445],\n                        [-440.59604167],\n                        [-543.88595187],\n                        [200.70483416],\n                        [215.88629903],\n                        [74.9456573],\n                        [913.85128645],\n                        [424.15996133],\n                        [-9.5797488],\n                        [-360.96841852],\n                        [27.214226],\n                        [150.87705909],\n                        [-492.17489392]])\n        np.testing.assert_array_almost_equal(\n            self.Y - self.model.predict(self.X),\n            np.hstack((res, res)),\n            decimal=3)\n\n        # loglike/_per_sample\n        self.assertRaises(ValueError,\n                          self.model.loglike, self.X, self.Y)\n\n    def test_ols_elastic_net_regularized(self):\n        # sklearn elastic net and l1 does not take sample_weights, will not test\n        pass\n\n    def test_ols_sample_weight_all_half(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X, self.Y, sample_weight=0.5)\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            np.array(((-3482258.63459582, 15.0618722713733, -0.358191792925910E-01,\n                       -2.02022980381683, -1.03322686717359, -0.511041056535807E-01,\n                       1829.15146461355),\n                      (-3482258.63459582, 15.0618722713733, -0.358191792925910E-01,\n                       -2.02022980381683, -1.03322686717359, -0.511041056535807E-01,\n                       1829.15146461355))).reshape(2, -1),\n            decimal=3)\n        # std.err of coefficient (calibrated by df_resid)\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.stderr, np.sqrt(old_div(9., self.data_longley.exog.shape[0]))),\n            np.array(((890420.383607373, 84.9149257747669, 0.334910077722432E-01,\n                       0.488399681651699, 0.214274163161675, 0.226073200069370,\n                       455.478499142212),\n                      (890420.383607373, 84.9149257747669, 0.334910077722432E-01,\n                       0.488399681651699, 0.214274163161675, 0.226073200069370,\n                       455.478499142212))).reshape(2, -1),\n            decimal=1)\n        # scale\n        np.testing.assert_array_almost_equal(\n            old_div(self.model.dispersion, (old_div(9., self.data_longley.exog.shape[0]))),\n            np.array(((92936.0061673238, 92936.0061673238),\n                      (92936.0061673238, 92936.0061673238))),\n            decimal=3)\n        # predict\n        res = np.array((267.34003, -94.01394, 46.28717, -410.11462,\n                        309.71459, -249.31122, -164.04896, -13.18036, 14.30477, 455.39409,\n                        -17.26893, -39.05504, -155.54997, -85.67131, 341.93151,\n                        -206.75783)).reshape(-1, 1)\n        np.testing.assert_array_almost_equal(\n            self.Y - self.model.predict(self.X),\n            np.hstack((res, res)),\n            decimal=3)\n        # loglike/_per_sample\n        self.assertRaises(ValueError,\n                          self.model.loglike, self.X, self.Y)\n\n    def test_ols_sample_weight_all_zero(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.assertRaises(ValueError, self.model.fit, self.X, self.Y, 0)\n\n    def test_ols_sample_weight_half_zero_half_one(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        len_half = 8\n        self.model.fit(self.X, self.Y,\n                       sample_weight=np.array([1] * len_half +\n                                              [0] * (self.data_longley.exog.shape[0] - len_half)))\n        self.model_half = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model_half.fit(self.X[:len_half], self.Y[:len_half])\n        # coefficient\n        np.testing.assert_array_almost_equal(\n            self.model.coef,\n            self.model_half.coef,\n            decimal=3)\n        # std.err\n        np.testing.assert_array_almost_equal(\n            self.model.stderr,\n            self.model_half.stderr,\n            decimal=3)\n\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion,\n            self.model_half.dispersion,\n            decimal=3)\n\n    # corner cases\n    def test_ols_one_data_point(self):\n        self.model = OLS(\n            solver='pinv', fit_intercept=True, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[0:1, :],\n                       self.Y[0:1, ], sample_weight=0.5)\n        # coef\n        self.assertEqual(self.model.coef.shape, (2, 7))\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model.dispersion, np.array([[0, 0], [0, 0]]), decimal=6)\n        # loglike_per_sample\n        np.testing.assert_array_equal(self.model.loglike_per_sample(\n            self.X[0:1, :], self.Y[0:1, ]), np.array([0]))\n        np.testing.assert_array_almost_equal(self.model.loglike_per_sample(\n            np.array(self.X[0:1, :].tolist() * 6),\n            np.array([[60323, 60323], [0, 60323], [60323, 60323],\n                      [60322, 60323], [60322, 60322], [60323, 60323]])),\n            np.array([0, -np.Infinity, 0, -np.Infinity, -np.Infinity, 0]), decimal=3)\n\n    def test_ols_multicolinearty(self):\n        self.model_col = OLS(\n            solver='pinv', fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        X = np.hstack([self.X[:, 0:1], self.X[:, 0:1]])\n        self.model_col.fit(X,\n                           self.Y, sample_weight=0.8)\n        self.model = OLS(\n            solver='pinv', fit_intercept=False, est_stderr=True,\n            reg_method=None,  alpha=0, l1_ratio=0,  tol=1e-4, max_iter=100,\n            coef=None, stderr=None,  dispersion=None)\n        self.model.fit(self.X[:, 0:1],\n                       self.Y, sample_weight=0.8)\n        # coef\n        np.testing.assert_array_almost_equal(\n            self.model_col.coef, np.array([[319.47969664, 319.47969664],\n                                           [319.47969664, 319.47969664]]).reshape(2, -1), decimal=3)\n        # stderr\n        self.assertEqual(self.model_col.stderr, None)\n        # scale\n        np.testing.assert_array_almost_equal(\n            self.model_col.dispersion, self.model.dispersion, decimal=3)\n        # loglike_per_sample\n        self.assertRaises(ValueError,\n                          self.model_col.loglike, X, self.Y)\n        np.testing.assert_array_almost_equal(\n            self.model_col.predict(X),\n            self.model.predict(self.X[:, 0:1]), decimal=3)\n"
  },
  {
    "path": "tests/test_SemiSupervisedIOHMM.py",
    "content": "from __future__ import print_function\nfrom __future__ import division\nfrom builtins import range\nfrom past.utils import old_div\nimport json\nimport unittest\n\n\nimport numpy as np\nimport pandas as pd\n\nfrom IOHMM import SemiSupervisedIOHMM\nfrom IOHMM import OLS, CrossEntropyMNL\n\n\nclass SemiSupervisedIOHMMTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_speed = pd.read_csv('examples/data/speed.csv')\n        cls.states = cls._mock_states()\n\n    @classmethod\n    def _mock_states(cls):\n        states = {}\n        corr = np.array(cls.data_speed['corr'])\n        for i in range(int(old_div(len(corr), 2))):\n            if corr[i] == 'cor':\n                states[i] = np.array([0, 1, 0, 0])\n                cls.data_speed.at[i, 'rt'] = 1\n            else:\n                states[i] = np.array([1, 0, 0, 0])\n                cls.data_speed.at[i, 'rt'] = 0\n        return states\n\n    def setUp(self):\n        np.random.seed(0)\n\n    def test_train_no_covariates(self):\n        np.random.seed(0)\n        self.model = SemiSupervisedIOHMM(num_states=4, max_EM_iter=100, EM_tol=1e-10)\n        self.model.set_models(\n            model_initial=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_transition=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_emissions=[OLS()])\n        self.model.set_inputs(covariates_initial=[], covariates_transition=[],\n                              covariates_emissions=[[]])\n        self.model.set_outputs([['rt']])\n        self.model.set_data([[self.data_speed, self.states]])\n        self.model.train()\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[0]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[1]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[2][0].coef,\n            np.array([[6.4]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[3][0].coef,\n            np.array([[5.5]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[2][0].dispersion, np.array([[0.051]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[3][0].dispersion, np.array([[0.032]]), decimal=2)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(np.array([[]]))),\n            np.array([[0.4, 0.6, 0, 0]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(np.array([[]]))),\n            np.array([[0.19, 0.81, 0, 0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[2].predict_log_proba(np.array([[]]))),\n            np.array([[0, 0, 0.93, 0.07]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[3].predict_log_proba(np.array([[]]))),\n            np.array([[0, 0, 0.11, 0.89]]), decimal=2)\n\n        # to_json\n        json_dict = self.model.to_json('tests/IOHMM_models/SemiSupervisedIOHMM/')\n        self.assertEqual(json_dict['data_type'], 'SemiSupervisedIOHMM')\n        self.assertSetEqual(\n            set(json_dict['properties'].keys()),\n            set(['num_states', 'EM_tol', 'max_EM_iter',\n                 'covariates_initial', 'covariates_transition',\n                 'covariates_emissions', 'responses_emissions',\n                 'model_initial', 'model_transition', 'model_emissions']))\n        with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json', 'w') as outfile:\n            json.dump(json_dict, outfile, indent=4, sort_keys=True)\n\n    def test_from_json(self):\n        with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json') as json_data:\n            json_dict = json.load(json_data)\n        self.model = SemiSupervisedIOHMM.from_json(json_dict)\n        self.assertEqual(type(self.model), SemiSupervisedIOHMM)\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[0]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[1]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[2][0].coef,\n            np.array([[6.4]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[3][0].coef,\n            np.array([[5.5]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[2][0].dispersion, np.array([[0.051]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[3][0].dispersion, np.array([[0.032]]), decimal=2)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(np.array([[]]))),\n            np.array([[0.4, 0.6, 0, 0]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(np.array([[]]))),\n            np.array([[0.19, 0.81, 0, 0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[2].predict_log_proba(np.array([[]]))),\n            np.array([[0, 0, 0.93, 0.07]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[3].predict_log_proba(np.array([[]]))),\n            np.array([[0, 0, 0.11, 0.89]]), decimal=2)\n\n    def test_from_config(self):\n        with open('tests/IOHMM_models/SemiSupervisedIOHMM/model.json') as json_data:\n            json_dict = json.load(json_data)\n        json_dict['properties'].update({\n            'model_initial': {\n                'data_type': 'CrossEntropyMNL',\n                'properties': {\n                    'reg_method': 'l2',\n                    'solver': 'newton-cg'\n                }\n            },\n            'model_transition': {\n                'data_type': 'CrossEntropyMNL',\n                'properties': {\n                    'reg_method': 'l2',\n                    'solver': 'newton-cg'\n                }\n            },\n            'model_emissions': [\n                {\n                    'data_type': 'OLS',\n                    'properties': {}\n                },\n            ]})\n        print(json_dict['properties']['model_initial'])\n        self.model = SemiSupervisedIOHMM.from_config(json_dict)\n        self.assertEqual(type(self.model), SemiSupervisedIOHMM)\n        self.model.set_data([[self.data_speed, self.states]])\n        self.model.train()\n\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[0]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[1]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[2][0].coef,\n            np.array([[6.4]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[3][0].coef,\n            np.array([[5.5]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[2][0].dispersion, np.array([[0.051]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[3][0].dispersion, np.array([[0.032]]), decimal=2)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(np.array([[]]))),\n            np.array([[0.4, 0.6, 0, 0]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(np.array([[]]))),\n            np.array([[0.19, 0.81, 0, 0]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[2].predict_log_proba(np.array([[]]))),\n            np.array([[0, 0, 0.93, 0.07]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[3].predict_log_proba(np.array([[]]))),\n            np.array([[0, 0, 0.11, 0.89]]), decimal=2)\n"
  },
  {
    "path": "tests/test_SupervisedIOHMM.py",
    "content": "from builtins import range\nimport json\nimport unittest\n\n\nimport numpy as np\nimport pandas as pd\n\nfrom IOHMM import SupervisedIOHMM\nfrom IOHMM import OLS, CrossEntropyMNL\n\n\nclass SupervisedIOHMMTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_speed = pd.read_csv('examples/data/speed.csv')\n        cls.states = cls._mock_states()\n\n    @classmethod\n    def _mock_states(cls):\n        states = {}\n        corr = np.array(cls.data_speed['corr'])\n        for i in range(len(corr)):\n            if corr[i] == 'cor':\n                states[i] = np.array([0, 1])\n            else:\n                states[i] = np.array([1, 0])\n        return states\n\n    def test_train_no_covariates(self):\n        self._mock_states()\n        self.model = SupervisedIOHMM(num_states=2)\n        self.model.set_models(\n            model_initial=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_transition=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_emissions=[OLS()])\n        self.model.set_inputs(covariates_initial=[], covariates_transition=[],\n                              covariates_emissions=[[]])\n        self.model.set_outputs([['rt']])\n        self.model.set_data([[self.data_speed, self.states]])\n        self.model.train()\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.705]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.137]]), decimal=3)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.128]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.224]]), decimal=3)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(np.array([[]]))),\n            np.array([[0.384, 0.616]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(np.array([[]]))),\n            np.array([[0.212, 0.788]]), decimal=3)\n\n        # to_json\n        json_dict = self.model.to_json('tests/IOHMM_models/SupervisedIOHMM/')\n        self.assertEqual(json_dict['data_type'], 'SupervisedIOHMM')\n        self.assertSetEqual(\n            set(json_dict['properties'].keys()),\n            set(['num_states',\n                 'covariates_initial', 'covariates_transition',\n                 'covariates_emissions', 'responses_emissions',\n                 'model_initial', 'model_transition', 'model_emissions']))\n        with open('tests/IOHMM_models/SupervisedIOHMM/model.json', 'w') as outfile:\n            json.dump(json_dict, outfile, indent=4, sort_keys=True)\n\n    def test_from_json(self):\n        with open('tests/IOHMM_models/SupervisedIOHMM/model.json') as json_data:\n            json_dict = json.load(json_data)\n        self.model = SupervisedIOHMM.from_json(json_dict)\n        self.assertEqual(type(self.model), SupervisedIOHMM)\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.705]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.137]]), decimal=3)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.128]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.224]]), decimal=3)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(np.array([[]]))),\n            np.array([[0.384, 0.616]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(np.array([[]]))),\n            np.array([[0.212, 0.788]]), decimal=3)\n\n    def test_from_config(self):\n        with open('tests/IOHMM_models/SupervisedIOHMM/model.json') as json_data:\n            json_dict = json.load(json_data)\n        json_dict['properties'].update({\n            'model_initial': {\n                'data_type': 'CrossEntropyMNL',\n                'properties': {\n                    'reg_method': 'l2',\n                    'solver': 'newton-cg'\n                }\n            },\n            'model_transition': {\n                'data_type': 'CrossEntropyMNL',\n                'properties': {\n                    'reg_method': 'l2',\n                    'solver': 'newton-cg'\n                }\n            },\n            'model_emissions': [\n                {\n                    'data_type': 'OLS',\n                    'properties': {}\n                },\n            ]})\n        self.model = SupervisedIOHMM.from_config(json_dict)\n        self.assertEqual(type(self.model), SupervisedIOHMM)\n        self.model.set_data([[self.data_speed, self.states]])\n        self.model.train()\n\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.705]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.137]]), decimal=3)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.128]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.224]]), decimal=3)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(np.array([[]]))),\n            np.array([[0.384, 0.616]]), decimal=3)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(np.array([[]]))),\n            np.array([[0.212, 0.788]]), decimal=3)\n"
  },
  {
    "path": "tests/test_UnSupervisedIOHMM.py",
    "content": "import json\nimport unittest\n\n\nimport numpy as np\nimport pandas as pd\n\nfrom IOHMM import UnSupervisedIOHMM\nfrom IOHMM import OLS, DiscreteMNL, CrossEntropyMNL\n\n\nclass UnSupervisedIOHMMTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.data_speed = pd.read_csv('examples/data/speed.csv')\n\n    def setUp(self):\n        np.random.seed(0)\n\n    def test_train_no_covariates(self):\n        self.model = UnSupervisedIOHMM(num_states=2, max_EM_iter=100, EM_tol=1e-6)\n        self.model.set_models(\n            model_initial=CrossEntropyMNL(solver='lbfgs', reg_method='l2'),\n            model_transition=CrossEntropyMNL(solver='lbfgs', reg_method='l2'),\n            model_emissions=[OLS()])\n        self.model.set_inputs(\n            covariates_initial=[],\n            covariates_transition=[],\n            covariates_emissions=[[]])\n        self.model.set_outputs([['rt']])\n        self.model.set_data([self.data_speed])\n        self.model.train()\n\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.5]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.4]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.037]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.063]]), decimal=2)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            self.model.model_transition[1].coef,\n            np.array([[2.4]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_transition[0].coef,\n            np.array([[-2]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(np.array([[]]))),\n            np.array([[0.08, 0.92]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(np.array([[]]))),\n            np.array([[0.88, 0.12]]), decimal=2)\n\n    def test_train_covariates_for_transition(self):\n        self.model = UnSupervisedIOHMM(num_states=2, max_EM_iter=100, EM_tol=1e-6)\n        self.model.set_models(\n            model_initial=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_transition=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_emissions=[OLS()])\n        self.model.set_inputs(\n            covariates_initial=[],\n            covariates_transition=['Pacc'],\n            covariates_emissions=[[]])\n        self.model.set_outputs([['rt']])\n        self.model.set_data([self.data_speed])\n        self.model.train()\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.5]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.4]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.036]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.063]]), decimal=2)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([312, 126]), decimal=0)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([112, 326]), decimal=0)\n\n    def test_train_multivariate(self):\n        self.model = UnSupervisedIOHMM(num_states=2, max_EM_iter=100, EM_tol=1e-6)\n        self.model.set_models(\n            model_initial=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_transition=CrossEntropyMNL(solver='newton-cg', reg_method='l2'),\n            model_emissions=[OLS(), DiscreteMNL(reg_method='l2')])\n        self.model.set_inputs(\n            covariates_initial=[],\n            covariates_transition=[],\n            covariates_emissions=[[], ['Pacc']])\n        self.model.set_outputs([['rt'], ['corr']])\n        self.model.set_data([self.data_speed])\n        self.model.train()\n\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.5]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.4]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.036]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.063]]), decimal=2)\n\n        # transition\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([387, 51]), decimal=0)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([37, 401.]), decimal=0)\n\n        # to_json\n        json_dict = self.model.to_json('tests/IOHMM_models/UnSupervisedIOHMM/')\n        self.assertEqual(json_dict['data_type'], 'UnSupervisedIOHMM')\n        self.assertSetEqual(\n            set(json_dict['properties'].keys()),\n            set(['num_states', 'EM_tol', 'max_EM_iter',\n                 'covariates_initial', 'covariates_transition',\n                 'covariates_emissions', 'responses_emissions',\n                 'model_initial', 'model_transition', 'model_emissions']))\n        with open('tests/IOHMM_models/UnSupervisedIOHMM/model.json', 'w') as outfile:\n            json.dump(json_dict, outfile, indent=4, sort_keys=True)\n\n    def test_from_json(self):\n        with open('tests/IOHMM_models/UnSupervisedIOHMM/model.json') as json_data:\n            json_dict = json.load(json_data)\n        self.model = UnSupervisedIOHMM.from_json(json_dict)\n        self.assertEqual(type(self.model), UnSupervisedIOHMM)\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.5]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.4]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.036]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.063]]), decimal=2)\n\n        # transition\n        self.model.set_data([self.data_speed])\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([387, 51]), decimal=0)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([37, 401.]), decimal=0)\n\n    def test_from_config(self):\n        with open('tests/IOHMM_models/UnSupervisedIOHMM/model.json') as json_data:\n            json_dict = json.load(json_data)\n        json_dict['properties'].update({\n            'model_initial': {\n                'data_type': 'CrossEntropyMNL',\n                'properties': {\n                    'reg_method': 'l2',\n                    'solver': 'newton-cg'\n                }\n            },\n            'model_transition': {\n                'data_type': 'CrossEntropyMNL',\n                'properties': {\n                    'reg_method': 'l2',\n                    'solver': 'newton-cg'\n                }\n            },\n            'model_emissions': [\n                {\n                    'data_type': 'OLS',\n                    'properties': {}\n                },\n                {\n                    'data_type': 'DiscreteMNL',\n                    'properties': {'reg_method': 'l2'}\n                }\n            ]})\n        self.model = UnSupervisedIOHMM.from_config(json_dict)\n        self.assertEqual(type(self.model), UnSupervisedIOHMM)\n        self.model.set_data([self.data_speed])\n        self.model.train()\n        # emission coefficients\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].coef,\n            np.array([[5.5]]), decimal=1)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].coef,\n            np.array([[6.4]]), decimal=1)\n\n        # emission dispersion\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[0][0].dispersion, np.array([[0.036]]), decimal=2)\n        np.testing.assert_array_almost_equal(\n            self.model.model_emissions[1][0].dispersion, np.array([[0.063]]), decimal=2)\n\n        # transition\n        self.model.set_data([self.data_speed])\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[0].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([387, 51]), decimal=0)\n        np.testing.assert_array_almost_equal(\n            np.exp(self.model.model_transition[1].predict_log_proba(\n                self.model.inp_transitions_all_sequences)).sum(axis=0),\n            np.array([37, 401.]), decimal=0)\n"
  }
]