Module cognet.cognet

Classes

class cognet

Aggregate related Qnet functions

Init

Expand source code

class cognet:
    """Aggregate related Qnet functions
    """

    def __init__(self):
        """Init
        """
        self.year = None
        self.n_jobs = 28
        self.qnet = None
        self.steps = 120
        self.num_qsamples = None
        self.all_samples = None
        self.samples = None
        self.samples_as_strings = None
        self.features = None
        self.cols = None
        self.immutable_vars = None
        self.mutable_vars = None
        self.poles = None
        self.polar_features = None
        self.polar_indices = None
        self.poles_dict = {}
        self.d0 = None
        self.qdistance_matrix_file = None
        self.dissonance_file = None
        self.s_null = None
        self.D_null = None
        self.mask_prob = 0.5
        self.variation_weight = None
        self.polar_matrix = None
    
    def load_from_model(self,
                        model,
                        samples_file=None,
                        im_vars=None,
                        m_vars=None):
        """load parameters from model object

        Args:
          model (Class): model obj for loading parameters
          samples_file (filepath): filepath and name for sample csv
          im_vars (list[str], optional): Not implemented yet. Defaults to None.
          m_vars (list[str], optional): Not implemented yet. Defaults to None.
        """
        if model is not None:
            self.qnet = model.myQnet
            self.cols = np.array(model.features)
            self.features = pd.DataFrame(columns=self.cols)
            if any(x is not None for x in [model.immutable_vars, model.mutable_vars]):
                if model.immutable_vars is not None:
                    self.immutable_vars = model.immutable_vars
                    self.mutable_vars = [x for x in self.features if x not in self.immutable_vars]
                elif model.mutable_vars is not None:
                    self.mutable_vars = model.mutable_vars
                    self.immutable_vars = [x for x in self.features if x not in self.mutable_vars]
            else:
                self.mutable_vars = self.features
            if all(x is None for x in [model.samples, samples_file]):
                raise ValueError("Please input samples if loading model after loading qnet")
            elif model.samples is not None:
                samples = pd.DataFrame(model.samples)
            elif samples_file is not None:
                samples = pd.read_csv(samples_file)
                
            self.samples = pd.concat([samples,self.features], axis=0)
            self.all_samples = self.samples
            self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
            self.s_null=['']*len(self.samples_as_strings[0])
            self.D_null=self.qnet.predict_distributions(self.s_null)
            variation_weight = []
            for d in self.D_null:
                v=[]
                for val in d.values():
                    v=np.append(v,val)
                variation_weight.append(entropy(v,base=len(v)))
            self.variation_weight = variation_weight
            
    def load_data(self,
                year,
                features_by_year,
                samples,
                qnet):
        '''load cols, features, samples, and qnet.

        Args:
          year (str): to identify cols/features.
          features_by_year (str): file containing all features by year of the dataset.
          samples (str): file of samples for that year.
          Qnet (str): Qnet file location.
        '''
        self.qnet = load_qnet(qnet)
        self.year = year
        self.cols = np.array((pd.read_csv(features_by_year,
                            keep_default_na=True, 
                            index_col=0).set_index(
                                'year')).loc[int(year)].apply(
                                    eval).values[0])
        self.features = pd.DataFrame(columns=self.cols)
        self.mutable_vars = [x for x in self.cols]
        #[self.cols].fillna('').values.astype(str)[:]

        self.samples=pd.read_csv(samples)
        self.samples = pd.concat([self.samples,self.features], axis=0)
        self.all_samples = self.samples
        self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
        self.s_null=['']*len(self.samples_as_strings[0])
        self.D_null=self.qnet.predict_distributions(self.s_null)
        variation_weight = []
        for d in self.D_null:
            v=[]
            for val in d.values():
                v=np.append(v,val)
            variation_weight.append(entropy(v,base=len(v)))
        self.variation_weight = variation_weight

    def set_immutable_vars(self,
                        IMMUTABLE_FILE):
        '''set vars to immutable and mutable, 
        can prob combine this with the load_data func: only set the immutable vars if necessary

        Args:
          IMMUTABLE_FILE (str): file containing the immutable features/vars
        '''
        if self.cols is None:
            raise ValueError("load_data first!")
        self.immutable_vars = pd.read_csv(IMMUTABLE_FILE,index_col=0).transpose()
        self.mutable_vars = None
        self.mutable_vars = [x for x in self.cols
                            if x.upper() not in self.immutable_vars.columns]
    
    def set_nsamples(self,
                    num_samples):
        '''select a subset of the samples

        Args:
          num_samples (int): Set num of samples to subset
        '''
        self.samples = self.all_samples
        if all(x is not None for x in [num_samples, self.samples]):
            if num_samples > len(self.samples.index):
                string = 'The number of selected samples ({}) ' + \
                    'is greater than the number of samples ({})!'
                string = string.format(num_samples, len(self.samples.index))
                raise ValueError(string)

            if num_samples == len(self.samples.index):
                string = 'The number of selected samples ({}) ' + \
                    'is equal to the number of samples ({})!'
                string = string.format(num_samples, len(self.samples.index))
                print(string)
            self.samples = self.samples.sample(num_samples)
            self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]

        elif self.samples is None:
            raise ValueError("load_data first!")

    def __variation_weight(self,
                        index):
        """
        """
        d_=self.D_null[index]
        v=[]
        for val in d_.values():
            v=np.append(v,val)
        return entropy(v,base=len(v))
    
    def getBaseFrequency(self, 
                        sample):
        '''get frequency of the variables
        helper func for qsampling

        Args:
          sample (list[str]): vector of sample, must have the same num of features as the qnet
        '''
        MUTABLE=pd.DataFrame(np.zeros(len(self.cols)),index=self.cols).transpose()
                
        for m in self.mutable_vars:
            MUTABLE[m]=1.0
        mutable_x=MUTABLE.values[0]
        base_frequency=mutable_x/mutable_x.sum()

        # commented out for now for testing using smaller qnet
        for i in range(len(base_frequency)):
            if base_frequency[i]>0.0:
                base_frequency[i]= self.variation_weight[i]*base_frequency[i]

        return base_frequency/base_frequency.sum()
    
    def qsampling(self,
                sample,
                steps,
                immutable=False):
        '''perturb the sample based on thet qnet distributions and number of steps

        Args:
          sample (1d array-like): sample vector, must have the same num of features as the qnet
          steps (int): number of steps to qsample
          immutable (bool): are there variables that are immutable?
        '''
        if all(x is not None for x in [self.mutable_vars, sample]):
            if immutable == True:
                return qsample(sample,self.qnet,steps,self.getBaseFrequency(self.samples))
            else:
                return qsample(sample,self.qnet,steps)
        elif self.mutable_vars is None:
            raise ValueError("load_data first!")

    def set_poles(self,
                POLEFILE,
                pole_1,
                pole_2,
                steps=0,
                mutable=False):
        '''set the poles and samples such that the samples contain features in poles

        Args:
          steps (int): number of steps to qsample
          POLEFILE (str): file containing poles samples and features
          mutable (boolean): Whether or not to set poles as the only mutable_vars
          pole_1 (str): column name for first pole to use
          pole_2 (str): column name for second pole to use
        '''
        invalid_count = 0
        if all(x is not None for x in [self.samples, self.qnet]):
            poles = pd.read_csv(POLEFILE, index_col=0)
            self.poles=poles.transpose()
            self.polar_features = pd.concat([self.poles, self.features], axis=0)
            poles_dict = {}
            for column in poles:
                p_ = self.polar_features.loc[column][self.cols].fillna('').values.astype(str)[:]
                poles_dict[column] = self.qsampling(p_,steps)
            self.poles_dict = poles_dict
            self.pL = self.poles_dict[pole_1]
            self.pR = self.poles_dict[pole_2]
            # self.pL = list(poles_dict.values())[0]
            # self.pR = list(poles_dict.values())[1]
            self.d0 = qdistance(self.pL, self.pR, self.qnet, self.qnet)
            
            cols = [x for x in self.poles.columns if x in self.samples.columns]
            self.samples=self.samples[cols]
        
            for x in self.poles.columns:
                if x not in self.samples.columns:
                    invalid_count += 1
                    self.samples[x]=np.nan

            self.samples = pd.concat([self.samples,self.features], axis=0)
            self.all_samples = self.samples
            self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
            
            if mutable:
                self.mutable_vars=[x for x in self.cols if x in self.poles.columns]
        elif self.samples is None:
            raise ValueError("load_data first!")
        
        print("{} pole features not found in sample features".format(invalid_count))

    def distance(self,
                sample1,
                sample2,
                nsteps1=0,
                nsteps2=0):
        """qsamples each sample set num of steps, then takes qdistance

        Args:
          sample1 (list[str]): sample vector 1, must have the same num of features as the qnet
          sample2 (list[str]): sample vector 2, must have the same num of features as the qnet
          nsteps1 (int, optional): number of steps to qsample for sample1
          nsteps2 (int, optional): number of steps to qsample for sample2

        Returns:
          float: qdistance
        """
        if self.qnet is None:
            raise ValueError("load qnet first!")
        bp1 = self.getBaseFrequency(sample1)
        bp2 = self.getBaseFrequency(sample2)
        sample1 = qsample(sample1, self.qnet, nsteps1)#, baseline_prob=bp1)
        sample2 = qsample(sample2, self.qnet, nsteps2)#, baseline_prob=bp2)
        return qdistance(sample1, sample2, self.qnet, self.qnet)
    
    def __distfunc(self, 
                x, 
                y):
        '''Compute distance between two samples

        Args:
          x (list[str]): first sample
          y (list[str]): second sample
        '''
        d=qdistance(x,y,self.qnet,self.qnet)
        return d
    
    def polarDistance(self,
                    i,
                    return_dict=None):
        """return the distance from a sample to the poles

        Args:
            i (int): index of sample to take
            return_dict (dict): dict used for multiple sample function

        Returns:
            [type]: [description]
        """
        samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
        p = samples_as_strings[i]
        distances = []
        for index, row in self.polar_features[self.cols].iterrows():
            row = row.fillna('').values.astype(str)[:]
            distances.append(self.distance(p, np.array(row)))
        if return_dict is not None:
            return_dict[i] = distances
        return distances
            
    def polarDistance_multiple(self,
                            outfile):
        """return the distance from all samples to the poles

        Args:
          outfile (str): desired output filename and path
        """
        if all(x is not None for x in [self.samples, self.cols,
                                    self.polar_features]):
            manager = mp.Manager()
            return_dict = manager.dict()
            processes = []
            
            for i in range(len(self.samples)):
                p = mp.Process(target=self.polarDistance, args=(i, return_dict))
                processes.append(p)

            [x.start() for x in processes]
            [x.join() for x in processes]

            pole_names = []
            for index, row in self.polar_features[self.cols].iterrows():
                pole_names.append(index)
            result=[x for x in return_dict.values()]
            result=pd.DataFrame(result,columns=pole_names).to_csv(outfile)
            
        else:
            raise ValueError("load data first!")
        return return_dict
        
    def distfunc_line(self,
                    i,
                    return_dict=None):
        '''compute the dist for a row, or vector of samples

        Args:
          i (int): row
        
        Return:
          numpy.ndarray(float)
        '''
        if all(x is not None for x in [self.samples, self.features]):
            w = self.samples.index.size
            line = np.zeros(w)
            y = self.samples_as_strings[i]
            for j in range(w):
                # only compute half of the distance matrix
                if j > i:
                    x = self.samples_as_strings[j]
                    line[j] = self.__distfunc(x, y)
        else:
            raise ValueError("load_data first!")
        if return_dict is not None:
            return_dict[i] = line
        return line
    
    def distfunc_multiples(self,
                        outfile):
        """compute distance matrix for all samples in the dataset

        Args:
          outfile (str): desired output filename and path
        """
        if all(x is not None for x in [self.samples, self.features]):
            manager = mp.Manager()
            return_dict = manager.dict()
            processes = []

            for i in range(len(self.samples)):
                p = mp.Process(target=self.distfunc_line, args=(i, return_dict))
                processes.append(p)
            
            [x.start() for x in processes]
            [x.join() for x in processes]
            result=[x for x in return_dict.values()]
            columns = [i for i in range(len(self.samples))]
            result=pd.DataFrame(result,columns=columns, index=columns).sort_index(ascending=False)
            result = result.to_numpy()
            result = pd.DataFrame(np.maximum(result, result.transpose()))
            result.to_csv(outfile)
        else:
            raise ValueError("load data first!")
        return return_dict
    
    def polar_separation(self,
                        nsteps=0):
        """returns the distance between poles as a qdistance matrix

        Args:
          nsteps (int, optional): [description]. Defaults to 0.
        """
        polar_arraydata = self.polar_features[self.cols].fillna('').values.astype(str)[:]
        samples_ = []
        for vector in polar_arraydata:
            bp = self.getBaseFrequency(vector)
            sample = qsample(vector, self.qnet, nsteps, baseline_prob=bp)
            samples_.append(sample)
        samples_ = np.array(samples_)
        self.polar_matrix = qdistance_matrix(samples_, samples_, self.qnet, self.qnet)
        return self.polar_matrix
        
    def embed(self,
            infile,
            name_pref,
            out_dir,
            pca_model=False,
            EMBED_BINARY=None  
    ):
        '''
        embed data

        Args:
          infile (str): input file to be embedded
          name_pref (str): preferred name for output file
          out_dir (str): output dir for results
          pca_model (bool): whether or not to generate PCA model
          EMBED_BINARY (os.path.abspath): path to embed binary
        '''
        if all(x is not None for x in [self.year]):
            yr = self.year
            PREF = name_pref
            FILE = infile

            if EMBED_BINARY is None:
                EMBED = pkg.util.get_data("cognet.bin", "__embed__.so") 
            else:
                EMBED = EMBED_BINARY
            DATAFILE = out_dir + 'data_' +yr
            EFILE = out_dir + PREF + '_E_' +yr
            DFILE = out_dir + PREF + '_D_' +yr

            pd.read_csv(FILE,header=None).to_csv(DATAFILE,sep=' ',header=None,index=None)
            STR=EMBED+' -f '+DATAFILE+' -E '+EFILE+' -D '+DFILE
            subprocess.call(STR,shell=True)
            if pca_model:
                embed_to_pca(EFILE, EFILE+'_PCA')
        elif self.year is None:
            raise ValueError("load_data first!")
    
    def __calc_d0(self,
                pole_1,
                pole_2):
        """calculate distance between two poles

        Args:
            pole_1 (list[str]): a polar vector, must have same number of features as qnet
            pole_2 (list[str]): a polar vector, must have same number of features as qnet
        """
        self.pL = self.poles_dict[pole_1]
        self.pR = self.poles_dict[pole_2]
        self.d0 = qdistance(self.pL, self.pR, self.qnet, self.qnet)
        
    def ideology(self,
                i,
                return_dict=None,
                pole_1=None,
                pole_2=None):
        """return ideology index (left-leaning or right-leaning) for a singular sample

        Args:
          i (int): index of sample
          pole_1 (int): index of Pole One to calc as base distance. Defaults to 0.
          pole_2 (int): index of Pole Two to calc as base distance. Defaults to 1.
          return_dict (dict, optional): dict containing results
        """
        if pole_1 is not None or pole_2 is not None:
            self.__calc_d0(pole_1, pole_2)
            
        p = self.samples_as_strings[i]
        dR = qdistance(self.pR, p, self.qnet, self.qnet)
        dL = qdistance(self.pL, p, self.qnet, self.qnet)
        ideology_index = (dR-dL)/self.d0
        if return_dict is not None:
            return_dict[i] = [ideology_index, dR, dL, self.d0]
        return [ideology_index, dR, dL, self.d0]

    def dispersion(self,
                i,
                return_dict=None):
        """qsamples a sample n times and takes distance matrix 
        to determine max and std of distances between qsamples

        Args:
            i (int): index of sample
            return_dict (dict): dict containing results

        Returns:
            list[float]: std and max of the distances btwn qsamples
        """
        p = self.samples_as_strings[i]
        Qset = [qsample(p, self.qnet, self.steps) for j in np.arange(self.num_qsamples)]
        Qset = np.array(Qset)

        matrix = (qdistance_matrix(Qset, Qset, self.qnet, self.qnet))
        Q = matrix.max()
        Qsd = matrix.std()
        if return_dict is not None:
            return_dict[i] = [Qsd, Q]
        return [Qsd, Q]
    
    def compute_DLI_samples(self,
                        type,
                        outfile,
                        num_qsamples=40,
                        steps=120,
                        n_jobs=28,
                        pole_1=0,
                        pole_2=1):
        """compute and save ideology index or dispersion for all samples

        Args:
          num_qsamples (int): number of qsamples to compute
          outfile (str): output file for results
          type (str): whether to calc dispersion or ideology
          steps (int): number of steps to qsample
          n_jobs (int, optional): sets the number of jobs for parallelization. Defaults to 28.
          pole_1 (int, optional): index of Pole One to calc as base distance. Defaults to 0.
          pole_2 (int, optional): index of Pole Two to calc as base distance. Defaults to 1.

        Raises:
            ValueError: set poles if poles are not set
            ValueError: load data if samples or features are not present
        """
        if all(x is not None for x in [self.samples, self.features,
                                    self.pL, self.pR]):
            self.num_qsamples = num_qsamples
            self.steps = steps
            if pole_1 != 0 or pole_2 != 1:
                self.__calc_d0(pole_1, pole_2)
            
            # testing
            # pd.DataFrame(self.samples_as_strings).to_csv('examples_results/class_allsamples_2018.csv')
            
            manager = mp.Manager()
            return_dict = manager.dict()
            processes = []

            if type == 'ideology':
                for i in range(len(self.samples)):
                    p = mp.Process(target=self.ideology, args=(i, return_dict))
                    processes.append(p)
                columns=['ideology', 'dR', 'dL', 'd0']
            elif type == 'dispersion':
                for i in range(len(self.samples)):
                    p = mp.Process(target=self.dispersion, args=(i, return_dict))
                    processes.append(p)
                columns=['Qsd', 'Qmax']
            else:
                raise ValueError("Type must be either dispersion or ideology!")
            
            [x.start() for x in processes]
            [x.join() for x in processes]
            result=[x for x in return_dict.values()]
            result=pd.DataFrame(result,columns=columns).to_csv(outfile)

        elif self.pL is None or self.pR is None:
            raise ValueError("set_poles first!")
        else:
            raise ValueError("load_data first!")
        return result

    def compute_polar_indices(self,
                            num_samples = None,
                            polar_comp = False,
                            POLEFILE = None,
                            steps = 5):
        '''set up polar indices for dissonance func

        Args:
          num_samples (int): subset of samples to take
          polar_comp (bool): whether or not to set poles
          POLEFILE (None): file containing pole samples and features
          steps (int): number of steps to qsample
        '''
        if all(x is not None for x in [self.samples, self.features, self.poles]):
            if num_samples is not None:
                self.set_nsamples(num_samples)

            # read sample data
            if polar_comp:
                self.set_poles(self.qnet, steps, POLEFILE)
            
            polar_features = pd.concat([self.poles, self.features], axis=0)
            self.polar_indices=np.where(polar_features[self.cols].fillna('XXXX').values[0]!='XXXX')[0]
        
        elif self.poles is None:
            raise ValueError("set_poles first!")
        else:
            raise ValueError("load_data first!")

    def dissonance(self,
                sample_index,
                return_dict=None,
                MISSING_VAL=0.0):
        '''compute dissonance for each sample_index, helper function for all_dissonance
        
        Args:
          sample_index (int): index of the sample to compute dissonance
          return_dict (dict): dict containing results
          MISSING_VAL (float): default dissonance value
        '''
        if all(x is not None for x in [self.samples, self.features, 
                                    self.poles]):
            s = self.samples_as_strings[sample_index]
            if self.polar_indices is None:
                self.polar_indices = range(len(s))

            Ds=self.qnet.predict_distributions(s)
            
            diss=np.ones(len(Ds))*MISSING_VAL
            for i in self.polar_indices:
                if s[i] != '':
                    if s[i] in Ds[i].keys():
                        diss[i]=1-Ds[i][s[i]]/np.max(
                            list(Ds[i].values())) 
                    else:
                        diss[i]=1.0
            if return_dict is not None:
                return_dict[sample_index] = diss[self.polar_indices]
            return diss[self.polar_indices]

        elif self.poles is None:
            raise ValueError("set_poles first!")
        else:
            raise ValueError("load_data first!")
    
    def dissonance_matrix(self,
                        output_file=None,
                        n_jobs=28):
        '''get the dissonance for all samples

        Args:
          output_file (str): directory and/or file for output
          n_jobs (int): number of jobs for pdqm
        '''
        if output_file is None:
            output_file = '/example_results/DISSONANCE_matrix.csv'
            
        manager = mp.Manager()
        return_dict = manager.dict()
        processes = []
        
        for i in range(len(self.samples)):
            p = mp.Process(target=self.dissonance, args=(i, return_dict))
            processes.append(p)

        [x.start() for x in processes]
        [x.join() for x in processes]

        result=[x for x in return_dict.values()]
        if self.polar_indices is not None:
            polar_features = pd.concat([self.poles, self.features], axis=0)
            cols = polar_features[self.cols].dropna(axis=1).columns
        else:
            cols = self.cols
        result=pd.DataFrame(result,columns=cols).to_csv(output_file)
        
        self.dissonance_file = output_file
        return return_dict
    
    def __choose_one(self,
                X):
        '''returns a random element of X

        Args:
          X (1D array-like): vector from which random element is to be chosen
        '''
        X=list(X)
        if len(X)>0:
            return X[np.random.randint(len(X))]
        return None

    def getMaskedSample(self,
                        s,
                        mask_prob=0.5,
                        allow_all_mutable=False):
        '''inputs a sample and randomly mask elements of the sample

        Args:
          s (list[str]): vector of sample, must have the same num of features as the qnet
          mask_prob (float): float btwn 0 and 1, prob to mask element of sample
          allow_all_mutable (bool): whether or not all variables are mutable
        '''
        if self.samples is not None:   
            MUTABLE=pd.DataFrame(np.zeros(len(self.cols)),index=self.cols).transpose()
            WITHVAL=[x for x in self.cols[np.where(s)[0]] if x in self.mutable_vars ]
            MASKrand=[x for x in WITHVAL if random.random() < mask_prob ]
            for m in MASKrand:
                MUTABLE[m]=1.0
            
            mutable_x=MUTABLE.values[0]
            base_frequency=mutable_x/mutable_x.sum()

            # if np.isnan(base_frequency).any():
            #     return np.nan,np.nan,np.nan
            #     return self.getMaskedSample(s)

            s1=s.copy()
            for i in range(len(base_frequency)):
                if base_frequency[i]>0.0001:
                    s1[i]=''
                    
            s_rand=np.copy(s)
            rnd_match_prob=[]        
            max_match_prob=[]        
            D=self.qnet.predict_distributions(s)
            for i in MASKrand:
                s_rand[np.where(
                    self.cols==i)[0][0]]=self.__choose_one(
                        self.D_null[np.where(self.cols==i)[0][0]].keys())
                rnd_match_prob=np.append(rnd_match_prob,1/len(
                    self.D_null[np.where(self.cols==i)[0][0]].keys()))
                max_match_prob=np.append(
                    max_match_prob,np.max(
                        list(D[np.where(
                            self.cols==i)[0][0]].values())))
                
            if allow_all_mutable:
                for m in mutable_vars:
                    MUTABLE[m]=1.0
                mutable_x=MUTABLE.values[0]
                base_frequency=mutable_x/mutable_x.sum()

            return s1,base_frequency,MASKrand,np.where(
                base_frequency)[0],np.mean(rnd_match_prob),np.mean(max_match_prob),s_rand
        else:
            raise ValueError("load_data first!")

    def randomMaskReconstruction(self,
                                index=None,
                                return_dict=None,
                                sample=None):
        """reconstruct the masked sample by qsampling and comparing to original
        set self.mask_prob and self.steps if needed

        Args:
          return_dict (dict): dict containing results. Defaults to None.
          sample (list[str], optional): sample vector, must have the same num of features as the qnet. Defaults to None.
          index (int): index of sample to take. Defaults to None.

        Raises:
          ValueError: Neither sample or index were given
          ValueError: Both sample and index were given

        Returns:
          [type]: [description]
        """
        if all(x is None for x in [sample, index]):
            raise ValueError("Must input either sample or index!")
        elif all(x is not None for x in [sample, index]):
            raise ValueError("Must input either sample or index not both!")
        elif sample is not None:
            s=np.array(pd.DataFrame(sample).fillna('').values.astype(str)[:])
        elif index is not None:
            s=self.samples_as_strings[index]
            
        s1,bp,mask_,maskindex,rmatch_u,rmatch,s_rand=self.getMaskedSample(s, 
                                                                        mask_prob=self.mask_prob)
        if np.isnan(bp).any():
            return_dict[index] = np.nan,np.nan,np.nan
            return np.nan,np.nan,np.nan

        qs=qsample(s1,self.qnet,self.steps,bp)

        dqestim=qdistance(s,qs,self.qnet,self.qnet)
        dactual=qdistance(s,s1,self.qnet,self.qnet)
        qdistance_time_end = time.time()

        cmpf=pd.DataFrame([s,qs,s_rand],columns=self.cols,index=['s','q','r'])[mask_].transpose()
        cmpf.index.name='gssvar'
        cmpf.to_csv('examples_results/CMPF_2018/CMPF-'+str(index)+'.csv')
        return_dict[index] = (1 - (dqestim/dactual))*100,rmatch_u,rmatch
        return (1 - (dqestim/dactual))*100,rmatch_u,rmatch,s,qs,s_rand,mask_

    def randomMaskReconstruction_multiple(self,
                                          out_file):
        '''runs and saves the results of the predicted masked sample

        Args:
          output_file (str): directory and/or file for output
        '''
        manager = mp.Manager()
        return_dict = manager.dict()
        processes = []
        
        for i in range(len(self.samples)):
            p = mp.Process(target=self.randomMaskReconstruction, args=(i, return_dict))
            processes.append(p)

        [x.start() for x in processes]
        [x.join() for x in processes]
        
        #result=pd.DataFrame(return_dict.items())[1]#, columns=['sample','rederr','r_prob','rand_err','s','q','r'])
        result=[x for x in return_dict.values() if isinstance(x, tuple)]
        # #result=pd.DataFrame(result.tolist())
        # print(result)
        # cmprdf=result[[3,4,5]]
        # mask_=result[[6]]
        # cmprdf.columns=['s','q','r']#[mask_].transpose()
        # cmprdf.to_csv("examples_results/CMPF_"+"tmp"+".csv")
        # print(cmprdf)
        # result=result[[0,1,2]]
        result=pd.DataFrame(result,columns=['rederr','r_prob','rand_err'])
        result.rederr=result.rederr.astype(float)

        if self.poles is not None:
            result.to_csv(out_file)
        else:
            result.to_csv(out_file)
        
        return result.rederr.mean(), result.rand_err.mean()

Methods

def compute_DLI_samples(self, type, outfile, num_qsamples=40, steps=120, n_jobs=28, pole_1=0, pole_2=1)

compute and save ideology index or dispersion for all samples

Args

num_qsamples : int

number of qsamples to compute

outfile : str

output file for results

type : str

whether to calc dispersion or ideology

steps : int

number of steps to qsample

n_jobs : int, optional

sets the number of jobs for parallelization. Defaults to 28.

pole_1 : int, optional

index of Pole One to calc as base distance. Defaults to 0.

pole_2 : int, optional

index of Pole Two to calc as base distance. Defaults to 1.

Raises

ValueError

set poles if poles are not set

ValueError

load data if samples or features are not present

Expand source code

def compute_DLI_samples(self,
                    type,
                    outfile,
                    num_qsamples=40,
                    steps=120,
                    n_jobs=28,
                    pole_1=0,
                    pole_2=1):
    """compute and save ideology index or dispersion for all samples

    Args:
      num_qsamples (int): number of qsamples to compute
      outfile (str): output file for results
      type (str): whether to calc dispersion or ideology
      steps (int): number of steps to qsample
      n_jobs (int, optional): sets the number of jobs for parallelization. Defaults to 28.
      pole_1 (int, optional): index of Pole One to calc as base distance. Defaults to 0.
      pole_2 (int, optional): index of Pole Two to calc as base distance. Defaults to 1.

    Raises:
        ValueError: set poles if poles are not set
        ValueError: load data if samples or features are not present
    """
    if all(x is not None for x in [self.samples, self.features,
                                self.pL, self.pR]):
        self.num_qsamples = num_qsamples
        self.steps = steps
        if pole_1 != 0 or pole_2 != 1:
            self.__calc_d0(pole_1, pole_2)
        
        # testing
        # pd.DataFrame(self.samples_as_strings).to_csv('examples_results/class_allsamples_2018.csv')
        
        manager = mp.Manager()
        return_dict = manager.dict()
        processes = []

        if type == 'ideology':
            for i in range(len(self.samples)):
                p = mp.Process(target=self.ideology, args=(i, return_dict))
                processes.append(p)
            columns=['ideology', 'dR', 'dL', 'd0']
        elif type == 'dispersion':
            for i in range(len(self.samples)):
                p = mp.Process(target=self.dispersion, args=(i, return_dict))
                processes.append(p)
            columns=['Qsd', 'Qmax']
        else:
            raise ValueError("Type must be either dispersion or ideology!")
        
        [x.start() for x in processes]
        [x.join() for x in processes]
        result=[x for x in return_dict.values()]
        result=pd.DataFrame(result,columns=columns).to_csv(outfile)

    elif self.pL is None or self.pR is None:
        raise ValueError("set_poles first!")
    else:
        raise ValueError("load_data first!")
    return result

def compute_polar_indices(self, num_samples=None, polar_comp=False, POLEFILE=None, steps=5)

set up polar indices for dissonance func

Args

num_samples : int

subset of samples to take

polar_comp : bool

whether or not to set poles

POLEFILE : None

file containing pole samples and features

steps : int

number of steps to qsample

Expand source code

def compute_polar_indices(self,
                        num_samples = None,
                        polar_comp = False,
                        POLEFILE = None,
                        steps = 5):
    '''set up polar indices for dissonance func

    Args:
      num_samples (int): subset of samples to take
      polar_comp (bool): whether or not to set poles
      POLEFILE (None): file containing pole samples and features
      steps (int): number of steps to qsample
    '''
    if all(x is not None for x in [self.samples, self.features, self.poles]):
        if num_samples is not None:
            self.set_nsamples(num_samples)

        # read sample data
        if polar_comp:
            self.set_poles(self.qnet, steps, POLEFILE)
        
        polar_features = pd.concat([self.poles, self.features], axis=0)
        self.polar_indices=np.where(polar_features[self.cols].fillna('XXXX').values[0]!='XXXX')[0]
    
    elif self.poles is None:
        raise ValueError("set_poles first!")
    else:
        raise ValueError("load_data first!")

def dispersion(self, i, return_dict=None)

qsamples a sample n times and takes distance matrix to determine max and std of distances between qsamples

Args

i : int

index of sample

return_dict : dict

dict containing results

Returns

list[float]

std and max of the distances btwn qsamples

Expand source code

def dispersion(self,
            i,
            return_dict=None):
    """qsamples a sample n times and takes distance matrix 
    to determine max and std of distances between qsamples

    Args:
        i (int): index of sample
        return_dict (dict): dict containing results

    Returns:
        list[float]: std and max of the distances btwn qsamples
    """
    p = self.samples_as_strings[i]
    Qset = [qsample(p, self.qnet, self.steps) for j in np.arange(self.num_qsamples)]
    Qset = np.array(Qset)

    matrix = (qdistance_matrix(Qset, Qset, self.qnet, self.qnet))
    Q = matrix.max()
    Qsd = matrix.std()
    if return_dict is not None:
        return_dict[i] = [Qsd, Q]
    return [Qsd, Q]

def dissonance(self, sample_index, return_dict=None, MISSING_VAL=0.0)

compute dissonance for each sample_index, helper function for all_dissonance

Args

sample_index : int

index of the sample to compute dissonance

return_dict : dict

dict containing results

MISSING_VAL : float

default dissonance value

Expand source code

def dissonance(self,
            sample_index,
            return_dict=None,
            MISSING_VAL=0.0):
    '''compute dissonance for each sample_index, helper function for all_dissonance
    
    Args:
      sample_index (int): index of the sample to compute dissonance
      return_dict (dict): dict containing results
      MISSING_VAL (float): default dissonance value
    '''
    if all(x is not None for x in [self.samples, self.features, 
                                self.poles]):
        s = self.samples_as_strings[sample_index]
        if self.polar_indices is None:
            self.polar_indices = range(len(s))

        Ds=self.qnet.predict_distributions(s)
        
        diss=np.ones(len(Ds))*MISSING_VAL
        for i in self.polar_indices:
            if s[i] != '':
                if s[i] in Ds[i].keys():
                    diss[i]=1-Ds[i][s[i]]/np.max(
                        list(Ds[i].values())) 
                else:
                    diss[i]=1.0
        if return_dict is not None:
            return_dict[sample_index] = diss[self.polar_indices]
        return diss[self.polar_indices]

    elif self.poles is None:
        raise ValueError("set_poles first!")
    else:
        raise ValueError("load_data first!")

def dissonance_matrix(self, output_file=None, n_jobs=28)

get the dissonance for all samples

Args

output_file : str

directory and/or file for output

n_jobs : int

number of jobs for pdqm

Expand source code

def dissonance_matrix(self,
                    output_file=None,
                    n_jobs=28):
    '''get the dissonance for all samples

    Args:
      output_file (str): directory and/or file for output
      n_jobs (int): number of jobs for pdqm
    '''
    if output_file is None:
        output_file = '/example_results/DISSONANCE_matrix.csv'
        
    manager = mp.Manager()
    return_dict = manager.dict()
    processes = []
    
    for i in range(len(self.samples)):
        p = mp.Process(target=self.dissonance, args=(i, return_dict))
        processes.append(p)

    [x.start() for x in processes]
    [x.join() for x in processes]

    result=[x for x in return_dict.values()]
    if self.polar_indices is not None:
        polar_features = pd.concat([self.poles, self.features], axis=0)
        cols = polar_features[self.cols].dropna(axis=1).columns
    else:
        cols = self.cols
    result=pd.DataFrame(result,columns=cols).to_csv(output_file)
    
    self.dissonance_file = output_file
    return return_dict

def distance(self, sample1, sample2, nsteps1=0, nsteps2=0)

qsamples each sample set num of steps, then takes qdistance

Args

sample1 : list[str]

sample vector 1, must have the same num of features as the qnet

sample2 : list[str]

sample vector 2, must have the same num of features as the qnet

nsteps1 : int, optional

number of steps to qsample for sample1

nsteps2 : int, optional

number of steps to qsample for sample2

Returns

float

qdistance

Expand source code

def distance(self,
            sample1,
            sample2,
            nsteps1=0,
            nsteps2=0):
    """qsamples each sample set num of steps, then takes qdistance

    Args:
      sample1 (list[str]): sample vector 1, must have the same num of features as the qnet
      sample2 (list[str]): sample vector 2, must have the same num of features as the qnet
      nsteps1 (int, optional): number of steps to qsample for sample1
      nsteps2 (int, optional): number of steps to qsample for sample2

    Returns:
      float: qdistance
    """
    if self.qnet is None:
        raise ValueError("load qnet first!")
    bp1 = self.getBaseFrequency(sample1)
    bp2 = self.getBaseFrequency(sample2)
    sample1 = qsample(sample1, self.qnet, nsteps1)#, baseline_prob=bp1)
    sample2 = qsample(sample2, self.qnet, nsteps2)#, baseline_prob=bp2)
    return qdistance(sample1, sample2, self.qnet, self.qnet)

def distfunc_line(self, i, return_dict=None)

compute the dist for a row, or vector of samples

Args

i : int

row

Return

numpy.ndarray(float)

Expand source code

def distfunc_line(self,
                i,
                return_dict=None):
    '''compute the dist for a row, or vector of samples

    Args:
      i (int): row
    
    Return:
      numpy.ndarray(float)
    '''
    if all(x is not None for x in [self.samples, self.features]):
        w = self.samples.index.size
        line = np.zeros(w)
        y = self.samples_as_strings[i]
        for j in range(w):
            # only compute half of the distance matrix
            if j > i:
                x = self.samples_as_strings[j]
                line[j] = self.__distfunc(x, y)
    else:
        raise ValueError("load_data first!")
    if return_dict is not None:
        return_dict[i] = line
    return line

def distfunc_multiples(self, outfile)

compute distance matrix for all samples in the dataset

Args

outfile : str

desired output filename and path

Expand source code

def distfunc_multiples(self,
                    outfile):
    """compute distance matrix for all samples in the dataset

    Args:
      outfile (str): desired output filename and path
    """
    if all(x is not None for x in [self.samples, self.features]):
        manager = mp.Manager()
        return_dict = manager.dict()
        processes = []

        for i in range(len(self.samples)):
            p = mp.Process(target=self.distfunc_line, args=(i, return_dict))
            processes.append(p)
        
        [x.start() for x in processes]
        [x.join() for x in processes]
        result=[x for x in return_dict.values()]
        columns = [i for i in range(len(self.samples))]
        result=pd.DataFrame(result,columns=columns, index=columns).sort_index(ascending=False)
        result = result.to_numpy()
        result = pd.DataFrame(np.maximum(result, result.transpose()))
        result.to_csv(outfile)
    else:
        raise ValueError("load data first!")
    return return_dict

def embed(self, infile, name_pref, out_dir, pca_model=False, EMBED_BINARY=None)

embed data

Args

infile : str

input file to be embedded

name_pref : str

preferred name for output file

out_dir : str

output dir for results

pca_model : bool

whether or not to generate PCA model

EMBED_BINARY : os.path.abspath

path to embed binary

Expand source code

def embed(self,
        infile,
        name_pref,
        out_dir,
        pca_model=False,
        EMBED_BINARY=None  
):
    '''
    embed data

    Args:
      infile (str): input file to be embedded
      name_pref (str): preferred name for output file
      out_dir (str): output dir for results
      pca_model (bool): whether or not to generate PCA model
      EMBED_BINARY (os.path.abspath): path to embed binary
    '''
    if all(x is not None for x in [self.year]):
        yr = self.year
        PREF = name_pref
        FILE = infile

        if EMBED_BINARY is None:
            EMBED = pkg.util.get_data("cognet.bin", "__embed__.so") 
        else:
            EMBED = EMBED_BINARY
        DATAFILE = out_dir + 'data_' +yr
        EFILE = out_dir + PREF + '_E_' +yr
        DFILE = out_dir + PREF + '_D_' +yr

        pd.read_csv(FILE,header=None).to_csv(DATAFILE,sep=' ',header=None,index=None)
        STR=EMBED+' -f '+DATAFILE+' -E '+EFILE+' -D '+DFILE
        subprocess.call(STR,shell=True)
        if pca_model:
            embed_to_pca(EFILE, EFILE+'_PCA')
    elif self.year is None:
        raise ValueError("load_data first!")

def getBaseFrequency(self, sample)

get frequency of the variables helper func for qsampling

Args

sample : list[str]

vector of sample, must have the same num of features as the qnet

Expand source code

def getBaseFrequency(self, 
                    sample):
    '''get frequency of the variables
    helper func for qsampling

    Args:
      sample (list[str]): vector of sample, must have the same num of features as the qnet
    '''
    MUTABLE=pd.DataFrame(np.zeros(len(self.cols)),index=self.cols).transpose()
            
    for m in self.mutable_vars:
        MUTABLE[m]=1.0
    mutable_x=MUTABLE.values[0]
    base_frequency=mutable_x/mutable_x.sum()

    # commented out for now for testing using smaller qnet
    for i in range(len(base_frequency)):
        if base_frequency[i]>0.0:
            base_frequency[i]= self.variation_weight[i]*base_frequency[i]

    return base_frequency/base_frequency.sum()

def getMaskedSample(self, s, mask_prob=0.5, allow_all_mutable=False)

inputs a sample and randomly mask elements of the sample

Args

s : list[str]

vector of sample, must have the same num of features as the qnet

mask_prob : float

float btwn 0 and 1, prob to mask element of sample

allow_all_mutable : bool

whether or not all variables are mutable

Expand source code

def getMaskedSample(self,
                    s,
                    mask_prob=0.5,
                    allow_all_mutable=False):
    '''inputs a sample and randomly mask elements of the sample

    Args:
      s (list[str]): vector of sample, must have the same num of features as the qnet
      mask_prob (float): float btwn 0 and 1, prob to mask element of sample
      allow_all_mutable (bool): whether or not all variables are mutable
    '''
    if self.samples is not None:   
        MUTABLE=pd.DataFrame(np.zeros(len(self.cols)),index=self.cols).transpose()
        WITHVAL=[x for x in self.cols[np.where(s)[0]] if x in self.mutable_vars ]
        MASKrand=[x for x in WITHVAL if random.random() < mask_prob ]
        for m in MASKrand:
            MUTABLE[m]=1.0
        
        mutable_x=MUTABLE.values[0]
        base_frequency=mutable_x/mutable_x.sum()

        # if np.isnan(base_frequency).any():
        #     return np.nan,np.nan,np.nan
        #     return self.getMaskedSample(s)

        s1=s.copy()
        for i in range(len(base_frequency)):
            if base_frequency[i]>0.0001:
                s1[i]=''
                
        s_rand=np.copy(s)
        rnd_match_prob=[]        
        max_match_prob=[]        
        D=self.qnet.predict_distributions(s)
        for i in MASKrand:
            s_rand[np.where(
                self.cols==i)[0][0]]=self.__choose_one(
                    self.D_null[np.where(self.cols==i)[0][0]].keys())
            rnd_match_prob=np.append(rnd_match_prob,1/len(
                self.D_null[np.where(self.cols==i)[0][0]].keys()))
            max_match_prob=np.append(
                max_match_prob,np.max(
                    list(D[np.where(
                        self.cols==i)[0][0]].values())))
            
        if allow_all_mutable:
            for m in mutable_vars:
                MUTABLE[m]=1.0
            mutable_x=MUTABLE.values[0]
            base_frequency=mutable_x/mutable_x.sum()

        return s1,base_frequency,MASKrand,np.where(
            base_frequency)[0],np.mean(rnd_match_prob),np.mean(max_match_prob),s_rand
    else:
        raise ValueError("load_data first!")

def ideology(self, i, return_dict=None, pole_1=None, pole_2=None)

return ideology index (left-leaning or right-leaning) for a singular sample

Args

i : int

index of sample

pole_1 : int

index of Pole One to calc as base distance. Defaults to 0.

pole_2 : int

index of Pole Two to calc as base distance. Defaults to 1.

return_dict : dict, optional

dict containing results

Expand source code

def ideology(self,
            i,
            return_dict=None,
            pole_1=None,
            pole_2=None):
    """return ideology index (left-leaning or right-leaning) for a singular sample

    Args:
      i (int): index of sample
      pole_1 (int): index of Pole One to calc as base distance. Defaults to 0.
      pole_2 (int): index of Pole Two to calc as base distance. Defaults to 1.
      return_dict (dict, optional): dict containing results
    """
    if pole_1 is not None or pole_2 is not None:
        self.__calc_d0(pole_1, pole_2)
        
    p = self.samples_as_strings[i]
    dR = qdistance(self.pR, p, self.qnet, self.qnet)
    dL = qdistance(self.pL, p, self.qnet, self.qnet)
    ideology_index = (dR-dL)/self.d0
    if return_dict is not None:
        return_dict[i] = [ideology_index, dR, dL, self.d0]
    return [ideology_index, dR, dL, self.d0]

def load_data(self, year, features_by_year, samples, qnet)

load cols, features, samples, and qnet.

Args

year : str

to identify cols/features.

features_by_year : str

file containing all features by year of the dataset.

samples : str

file of samples for that year.

Qnet : str

Qnet file location.

Expand source code

def load_data(self,
            year,
            features_by_year,
            samples,
            qnet):
    '''load cols, features, samples, and qnet.

    Args:
      year (str): to identify cols/features.
      features_by_year (str): file containing all features by year of the dataset.
      samples (str): file of samples for that year.
      Qnet (str): Qnet file location.
    '''
    self.qnet = load_qnet(qnet)
    self.year = year
    self.cols = np.array((pd.read_csv(features_by_year,
                        keep_default_na=True, 
                        index_col=0).set_index(
                            'year')).loc[int(year)].apply(
                                eval).values[0])
    self.features = pd.DataFrame(columns=self.cols)
    self.mutable_vars = [x for x in self.cols]
    #[self.cols].fillna('').values.astype(str)[:]

    self.samples=pd.read_csv(samples)
    self.samples = pd.concat([self.samples,self.features], axis=0)
    self.all_samples = self.samples
    self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
    self.s_null=['']*len(self.samples_as_strings[0])
    self.D_null=self.qnet.predict_distributions(self.s_null)
    variation_weight = []
    for d in self.D_null:
        v=[]
        for val in d.values():
            v=np.append(v,val)
        variation_weight.append(entropy(v,base=len(v)))
    self.variation_weight = variation_weight

def load_from_model(self, model, samples_file=None, im_vars=None, m_vars=None)

load parameters from model object

Args

model : Class

model obj for loading parameters

samples_file : filepath

filepath and name for sample csv

im_vars : list[str], optional

Not implemented yet. Defaults to None.

m_vars : list[str], optional

Not implemented yet. Defaults to None.

Expand source code

def load_from_model(self,
                    model,
                    samples_file=None,
                    im_vars=None,
                    m_vars=None):
    """load parameters from model object

    Args:
      model (Class): model obj for loading parameters
      samples_file (filepath): filepath and name for sample csv
      im_vars (list[str], optional): Not implemented yet. Defaults to None.
      m_vars (list[str], optional): Not implemented yet. Defaults to None.
    """
    if model is not None:
        self.qnet = model.myQnet
        self.cols = np.array(model.features)
        self.features = pd.DataFrame(columns=self.cols)
        if any(x is not None for x in [model.immutable_vars, model.mutable_vars]):
            if model.immutable_vars is not None:
                self.immutable_vars = model.immutable_vars
                self.mutable_vars = [x for x in self.features if x not in self.immutable_vars]
            elif model.mutable_vars is not None:
                self.mutable_vars = model.mutable_vars
                self.immutable_vars = [x for x in self.features if x not in self.mutable_vars]
        else:
            self.mutable_vars = self.features
        if all(x is None for x in [model.samples, samples_file]):
            raise ValueError("Please input samples if loading model after loading qnet")
        elif model.samples is not None:
            samples = pd.DataFrame(model.samples)
        elif samples_file is not None:
            samples = pd.read_csv(samples_file)
            
        self.samples = pd.concat([samples,self.features], axis=0)
        self.all_samples = self.samples
        self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
        self.s_null=['']*len(self.samples_as_strings[0])
        self.D_null=self.qnet.predict_distributions(self.s_null)
        variation_weight = []
        for d in self.D_null:
            v=[]
            for val in d.values():
                v=np.append(v,val)
            variation_weight.append(entropy(v,base=len(v)))
        self.variation_weight = variation_weight

def polarDistance(self, i, return_dict=None)

return the distance from a sample to the poles

Args

i : int

index of sample to take

return_dict : dict

dict used for multiple sample function

Returns

[type]

[description]

Expand source code

def polarDistance(self,
                i,
                return_dict=None):
    """return the distance from a sample to the poles

    Args:
        i (int): index of sample to take
        return_dict (dict): dict used for multiple sample function

    Returns:
        [type]: [description]
    """
    samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
    p = samples_as_strings[i]
    distances = []
    for index, row in self.polar_features[self.cols].iterrows():
        row = row.fillna('').values.astype(str)[:]
        distances.append(self.distance(p, np.array(row)))
    if return_dict is not None:
        return_dict[i] = distances
    return distances

def polarDistance_multiple(self, outfile)

return the distance from all samples to the poles

Args

outfile : str

desired output filename and path

Expand source code

def polarDistance_multiple(self,
                        outfile):
    """return the distance from all samples to the poles

    Args:
      outfile (str): desired output filename and path
    """
    if all(x is not None for x in [self.samples, self.cols,
                                self.polar_features]):
        manager = mp.Manager()
        return_dict = manager.dict()
        processes = []
        
        for i in range(len(self.samples)):
            p = mp.Process(target=self.polarDistance, args=(i, return_dict))
            processes.append(p)

        [x.start() for x in processes]
        [x.join() for x in processes]

        pole_names = []
        for index, row in self.polar_features[self.cols].iterrows():
            pole_names.append(index)
        result=[x for x in return_dict.values()]
        result=pd.DataFrame(result,columns=pole_names).to_csv(outfile)
        
    else:
        raise ValueError("load data first!")
    return return_dict

def polar_separation(self, nsteps=0)

returns the distance between poles as a qdistance matrix

Args

nsteps : int, optional

[description]. Defaults to 0.

Expand source code

def polar_separation(self,
                    nsteps=0):
    """returns the distance between poles as a qdistance matrix

    Args:
      nsteps (int, optional): [description]. Defaults to 0.
    """
    polar_arraydata = self.polar_features[self.cols].fillna('').values.astype(str)[:]
    samples_ = []
    for vector in polar_arraydata:
        bp = self.getBaseFrequency(vector)
        sample = qsample(vector, self.qnet, nsteps, baseline_prob=bp)
        samples_.append(sample)
    samples_ = np.array(samples_)
    self.polar_matrix = qdistance_matrix(samples_, samples_, self.qnet, self.qnet)
    return self.polar_matrix

def qsampling(self, sample, steps, immutable=False)

perturb the sample based on thet qnet distributions and number of steps

Args

sample : 1d array-like

sample vector, must have the same num of features as the qnet

steps : int

number of steps to qsample

immutable : bool

are there variables that are immutable?

Expand source code

def qsampling(self,
            sample,
            steps,
            immutable=False):
    '''perturb the sample based on thet qnet distributions and number of steps

    Args:
      sample (1d array-like): sample vector, must have the same num of features as the qnet
      steps (int): number of steps to qsample
      immutable (bool): are there variables that are immutable?
    '''
    if all(x is not None for x in [self.mutable_vars, sample]):
        if immutable == True:
            return qsample(sample,self.qnet,steps,self.getBaseFrequency(self.samples))
        else:
            return qsample(sample,self.qnet,steps)
    elif self.mutable_vars is None:
        raise ValueError("load_data first!")

def randomMaskReconstruction(self, index=None, return_dict=None, sample=None)

reconstruct the masked sample by qsampling and comparing to original set self.mask_prob and self.steps if needed

Args

return_dict : dict

dict containing results. Defaults to None.

sample : list[str], optional

sample vector, must have the same num of features as the qnet. Defaults to None.

index : int

index of sample to take. Defaults to None.

Raises

ValueError

Neither sample or index were given

ValueError

Both sample and index were given

Returns

[type]

[description]

Expand source code

def randomMaskReconstruction(self,
                            index=None,
                            return_dict=None,
                            sample=None):
    """reconstruct the masked sample by qsampling and comparing to original
    set self.mask_prob and self.steps if needed

    Args:
      return_dict (dict): dict containing results. Defaults to None.
      sample (list[str], optional): sample vector, must have the same num of features as the qnet. Defaults to None.
      index (int): index of sample to take. Defaults to None.

    Raises:
      ValueError: Neither sample or index were given
      ValueError: Both sample and index were given

    Returns:
      [type]: [description]
    """
    if all(x is None for x in [sample, index]):
        raise ValueError("Must input either sample or index!")
    elif all(x is not None for x in [sample, index]):
        raise ValueError("Must input either sample or index not both!")
    elif sample is not None:
        s=np.array(pd.DataFrame(sample).fillna('').values.astype(str)[:])
    elif index is not None:
        s=self.samples_as_strings[index]
        
    s1,bp,mask_,maskindex,rmatch_u,rmatch,s_rand=self.getMaskedSample(s, 
                                                                    mask_prob=self.mask_prob)
    if np.isnan(bp).any():
        return_dict[index] = np.nan,np.nan,np.nan
        return np.nan,np.nan,np.nan

    qs=qsample(s1,self.qnet,self.steps,bp)

    dqestim=qdistance(s,qs,self.qnet,self.qnet)
    dactual=qdistance(s,s1,self.qnet,self.qnet)
    qdistance_time_end = time.time()

    cmpf=pd.DataFrame([s,qs,s_rand],columns=self.cols,index=['s','q','r'])[mask_].transpose()
    cmpf.index.name='gssvar'
    cmpf.to_csv('examples_results/CMPF_2018/CMPF-'+str(index)+'.csv')
    return_dict[index] = (1 - (dqestim/dactual))*100,rmatch_u,rmatch
    return (1 - (dqestim/dactual))*100,rmatch_u,rmatch,s,qs,s_rand,mask_

def randomMaskReconstruction_multiple(self, out_file)

runs and saves the results of the predicted masked sample

Args

output_file : str

directory and/or file for output

Expand source code

def randomMaskReconstruction_multiple(self,
                                      out_file):
    '''runs and saves the results of the predicted masked sample

    Args:
      output_file (str): directory and/or file for output
    '''
    manager = mp.Manager()
    return_dict = manager.dict()
    processes = []
    
    for i in range(len(self.samples)):
        p = mp.Process(target=self.randomMaskReconstruction, args=(i, return_dict))
        processes.append(p)

    [x.start() for x in processes]
    [x.join() for x in processes]
    
    #result=pd.DataFrame(return_dict.items())[1]#, columns=['sample','rederr','r_prob','rand_err','s','q','r'])
    result=[x for x in return_dict.values() if isinstance(x, tuple)]
    # #result=pd.DataFrame(result.tolist())
    # print(result)
    # cmprdf=result[[3,4,5]]
    # mask_=result[[6]]
    # cmprdf.columns=['s','q','r']#[mask_].transpose()
    # cmprdf.to_csv("examples_results/CMPF_"+"tmp"+".csv")
    # print(cmprdf)
    # result=result[[0,1,2]]
    result=pd.DataFrame(result,columns=['rederr','r_prob','rand_err'])
    result.rederr=result.rederr.astype(float)

    if self.poles is not None:
        result.to_csv(out_file)
    else:
        result.to_csv(out_file)
    
    return result.rederr.mean(), result.rand_err.mean()

def set_immutable_vars(self, IMMUTABLE_FILE)

set vars to immutable and mutable, can prob combine this with the load_data func: only set the immutable vars if necessary

Args

IMMUTABLE_FILE : str

file containing the immutable features/vars

Expand source code

def set_immutable_vars(self,
                    IMMUTABLE_FILE):
    '''set vars to immutable and mutable, 
    can prob combine this with the load_data func: only set the immutable vars if necessary

    Args:
      IMMUTABLE_FILE (str): file containing the immutable features/vars
    '''
    if self.cols is None:
        raise ValueError("load_data first!")
    self.immutable_vars = pd.read_csv(IMMUTABLE_FILE,index_col=0).transpose()
    self.mutable_vars = None
    self.mutable_vars = [x for x in self.cols
                        if x.upper() not in self.immutable_vars.columns]

def set_nsamples(self, num_samples)

select a subset of the samples

Args

num_samples : int

Set num of samples to subset

Expand source code

def set_nsamples(self,
                num_samples):
    '''select a subset of the samples

    Args:
      num_samples (int): Set num of samples to subset
    '''
    self.samples = self.all_samples
    if all(x is not None for x in [num_samples, self.samples]):
        if num_samples > len(self.samples.index):
            string = 'The number of selected samples ({}) ' + \
                'is greater than the number of samples ({})!'
            string = string.format(num_samples, len(self.samples.index))
            raise ValueError(string)

        if num_samples == len(self.samples.index):
            string = 'The number of selected samples ({}) ' + \
                'is equal to the number of samples ({})!'
            string = string.format(num_samples, len(self.samples.index))
            print(string)
        self.samples = self.samples.sample(num_samples)
        self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]

    elif self.samples is None:
        raise ValueError("load_data first!")

def set_poles(self, POLEFILE, pole_1, pole_2, steps=0, mutable=False)

set the poles and samples such that the samples contain features in poles

Args

steps : int

number of steps to qsample

POLEFILE : str

file containing poles samples and features

mutable : boolean

Whether or not to set poles as the only mutable_vars

pole_1 : str

column name for first pole to use

pole_2 : str

column name for second pole to use

Expand source code

def set_poles(self,
            POLEFILE,
            pole_1,
            pole_2,
            steps=0,
            mutable=False):
    '''set the poles and samples such that the samples contain features in poles

    Args:
      steps (int): number of steps to qsample
      POLEFILE (str): file containing poles samples and features
      mutable (boolean): Whether or not to set poles as the only mutable_vars
      pole_1 (str): column name for first pole to use
      pole_2 (str): column name for second pole to use
    '''
    invalid_count = 0
    if all(x is not None for x in [self.samples, self.qnet]):
        poles = pd.read_csv(POLEFILE, index_col=0)
        self.poles=poles.transpose()
        self.polar_features = pd.concat([self.poles, self.features], axis=0)
        poles_dict = {}
        for column in poles:
            p_ = self.polar_features.loc[column][self.cols].fillna('').values.astype(str)[:]
            poles_dict[column] = self.qsampling(p_,steps)
        self.poles_dict = poles_dict
        self.pL = self.poles_dict[pole_1]
        self.pR = self.poles_dict[pole_2]
        # self.pL = list(poles_dict.values())[0]
        # self.pR = list(poles_dict.values())[1]
        self.d0 = qdistance(self.pL, self.pR, self.qnet, self.qnet)
        
        cols = [x for x in self.poles.columns if x in self.samples.columns]
        self.samples=self.samples[cols]
    
        for x in self.poles.columns:
            if x not in self.samples.columns:
                invalid_count += 1
                self.samples[x]=np.nan

        self.samples = pd.concat([self.samples,self.features], axis=0)
        self.all_samples = self.samples
        self.samples_as_strings = self.samples[self.cols].fillna('').values.astype(str)[:]
        
        if mutable:
            self.mutable_vars=[x for x in self.cols if x in self.poles.columns]
    elif self.samples is None:
        raise ValueError("load_data first!")
    
    print("{} pole features not found in sample features".format(invalid_count))