Bugfixes and improvements in MVFTS and DEHO

pyFTS/common/fts.py

@@ -332,7 +332,7 @@ class FTS(object):
 
         dump = kwargs.get('dump', None)
 
-        num_batches = kwargs.get('num_batches', 10)
+        num_batches = kwargs.get('num_batches', None)
 
         save = kwargs.get('save_model', False)  # save model on disk
 
@@ -345,6 +345,8 @@ class FTS(object):
         batch_save_interval = kwargs.get('batch_save_interval', 10)
 
         if distributed is not None and distributed:
+            if num_batches is None:
+                num_batches = 10
 
             if distributed == 'dispy':
                 from pyFTS.distributed import dispy

pyFTS/data/Malaysia.py

@@ -30,5 +30,4 @@ def get_dataframe():
 
     return df
 
-    return df
 

pyFTS/hyperparam/Evolutionary.py

@@ -69,13 +69,16 @@ def initial_population(n, **kwargs):
     :param n: the size of the population
     :return: a list with n random individuals
     """
+
+    create_random_individual = kwargs.get('random_individual', random_genotype)
+
     pop = []
     for i in range(n):
-        pop.append(random_genotype(**kwargs))
+        pop.append(create_random_individual(**kwargs))
     return pop
 
 
-def phenotype(individual, train, fts_method, parameters={}):
+def phenotype(individual, train, fts_method, parameters={}, **kwargs):
     """
     Instantiate the genotype, creating a fitted model with the genotype hyperparameters
 
@@ -96,10 +99,10 @@ def phenotype(individual, train, fts_method, parameters={}):
     else:
         mf = Membership.trimf
 
-    #if individual['partitioner'] == 1:
-    partitioner = Grid.GridPartitioner(data=train, npart=individual['npart'], func=mf)
-    #elif individual['partitioner'] == 2:
-    #    partitioner = Entropy.EntropyPartitioner(data=train, npart=individual['npart'], func=mf)
+    if individual['partitioner'] == 1:
+        partitioner = Grid.GridPartitioner(data=train, npart=individual['npart'], func=mf)
+    elif individual['partitioner'] == 2:
+        partitioner = Entropy.EntropyPartitioner(data=train, npart=individual['npart'], func=mf)
 
     model = fts_method(partitioner=partitioner,
                                        lags=individual['lags'],
@@ -243,8 +246,10 @@ def crossover(population, **kwargs):
 
     n = len(population) - 1
 
-    r1 = random.randint(0, n)
-    r2 = random.randint(0, n)
+    r1, r2 = 0, 0
+    while r1 == r2:
+        r1 = random.randint(0, n)
+        r2 = random.randint(0, n)
 
     if population[r1]['f1'] < population[r2]['f1']:
         best = population[r1]
@@ -304,9 +309,6 @@ def mutation(individual, **kwargs):
     :param pmut: individual probability o
     :return:
     """
-    import numpy.random
-
-    print('mutation')
 
     individual['npart'] = min(50, max(3, int(individual['npart'] + np.random.normal(0, 4))))
     individual['alpha'] = min(.5, max(0, individual['alpha'] + np.random.normal(0, .5)))
@@ -572,6 +574,7 @@ def execute(datasetname, dataset, **kwargs):
     :keyword parameters: dict with model specific arguments for fts_method
     :keyword elitism: A boolean value indicating if the best individual must always survive to next population
     :keyword initial_operator: a function that receives npop and return a random population with size npop
+    :keyword random_individual: create an random genotype
     :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness
     :keyword selection_operator: a function that receives the whole population and return a selected individual
     :keyword crossover_operator: a function that receives the whole population and return a descendent individual

pyFTS/hyperparam/Util.py

@@ -1,9 +1,10 @@
 """
-Common facilities for hyperparameter tunning
+Common facilities for hyperparameter optimization
 """
 
 import sqlite3
 
+
 def open_hyperparam_db(name):
     """
     Open a connection with a Sqlite database designed to store benchmark results.
@@ -66,4 +67,4 @@ def insert_hyperparam(data, conn):
               + "Transformation, mf, 'Order', Partitioner, Partitions, "
               + "alpha, lags, Measure, Value) "
               + "VALUES(datetime('now'),?,?,?,?,?,?,?,?,?,?,?,?)", data)
-    conn.commit()
+    conn.commit()

pyFTS/hyperparam/mvfts.py

@@ -1,25 +1,58 @@
 """
 Distributed Evolutionary Hyperparameter Optimization (DEHO) for MVFTS
+
+variables: A list of dictionaries, where each dictionary contains
+- name: Variable name
+- data_label: data label
+- type: common | seasonal
+- seasonality:
+
+target_variable
+
+genotype: A dictionary containing
+- variables: a list with the selected variables, each instance is the index of a variable in variables
+- params: a list of dictionaries, where each dictionary contains {mf, npart, partitioner, alpha}
+
 """
 
+
 import numpy as np
 import pandas as pd
 import math
+import time
 import random
+import logging
+from pyFTS.common import Util
+from pyFTS.benchmarks import Measures
+from pyFTS.partitioners import Grid, Entropy  # , Huarng
+from pyFTS.common import Membership
+from pyFTS.models import hofts, ifts, pwfts
+from pyFTS.hyperparam import Util as hUtil
+from pyFTS.distributed import dispy as dUtil
 from pyFTS.hyperparam import Evolutionary
+from pyFTS.models.multivariate import mvfts, wmvfts, variable
+from pyFTS.models.seasonal import partitioner as seasonal
+from pyFTS.models.seasonal.common import DateTime
 
 
-def genotype(vars, params, f1, f2):
+def genotype(vars, params, tparams, f1=None, f2=None):
     """
     Create the individual genotype
 
-    :param vars: dictionary with variable names, types, and other parameters
+    :param variables: dictionary with explanatory variable names, types, and other parameters
     :param params: dictionary with variable hyperparameters var: {mf, npart, partitioner, alpha}
+    :param tparams: dictionary with target variable hyperparameters var: {mf, npart, partitioner, alpha}
     :param f1: accuracy fitness value
     :param f2: parsimony fitness value
     :return: the genotype, a dictionary with all hyperparameters
     """
-    ind = dict(vars=vars, params=params, f1=f1, f2=f2)
+    ind = dict(
+        explanatory_variables=vars,
+        explanatory_params=params,
+        target_params = tparams,
+        f1=f1,
+        f2=f2
+    )
     return ind
 
 
@@ -29,21 +62,365 @@ def random_genotype(**kwargs):
 
     :return: the genotype, a dictionary with all hyperparameters
     """
-    order = random.randint(1, 3)
-    lags = [k for k in np.arange(1, order+1)]
+    vars = kwargs.get('variables',None)
+
+    tvar = kwargs.get('target_variable',None)
+
+    l = len(vars)
+
+    nvar = np.random.randint(1,l,1) # the number of variables
+
+    explanatory_variables = np.unique(np.random.randint(0, l, nvar)).tolist() #indexes of the variables
+
+    explanatory_params = []
+
+    for v in explanatory_variables:
+        param = {
+            'mf': random.randint(1, 4),
+            'npart': random.randint(10, 50),
+            'partitioner': 1, #random.randint(1, 2),
+            'alpha': random.uniform(0, .5)
+        }
+        explanatory_params.append(param)
+
+    target_params = {
+            'mf': random.randint(1, 4),
+            'npart': random.randint(10, 50),
+            'partitioner': 1, #random.randint(1, 2),
+            'alpha': random.uniform(0, .5)
+        }
+
     return genotype(
-        random.randint(1, 4),
-        random.randint(10, 100),
-        random.randint(1, 2),
-        order,
-        random.uniform(0, .5),
-        lags,
-        None,
-        None
+        explanatory_variables,
+        explanatory_params,
+        target_params
     )
 
 
+def phenotype(individual, train, fts_method, parameters={}, **kwargs):
+    vars = kwargs.get('variables', None)
+    tvar = kwargs.get('target_variable', None)
 
-def phenotype(individual, train, fts_method, parameters={}):
-    pass
+    explanatory_vars = []
 
+    for ct, vix in enumerate(individual['explanatory_variables']):
+        var = vars[vix]
+        params = individual['explanatory_params'][ct]
+
+        mf = phenotype_mf(params)
+
+        partitioner = phenotype_partitioner(params)
+
+        if var['type'] == 'common':
+            tmp = variable.Variable(var['name'], data_label=var['data_label'], alias=var['name'], partitioner=partitioner,
+                                   partitioner_specific={'mf': mf}, npart=params['npart'], alpha_cut=params['alpha'],
+                                    data=train)
+        elif var['type'] == 'seasonal':
+            sp = {'seasonality': var['seasonality'], 'mf': mf }
+            tmp = variable.Variable(var['name'], data_label=var['data_label'], alias=var['name'],
+                                    partitioner=seasonal.TimeGridPartitioner,
+                                    partitioner_specific=sp, npart=params['npart'], alpha_cut=params['alpha'],
+                                    data=train)
+
+        explanatory_vars.append(tmp)
+
+    tparams = individual['target_params']
+
+    partitioner = phenotype_partitioner(tparams)
+    mf = phenotype_mf(tparams)
+
+    target_var = variable.Variable(tvar['name'], data_label=tvar['data_label'], alias=tvar['name'], partitioner=partitioner,
+                                   partitioner_specific={'mf': mf}, npart=tparams['npart'], alpha_cut=tparams['alpha'],
+                                    data=train)
+
+    model = fts_method(explanatory_variables=explanatory_vars, target_variable=target_var, **parameters)
+    model.fit(train, **parameters)
+
+    return model
+
+
+def phenotype_partitioner(params):
+    if params['partitioner'] == 1:
+        partitioner = Grid.GridPartitioner
+    elif params['partitioner'] == 2:
+        partitioner = Entropy.EntropyPartitioner
+    return partitioner
+
+
+def phenotype_mf(params):
+    if params['mf'] == 1:
+        mf = Membership.trimf
+    elif params['mf'] == 2:
+        mf = Membership.trapmf
+    elif params['mf'] == 3 and params['partitioner'] != 2:
+        mf = Membership.gaussmf
+    else:
+        mf = Membership.trimf
+    return mf
+
+
+def evaluate(dataset, individual, **kwargs):
+    """
+    Evaluate an individual using a sliding window cross validation over the dataset.
+
+    :param dataset: Evaluation dataset
+    :param individual: genotype to be tested
+    :param window_size: The length of scrolling window for train/test on dataset
+    :param train_rate: The train/test split ([0,1])
+    :param increment_rate: The increment of the scrolling window, relative to the window_size ([0,1])
+    :param parameters: dict with model specific arguments for fit method.
+    :return: a tuple (len_lags, rmse) with the parsimony fitness value and the accuracy fitness value
+    """
+    from pyFTS.models import hofts, ifts, pwfts
+    from pyFTS.common import Util
+    from pyFTS.benchmarks import Measures
+    from pyFTS.hyperparam.Evolutionary import __measures
+    from pyFTS.hyperparam.mvfts import phenotype
+    from pyFTS.models.multivariate import mvfts, wmvfts, partitioner, variable, cmvfts,grid, granular, common
+    import numpy as np
+
+    window_size = kwargs.get('window_size', 800)
+    train_rate = kwargs.get('train_rate', .8)
+    increment_rate = kwargs.get('increment_rate', .2)
+    fts_method = kwargs.get('fts_method', wmvfts.WeightedMVFTS)
+    parameters = kwargs.get('parameters',{})
+    tvar = kwargs.get('target_variable', None)
+
+    if individual['f1'] is not None and individual['f2'] is not None:
+        return { key: individual[key] for key in __measures }
+
+    errors = []
+    lengths = []
+
+    kwargs2 = kwargs.copy()
+    kwargs2.pop('fts_method')
+    if 'parameters' in kwargs2:
+        kwargs2.pop('parameters')
+
+    for count, train, test in Util.sliding_window(dataset, window_size, train=train_rate, inc=increment_rate):
+
+        try:
+
+            model = phenotype(individual, train, fts_method=fts_method, parameters=parameters, **kwargs2)
+
+            forecasts = model.predict(test)
+
+            rmse = Measures.rmse(test[tvar['data_label']].values[model.max_lag:], forecasts[:-1])
+            lengths.append(len(model))
+
+            errors.append(rmse)
+
+        except Exception as ex:
+            logging.exception("Error")
+
+            lengths.append(np.nan)
+            errors.append(np.nan)
+
+    try:
+        _rmse = np.nanmean(errors)
+        _len = np.nanmean(lengths)
+
+        f1 = np.nansum([.6 * _rmse, .4 * np.nanstd(errors)])
+        f2 = np.nansum([.9 * _len, .1 * np.nanstd(lengths)])
+
+        return {'f1': f1, 'f2': f2, 'rmse': _rmse, 'size': _len }
+    except Exception as ex:
+        logging.exception("Error")
+        return {'f1': np.inf, 'f2': np.inf, 'rmse': np.inf, 'size': np.inf}
+
+
+def crossover(population, **kwargs):
+    """
+    Crossover operation between two parents
+
+    :param population: the original population
+    :return: a genotype
+    """
+    import random
+
+    n = len(population) - 1
+
+    r1,r2 = 0,0
+    while r1 == r2:
+        r1 = random.randint(0, n)
+        r2 = random.randint(0, n)
+
+    if population[r1]['f1'] < population[r2]['f1']:
+        best = population[r1]
+        worst = population[r2]
+    else:
+        best = population[r2]
+        worst = population[r1]
+
+    rnd = random.uniform(0, 1)
+    nvar = len(best['explanatory_variables']) if rnd < .7 else len(worst['explanatory_variables'])
+
+    explanatory_variables = []
+    explanatory_params = []
+    for ct in np.arange(nvar):
+        if ct < len(best['explanatory_variables']) and ct < len(worst['explanatory_variables']):
+            rnd = random.uniform(0, 1)
+            ix = best['explanatory_variables'][ct] if rnd < .7 else worst['explanatory_variables'][ct]
+        elif ct < len(best['explanatory_variables']):
+            ix = best['explanatory_variables'][ct]
+        elif ct < len(worst['explanatory_variables']):
+            ix = worst['explanatory_variables'][ct]
+
+        if ix in explanatory_variables:
+            continue
+
+        if ix in best['explanatory_variables'] and ix in worst['explanatory_variables']:
+            bix = best['explanatory_variables'].index(ix)
+            wix = worst['explanatory_variables'].index(ix)
+            param = crossover_variable_params(best['explanatory_params'][bix], worst['explanatory_params'][wix])
+        elif ix in best['explanatory_variables']:
+            bix = best['explanatory_variables'].index(ix)
+            param = best['explanatory_params'][bix]
+        elif ix in worst['explanatory_variables']:
+            wix = worst['explanatory_variables'].index(ix)
+            param = worst['explanatory_params'][wix]
+
+        explanatory_variables.append(ix)
+        explanatory_params.append(param)
+
+    tparams = crossover_variable_params(best['target_params'], worst['target_params'])
+
+    descendent = genotype(explanatory_variables, explanatory_params, tparams, None, None)
+
+    return descendent
+
+
+def crossover_variable_params(best, worst):
+    npart = int(round(.7 * best['npart'] + .3 * worst['npart']))
+    alpha = float(.7 * best['alpha'] + .3 * worst['alpha'])
+    rnd = random.uniform(0, 1)
+    mf = best['mf'] if rnd < .7 else worst['mf']
+    rnd = random.uniform(0, 1)
+    partitioner = best['partitioner'] if rnd < .7 else worst['partitioner']
+    param = {'partitioner': partitioner, 'npart': npart, 'alpha': alpha, 'mf': mf}
+    return param
+
+def mutation(individual, **kwargs):
+    """
+    Mutation operator
+
+    :param individual: an individual genotype
+    :param pmut: individual probability o
+    :return:
+    """
+
+    for ct in np.arange(len(individual['explanatory_variables'])):
+        rnd = random.uniform(0, 1)
+        if rnd > .5:
+            mutate_variable_params(individual['explanatory_params'][ct])
+
+    rnd = random.uniform(0, 1)
+    if rnd > .5:
+        mutate_variable_params(individual['target_params'])
+
+    individual['f1'] = None
+    individual['f2'] = None
+
+    return individual
+
+
+def mutate_variable_params(param):
+    param['npart'] = min(50, max(3, int(param['npart'] + np.random.normal(0, 4))))
+    param['alpha'] = min(.5, max(0, param['alpha'] + np.random.normal(0, .5)))
+    param['mf'] = random.randint(1, 4)
+    param['partitioner'] = random.randint(1, 2)
+
+
+def execute(datasetname, dataset, **kwargs):
+    """
+    Batch execution of Distributed Evolutionary Hyperparameter Optimization (DEHO) for monovariate methods
+
+    :param datasetname:
+    :param dataset: The time series to optimize the FTS
+    :keyword database_file:
+    :keyword experiments:
+    :keyword distributed:
+    :keyword ngen: An integer value with the maximum number of generations, default value: 30
+    :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7
+    :keyword npop: An integer value with the population size, default value: 20
+    :keyword pcross: A float value between 0 and 1 with the probability of crossover, default: .5
+    :keyword psel: A float value between 0 and 1 with the probability of selection, default: .5
+    :keyword pmut: A float value between 0 and 1 with the probability of mutation, default: .3
+    :keyword fts_method: The MVFTS method to optimize
+    :keyword parameters: dict with model specific arguments for fts_method
+    :keyword elitism: A boolean value indicating if the best individual must always survive to next population
+    :keyword selection_operator: a function that receives the whole population and return a selected individual
+    :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset
+    :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])
+    :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,
+             relative to the window_size ([0,1])
+    :keyword collect_statistics: A boolean value indicating to collect statistics for each generation
+    :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),
+             or parallel and distributed (distributed='dispy' or distributed='spark')
+    :keyword cluster: If distributed='dispy' the list of cluster nodes, else if distributed='spark' it is the master node
+    :return: the best genotype
+    """
+
+    experiments = kwargs.get('experiments', 30)
+
+    distributed = kwargs.get('distributed', False)
+
+    fts_method = kwargs.get('fts_method', hofts.WeightedHighOrderFTS)
+    shortname = str(fts_method.__module__).split('.')[-1]
+
+    kwargs['mutation_operator'] = mutation
+    kwargs['crossover_operator'] = crossover
+    kwargs['evaluation_operator'] = evaluate
+    kwargs['random_individual'] = random_genotype
+
+    if distributed == 'dispy':
+        nodes = kwargs.get('nodes', ['127.0.0.1'])
+        cluster, http_server = dUtil.start_dispy_cluster(evaluate, nodes=nodes)
+        kwargs['cluster'] = cluster
+
+    ret = []
+    for i in np.arange(experiments):
+        print("Experiment {}".format(i))
+
+        start = time.time()
+        ret, statistics = Evolutionary.GeneticAlgorithm(dataset, **kwargs)
+        end = time.time()
+        ret['time'] = end - start
+        experiment = {'individual': ret, 'statistics': statistics}
+
+        ret = process_experiment(shortname, experiment, datasetname)
+
+    if distributed == 'dispy':
+        dUtil.stop_dispy_cluster(cluster, http_server)
+
+    return ret
+
+
+def process_experiment(fts_method, result, datasetname):
+    """
+    Persist the results of an DEHO execution in sqlite database (best hyperparameters) and json file (generation statistics)
+
+    :param fts_method:
+    :param result:
+    :param datasetname:
+    :param conn:
+    :return:
+    """
+
+    log_result(datasetname, fts_method, result['individual'])
+    persist_statistics(datasetname, result['statistics'])
+    return result['individual']
+
+
+def persist_statistics(datasetname, statistics):
+    import json
+    with open('statistics_{}.json'.format(datasetname), 'w') as file:
+        file.write(json.dumps(statistics))
+
+
+def log_result(datasetname, fts_method, result):
+    import json
+    with open('result_{}{}.json'.format(fts_method,datasetname), 'w') as file:
+        file.write(json.dumps(result))
+
+        print(result)

pyFTS/models/multivariate/FLR.py

@@ -19,7 +19,7 @@ class FLR(object):
         self.RHS = set
 
     def __str__(self):
-        return "{} -> {}".format([self.LHS[k] for k in self.LHS.keys()], self.RHS)
+        return "{} -> {}".format([k for k in self.LHS.values()], self.RHS)
 
 
 

pyFTS/models/multivariate/mvfts.py

@@ -3,6 +3,7 @@ from pyFTS.partitioners import Grid
 from pyFTS.models.multivariate import FLR as MVFLR, common, flrg as mvflrg
 from itertools import product
 from types import LambdaType
+from copy import deepcopy
 
 import numpy as np
 import pandas as pd
@@ -75,19 +76,19 @@ class MVFTS(fts.FTS):
         for path in product_dict(**lags):
             flr = MVFLR.FLR()
 
-            for var, fset in path.items():
-                flr.set_lhs(var, fset)
+            flr.LHS = path
+
+            #for var, fset in path.items():
+            #    flr.set_lhs(var, fset)
 
             if len(flr.LHS.keys()) == len(self.explanatory_variables):
                 flrs.append(flr)
-            else:
-                print(flr)
 
         return flrs
 
     def generate_flrs(self, data):
         flrs = []
-        for ct in range(1, len(data.index)):
+        for ct in np.arange(1, len(data.index)):
             ix = data.index[ct-1]
             data_point = self.format_data( data.loc[ix] )
 
@@ -99,8 +100,9 @@ class MVFTS(fts.FTS):
 
             for flr in tmp_flrs:
                 for v, s in target:
-                    flr.set_rhs(s)
-                    flrs.append(flr)
+                    new_flr = deepcopy(flr)
+                    new_flr.set_rhs(s)
+                    flrs.append(new_flr)
 
         return flrs
 
@@ -113,7 +115,6 @@ class MVFTS(fts.FTS):
 
             self.flrgs[flrg.get_key()].append_rhs(flr.RHS)
 
-
     def train(self, data, **kwargs):
 
         ndata = self.apply_transformations(data)

pyFTS/models/seasonal/partitioner.py

@@ -72,55 +72,54 @@ class TimeGridPartitioner(partitioner.Partitioner):
             partlen = self.season.value / self.partitions
             pl2 = partlen / 2
 
-        count = 0
-        for c in np.arange(self.min, self.max, partlen):
+        for count, midpoint in enumerate(np.arange(self.min, self.max, partlen)):
             set_name = self.get_name(count)
             if self.membership_function == Membership.trimf:
-                if c == self.min:
+                if midpoint == self.min or count == 0:
                     tmp = Composite(set_name, superset=True, **kwargs)
                     tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf,
                                             [self.season.value - pl2, self.season.value,
                                              self.season.value + pl2], self.season.value, alpha=1,
                                             **kwargs))
                     tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf,
-                                            [c - partlen, c, c + partlen], c,
+                                            [midpoint - partlen, midpoint, midpoint + partlen], midpoint,
                                             **kwargs))
-                    tmp.centroid = c
+                    tmp.centroid = midpoint
                     sets[set_name] = tmp
-                elif c == self.max - partlen:
+                elif midpoint == self.max - partlen or count == self.partitions - 1:
                     tmp = Composite(set_name, superset=True, **kwargs)
                     tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf,
                                             [-pl2, 0.0,
                                              pl2], 0.0, alpha=1,
                                             **kwargs))
                     tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf,
-                                            [c - partlen, c, c + partlen], c,
+                                            [midpoint - partlen, midpoint, midpoint + partlen], midpoint,
                                             **kwargs))
-                    tmp.centroid = c
+                    tmp.centroid = midpoint
                     sets[set_name] = tmp
                 else:
                     sets[set_name] = FuzzySet(self.season, set_name, Membership.trimf,
-                                         [c - partlen, c, c + partlen], c,
+                                         [midpoint - partlen, midpoint, midpoint + partlen], midpoint,
                                          **kwargs)
             elif self.membership_function == Membership.gaussmf:
-                sets[set_name] = FuzzySet(self.season, set_name, Membership.gaussmf, [c, partlen / 3], c,
+                sets[set_name] = FuzzySet(self.season, set_name, Membership.gaussmf, [midpoint, partlen / 3], midpoint,
                                      **kwargs)
             elif self.membership_function == Membership.trapmf:
                 q = partlen / 4
-                if c == self.min:
+                if midpoint == self.min:
                     tmp = Composite(set_name, superset=True)
                     tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf,
                                             [self.season.value - pl2, self.season.value,
                                              self.season.value + 0.0000001], 0,
                                             **kwargs))
                     tmp.append_set(FuzzySet(self.season, set_name, Membership.trapmf,
-                                            [c - partlen, c - q, c + q, c + partlen], c,
+                                            [midpoint - partlen, midpoint - q, midpoint + q, midpoint + partlen], midpoint,
                                             **kwargs))
-                    tmp.centroid = c
+                    tmp.centroid = midpoint
                     sets[set_name] = tmp
                 else:
                     sets[set_name] = FuzzySet(self.season, set_name, Membership.trapmf,
-                                         [c - partlen, c - q, c + q, c + partlen], c,
+                                         [midpoint - partlen, midpoint - q, midpoint + q, midpoint + partlen], midpoint,
                                          **kwargs)
             count += 1
 

pyFTS/partitioners/partitioner.py

@@ -169,9 +169,12 @@ class Partitioner(object):
         if method == 'fuzzy' and mode == 'vector':
             return mv
         elif method == 'fuzzy' and mode == 'sets':
-            ix = np.ravel(np.argwhere(mv > 0.))
-            sets = [self.ordered_sets[i] for i in ix]
-            return sets
+            try:
+                ix = np.ravel(np.argwhere(mv > 0.))
+                sets = [self.ordered_sets[i] for i in ix if i < self.partitions]
+                return sets
+            except Exception as ex:
+                return None
         elif method == 'maximum' and mode == 'sets':
             mx = max(mv)
             ix = np.ravel(np.argwhere(mv == mx))

pyFTS/tests/hyperparam.py

@@ -1,20 +1,23 @@
 import numpy as np
 import pandas as pd
-from pyFTS.hyperparam import GridSearch, Evolutionary
+from pyFTS.hyperparam import GridSearch, Evolutionary, mvfts as deho_mv
 from pyFTS.models import pwfts
+from pyFTS.models.multivariate import mvfts, wmvfts
+from pyFTS.models.seasonal.common import DateTime
 
 
 def get_dataset():
-    from pyFTS.data import SONDA
-    #from pyFTS.data import Malaysia
+    #from pyFTS.data import SONDA
+    from pyFTS.data import Malaysia
 
-    data = [k for k in SONDA.get_data('ws_10m') if k > 0.1 and k != np.nan and k is not None]
-    data = [np.nanmean(data[k:k+60]) for k in np.arange(0,len(data),60)]
+    #data = [k for k in SONDA.get_data('ws_10m') if k > 0.1 and k != np.nan and k is not None]
+    #data = [np.nanmean(data[k:k+60]) for k in np.arange(0,len(data),60)]
     #data = pd.read_csv('https://query.data.world/s/6xfb5useuotbbgpsnm5b2l3wzhvw2i', sep=';')
-    #data = Malaysia.get_data('temperature')
+    data = Malaysia.get_dataframe()
+    data['time'] = pd.to_datetime(data["time"], format='%m/%d/%y %I:%M %p')
 
-    return 'SONDA.ws_10m', data
-    #return 'Malaysia.temperature', data #train, test
+    #return 'SONDA.ws_10m', data
+    return 'Malaysia', data.iloc[:5000] #train, test
     #return 'Malaysia.temperature', data  # train, test
 
 '''
@@ -43,6 +46,30 @@ datsetname, dataset  = get_dataset()
 #GridSearch.execute(hyperparams, datsetname, dataset, nodes=nodes,
 #                   window_size=10000, train_rate=.9, increment_rate=1,)
 
+explanatory_variables =[
+    {'name': 'Load', 'data_label': 'load', 'type': 'common'},
+    {'name': 'Temperature', 'data_label': 'temperature', 'type': 'common'},
+    {'name': 'Daily', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.minute_of_day, 'npart': 24 },
+    {'name': 'Weekly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_week, 'npart': 7 },
+    #{'name': 'Monthly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_month, 'npart': 4 },
+    {'name': 'Yearly', 'data_label': 'time', 'type': 'seasonal', 'seasonality': DateTime.day_of_year, 'npart': 12 }
+]
+
+target_variable = {'name': 'Load', 'data_label': 'load', 'type': 'common'}
+nodes=['192.168.28.38']
+deho_mv.execute(datsetname, dataset,
+              ngen=10, npop=10,psel=0.6, pcross=.5, pmut=.3,
+              window_size=5000, train_rate=.9, increment_rate=1,
+              experiments=1,
+              fts_method=wmvfts.WeightedMVFTS,
+              variables=explanatory_variables,
+              target_variable=target_variable,
+              distributed='dispy', nodes=nodes,
+              #parameters=dict(num_batches=5)
+              #parameters=dict(distributed='dispy', nodes=nodes, num_batches=5)
+              )
+
+'''
 ret = Evolutionary.execute(datsetname, dataset,
                            ngen=30, npop=20,psel=0.6, pcross=.5, pmut=.3,
                            window_size=10000, train_rate=.9, increment_rate=.3,
@@ -50,7 +77,7 @@ ret = Evolutionary.execute(datsetname, dataset,
                            fts_method=pwfts.ProbabilisticWeightedFTS,
                            database_file='experiments.db',
                            distributed='dispy', nodes=nodes)
-
+'''
 #res = GridSearch.cluster_method({'mf':1, 'partitioner': 1, 'npart': 10, 'lags':[1], 'alpha': 0.0, 'order': 1},
 #                          dataset, window_size = 10000, train_rate = .9, increment_rate = 1)
 

pyFTS/tests/multivariate.py

@@ -16,33 +16,39 @@ from pyFTS.models.seasonal.common import DateTime
 from pyFTS.models.multivariate import common, variable, mvfts
 from pyFTS.partitioners import Grid
 from pyFTS.common import Membership
-
-
 import os
 
-from pyFTS.data import NASDAQ
+from pyFTS.data import Malaysia, Enrollments
 
-train_data = NASDAQ.get_data()[:2000]
-test_data = NASDAQ.get_data()[2000:3000]
+df = Malaysia.get_dataframe()
+df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p')
 
-from pyFTS.partitioners import Grid
+train_mv = df.iloc[:4500]
+test_mv = df.iloc[4500:5000]
 
-partitioner = Grid.GridPartitioner(data=train_data, npart=35)
+del(df)
 
-from pyFTS.models import pwfts, hofts
+sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
 
-#model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner, order=2)
-#from pyFTS.models.incremental import TimeVariant
+vhour = variable.Variable("Hour", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24,
+                          data=train_mv, partitioner_specific=sp, alpha_cut=.3)
 
-#model = TimeVariant.Retrainer(partitioner_method=Grid.GridPartitioner, partitioner_params={'npart': 35},
-#                              fts_method=pwfts.ProbabilisticWeightedFTS, fts_params={}, order=2 ,
-#                              batch_size=100, window_length=500)
+vtemp = variable.Variable("Temperature", data_label="temperature", alias='temp',
+                         partitioner=Grid.GridPartitioner, npart=15, func=Membership.gaussmf,
+                         data=train_mv, alpha_cut=.3)
 
-model = hofts.HighOrderFTS(partitioner=partitioner, order=2)
-model.fit(train_data)
+vload = variable.Variable("Load", data_label="load", alias='load',
+                         partitioner=Grid.GridPartitioner, npart=20, func=Membership.trimf,
+                         data=train_mv, alpha_cut=.3)
 
-print(model.predict(test_data, steps_ahead=10))
+model = mvfts.MVFTS(explanatory_variables=[vhour, vtemp, vload], target_variable=vload)
+#fs = Grid.GridPartitioner(data=Enrollments.get_data(), npart=10)
+#print(fs)
+#model = pwfts.ProbabilisticWeightedFTS(partitioner=vload.partitioner, order=2)
+model.fit(train_mv) #, num_batches=10) #, distributed='dispy',nodes=['192.168.0.110'])
+#model.fit(Enrollments.get_data()) #, num_batches=20) #, distributed='dispy',nodes=['192.168.0.110'])
 
+print(model)
 
 '''
 def sample_by_hour(data):