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.gitignore
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.gitignore
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@ -1,57 +0,0 @@
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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downloads/
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sdist/
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var/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*,cover
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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2
MANIFEST
2
MANIFEST
@ -104,7 +104,7 @@ pyFTS/models/seasonal/common.py
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pyFTS/models/seasonal/msfts.py
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pyFTS/models/seasonal/partitioner.py
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pyFTS/models/seasonal/sfts.py
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pyFTS/partitioners/KMeans.py
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pyFTS/partitioners/CMeans.py
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pyFTS/partitioners/Entropy.py
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pyFTS/partitioners/FCM.py
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pyFTS/partitioners/Grid.py
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@ -38,7 +38,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
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2. **Universe of Discourse Partitioning**: This is the most important step. Here, the range of values of the numerical time series *Y(t)* will be splited in overlapped intervals and for each interval will be created a Fuzzy Set. This step is performed by pyFTS.partition module and its classes (for instance GridPartitioner, EntropyPartitioner, etc). The main parameters are:
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- the number of intervals
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- which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py))
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- partition scheme ([GridPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py), [EntropyPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py)[3], [FCMPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py), [KMeansPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/KMeans.py), [HuarngPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py)[4])
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- partition scheme ([GridPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py), [EntropyPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py)[3], [FCMPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py), [CMeansPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py), [HuarngPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py)[4])
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Check out the jupyter notebook on [notebooks/Partitioners.ipynb](https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb) for sample codes.
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@ -28,10 +28,10 @@ pyFTS.partitioners.Class module
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:undoc-members:
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:show-inheritance:
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pyFTS.partitioners.KMeans module
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pyFTS.partitioners.CMeans module
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--------------------------------
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.. automodule:: pyFTS.partitioners.KMeans
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.. automodule:: pyFTS.partitioners.CMeans
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:members:
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:undoc-members:
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:show-inheritance:
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@ -1,5 +1,5 @@
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from pyFTS.common.transformations.transformation import Transformation
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# from pandas import datetime
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from pandas import datetime
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from sklearn.linear_model import LinearRegression
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import numpy as np
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import pandas as pd
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@ -146,7 +146,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
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new_data_point[self.target_variable.data_label] = tmp.expected_value()
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sample = pd.concat([sample, pd.DataFrame([new_data_point])], ignore_index=True)
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sample = sample.append(new_data_point, ignore_index=True)
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return ret[-steps:]
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@ -199,7 +199,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
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for k in np.arange(0, steps):
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sample = ret.iloc[k:self.order+k]
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tmp = self.forecast_multivariate(sample, **kwargs)
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ret = pd.concat([ret, pd.DataFrame([tmp])], ignore_index=True)
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ret = ret.append(tmp, ignore_index=True)
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return ret
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@ -211,7 +211,7 @@ class MVFTS(fts.FTS):
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new_data_point[self.target_variable.data_label] = tmp
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ndata = pd.concat([ndata, pd.DataFrame([new_data_point])], ignore_index=True)
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ndata = ndata.append(new_data_point, ignore_index=True)
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return ret[-steps:]
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@ -307,8 +307,8 @@ class MVFTS(fts.FTS):
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new_data_point_lo[self.target_variable.data_label] = min(tmp_lo)
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new_data_point_up[self.target_variable.data_label] = max(tmp_up)
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lo = pd.concat([lo, pd.DataFrame([new_data_point_lo])], ignore_index=True)
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up = pd.concat([up, pd.DataFrame([new_data_point_up])], ignore_index=True)
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lo = lo.append(new_data_point_lo, ignore_index=True)
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up = up.append(new_data_point_up, ignore_index=True)
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return ret[-steps:]
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@ -14,15 +14,15 @@ def distance(x, y):
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return math.sqrt(tmp)
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def k_means(k, dados, tam):
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# Инициализирует центроиды, выбирая случайные элементы из множества
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def c_means(k, dados, tam):
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# Inicializa as centróides escolhendo elementos aleatórios dos conjuntos
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centroides = [dados[rnd.randint(0, len(dados)-1)] for kk in range(0, k)]
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grupos = [-1 for x in range(0, len(dados))]
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it_semmodificacao = 0
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# для каждого экземпляра
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# para cada instância
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iteracoes = 0
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while iteracoes < 1000 and it_semmodificacao < 10:
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inst_count = 0
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@ -31,7 +31,7 @@ def k_means(k, dados, tam):
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for instancia in dados:
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# проверяет расстояние до каждого центроида
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# verifica a distância para cada centroide
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grupo_count = 0
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dist = 10000
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@ -41,7 +41,7 @@ def k_means(k, dados, tam):
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tmp = distance(instancia, grupo)
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if tmp < dist:
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dist = tmp
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# ассоциирует центроид с наименьшим расстоянием до экземпляра
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# associa a a centroide de menor distância à instância
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grupos[inst_count] = grupo_count
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grupo_count = grupo_count + 1
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@ -55,7 +55,7 @@ def k_means(k, dados, tam):
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else:
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it_semmodificacao = 0
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# обновляет каждый центроид на основе средних значений всех связанных с ним экземпляров
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# atualiza cada centroide com base nos valores médios de todas as instâncias à ela associadas
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grupo_count = 0
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for grupo in centroides:
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total_inst = functools.reduce(operator.add, [1 for xx in grupos if xx == grupo_count], 0)
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@ -77,21 +77,21 @@ def k_means(k, dados, tam):
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return centroides
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class KMeansPartitioner(partitioner.Partitioner):
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class CMeansPartitioner(partitioner.Partitioner):
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def __init__(self, **kwargs):
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super(KMeansPartitioner, self).__init__(name="KMeans", **kwargs)
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super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs)
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def build(self, data):
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sets = {}
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kwargs = {'type': self.type, 'variable': self.variable}
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centroides = k_means(self.partitions, data, 1)
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centroides = c_means(self.partitions, data, 1)
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centroides.append(self.max)
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centroides.append(self.min)
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centroides = list(set(centroides))
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centroides.sort()
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for c in range(1, len(centroides) - 1):
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for c in np.arange(1, len(centroides) - 1):
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_name = self.get_name(c)
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sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf,
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[round(centroides[c - 1], 3), round(centroides[c], 3), round(centroides[c + 1], 3)],
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@ -36,7 +36,7 @@ def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
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centroids = [data[rnd.randint(0, data_length - 1)] for kk in range(0, k)]
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# Membership table
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membership_table = np.zeros((data_length, k))
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membership_table = np.zeros((k, data_length)) #[[0 for kk in range(0, k)] for xx in range(0, data_length)]
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mean_change = 1000
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@ -50,12 +50,12 @@ def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
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inst_count = 0
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for instance in data:
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dist_groups = np.zeros(k)
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dist_groups = np.zeros(k) #[0 for xx in range(0, k)]
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for group_count, group in enumerate(centroids):
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dist_groups[group_count] = fuzzy_distance(group, instance)
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# dist_groups_total = functools.reduce(operator.add, [xk for xk in dist_groups])
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dist_groups_total = functools.reduce(operator.add, [xk for xk in dist_groups])
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for grp in range(0, k):
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if dist_groups[grp] == 0:
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@ -19,13 +19,13 @@ class HuarngPartitioner(partitioner.Partitioner):
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def build(self, data):
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diff = Transformations.Differential(1)
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data2 = diff.apply(data)
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divs = np.abs( np.mean(data2) / 2 )
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davg = np.abs( np.mean(data2) / 2 )
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if divs <= 1.0:
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if davg <= 1.0:
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base = 0.1
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elif 1 < divs <= 10:
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elif 1 < davg <= 10:
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base = 1.0
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elif 10 < divs <= 100:
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elif 10 < davg <= 100:
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base = 10
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else:
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base = 100
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@ -18,20 +18,19 @@ all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitio
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mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
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def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
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def plot_sets(data, sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
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"""
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Plot all fuzzy sets in a Partitioner
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"""
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num = len(sets)
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num_cols_plot = 1
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if axis is None:
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fig, axes = plt.subplots(nrows=num, ncols=num_cols_plot, figsize=size, squeeze=False)
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for k in range(num):
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fig, axes = plt.subplots(nrows=num, ncols=1,figsize=size)
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for k in np.arange(0,num):
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ticks = []
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x = []
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ax = axes[k, num_cols_plot-1] if axis is None else axis
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ax = axes[k] if axis is None else axis
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ax.set_title(titles[k])
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ax.set_ylim([0, 1.1])
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for key in sets[k].keys():
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@ -55,7 +54,7 @@ def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, a
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Util.show_and_save_image(fig, file, save)
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def plot_partitioners(objs, tam=[12, 10], save=False, file=None, axis=None):
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def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None, axis=None):
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sets = [k.sets for k in objs]
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titles = [k.name for k in objs]
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plot_sets(sets, titles, tam, save, file, axis)
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@ -1,7 +1,6 @@
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from pyFTS.common import FuzzySet, Membership
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import numpy as np
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from scipy.spatial import KDTree
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import warnings
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class Partitioner(object):
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@ -46,9 +45,6 @@ class Partitioner(object):
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if kwargs.get('preprocess',True):
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data = kwargs.get('data',[None])
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if isinstance(data, np.ndarray) and len(data.shape) > 1:
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warnings.warn(f"An ndarray of dimension greater than 1 is used. shape.len(): {len(data.shape)}")
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if self.indexer is not None:
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ndata = self.indexer.get_data(data)
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@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D
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import datetime
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import pandas as pd
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from pyFTS.partitioners import Grid, KMeans, FCM, Entropy
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from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
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from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
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from pyFTS import sfts
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from pyFTS.models import msfts
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1
setup.py
1
setup.py
@ -26,7 +26,6 @@ setuptools.setup(
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.10',
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'Programming Language :: Python :: 3.11',
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'Intended Audience :: Science/Research',
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'Intended Audience :: Developers',
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'Intended Audience :: Education',
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