Bugfix in partitioners.Entropy, not catching fuzzy set names correctly
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@ -94,8 +94,8 @@ class EntropyPartitioner(partitioner.Partitioner):
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partitions.append(self.max)
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partitions = list(set(partitions))
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partitions.sort()
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for c in np.arange(1, len(partitions) - 1):
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_name = self.get_name(c)
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for c in np.arange(1, len(partitions)-1):
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_name = self.get_name(c-1)
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if self.membership_function == Membership.trimf:
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sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf,
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[partitions[c - 1], partitions[c], partitions[c + 1]],partitions[c], **kwargs)
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@ -25,14 +25,29 @@ from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia, Enrollments
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from pyFTS.partitioners import Grid
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from pyFTS.models import pwfts, tsaur
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dataset = pd.read_csv('/home/petronio/Downloads/kalang.csv', sep=',')
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dataset = pd.read_csv('/home/petronio/Downloads/Klang-daily Max.csv', sep=',')
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dataset['date'] = pd.to_datetime(dataset["date"], format='%Y-%m-%d %H:%M:%S')
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dataset['date'] = pd.to_datetime(dataset["Day/Month/Year"], format='%m/%d/%Y')
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dataset['value'] = dataset['Daily-Max API']
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train_uv = dataset['value'].values[:24505]
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test_uv = dataset['value'].values[24505:]
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partitioner = Grid.GridPartitioner(data=train_uv, npart=35)
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train_uv = dataset['value'].values[:732]
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test_uv = dataset['value'].values[732:]
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from itertools import product
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levels = ['VeryLow', 'Low', 'Medium', 'High', 'VeryHigh']
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sublevels = [str(k) for k in np.arange(0, 7)]
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names = []
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for combination in product(*[levels, sublevels]):
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names.append(combination[0] + combination[1])
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print(names)
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#partitioner = Grid.GridPartitioner(data=train_uv, npart=35, names=names)
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partitioner = Entropy.EntropyPartitioner(data=train_uv,npart=35, names=names)
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print(partitioner)
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model = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner) #, order=2, lags=[3,4])
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#model = tsaur.MarkovWeightedFTS(partitioner=partitioner)
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@ -3,7 +3,7 @@ import pandas as pd
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import time
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from pyFTS.data import Enrollments, TAIEX, SONDA
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from pyFTS.partitioners import Grid, Simple
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from pyFTS.partitioners import Grid, Simple, Entropy
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from pyFTS.common import Util
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from pyspark import SparkConf
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@ -114,7 +114,7 @@ vhour = variable.Variable("Hour", data_label="date", partitioner=seasonal.TimeGr
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data=train_mv, partitioner_specific=sp, data_type=pd.datetime, mask='%Y-%m-%d %H:%M:%S')
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vvalue = variable.Variable("Pollution", data_label="value", alias='value',
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partitioner=Grid.GridPartitioner, npart=35, data_type=np.float64,
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partitioner=Entropy.EntropyPartitioner, npart=35, data_type=np.float64,
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data=train_mv)
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fs = grid.GridCluster(explanatory_variables=[vhour, vvalue], target_variable=vvalue)
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@ -122,7 +122,7 @@ fs = grid.GridCluster(explanatory_variables=[vhour, vvalue], target_variable=vva
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model = cmvfts.ClusteredMVFTS(explanatory_variables=[vhour, vvalue], target_variable=vvalue,
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partitioner=fs)
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model.fit(train_mv, distributed='spark', url='spark://192.168.0.106:7077')
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model.fit(train_mv) #, distributed='spark', url='spark://192.168.0.106:7077')
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#'''
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#print(model)
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