Bugfixes in MVFTS and DEHO

This commit is contained in:
Petrônio Cândido 2019-08-05 14:20:10 -03:00
parent 876de2721d
commit 5b7e4edcd7
4 changed files with 31 additions and 21 deletions

View File

@ -75,9 +75,14 @@ def random_genotype(**kwargs):
explanatory_params = []
for v in explanatory_variables:
var = vars[v]
if var['type'] == 'common':
npart = random.randint(7, 50)
else:
npart = var['npart']
param = {
'mf': random.randint(1, 4),
'npart': random.randint(10, 50),
'npart': npart,
'partitioner': 1, #random.randint(1, 2),
'alpha': random.uniform(0, .5)
}
@ -85,7 +90,7 @@ def random_genotype(**kwargs):
target_params = {
'mf': random.randint(1, 4),
'npart': random.randint(10, 50),
'npart': random.randint(7, 50),
'partitioner': 1, #random.randint(1, 2),
'alpha': random.uniform(0, .5)
}
@ -133,6 +138,8 @@ def phenotype(individual, train, fts_method, parameters={}, **kwargs):
partitioner_specific={'mf': mf}, npart=tparams['npart'], alpha_cut=tparams['alpha'],
data=train)
explanatory_vars.append(target_var)
model = fts_method(explanatory_variables=explanatory_vars, target_variable=target_var, **parameters)
model.fit(train, **parameters)
@ -171,6 +178,7 @@ def evaluate(dataset, individual, **kwargs):
: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
"""
import logging
from pyFTS.models import hofts, ifts, pwfts
from pyFTS.common import Util
from pyFTS.benchmarks import Measures

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@ -2,7 +2,7 @@ from pyFTS.common import FuzzySet, Membership
import numpy as np
from scipy.spatial import KDTree
import matplotlib.pylab as plt
import logging
class Partitioner(object):
"""
@ -154,7 +154,6 @@ class Partitioner(object):
method = kwargs.get('method', 'fuzzy')
nearest = self.search(data, type='index')
mv = np.zeros(self.partitions)
for ix in nearest:
@ -317,16 +316,20 @@ class Partitioner(object):
it represents the fuzzy set name.
:return: the fuzzy set
"""
if isinstance(item, (int, np.int, np.int8, np.int16, np.int32, np.int64)):
if item < 0 or item >= self.partitions:
raise ValueError("The fuzzy set index must be between 0 and {}.".format(self.partitions))
return self.sets[self.ordered_sets[item]]
elif isinstance(item, str):
if item not in self.sets:
raise ValueError("The fuzzy set with name {} does not exist.".format(item))
return self.sets[item]
else:
raise ValueError("The parameter 'item' must be an integer or a string and the value informed was {} of type {}!".format(item, type(item)))
try:
if isinstance(item, (int, np.int, np.int8, np.int16, np.int32, np.int64)):
if item < 0 or item >= self.partitions:
raise ValueError("The fuzzy set index must be between 0 and {}.".format(self.partitions))
return self.sets[self.ordered_sets[item]]
elif isinstance(item, str):
if item not in self.sets:
raise ValueError("The fuzzy set with name {} does not exist.".format(item))
return self.sets[item]
else:
raise ValueError("The parameter 'item' must be an integer or a string and the value informed was {} of type {}!".format(item, type(item)))
except Exception as ex:
logging.exception("Error")
def __iter__(self):
"""

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@ -17,7 +17,7 @@ def get_dataset():
data['time'] = pd.to_datetime(data["time"], format='%m/%d/%y %I:%M %p')
#return 'SONDA.ws_10m', data
return 'Malaysia', data.iloc[:5000] #train, test
return 'Malaysia', data.iloc[:2000] #train, test
#return 'Malaysia.temperature', data # train, test
'''
@ -47,7 +47,6 @@ datsetname, dataset = get_dataset()
# 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 },
@ -59,13 +58,13 @@ 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,
window_size=2000, 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)
#distributed='dispy', nodes=nodes,
parameters=dict(num_batches=5)
#parameters=dict(distributed='dispy', nodes=nodes, num_batches=5)
)

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@ -23,8 +23,8 @@ from pyFTS.data import Malaysia, Enrollments
df = Malaysia.get_dataframe()
df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p')
train_mv = df.iloc[:4500]
test_mv = df.iloc[4500:5000]
train_mv = df.iloc[:1800]
test_mv = df.iloc[1800:2000]
del(df)