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Bugfixes and improvements on pwfts and multivariate.granular

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This commit is contained in:
Petrônio Cândido 2019-06-22 18:29:40 -03:00
parent 35898f338a
commit f3cf757e96
8 changed files with 69 additions and 79 deletions
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3
pyFTS/benchmarks/Measures.py
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@ -300,6 +300,9 @@ def crps(targets, densities):
targets = [targets]
n = len(densities)
if n == 0:
return np.nan
for ct, df in enumerate(densities):
_crps += np.nansum([(df.cumulative(bin) - (1 if bin >= targets[ct] else 0)) ** 2 for bin in df.bins])

2
pyFTS/common/Util.py
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@ -216,7 +216,7 @@ def plot_distribution2(probabilitydist, data, **kwargs):
if kwargs.get('median',True):
y = [data[start_at]]
for pd in probabilitydist:
qts = pd.quantile(.5)
qts = pd.quantile([.5])
y.append(qts[0])
ax.plot(x, y, color='red', label='Median')

31
pyFTS/hyperparam/Evolutionary.py
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@ -139,24 +139,33 @@ def evaluate(dataset, individual, **kwargs):
for count, train, test in Util.sliding_window(dataset, window_size, train=train_rate, inc=increment_rate):
model = phenotype(individual, train, fts_method=fts_method, parameters=parameters)
try:
forecasts = model.predict(test)
model = phenotype(individual, train, fts_method=fts_method, parameters=parameters)
rmse = Measures.rmse(test[model.max_lag:], forecasts[:-1])
lengths.append(len(model))
forecasts = model.predict(test)
errors.append(rmse)
rmse = Measures.rmse(test[model.max_lag:], forecasts[:-1])
lengths.append(len(model))
_lags = sum(model.lags) * 100
errors.append(rmse)
_rmse = np.nanmean(errors)
_len = np.nanmean(lengths)
except:
lengths.append(np.nan)
errors.append(np.nan)
f1 = np.nansum([.6 * _rmse, .4 * np.nanstd(errors)])
f2 = np.nansum([.4 * _len, .6 * _lags])
try:
_lags = sum(model.lags) * 100
return {'f1': f1, 'f2': f2, 'rmse': _rmse, 'size': _len }
_rmse = np.nanmean(errors)
_len = np.nanmean(lengths)
f1 = np.nansum([.6 * _rmse, .4 * np.nanstd(errors)])
f2 = np.nansum([.4 * _len, .6 * _lags])
return {'f1': f1, 'f2': f2, 'rmse': _rmse, 'size': _len }
except:
return {'f1': np.inf, 'f2': np.inf, 'rmse': np.inf, 'size': np.inf}
def tournament(population, objective, **kwargs):

4
pyFTS/models/multivariate/granular.py
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@ -15,8 +15,8 @@ class GranularWMVFTS(cmvfts.ClusteredMVFTS):
"""The most recent trained model"""
self.knn = kwargs.get('knn', 2)
self.order = kwargs.get("order", 2)
self.shortname = "GranularWMVFTS"
self.name = "Granular Weighted Multivariate FTS"
self.shortname = "FIG-FTS"
self.name = "Fuzzy Information Granular FTS"
self.mode = kwargs.get('mode','sets')
def train(self, data, **kwargs):

4
pyFTS/models/pwfts.py
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@ -165,8 +165,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
def pwflrg_lhs_memberhip_fuzzyfied(self, flrg, sample):
vals = []
for ct, fuzz in enumerate(sample):
vals.append([mv for fset, mv in fuzz if fset == flrg.LHS[ct]])
for ct in range(len(flrg.LHS)): # fuzz in enumerate(sample):
vals.append([mv for fset, mv in sample[ct] if fset == flrg.LHS[ct]])
return np.nanprod(vals)

2
pyFTS/probabilistic/ProbabilityDistribution.py
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@ -217,7 +217,7 @@ class ProbabilityDistribution(object):
else:
try:
k = self.bin_index.find_ge(values)
return self.cdf[values]
return self.cdf[k]
except:
return np.nan

41
pyFTS/tests/multivariate.py
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@ -19,9 +19,33 @@ from pyFTS.common import Membership
import os
'''
def sample_by_hour(data):
return [np.nanmean(data[k:k+60]) for k in np.arange(0,len(data),60)]
def sample_date_by_hour(data):
return [data[k] for k in np.arange(0,len(data),60)]
from pyFTS.data import SONDA
sonda = SONDA.get_dataframe()[['datahora','glo_avg','ws_10m']]
sonda = sonda.drop(sonda.index[np.where(sonda["ws_10m"] <= 0.01)])
sonda = sonda.drop(sonda.index[np.where(sonda["glo_avg"] <= 0.01)])
sonda = sonda.dropna()
sonda['datahora'] = pd.to_datetime(sonda["datahora"], format='%Y-%m-%d %H:%M:%S')
from pyFTS.data import SONDA, Malaysia
var = {
'datahora': sample_date_by_hour(sonda['datahora'].values),
'glo_avg': sample_by_hour(sonda['glo_avg'].values),
'ws_10m': sample_by_hour(sonda['ws_10m'].values)
}
df = pd.DataFrame(var)
'''
from pyFTS.data import Malaysia
df = Malaysia.get_dataframe()
df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p')
@ -39,11 +63,10 @@ variables = {
alpha_cut=.25)
}
methods = [mvfts.MVFTS, wmvfts.WeightedMVFTS, granular.GranularWMVFTS]
#methods = [granular.GranularWMVFTS]
methods = [granular.GranularWMVFTS]
parameters = [
{},{},
dict(fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both',
order=1, knn=1)
]
@ -52,16 +75,16 @@ bchmk.multivariate_sliding_window_benchmarks2(df, 10000, train=0.9, inc=0.25,
methods=methods,
methods_parameters=parameters,
variables=variables,
target_variable='Load',
type='interval',
target_variable='Temperature',
type='distribution',
steps_ahead=[1],
distributed=False,
nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="experiments.db", dataset='Malaysia',
file="experiments.db", dataset='Malaysia.temperature',
tag="experiments"
)
'''
from pyFTS.data import lorentz
df = lorentz.get_dataframe(iterations=5000)

61
pyFTS/tests/pwfts.py
View File

@ -19,63 +19,18 @@ from pyFTS.models.seasonal import partitioner as seasonal
from pyFTS.models.seasonal.common import DateTime
from pyFTS.common import Membership
def sample_by_hour(data):
return [np.nanmean(data[k:k+60]) for k in np.arange(0,len(data),60)]
def sample_date_by_hour(data):
return [data[k] for k in np.arange(0,len(data),60)]
from pyFTS.data import SONDA
sonda = SONDA.get_dataframe()[['datahora','glo_avg','ws_10m']]
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)]
sonda = sonda.drop(sonda.index[np.where(sonda["ws_10m"] <= 0.01)])
sonda = sonda.drop(sonda.index[np.where(sonda["glo_avg"] <= 0.01)])
sonda = sonda.dropna()
sonda['datahora'] = pd.to_datetime(sonda["datahora"], format='%Y-%m-%d %H:%M:%S')
train = data[:9000]
test = data[9000:10000]
fs = Grid.GridPartitioner(data=train, npart=95)
var = {
'datahora': sample_date_by_hour(sonda['datahora'].values),
'glo_avg': sample_by_hour(sonda['glo_avg'].values),
'ws_10m': sample_by_hour(sonda['ws_10m'].values),
}
model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=3)
df = pd.DataFrame(var)
train_mv = df.iloc[:9000]
test_mv = df.iloc[9000:10000]
fig, ax = plt.subplots(nrows=2, ncols=1, figsize=[10,3])
sp = {'seasonality': DateTime.month, 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']}
vmonth = variable.Variable("Month", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=12,
data=train_mv, partitioner_specific=sp, alpha_cut=.3)
vmonth.partitioner.plot(ax[0])
vwin = variable.Variable("Wind", data_label="ws_10m", alias='wind',
partitioner=Grid.GridPartitioner, npart=15, func=Membership.gaussmf,
data=train_mv, alpha_cut=.25)
vwin.partitioner.plot(ax[1])
plt.tight_layout()
order = 3
knn = 2
model = granular.GranularWMVFTS(explanatory_variables=[vmonth, vwin], target_variable=vwin,
fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both',
order=order, knn=knn)
model.fit(train_mv)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,3])
ax.plot(test_mv['ws_10m'].values[:100], label='original')
forecasts = model.predict(test_mv.iloc[:100], type='distribution')
Util.plot_distribution2(forecasts, test_mv['ws_10m'].values[:100], start_at=model.order-1, ax=ax)
model.fit(train)
model.predict(test)