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- Issue #3 - Code documentation with PEP 257 compliance

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Petrônio Cândido de Lima e Silva 2017-05-05 15:01:45 -03:00
parent 18e795bcd3
commit 9da53a4845
6 changed files with 83 additions and 12 deletions
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2
benchmarks/Measures.py
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@ -200,6 +200,7 @@ def crps(targets, densities):
def get_point_statistics(data, model, indexer=None):
"""Condensate all measures for point forecasters"""
if indexer is not None:
ndata = np.array(indexer.get_data(data[model.order:]))
else:
@ -232,6 +233,7 @@ def get_point_statistics(data, model, indexer=None):
def get_interval_statistics(original, model):
"""Condensate all measures for interval forecasters"""
ret = list()
forecasts = model.forecastInterval(original)
ret.append(round(sharpness(forecasts), 2))

63
benchmarks/benchmarks.py
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@ -1,6 +1,9 @@
#!/usr/bin/python
# -*- coding: utf8 -*-
"""Benchmarks to FTS methods"""
import numpy as np
import pandas as pd
import time
@ -12,7 +15,7 @@ import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# from sklearn.cross_validation import KFold
from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM
from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util
from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util, quantreg
from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts
from copy import deepcopy
@ -27,17 +30,38 @@ styles = ['-','--','-.',':','.']
nsty = len(styles)
def get_benchmark_point_methods():
"""Return all non FTS methods for point forecast"""
return [naive.Naive, arima.ARIMA]
def get_point_methods():
"""Return all FTS methods for point forecast"""
return [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]
def get_benchmark_interval_methods():
"""Return all non FTS methods for interval forecast"""
return [quantreg.QuantileRegression]
def get_interval_methods():
"""Return all FTS methods for interval forecast"""
return [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
def external_point_sliding_window(models, parameters, data, windowsize,train=0.8, dump=False, save=False, file=None, sintetic=True):
def external_point_sliding_window(models, parameters, data, windowsize,train=0.8, dump=False,
save=False, file=None, sintetic=True):
"""
Sliding window benchmarks for non FTS point forecasters
:param models: non FTS point forecasters
:param parameters: parameters for each model
:param data: data set
:param windowsize: size of sliding window
:param train: percentual of sliding window data used to train the models
:param dump:
:param save: save results
:param file: file path to save the results
:param sintetic: if true only the average and standard deviation of the results
:return: DataFrame with the results
"""
objs = {}
lcolors = {}
rmse = {}
@ -91,6 +115,23 @@ def external_point_sliding_window(models, parameters, data, windowsize,train=0.8
def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[Grid.GridPartitioner],
partitions=[10], max_order=3,transformation=None,indexer=None,dump=False,
save=False, file=None, sintetic=True):
"""
Sliding window benchmarks for FTS point forecasters
:param data:
:param windowsize: size of sliding window
:param train: percentual of sliding window data used to train the models
:param models: FTS point forecasters
:param partitioners: Universe of Discourse partitioner
:param partitions: the max number of partitions on the Universe of Discourse
:param max_order: the max order of the models (for high order models)
:param transformation: data transformation
:param indexer: seasonal indexer
:param dump:
:param save: save results
:param file: file path to save the results
:param sintetic: if true only the average and standard deviation of the results
:return: DataFrame with the results
"""
_process_start = time.time()
@ -210,7 +251,23 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
def all_point_forecasters(data_train, data_test, partitions, max_order=3, statistics=True, residuals=True,
series=True, save=False, file=None, tam=[20, 5], models=None, transformation=None,
distributions=False):
"""
Fixed data benchmark for FTS point forecasters
:param data_train: data used to train the models
:param data_test: data used to test the models
:param partitions: the max number of partitions on the Universe of Discourse
:param max_order: the max order of the models (for high order models)
:param statistics: print statistics
:param residuals: print and plot residuals
:param series: plot time series
:param save: save results
:param file: file path to save the results
:param tam: figure dimensions to plot the graphs
:param models: list of models to benchmark
:param transformation: data transformation
:param distributions: plot distributions
:return:
"""
if models is None:
models = get_point_methods()

8
benchmarks/distributed_benchmarks.py
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@ -1,3 +1,11 @@
"""
dispy Distributed Benchmarks to FTS methods
To enable a dispy cluster node:
python3 /usr/local/bin/dispynode.py -i [local IP] -d
"""
import random
import dispy
import dispy.httpd

16
benchmarks/parallel_benchmarks.py
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@ -1,3 +1,7 @@
"""
joblib Parallelized Benchmarks to FTS methods
"""
from copy import deepcopy
from joblib import Parallel, delayed
import multiprocessing
@ -64,9 +68,9 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
for model in benchmarks.get_point_methods():
mfts = model("")
if mfts.isHighOrder:
if mfts.is_high_order:
for order in np.arange(1, max_order + 1):
if order >= mfts.minOrder:
if order >= mfts.min_order:
mfts = model("")
mfts.order = order
pool.append(mfts)
@ -162,9 +166,9 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
for model in benchmarks.get_interval_methods():
mfts = model("")
if mfts.isHighOrder:
if mfts.is_high_order:
for order in np.arange(1, max_order + 1):
if order >= mfts.minOrder:
if order >= mfts.min_order:
mfts = model("")
mfts.order = order
pool.append(mfts)
@ -261,9 +265,9 @@ def ahead_sliding_window(data, windowsize, train, steps,resolution, models=None,
for model in benchmarks.get_interval_methods():
mfts = model("")
if mfts.isHighOrder:
if mfts.is_high_order:
for order in np.arange(1, max_order + 1):
if order >= mfts.minOrder:
if order >= mfts.min_order:
mfts = model("")
mfts.order = order
pool.append(mfts)

2
tests/general.py
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@ -55,7 +55,7 @@ from pyFTS.benchmarks import distributed_benchmarks as bchmk
bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
partitions= np.arange(10,200,step=5), #transformation=diff,
dump=False, save=False, file="experiments/nasdaq_point_distributed.csv",
dump=True, save=False, file="experiments/nasdaq_point_distributed.csv",
nodes=['192.168.1.42']) #, depends=[hofts, ifts])
#bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])

4
yu.py
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@ -3,8 +3,8 @@ from pyFTS.common import FuzzySet,FLR
from pyFTS import fts
class WeightedFLRG(fts.FTS):
def __init__(self, order, LHS, **kwargs):
class WeightedFLRG(object):
def __init__(self, LHS, **kwargs):
self.LHS = LHS
self.RHS = []
self.count = 1.0