Bugfixes in ProbabilityDistribution
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Petrônio Cândido
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e3f5faee2b
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86
pyFTS/benchmarks/BSTS.py
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86
pyFTS/benchmarks/BSTS.py
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@ -0,0 +1,86 @@
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#!/usr/bin/python
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# -*- coding: utf8 -*-
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import numpy as np
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import pandas as pd
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import pyflux as pf
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import scipy.stats as st
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from pyFTS.common import SortedCollection, fts
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from pyFTS.probabilistic import ProbabilityDistribution
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class ARIMA(fts.FTS):
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"""
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Façade for statsmodels.tsa.arima_model
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"""
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def __init__(self, **kwargs):
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super(ARIMA, self).__init__(**kwargs)
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self.name = "BSTS"
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self.detail = "Bayesian Structural Time Series"
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self.is_high_order = True
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.has_probability_forecasting = True
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self.model = None
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self.model_fit = None
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self.trained_data = None
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self.p = 1
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self.d = 0
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self.q = 0
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self.benchmark_only = True
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self.min_order = 1
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self.alpha = kwargs.get("alpha", 0.05)
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self.order = kwargs.get("order", (1,0,0))
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self._decompose_order(self.order)
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self.model = None
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def _decompose_order(self, order):
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if isinstance(order, (tuple, set, list)):
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self.p = order[0]
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self.d = order[1]
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self.q = order[2]
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self.order = self.p + self.q + (self.q - 1 if self.q > 0 else 0)
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self.max_lag = self.order
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self.d = len(self.transformations)
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self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ") - " + str(self.alpha)
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def train(self, data, **kwargs):
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if 'order' in kwargs:
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order = kwargs.pop('order')
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self._decompose_order(order)
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if self.indexer is not None:
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data = self.indexer.get_data(data)
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try:
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self.model = pf.ARIMA(data=data, ar=self.p, ma=self.q, integ=self.d, family=pf.Normal())
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self.model_fit = self.model.fit('M-H', nsims=20000)
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except Exception as ex:
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print(ex)
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self.model_fit = None
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def forecast(self, ndata, **kwargs):
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return ret
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def forecast_interval(self, data, **kwargs):
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return ret
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def forecast_ahead_interval(self, ndata, steps, **kwargs):
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return ret
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def forecast_distribution(self, data, **kwargs):
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return ret
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def forecast_ahead_distribution(self, data, steps, **kwargs):
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return ret
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@ -239,7 +239,6 @@ class EnsembleFTS(fts.FTS):
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return ret
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def forecast_ahead_distribution(self, data, steps, **kwargs):
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if 'method' in kwargs:
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self.point_method = kwargs.get('method','mean')
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@ -1,8 +1,7 @@
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from pyFTS.common import FuzzySet
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from scipy.spatial import KDTree
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from pyFTS.common import FuzzySet,SortedCollection,tree
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from pyFTS.probabilistic import kde
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@ -32,7 +31,9 @@ class ProbabilityDistribution(object):
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data = kwargs.get("data", None)
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if self.type == "KDE":
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self.kde = kde.KernelSmoothing(kwargs.get("h", 0.5), kwargs.get("kernel", "epanechnikov"))
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self.kde = kde.KernelSmoothing(h=kwargs.get("h", 0.5), kernel=kwargs.get("kernel", "epanechnikov"))
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if self.data is not None:
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_min = np.nanmin(data)
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_min = _min * .7 if _min > 0 else _min * 1.3
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_max = np.nanmax(data)
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@ -42,13 +43,13 @@ class ProbabilityDistribution(object):
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self.nbins = kwargs.get("num_bins", 100)
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if self.bins is None:
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self.bins = np.linspace(self.uod[0], self.uod[1], int(self.nbins)).tolist()
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self.bins = np.linspace(int(self.uod[0]), int(self.uod[1]), int(self.nbins)).tolist()
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self.labels = [str(k) for k in self.bins]
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if self.uod is not None:
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self.resolution = (self.uod[1] - self.uod[0]) / self.nbins
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self.bin_index = KDTree(self.bins)
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self.bin_index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
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self.quantile_index = None
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self.distribution = {}
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self.cdf = None
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@ -68,8 +69,7 @@ class ProbabilityDistribution(object):
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:param value: A value in the universe of discourse from the distribution
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:param density: The probability density to assign to the value
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"""
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_, ix = self.bin_index.query([value], 1)
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k = self.labels[ix]
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k = self.bin_index.find_ge(value)
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self.distribution[k] = density
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def append(self, values):
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@ -80,8 +80,7 @@ class ProbabilityDistribution(object):
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"""
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if self.type == "histogram":
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for k in values:
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_, ix = self.bin_index.query([k], 1)
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v = self.labels[ix]
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v = self.bin_index.find_ge(k)
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self.distribution[v] += 1
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self.count += 1
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else:
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@ -99,12 +98,8 @@ class ProbabilityDistribution(object):
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"""
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if self.type == "histogram":
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for interval in intervals:
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_, ix1 = self.bin_index.query([interval[0]], 1)
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_, ix2 = self.bin_index.query([interval[1]], 1)
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for k in range(ix1, ix2):
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v = self.labels[k]
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self.distribution[v] += 1
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for k in self.bin_index.inside(interval[0], interval[1]):
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self.distribution[k] += 1
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self.count += 1
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def density(self, values):
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@ -123,15 +118,13 @@ class ProbabilityDistribution(object):
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for k in values:
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if self.type == "histogram":
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_, ix = self.bin_index.query([k], 1)
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v = self.labels[ix]
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v = self.bin_index.find_ge(k)
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ret.append(self.distribution[v] / (self.count + 1e-5))
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elif self.type == "KDE":
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v = self.kde.probability(k, self.data)
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v = self.kde.probability(k, data=self.data)
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ret.append(v)
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else:
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_, ix = self.bin_index.query([k], 1)
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v = self.labels[ix]
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v = self.bin_index.find_ge(k)
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ret.append(self.distribution[v])
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if scalar:
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@ -158,7 +151,7 @@ class ProbabilityDistribution(object):
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self.distribution = dist
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self.labels = [str(k) for k in self.bins]
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self.bin_index = KDTree(self.bins)
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self.bin_index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
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self.quantile_index = None
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self.cdf = None
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self.qtl = None
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@ -187,11 +180,11 @@ class ProbabilityDistribution(object):
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_keys = [float(k) for k in sorted(self.qtl.keys())]
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self.quantile_index = KDTree(_keys)
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self.quantile_index = SortedCollection.SortedCollection(iterable=_keys)
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def cumulative(self, values):
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"""
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Return the cumulative probability densities for the input values,
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Return the cumulative probability densities for the input values,
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such that F(x) = P(X <= x)
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:param values: A list of input values
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@ -203,17 +196,15 @@ class ProbabilityDistribution(object):
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if isinstance(values, list):
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ret = []
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for val in values:
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_, ix = self.bin_index.query([val], 1)
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k = self.labels[ix]
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k = self.bin_index.find_ge(val)
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ret.append(self.cdf[k])
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else:
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_, ix = self.bin_index.query([values], 1)
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k = self.labels[ix]
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k = self.bin_index.find_ge(values)
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return self.cdf[values]
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def quantile(self, values):
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"""
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Return the Universe of Discourse values in relation to the quantile input values,
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Return the Universe of Discourse values in relation to the quantile input values,
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such that Q(tau) = min( {x | F(x) >= tau })
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:param values: input values
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@ -225,12 +216,10 @@ class ProbabilityDistribution(object):
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if isinstance(values, list):
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ret = []
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for val in values:
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_, ix = self.quantile_index.query([val], 1)
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k = self.labels[ix]
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k = self.quantile_index.find_ge(val)
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ret.append(self.qtl[str(k)][0])
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else:
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_, ix = self.quantile_index.query([values], 1)
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k = self.labels[ix]
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k = self.quantile_index.find_ge(values)
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ret = self.qtl[str(k)]
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return ret
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@ -10,13 +10,17 @@ import numpy as np
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class KernelSmoothing(object):
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"""Kernel Density Estimation"""
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def __init__(self,h, kernel="epanechnikov"):
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self.h = h
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def __init__(self, **kwargs):
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self.h = kwargs.get('h',.5)
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"""Width parameter"""
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self.kernel = kernel
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self.kernel = kwargs.get("kernel", "epanechnikov")
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"""Kernel function"""
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self.data = kwargs.get("data",None)
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self.transf = Transformations.Scale(min=0,max=1)
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if self.data is not None:
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self.data = self.transf.apply(self.data)
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def kernel_function(self, u):
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"""
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Apply the kernel
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@ -45,7 +49,7 @@ class KernelSmoothing(object):
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elif self.kernel == "exponential":
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return 0.5 * np.exp(-np.abs(u))
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def probability(self, x, data):
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def probability(self, x, **kwargs):
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"""
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Probability of the point x on data
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@ -53,10 +57,14 @@ class KernelSmoothing(object):
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:param data:
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:return:
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"""
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l = len(data)
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ndata = self.transf.apply(data)
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if self.data is None:
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self.data = kwargs.get('data',None)
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self.data = self.transf.apply(self.data)
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l = len(self.data)
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nx = self.transf.apply(x)
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p = sum([self.kernel_function((nx - k)/self.h) for k in ndata]) / l*self.h
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p = sum([self.kernel_function((nx - k)/self.h) for k in self.data]) / l*self.h
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return p
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@ -21,25 +21,12 @@ from pyFTS.data import Enrollments, TAIEX
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x = [k for k in np.arange(-2*np.pi, 2*np.pi, 0.15)]
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y = [np.sin(k) for k in x]
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from pyFTS.models import hofts
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from pyFTS.partitioners import Grid, FCM, CMeans, Entropy
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from pyFTS.benchmarks import Measures
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from pyFTS.probabilistic import ProbabilityDistribution
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metodos = [FCM.FCMPartitioner]
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k = 35
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rows = []
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for contador, metodo in enumerate(metodos):
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print(metodo)
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part = metodo(data=y, npart=k)
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model = hofts.HighOrderFTS(order=2, partitioner=part)
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model.fit(y)
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forecasts = model.predict(y)
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for o in range(model.order):
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forecasts.insert(0, None)
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pd = ProbabilityDistribution.ProbabilityDistribution(type='histogram', data=y, num_bins=30)
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print(pd.quantile([.5]))
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print(pd.cdf)
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'''
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model = fts.FCM_FTS(partitioner=fs, order=1)
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