Improvements on benchmarks.knn
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Petrônio Cândido
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@ -643,7 +643,7 @@ def run_point2(fts_method, order, partitioner_method, partitions, transformation
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import time
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from pyFTS.models import yu, chen, hofts, pwfts,ismailefendi,sadaei, song, cheng, hwang
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures, naive, arima, quantreg
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from pyFTS.benchmarks import Measures, naive, arima, quantreg, benchmarks
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from pyFTS.common import Transformations
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tmp = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
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@ -664,7 +664,7 @@ def run_point2(fts_method, order, partitioner_method, partitions, transformation
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method = kwargs.get('method', None)
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parameters = kwargs.get('parameters', {})
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mfts, pttr = __build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
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mfts, pttr = benchmarks.__build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
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transformation)
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_start = time.time()
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@ -691,7 +691,7 @@ def run_interval2(fts_method, order, partitioner_method, partitions, transformat
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import time
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from pyFTS.models import hofts,ifts,pwfts
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures, arima, quantreg, BSTS
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from pyFTS.benchmarks import Measures, arima, quantreg, BSTS, benchmarks
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tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, ifts.WeightedIntervalFTS, pwfts.ProbabilisticWeightedFTS]
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@ -705,7 +705,7 @@ def run_interval2(fts_method, order, partitioner_method, partitions, transformat
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method = kwargs.get('method', None)
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parameters = kwargs.get('parameters',{})
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mfts, pttr = __build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
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mfts, pttr = benchmarks.__build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
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transformation)
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_start = time.time()
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mfts.fit(train_data, **kwargs)
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@ -735,7 +735,7 @@ def run_probabilistic2(fts_method, order, partitioner_method, partitions, transf
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from pyFTS.models import hofts, ifts, pwfts
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from pyFTS.models.ensemble import ensemble
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures, arima, quantreg, knn
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from pyFTS.benchmarks import Measures, arima, quantreg, knn, benchmarks
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from pyFTS.models.seasonal import SeasonalIndexer
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tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS, arima.ARIMA,
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@ -751,9 +751,8 @@ def run_probabilistic2(fts_method, order, partitioner_method, partitions, transf
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method = kwargs.get('method', None)
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parameters = kwargs.get('parameters', {})
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mfts, pttr = __build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
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mfts, pttr = benchmarks.__build_model(fts_method, order, parameters, partitioner_method, partitions, train_data,
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transformation)
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if mfts.has_seasonality:
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mfts.indexer = indexer
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@ -5,17 +5,20 @@ import numpy as np
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from statsmodels.tsa.tsatools import lagmat
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from pyFTS.common import fts
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from pyFTS.probabilistic import ProbabilityDistribution
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from sklearn.neighbors import KDTree
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from itertools import product
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from pyFTS.models.ensemble.ensemble import sampler
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class KNearestNeighbors(fts.FTS):
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"""
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K-Nearest Neighbors
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A façade for sklearn.neighbors
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"""
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def __init__(self, **kwargs):
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super(KNearestNeighbors, self).__init__(**kwargs)
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self.name = "kNN"
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self.shortname = "kNN"
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self.detail = "K-Nearest Neighbors"
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self.uod_clip = False
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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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@ -26,30 +29,113 @@ class KNearestNeighbors(fts.FTS):
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self.lag = None
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self.k = kwargs.get("k", 30)
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self.uod = None
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self.kdtree = None
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self.values = None
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def _prepare_x(self, data):
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l = len(data)
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X = []
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if l == self.order:
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l += 1
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for t in np.arange(self.order, l):
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X.append([data[t - k - 1] for k in np.arange(self.order)])
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return X
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def _prepare_xy(self, data):
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l = len(data)
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X = []
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Y = []
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for t in np.arange(self.order, l):
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X.append([data[t - k - 1] for k in np.arange(self.order)])
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Y.append(data[t])
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return (X,Y)
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def train(self, data, **kwargs):
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self.data = np.array(data)
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X,Y = self._prepare_xy(data)
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self.kdtree = KDTree(X)
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self.values = Y
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def knn(self, sample):
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X = self._prepare_x(sample)
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_, ix = self.kdtree.query(X, self.k)
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if self.order == 1:
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dist = np.apply_along_axis(lambda x: (x - sample) ** 2, 0, self.data)
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ix = np.argsort(dist) + 1
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else:
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dist = []
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for k in np.arange(self.order, len(self.data)):
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dist.append(sum([ (self.data[k - kk] - sample[kk])**2 for kk in range(self.order)]))
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ix = np.argsort(np.array(dist)) + self.order + 1
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return [self.values[k] for k in ix.flatten() ]
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ix2 = np.clip(ix[:self.k], 0, len(self.data)-1)
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return self.data[ix2]
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def forecast(self, data, **kwargs):
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ret = []
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for k in np.arange(self.order, len(data)):
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sample = data[k-self.order : k]
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forecasts = self.knn(sample)
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ret.append(np.nanmean(forecasts))
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return ret
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def forecast_ahead(self, data, steps, **kwargs):
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start = kwargs.get('start', self.order)
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sample = [k for k in data[start - self.order: start]]
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for k in np.arange(self.order, steps + self.order):
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tmp = self.forecast(sample[k-self.order:k])
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sample.append(tmp)
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return sample[-steps]
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def forecast_interval(self, data, **kwargs):
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alpha = kwargs.get('alpha',self.alpha)
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ret = []
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for k in np.arange(self.order, len(data)):
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sample = data[k-self.order : k]
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forecasts = self.knn(sample)
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i = np.percentile(forecasts, [alpha*100, (1-alpha)*100]).tolist()
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ret.append(i)
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return ret
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def forecast_ahead_interval(self, data, steps, **kwargs):
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alpha = kwargs.get('alpha', self.alpha)
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ret = []
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start = kwargs.get('start', self.order)
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sample = [[k] for k in data[start - self.order: start]]
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for k in np.arange(self.order, steps + self.order):
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forecasts = []
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lags = [sample[k - i - 1] for i in np.arange(0, self.order)]
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# Trace the possible paths
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for path in product(*lags):
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forecasts.extend(self.knn(path))
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sample.append(sampler(forecasts, np.arange(.1, 1, 0.1), bounds=True))
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interval = np.percentile(forecasts, [alpha*100, (1-alpha)*100]).tolist()
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ret.append(interval)
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return ret
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def forecast_distribution(self, data, **kwargs):
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ret = []
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smooth = kwargs.get("smooth", "KDE")
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alpha = kwargs.get("alpha", None)
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smooth = kwargs.get("smooth", "histogram")
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uod = self.get_UoD()
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@ -65,4 +151,32 @@ class KNearestNeighbors(fts.FTS):
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return ret
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def forecast_ahead_distribution(self, data, steps, **kwargs):
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smooth = kwargs.get("smooth", "histogram")
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ret = []
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start = kwargs.get('start', self.order)
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uod = self.get_UoD()
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sample = [[k] for k in data[start - self.order: start]]
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for k in np.arange(self.order, steps + self.order):
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forecasts = []
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lags = [sample[k - i - 1] for i in np.arange(0, self.order)]
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# Trace the possible paths
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for path in product(*lags):
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forecasts.extend(self.knn(path))
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dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, data=forecasts,
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name="", **kwargs)
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ret.append(dist)
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sample.append(sampler(forecasts, np.arange(.1, 1, 0.1), bounds=True))
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return ret
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@ -519,8 +519,10 @@ class FTS(object):
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return data
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def get_UoD(self):
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#return [self.original_min, self.original_max]
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return [self.partitioner.min, self.partitioner.max]
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if self.partitioner is not None:
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return [self.partitioner.min, self.partitioner.max]
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else:
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return [self.original_min, self.original_max]
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def __str__(self):
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"""String representation of the model"""
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@ -167,8 +167,7 @@ class EnsembleFTS(fts.FTS):
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if "method" in kwargs:
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self.interval_method = kwargs.get('method','quantile')
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if 'alpha' in kwargs:
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self.alpha = kwargs.get('alpha',0.05)
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self.alpha = kwargs.get('alpha', self.alpha)
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l = len(data)
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@ -189,15 +188,12 @@ class EnsembleFTS(fts.FTS):
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if 'method' in kwargs:
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self.interval_method = kwargs.get('method','quantile')
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if 'alpha' in kwargs:
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self.alpha = kwargs.get('alpha', self.alpha)
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self.alpha = kwargs.get('alpha', self.alpha)
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ret = []
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start = kwargs.get('start', self.order)
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uod = self.get_UoD()
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sample = [[k] for k in data[start - self.order: start]]
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for k in np.arange(self.order, steps + self.order):
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@ -13,29 +13,51 @@ from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple
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from pyFTS.benchmarks import benchmarks as bchmk, Measures
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from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts
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from pyFTS.models.ensemble import ensemble
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from pyFTS.common import Transformations, Membership
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from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc
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from pyFTS.common import Transformations, Membership, Util
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from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc, knn
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from pyFTS.fcm import fts, common, GA
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from pyFTS.data import TAIEX, NASDAQ, SP500
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train = TAIEX.get_data()[:800]
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test = TAIEX.get_data()[800:1000]
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order = 2
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model = knn.KNearestNeighbors(order=order)
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model.fit(train)
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horizon=7
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intervals05 = model.predict(test[:10], type='interval', alpha=.05, steps_ahead=horizon)
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print(test[:10])
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print(intervals05)
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intervals25 = model.predict(test[:10], type='interval', alpha=.25, steps_ahead=horizon)
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distributions = model.predict(test[:10], type='distribution', steps_ahead=horizon, smoothing=0.01, num_bins=100)
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fig, ax = plt.subplots(nrows=1, ncols=1,figsize=[15,5])
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ax.plot(test[:10], label='Original',color='black')
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Util.plot_interval2(intervals05, test[:10], start_at=model.order, ax=ax, color='green', label='alpha=.05'.format(model.order))
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Util.plot_interval2(intervals25, test[:10], start_at=model.order, ax=ax, color='green', label='alpha=.25'.format(model.order))
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Util.plot_distribution2(distributions, test[:10], start_at=model.order, ax=ax, cmap="Blues")
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print("")
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'''
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datasets = {}
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datasets['TAIEX'] = TAIEX.get_data()[:5000]
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datasets['NASDAQ'] = NASDAQ.get_data()[:5000]
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datasets['SP500'] = SP500.get_data()[10000:15000]
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methods = [ensemble.SimpleEnsembleFTS]*8
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methods = [ensemble.SimpleEnsembleFTS]*4
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methods_parameters = [
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{'name': 'EnsembleFTS-HOFTS-10-.05', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .05},
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{'name': 'EnsembleFTS-HOFTS-5-.05', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .05},
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{'name': 'EnsembleFTS-HOFTS-10-.25', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .25},
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{'name': 'EnsembleFTS-HOFTS-5-.25', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .25},
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{'name': 'EnsembleFTS-WHOFTS-10-.05', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .05},
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{'name': 'EnsembleFTS-WHOFTS-5-.05', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .05},
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{'name': 'EnsembleFTS-WHOFTS-10-.25', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,10), 'alpha': .25},
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{'name': 'EnsembleFTS-WHOFTS-5-.25', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,5), 'alpha': .25},
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{'name': 'EnsembleFTS-HOFTS-10', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,10)},
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{'name': 'EnsembleFTS-HOFTS-5', 'fts_method': hofts.HighOrderFTS, 'partitions': np.arange(20,50,5)},
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{'name': 'EnsembleFTS-WHOFTS-10', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,10)},
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{'name': 'EnsembleFTS-WHOFTS-5', 'fts_method': hofts.WeightedHighOrderFTS, 'partitions': np.arange(20,50,5)}
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]
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for dataset_name, dataset in datasets.items():
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@ -46,6 +68,7 @@ for dataset_name, dataset in datasets.items():
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transformations=[None],
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orders=[3],
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partitions=[None],
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type='interval',
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#distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
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file="tmp.db", dataset=dataset_name, tag="gridsearch")
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type='distribution',
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distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
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file="experiments.db", dataset=dataset_name, tag="gridsearch")
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'''
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