Refactoring: Type Hints
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@ -12,15 +12,15 @@ class FuzzySet(object):
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"""
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Create a Fuzzy Set
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"""
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self.name = name
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self.name : str = name
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"""The fuzzy set name"""
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self.mf = mf
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self.mf = mf
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"""The membership function"""
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self.parameters = parameters
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self.parameters : list = parameters
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"""The parameters of the membership function"""
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self.centroid = centroid
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self.centroid : float = centroid
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"""The fuzzy set center of mass (or midpoint)"""
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self.alpha = alpha
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self.alpha : float = alpha
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"""The alpha cut value"""
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self.type = kwargs.get('type', 'common')
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"""The fuzzy set type (common, composite, nonstationary, etc)"""
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@ -170,7 +170,7 @@ def set_ordered(fuzzy_sets):
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return [k.name for k in sorted(tmp1, key=lambda x: x.centroid)]
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def fuzzyfy_instance(inst, fuzzy_sets, ordered_sets=None):
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def fuzzyfy_instance(inst, fuzzy_sets : dict , ordered_sets : list =None):
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"""
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Calculate the membership values for a data point given fuzzy sets
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@ -191,7 +191,7 @@ def fuzzyfy_instance(inst, fuzzy_sets, ordered_sets=None):
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return mv
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def fuzzyfy_instances(data, fuzzy_sets, ordered_sets=None):
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def fuzzyfy_instances(data : list, fuzzy_sets : dict, ordered_sets=None) -> list:
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"""
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Calculate the membership values for a data point given fuzzy sets
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@ -209,7 +209,7 @@ def fuzzyfy_instances(data, fuzzy_sets, ordered_sets=None):
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return ret
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def get_fuzzysets(inst, fuzzy_sets, ordered_sets=None, alpha_cut=0.0):
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def get_fuzzysets(inst, fuzzy_sets : dict, ordered_sets : list =None, alpha_cut : flota =0.0) -> list:
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"""
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Return the fuzzy sets which membership value for a inst is greater than the alpha_cut
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@ -232,7 +232,7 @@ def get_fuzzysets(inst, fuzzy_sets, ordered_sets=None, alpha_cut=0.0):
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raise ex
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def get_maximum_membership_fuzzyset(inst, fuzzy_sets, ordered_sets=None):
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def get_maximum_membership_fuzzyset(inst, fuzzy_sets, ordered_sets=None) -> FuzzySet:
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"""
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Fuzzify a data point, returning the fuzzy set with maximum membership value
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@ -248,7 +248,7 @@ def get_maximum_membership_fuzzyset(inst, fuzzy_sets, ordered_sets=None):
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return fuzzy_sets[key]
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def get_maximum_membership_fuzzyset_index(inst, fuzzy_sets):
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def get_maximum_membership_fuzzyset_index(inst, fuzzy_sets) -> int:
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"""
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Fuzzify a data point, returning the fuzzy set with maximum membership value
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@ -15,6 +15,3 @@ from pyFTS.common.transformations.trend import LinearTrend
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from pyFTS.common.transformations.som import SOMTransformation
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from pyFTS.common.transformations.autoencoder import AutoencoderTransformation
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from pyFTS.common.transformations.normalization import Normalization
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@ -1,6 +1,9 @@
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import numpy as np
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import pandas as pd
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from pyFTS.common import FuzzySet, SortedCollection, tree, Util
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from pyFTS.common import FuzzySet, Util
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from pyFTS.common.transformations import transformation
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from pyFTS.partitioners import partitioner
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from pyFTS.probabilistic import ProbabilityDistribution
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class FTS(object):
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@ -11,72 +14,72 @@ class FTS(object):
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"""
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Create a Fuzzy Time Series model
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"""
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self.flrgs = {}
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self.flrgs: dict = {}
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"""The list of Fuzzy Logical Relationship Groups - FLRG"""
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self.order = kwargs.get('order',1)
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self.order : int = kwargs.get('order',1)
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"""A integer with the model order (number of past lags are used on forecasting)"""
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self.shortname = kwargs.get('name',"")
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self.shortname : str = kwargs.get('name',"")
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"""A string with a short name or alias for the model"""
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self.name = kwargs.get('name',"")
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self.name : str = kwargs.get('name',"")
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"""A string with the model name"""
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self.detail = kwargs.get('name',"")
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self.detail : str = kwargs.get('name',"")
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"""A string with the model detailed information"""
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self.is_wrapper = False
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self.is_wrapper : bool = False
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"""Indicates that this model is a wrapper for other(s) method(s)"""
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self.is_high_order = False
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self.is_high_order : bool = False
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"""A boolean value indicating if the model support orders greater than 1, default: False"""
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self.min_order = 1
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self.min_order : int = 1
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"""In high order models, this integer value indicates the minimal order supported for the model, default: 1"""
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self.has_seasonality = False
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self.has_seasonality : bool = False
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"""A boolean value indicating if the model supports seasonal indexers, default: False"""
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self.has_point_forecasting = True
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self.has_point_forecasting : bool = True
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"""A boolean value indicating if the model supports point forecasting, default: True"""
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self.has_interval_forecasting = False
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self.has_interval_forecasting : bool = False
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"""A boolean value indicating if the model supports interval forecasting, default: False"""
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self.has_probability_forecasting = False
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self.has_probability_forecasting : bool = False
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"""A boolean value indicating if the model support probabilistic forecasting, default: False"""
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self.is_multivariate = False
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self.is_multivariate : bool = False
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"""A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False"""
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self.is_clustered = False
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self.is_clustered : bool = False
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"""A boolean value indicating if the model support multivariate time series (Pandas DataFrame), but works like
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a monovariate method, default: False"""
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self.dump = False
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self.transformations = []
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self.dump : bool = False
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self.transformations : list[transformation.Transformation] = []
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"""A list with the data transformations (common.Transformations) applied on model pre and post processing, default: []"""
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self.transformations_param = []
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self.transformations_param : list = []
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"""A list with the specific parameters for each data transformation"""
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self.original_max = 0
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self.original_max : float = 0.0
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"""A float with the upper limit of the Universe of Discourse, the maximal value found on training data"""
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self.original_min = 0
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self.original_min : float = 0.0
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"""A float with the lower limit of the Universe of Discourse, the minimal value found on training data"""
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self.partitioner = kwargs.get("partitioner", None)
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self.partitioner : partitioner.Partitioner = kwargs.get("partitioner", None)
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"""A pyFTS.partitioners.Partitioner object with the Universe of Discourse partitioner used on the model. This is a mandatory dependecy. """
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if self.partitioner != None:
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self.sets = self.partitioner.sets
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self.auto_update = False
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self.auto_update : bool = False
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"""A boolean value indicating that model is incremental"""
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self.benchmark_only = False
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self.benchmark_only : bool = False
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"""A boolean value indicating a façade for external (non-FTS) model used on benchmarks or ensembles."""
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self.indexer = kwargs.get("indexer", None)
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"""An pyFTS.models.seasonal.Indexer object for indexing the time series data"""
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self.uod_clip = kwargs.get("uod_clip", True)
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self.uod_clip : bool = kwargs.get("uod_clip", True)
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"""Flag indicating if the test data will be clipped inside the training Universe of Discourse"""
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self.alpha_cut = kwargs.get("alpha_cut", 0.0)
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self.alpha_cut : float = kwargs.get("alpha_cut", 0.0)
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"""A float with the minimal membership to be considered on fuzzyfication process"""
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self.lags = kwargs.get("lags", None)
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self.lags : list[int] = kwargs.get("lags", None)
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"""The list of lag indexes for high order models"""
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self.max_lag = self.order
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self.max_lag : int = self.order
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"""A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags
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needed to forecast a single step ahead"""
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self.log = pd.DataFrame([],columns=["Datetime","Operation","Value"])
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self.log : pd.DataFrame = pd.DataFrame([],columns=["Datetime","Operation","Value"])
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""""""
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self.is_time_variant = False
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self.is_time_variant : bool = False
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"""A boolean value indicating if this model is time variant"""
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self.standard_horizon = kwargs.get("standard_horizon", 1)
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self.standard_horizon : int = kwargs.get("standard_horizon", 1)
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"""Standard forecasting horizon (Default: 1)"""
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def fuzzy(self, data):
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def fuzzy(self, data) -> dict:
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"""
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Fuzzify a data point
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@ -198,7 +201,7 @@ class FTS(object):
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return ret
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def forecast(self, data, **kwargs):
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def forecast(self, data, **kwargs) -> list:
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"""
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Point forecast one step ahead
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@ -208,7 +211,7 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform one step ahead point forecasts!')
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def forecast_interval(self, data, **kwargs):
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def forecast_interval(self, data, **kwargs) -> list:
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"""
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Interval forecast one step ahead
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@ -218,7 +221,7 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform one step ahead interval forecasts!')
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def forecast_distribution(self, data, **kwargs):
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def forecast_distribution(self, data, **kwargs) -> list[ProbabilityDistribution.ProbabilityDistribution]:
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"""
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Probabilistic forecast one step ahead
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@ -228,7 +231,7 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform one step ahead distribution forecasts!')
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def forecast_multivariate(self, data, **kwargs):
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def forecast_multivariate(self, data, **kwargs) -> pd.DataFrame:
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"""
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Multivariate forecast one step ahead
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@ -239,7 +242,7 @@ class FTS(object):
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raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')
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def forecast_ahead(self, data, steps, **kwargs):
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def forecast_ahead(self, data, steps, **kwargs) -> list:
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"""
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Point forecast from 1 to H steps ahead, where H is given by the steps parameter
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@ -269,7 +272,7 @@ class FTS(object):
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return ret[-steps:]
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def forecast_ahead_interval(self, data, steps, **kwargs):
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def forecast_ahead_interval(self, data, steps, **kwargs) -> list:
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"""
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Interval forecast from 1 to H steps ahead, where H is given by the steps parameter
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@ -280,7 +283,7 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform multi step ahead interval forecasts!')
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def forecast_ahead_distribution(self, data, steps, **kwargs):
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def forecast_ahead_distribution(self, data, steps, **kwargs) -> list[ProbabilityDistribution.ProbabilityDistribution]:
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"""
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Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter
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@ -291,7 +294,7 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform multi step ahead distribution forecasts!')
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def forecast_ahead_multivariate(self, data, steps, **kwargs):
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def forecast_ahead_multivariate(self, data, steps, **kwargs) -> pd.DataFrame:
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"""
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Multivariate forecast n step ahead
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@ -302,7 +305,7 @@ class FTS(object):
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"""
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raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')
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def forecast_step(self, data, step, **kwargs):
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def forecast_step(self, data, step, **kwargs) -> list:
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"""
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Point forecast for H steps ahead, where H is given by the step parameter
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@ -573,7 +576,7 @@ class FTS(object):
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else:
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return data
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def get_UoD(self):
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def get_UoD(self) -> set:
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"""
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Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,
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the known minimum and maximum values of the time series.
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@ -585,7 +588,7 @@ class FTS(object):
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else:
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return (self.original_min, self.original_max)
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def offset(self):
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def offset(self) -> int:
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"""
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Returns the number of lags to skip in the input test data in order to synchronize it with
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the forecasted values given by the predict function. This is necessary due to the order of the
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@ -9,7 +9,7 @@ import pandas as pd
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import numpy as np
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def get_data():
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def get_data() -> np.ndarray:
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"""
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Get a simple univariate time series data.
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@ -19,7 +19,7 @@ def get_data():
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dat = np.array(dat["Passengers"])
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return dat
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def get_dataframe():
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def get_dataframe() -> pd.DataFrame:
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"""
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Get the complete multivariate time series data.
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@ -16,7 +16,7 @@ class SignalEmulator(object):
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self.components = []
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"""Components of the signal"""
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def stationary_gaussian(self, mu, sigma, **kwargs):
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def stationary_gaussian(self, mu:float, sigma:float, **kwargs):
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"""
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Creates a continuous Gaussian signal with mean mu and variance sigma.
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@ -36,7 +36,7 @@ class SignalEmulator(object):
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'parameters': parameters, 'args': kwargs})
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return self
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def incremental_gaussian(self, mu, sigma, **kwargs):
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def incremental_gaussian(self, mu:float, sigma:float, **kwargs):
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"""
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Creates an additive gaussian interference on a previous signal
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@ -7,7 +7,7 @@ from pathlib import Path
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from urllib import request
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def get_dataframe(filename, url, sep=";", compression='infer'):
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def get_dataframe(filename: str, url: str, sep:str=";", compression:str='infer') -> pd.DataFrame:
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"""
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This method check if filename already exists, read the file and return its data.
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If the file don't already exists, it will be downloaded and decompressed.
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@ -9,7 +9,7 @@ from pyFTS.partitioners import partitioner
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class SingletonPartitioner(partitioner.Partitioner):
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"""Singleton Partitioner"""
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"""Singleton Partitioner: Create singleton fuzzy sets for each distinct value in UoD"""
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def __init__(self, **kwargs):
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"""
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@ -17,12 +17,12 @@ class SingletonPartitioner(partitioner.Partitioner):
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"""
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super(SingletonPartitioner, self).__init__(name="Singleton", **kwargs)
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def build(self, data):
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def build(self, data : list):
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sets = {}
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kwargs = {'type': self.type, 'variable': self.variable}
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for count, instance in enumerate(data):
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for count, instance in enumerate(set(data)):
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_name = self.get_name(count)
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sets[_name] = FuzzySet.FuzzySet(_name, Membership.singleton, [instance], instance, **kwargs)
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mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
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def plot_sets(data, sets, titles, size=[12, 10], save=False, file=None, axis=None):
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def plot_sets(data, sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
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"""
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Plot all fuzzy sets in a Partitioner
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"""
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num = len(sets)
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if axis is None:
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@ -53,7 +57,7 @@ def plot_sets(data, sets, titles, size=[12, 10], save=False, file=None, axis=Non
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def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None, axis=None):
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sets = [k.sets for k in objs]
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titles = [k.name for k in objs]
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plot_sets(data, sets, titles, tam, save, file, axis)
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plot_sets(sets, titles, tam, save, file, axis)
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def explore_partitioners(data, npart, methods=None, mf=None, transformation=None,
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@ -84,6 +88,6 @@ def explore_partitioners(data, npart, methods=None, mf=None, transformation=None
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obj.name = obj.name + " - " + obj.membership_function.__name__
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objs.append(obj)
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plot_partitioners(data, objs, size, save, file)
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plot_partitioners(objs, size, save, file)
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return objs
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@ -28,5 +28,5 @@ def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=
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objs = np.ravel(objs).tolist()
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Util.plot_partitioners(data, objs, tam, save, file)
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Util.plot_partitioners(objs, tam, save, file)
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@ -14,16 +14,17 @@ class Partitioner(object):
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"""
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Universe of Discourse partitioner scheme. Split data on several fuzzy sets
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"""
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self.name = kwargs.get('name',"")
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self.name : str = kwargs.get('name',"")
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"""partitioner name"""
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self.partitions = kwargs.get('npart', 10)
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self.partitions : int = kwargs.get('npart', 10)
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"""The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created"""
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self.sets = {}
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self.sets : dict = {}
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"""The fuzzy sets dictionary"""
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self.membership_function = kwargs.get('func', Membership.trimf)
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"""Fuzzy membership function (pyFTS.common.Membership)"""
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self.setnames = kwargs.get('names', None)
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self.setnames : list' = kwargs.get('names', None)
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"""list of partitions names. If None is given the partitions will be auto named with prefix"""
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self.prefix = kwargs.get('prefix', 'A')
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self.prefix : str'' = kwargs.get('prefix', 'A')
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"""prefix of auto generated partition names"""
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self.transformation = kwargs.get('transformation', None)
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"""data transformation to be applied on data"""
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@ -34,13 +35,13 @@ class Partitioner(object):
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"""The type of fuzzy sets that are generated by this partitioner"""
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self.ordered_sets = None
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"""A ordered list of the fuzzy sets names, sorted by their middle point"""
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self.kdtree = None
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self.kdtree : KDTree = None
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"""A spatial index to help in fuzzyfication"""
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self.margin = kwargs.get("margin", 0.1)
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self.margin : float = kwargs.get("margin", 0.1)
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"""The upper and lower exceeding margins for the known UoD. The default value is .1"""
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self.lower_margin = kwargs.get("lower_margin", self.margin)
|
||||
self.lower_margin : float = kwargs.get("lower_margin", self.margin)
|
||||
"""Specific lower exceeding margins for the known UoD. The default value is the self.margin parameter"""
|
||||
self.upper_margin = kwargs.get("lower_margin", self.margin)
|
||||
self.upper_margin : float = kwargs.get("lower_margin", self.margin)
|
||||
"""Specific upper exceeding margins for the known UoD. The default value is the self.margin parameter"""
|
||||
|
||||
if kwargs.get('preprocess',True):
|
||||
|
@ -8,6 +8,7 @@ from typing import Tuple
|
||||
|
||||
|
||||
class SOMPartitioner:
|
||||
"""Self Organized Map Partitioner"""
|
||||
def __init__(self,
|
||||
grid_dimension: Tuple,
|
||||
**kwargs):
|
||||
@ -19,8 +20,8 @@ class SOMPartitioner:
|
||||
|
||||
|
||||
# debug attributes
|
||||
self.name = 'Kohonen Self Organizing Maps FTS'
|
||||
self.shortname = 'SOM-FTS'
|
||||
self.name = 'Kohonen Self Organizing Map Partitioner'
|
||||
self.shortname = 'SOM-Partitioner'
|
||||
|
||||
def __repr__(self):
|
||||
status = "is trained" if self.is_trained else "not trained"
|
||||
|
Loading…
Reference in New Issue
Block a user