pyFTS.common package¶
Module contents¶
Submodules¶
pyFTS.common.Composite module¶
Composite Fuzzy Sets
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class
pyFTS.common.Composite.
FuzzySet
(name, superset=False, **kwargs)¶ Bases:
pyFTS.common.FuzzySet.FuzzySet
Composite Fuzzy Set
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append
(mf, parameters)¶ Adds a new function to composition
Parameters: - mf –
- parameters –
Returns:
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append_set
(set)¶ Adds a new function to composition
Parameters: - mf –
- parameters –
Returns:
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membership
(x)¶ Calculate the membership value of a given input
Parameters: x – input value Returns: membership value of x at this fuzzy set
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transform
(x)¶ Preprocess the data point for non native types
Parameters: x – Returns: return a native type value for the structured type
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pyFTS.common.FLR module¶
This module implements functions for Fuzzy Logical Relationship generation
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class
pyFTS.common.FLR.
FLR
(LHS, RHS)¶ Bases:
object
Fuzzy Logical Relationship
Represents a temporal transition of the fuzzy set LHS on time t for the fuzzy set RHS on time t+1.
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class
pyFTS.common.FLR.
IndexedFLR
(index, LHS, RHS)¶ Bases:
pyFTS.common.FLR.FLR
Season Indexed Fuzzy Logical Relationship
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pyFTS.common.FLR.
generate_high_order_recurrent_flr
(fuzzyData)¶ Create a ordered FLR set from a list of fuzzy sets with recurrence
Parameters: fuzzyData – ordered list of fuzzy sets Returns: ordered list of FLR
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pyFTS.common.FLR.
generate_indexed_flrs
(sets, indexer, data, transformation=None, alpha_cut=0.0)¶ Create a season-indexed ordered FLR set from a list of fuzzy sets with recurrence
Parameters: - sets – fuzzy sets
- indexer – seasonality indexer
- data – original data
Returns: ordered list of FLR
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pyFTS.common.FLR.
generate_non_recurrent_flrs
(fuzzyData)¶ Create a ordered FLR set from a list of fuzzy sets without recurrence
Parameters: fuzzyData – ordered list of fuzzy sets Returns: ordered list of FLR
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pyFTS.common.FLR.
generate_recurrent_flrs
(fuzzyData)¶ Create a ordered FLR set from a list of fuzzy sets with recurrence
Parameters: fuzzyData – ordered list of fuzzy sets Returns: ordered list of FLR
pyFTS.common.FuzzySet module¶
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class
pyFTS.common.FuzzySet.
FuzzySet
(name, mf, parameters, centroid, alpha=1.0, **kwargs)¶ Bases:
object
Fuzzy Set
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membership
(x)¶ Calculate the membership value of a given input
Parameters: x – input value Returns: membership value of x at this fuzzy set
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partition_function
(uod=None, nbins=100)¶ Calculate the partition function over the membership function.
Parameters: - uod –
- nbins –
Returns:
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transform
(x)¶ Preprocess the data point for non native types
Parameters: x – Returns: return a native type value for the structured type
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pyFTS.common.FuzzySet.
check_bounds
(data, fuzzy_sets, ordered_sets)¶
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pyFTS.common.FuzzySet.
check_bounds_index
(data, fuzzy_sets, ordered_sets)¶
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pyFTS.common.FuzzySet.
fuzzyfy
(data, partitioner, **kwargs)¶ A general method for fuzzyfication.
Parameters: - data – input value to be fuzzyfied
- partitioner – a trained pyFTS.partitioners.Partitioner object
- kwargs – dict, optional arguments
- alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)
- method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
- mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) ) :returns a list with the fuzzyfied values, depending on the mode
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pyFTS.common.FuzzySet.
fuzzyfy_instance
(inst, fuzzy_sets, ordered_sets=None)¶ Calculate the membership values for a data point given fuzzy sets
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
Returns: array of membership values
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pyFTS.common.FuzzySet.
fuzzyfy_instances
(data, fuzzy_sets, ordered_sets=None)¶ Calculate the membership values for a data point given fuzzy sets
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
Returns: array of membership values
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pyFTS.common.FuzzySet.
fuzzyfy_series
(data, fuzzy_sets, method='maximum', alpha_cut=0.0, ordered_sets=None)¶
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pyFTS.common.FuzzySet.
fuzzyfy_series_old
(data, fuzzy_sets, method='maximum')¶
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pyFTS.common.FuzzySet.
get_fuzzysets
(inst, fuzzy_sets, ordered_sets=None, alpha_cut=0.0)¶ Return the fuzzy sets which membership value for a inst is greater than the alpha_cut
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
- alpha_cut – Minimal membership to be considered on fuzzyfication process
Returns: array of membership values
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pyFTS.common.FuzzySet.
get_maximum_membership_fuzzyset
(inst, fuzzy_sets, ordered_sets=None)¶ Fuzzify a data point, returning the fuzzy set with maximum membership value
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
Returns: fuzzy set with maximum membership
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pyFTS.common.FuzzySet.
get_maximum_membership_fuzzyset_index
(inst, fuzzy_sets)¶ Fuzzify a data point, returning the fuzzy set with maximum membership value
Parameters: - inst – data point
- fuzzy_sets – dict of fuzzy sets
Returns: fuzzy set with maximum membership
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pyFTS.common.FuzzySet.
grant_bounds
(data, fuzzy_sets, ordered_sets)¶
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pyFTS.common.FuzzySet.
set_ordered
(fuzzy_sets)¶ Order a fuzzy set list by their centroids
Parameters: fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object. Returns: a list with the fuzzy sets names ordered by their centroids.
pyFTS.common.Membership module¶
Membership functions for Fuzzy Sets
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pyFTS.common.Membership.
bellmf
(x, parameters)¶ Bell shaped membership function
Parameters: - x –
- parameters –
Returns:
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pyFTS.common.Membership.
gaussmf
(x, parameters)¶ Gaussian fuzzy membership function
Parameters: - x – data point
- parameters – a list with 2 real values (mean and variance)
Returns: the membership value of x given the parameters
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pyFTS.common.Membership.
sigmf
(x, parameters)¶ Sigmoid / Logistic membership function
Parameters: - x –
- parameters – an list with 2 real values (smoothness and midpoint)
:return
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pyFTS.common.Membership.
singleton
(x, parameters)¶ Singleton membership function, a single value fuzzy function
Parameters: - x –
- parameters – a list with one real value
:returns
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pyFTS.common.Membership.
trapmf
(x, parameters)¶ Trapezoidal fuzzy membership function
Parameters: - x – data point
- parameters – a list with 4 real values
Returns: the membership value of x given the parameters
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pyFTS.common.Membership.
trimf
(x, parameters)¶ Triangular fuzzy membership function
Parameters: - x – data point
- parameters – a list with 3 real values
Returns: the membership value of x given the parameters
pyFTS.common.SortedCollection module¶
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class
pyFTS.common.SortedCollection.
SortedCollection
(iterable=(), key=None)¶ Bases:
object
Sequence sorted by a key function.
SortedCollection() is much easier to work with than using bisect() directly. It supports key functions like those use in sorted(), min(), and max(). The result of the key function call is saved so that keys can be searched efficiently.
Instead of returning an insertion-point which can be hard to interpret, the five find-methods return a specific item in the sequence. They can scan for exact matches, the last item less-than-or-equal to a key, or the first item greater-than-or-equal to a key.
Once found, an item’s ordinal position can be located with the index() method. New items can be added with the insert() and insert_right() methods. Old items can be deleted with the remove() method.
The usual sequence methods are provided to support indexing, slicing, length lookup, clearing, copying, forward and reverse iteration, contains checking, item counts, item removal, and a nice looking repr.
Finding and indexing are O(log n) operations while iteration and insertion are O(n). The initial sort is O(n log n).
The key function is stored in the ‘key’ attibute for easy introspection or so that you can assign a new key function (triggering an automatic re-sort).
In short, the class was designed to handle all of the common use cases for bisect but with a simpler API and support for key functions.
>>> from pprint import pprint >>> from operator import itemgetter
>>> s = SortedCollection(key=itemgetter(2)) >>> for record in [ ... ('roger', 'young', 30), ... ('angela', 'jones', 28), ... ('bill', 'smith', 22), ... ('david', 'thomas', 32)]: ... s.insert(record)
>>> pprint(list(s)) # show records sorted by age [('bill', 'smith', 22), ('angela', 'jones', 28), ('roger', 'young', 30), ('david', 'thomas', 32)]
>>> s.find_le(29) # find oldest person aged 29 or younger ('angela', 'jones', 28) >>> s.find_lt(28) # find oldest person under 28 ('bill', 'smith', 22) >>> s.find_gt(28) # find youngest person over 28 ('roger', 'young', 30)
>>> r = s.find_ge(32) # find youngest person aged 32 or older >>> s.index(r) # get the index of their record 3 >>> s[3] # fetch the record at that index ('david', 'thomas', 32)
>>> s.key = itemgetter(0) # now sort by first name >>> pprint(list(s)) [('angela', 'jones', 28), ('bill', 'smith', 22), ('david', 'thomas', 32), ('roger', 'young', 30)]
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around
(k)¶
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between
(ge, le)¶
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clear
()¶
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copy
()¶
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count
(item)¶ Return number of occurrences of item
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find
(k)¶ Return first item with a key == k. Raise ValueError if not found.
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find_ge
(k)¶ Return first item with a key >= equal to k. Raise ValueError if not found
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find_gt
(k)¶ Return first item with a key > k. Raise ValueError if not found
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find_le
(k)¶ Return last item with a key <= k. Raise ValueError if not found.
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find_lt
(k)¶ Return last item with a key < k. Raise ValueError if not found.
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index
(item)¶ Find the position of an item. Raise ValueError if not found.
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insert
(item)¶ Insert a new item. If equal keys are found, add to the left
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insert_right
(item)¶ Insert a new item. If equal keys are found, add to the right
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inside
(ge, le)¶
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key
¶ key function
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remove
(item)¶ Remove first occurence of item. Raise ValueError if not found
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pyFTS.common.Transformations module¶
Common data transformation used on pre and post processing of the FTS
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class
pyFTS.common.Transformations.
AdaptiveExpectation
(parameters)¶ Bases:
pyFTS.common.Transformations.Transformation
Adaptive Expectation post processing
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apply
(data, param=None, **kwargs)¶ Apply the transformation on input data
Parameters: - data – input data
- param –
- kwargs –
Returns: numpy array with transformed data
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inverse
(data, param, **kwargs)¶ Parameters: - data – transformed data
- param –
- kwargs –
Returns: numpy array with inverse transformed data
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parameters
¶
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class
pyFTS.common.Transformations.
BoxCox
(plambda)¶ Bases:
pyFTS.common.Transformations.Transformation
Box-Cox power transformation
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apply
(data, param=None, **kwargs)¶ Apply the transformation on input data
Parameters: - data – input data
- param –
- kwargs –
Returns: numpy array with transformed data
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inverse
(data, param=None, **kwargs)¶ Parameters: - data – transformed data
- param –
- kwargs –
Returns: numpy array with inverse transformed data
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parameters
¶
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class
pyFTS.common.Transformations.
Differential
(lag)¶ Bases:
pyFTS.common.Transformations.Transformation
Differentiation data transform
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apply
(data, param=None, **kwargs)¶ Apply the transformation on input data
Parameters: - data – input data
- param –
- kwargs –
Returns: numpy array with transformed data
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inverse
(data, param, **kwargs)¶ Parameters: - data – transformed data
- param –
- kwargs –
Returns: numpy array with inverse transformed data
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parameters
¶
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class
pyFTS.common.Transformations.
Scale
(min=0, max=1)¶ Bases:
pyFTS.common.Transformations.Transformation
Scale data inside a interval [min, max]
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apply
(data, param=None, **kwargs)¶ Apply the transformation on input data
Parameters: - data – input data
- param –
- kwargs –
Returns: numpy array with transformed data
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inverse
(data, param, **kwargs)¶ Parameters: - data – transformed data
- param –
- kwargs –
Returns: numpy array with inverse transformed data
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parameters
¶
-
-
class
pyFTS.common.Transformations.
Transformation
(**kwargs)¶ Bases:
object
Data transformation used on pre and post processing of the FTS
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apply
(data, param, **kwargs)¶ Apply the transformation on input data
Parameters: - data – input data
- param –
- kwargs –
Returns: numpy array with transformed data
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inverse
(data, param, **kwargs)¶ Parameters: - data – transformed data
- param –
- kwargs –
Returns: numpy array with inverse transformed data
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pyFTS.common.Transformations.
Z
(original)¶
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pyFTS.common.Transformations.
aggregate
(original, operation)¶
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pyFTS.common.Transformations.
roi
(original)¶
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pyFTS.common.Transformations.
smoothing
(original, lags)¶
pyFTS.common.Util module¶
Common facilities for pyFTS
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pyFTS.common.Util.
current_milli_time
()¶
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pyFTS.common.Util.
draw_sets_on_axis
(axis, model, size)¶
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pyFTS.common.Util.
enumerate2
(xs, start=0, step=1)¶
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pyFTS.common.Util.
load_env
(file)¶
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pyFTS.common.Util.
load_obj
(file)¶ Load to memory an object stored filesystem. This function depends on Dill package
Parameters: file – file name where the object is stored Returns: object
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pyFTS.common.Util.
persist_env
(file)¶ Persist an entire environment on file. This function depends on Dill package
Parameters: file – file name to store the environment
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pyFTS.common.Util.
persist_obj
(obj, file)¶ Persist an object on filesystem. This function depends on Dill package
Parameters: - obj – object on memory
- file – file name to store the object
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pyFTS.common.Util.
plot_compared_intervals_ahead
(original, models, colors, distributions, time_from, time_to, intervals=True, save=False, file=None, tam=[20, 5], resolution=None, cmap='Blues', linewidth=1.5)¶ Plot the forecasts of several one step ahead models, by point or by interval
Parameters: - original – Original time series data (list)
- models – List of models to compare
- colors – List of models colors
- distributions – True to plot a distribution
- time_from – index of data poit to start the ahead forecasting
- time_to – number of steps ahead to forecast
- interpol – Fill space between distribution plots
- save – Save the picture on file
- file – Filename to save the picture
- tam – Size of the picture
- resolution –
- cmap – Color map to be used on distribution plot
- option – Distribution type to be passed for models
Returns:
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pyFTS.common.Util.
plot_density_rectange
(ax, cmap, density, fig, resolution, time_from, time_to)¶ Auxiliar function to plot_compared_intervals_ahead
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pyFTS.common.Util.
plot_distribution
(ax, cmap, probabilitydist, fig, time_from, reference_data=None)¶ Plot forecasted ProbabilityDistribution objects on a matplotlib axis
Parameters: - ax – matplotlib axis
- cmap – matplotlib colormap name
- probabilitydist – list of ProbabilityDistribution objects
- fig – matplotlib figure
- time_from – starting time (on x axis) to begin the plots
- reference_data –
Returns:
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pyFTS.common.Util.
plot_distribution2
(probabilitydist, data, **kwargs)¶ Plot distributions in y-axis over the time (x-axis)
Parameters: - probabilitydist – the forecasted probability distributions to plot
- data – the original test sample
- start_at – the time index (inside of data) to start to plot the probability distributions
- ax – a matplotlib axis. If no value was provided a new axis is created.
- cmap – a matplotlib colormap name, the default value is ‘Blues’
- quantiles – the list of quantiles intervals to plot, e. g. [.05, .25, .75, .95]
- median – a boolean value indicating if the median value will be plot.
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pyFTS.common.Util.
plot_distribution_tiled
(distributions, data=None, rows=5, cols=5, index=None, axis=None, size=[10, 20])¶ Plot one distribution individually in each axis, with probability in y-axis and UoD on x-axis
Parameters: - distributions –
- data –
- rows –
- cols –
- index –
- axis –
- size –
Returns:
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pyFTS.common.Util.
plot_interval
(axis, intervals, order, label, color='red', typeonlegend=False, ls='-', linewidth=1)¶ Plot forecasted intervals on matplotlib
Parameters: - axis – matplotlib axis
- intervals – list of forecasted intervals
- order – order of the model that create the forecasts
- label – figure label
- color – matplotlib color name
- typeonlegend –
- ls – matplotlib line style
- linewidth – matplotlib width
Returns:
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pyFTS.common.Util.
plot_interval2
(intervals, data, **kwargs)¶ Plot forecasted intervals on matplotlib
Parameters: - intervals – list of forecasted intervals
- data – the original test sample
- start_at – the time index (inside of data) to start to plot the intervals
- label – figure label
- color – matplotlib color name
- typeonlegend –
- ls – matplotlib line style
- linewidth – matplotlib width
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pyFTS.common.Util.
plot_probability_distributions
(pmfs, lcolors, tam=[15, 7])¶
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pyFTS.common.Util.
plot_rules
(model, size=[5, 5], axis=None, rules_by_axis=None, columns=1)¶ Plot the FLRG rules of a FTS model on a matplotlib axis
Parameters: - model – FTS model
- size – figure size
- axis – matplotlib axis
- rules_by_axis – number of rules plotted by column
- columns – number of columns
Returns:
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pyFTS.common.Util.
show_and_save_image
(fig, file, flag, lgd=None)¶ Show and image and save on file
Parameters: - fig – Matplotlib Figure object
- file – filename to save the picture
- flag – if True the image will be saved
- lgd – legend
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pyFTS.common.Util.
sliding_window
(data, windowsize, train=0.8, inc=0.1, **kwargs)¶ Sliding window method of cross validation for time series
Parameters: - data – the entire dataset
- windowsize – window size
- train – percentual of the window size will be used for training the models
- inc – percentual of data used for slide the window
Returns: window count, training set, test set
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pyFTS.common.Util.
uniquefilename
(name)¶
pyFTS.common.flrg module¶
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class
pyFTS.common.flrg.
FLRG
(order, **kwargs)¶ Bases:
object
Fuzzy Logical Relationship Group
Group a set of FLR’s with the same LHS. Represents the temporal patterns for time t+1 (the RHS fuzzy sets) when the LHS pattern is identified on time t.
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append_rhs
(set, **kwargs)¶
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get_key
()¶ Returns a unique identifier for this FLRG
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get_lower
(sets)¶ Returns the lower bound value for the RHS fuzzy sets
Parameters: sets – fuzzy sets Returns: lower bound value
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get_membership
(data, sets)¶ Returns the membership value of the FLRG for the input data
Parameters: - data – input data
- sets – fuzzy sets
Returns: the membership value
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get_midpoint
(sets)¶ Returns the midpoint value for the RHS fuzzy sets
Parameters: sets – fuzzy sets Returns: the midpoint value
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get_midpoints
(sets)¶
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get_upper
(sets)¶ Returns the upper bound value for the RHS fuzzy sets
Parameters: sets – fuzzy sets Returns: upper bound value
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reset_calculated_values
()¶
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pyFTS.common.fts module¶
-
class
pyFTS.common.fts.
FTS
(**kwargs)¶ Bases:
object
Fuzzy Time Series object model
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append_log
(operation, value)¶
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append_rule
(flrg)¶ Append FLRG rule to the model
Parameters: flrg – rule Returns:
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append_transformation
(transformation)¶
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apply_inverse_transformations
(data, params=None, **kwargs)¶ Apply the data transformations for data postprocessing
Parameters: - data – input data
- params – transformation parameters
- updateUoD –
- kwargs –
Returns: postprocessed data
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apply_transformations
(data, params=None, updateUoD=False, **kwargs)¶ Apply the data transformations for data preprocessing
Parameters: - data – input data
- params – transformation parameters
- updateUoD –
- kwargs –
Returns: preprocessed data
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clip_uod
(ndata)¶
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clone_parameters
(model)¶ Import the parameters values from other model
Parameters: model – a model to clone the parameters
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fit
(ndata, **kwargs)¶ Fit the model’s parameters based on the training data.
Parameters: - ndata – training time series data
- kwargs –
- num_batches – split the training data in num_batches to save memory during the training process
- save_model – save final model on disk
- batch_save – save the model between each batch
- file_path – path to save the model
- distributed – boolean, indicate if the training procedure will be distributed in a dispy cluster
- nodes – a list with the dispy cluster nodes addresses
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forecast
(data, **kwargs)¶ Point forecast one step ahead
Parameters: - data – time series data with the minimal length equal to the max_lag of the model
- kwargs – model specific parameters
Returns: a list with the forecasted values
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forecast_ahead
(data, steps, **kwargs)¶ Point forecast n steps ahead
Parameters: - data – time series data with the minimal length equal to the max_lag of the model
- steps – the number of steps ahead to forecast (default: 1)
- start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
Returns: a list with the forecasted values
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forecast_ahead_distribution
(data, steps, **kwargs)¶ Probabilistic forecast n steps ahead
Parameters: - data – time series data with the minimal length equal to the max_lag of the model
- steps – the number of steps ahead to forecast
- start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
Returns: a list with the forecasted Probability Distributions
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forecast_ahead_interval
(data, steps, **kwargs)¶ Interval forecast n steps ahead
Parameters: - data – time series data with the minimal length equal to the max_lag of the model
- steps – the number of steps ahead to forecast
- start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
Returns: a list with the forecasted intervals
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forecast_ahead_multivariate
(data, steps, **kwargs)¶ Multivariate forecast n step ahead
Parameters: - data – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model
- steps – the number of steps ahead to forecast
- start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
Returns: a Pandas Dataframe object representing the forecasted values for each variable
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forecast_distribution
(data, **kwargs)¶ Probabilistic forecast one step ahead
Parameters: - data – time series data with the minimal length equal to the max_lag of the model
- kwargs – model specific parameters
Returns: a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions
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forecast_interval
(data, **kwargs)¶ Interval forecast one step ahead
Parameters: - data – time series data with the minimal length equal to the max_lag of the model
- kwargs – model specific parameters
Returns: a list with the prediction intervals
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forecast_multivariate
(data, **kwargs)¶ Multivariate forecast one step ahead
Parameters: - data – Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model
- kwargs – model specific parameters
Returns: a Pandas Dataframe object representing the forecasted values for each variable
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fuzzy
(data)¶ Fuzzify a data point
Parameters: data – data point Returns: maximum membership fuzzy set
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get_UoD
()¶ Returns the interval of the known bounds of the universe of discourse (UoD), i. e., the known minimum and maximum values of the time series.
Returns: A set with the lower and the upper bounds of the UoD
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len_total
()¶ Total length of the model, adding the number of terms in all rules
Returns:
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merge
(model)¶ Merge the FLRG rules from other model
Parameters: model – source model Returns:
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offset
()¶ Returns the number of lags to skip in the input test data in order to synchronize it with the forecasted values given by the predict function. This is necessary due to the order of the model, among other parameters.
Returns: An integer with the number of lags to skip
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predict
(data, **kwargs)¶ Forecast using trained model
Parameters: - data – time series with minimal length to the order of the model
- type – the forecasting type, one of these values: point(default), interval, distribution or multivariate.
- steps_ahead – The forecasting horizon, i. e., the number of steps ahead to forecast (default value: 1)
- start_at – in the multi step forecasting, the index of the data where to start forecasting (default value: 0)
- distributed – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster (default value: False)
- nodes – a list with the dispy cluster nodes addresses
- explain – try to explain, step by step, the one-step-ahead point forecasting result given the input data. (default value: False)
- generators – for multivariate methods on multi step ahead forecasting, generators is a dict where the keys are the dataframe columun names (except the target_variable) and the values are lambda functions that accept one value (the actual value of the variable) and return the next value or trained FTS models that accept the actual values and forecast new ones.
Returns: a numpy array with the forecasted data
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reset_calculated_values
()¶ Reset all pre-calculated values on the FLRG’s
Returns:
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train
(data, **kwargs)¶ Method specific parameter fitting
Parameters: - data – training time series data
- kwargs – Method specific parameters
-