From dbbedce622cd87b036bbf1ee3512662192abd97d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= Date: Fri, 24 Jan 2020 00:42:51 -0300 Subject: [PATCH] Removing dispy imports on hyperparam --- docs/build/doctrees/environment.pickle | Bin 1853121 -> 1854280 bytes docs/build/doctrees/index.doctree | Bin 15448 -> 15410 bytes docs/build/doctrees/modules.doctree | Bin 2573 -> 2535 bytes docs/build/doctrees/pyFTS.benchmarks.doctree | Bin 423128 -> 427900 bytes docs/build/doctrees/pyFTS.common.doctree | Bin 327194 -> 345155 bytes docs/build/doctrees/pyFTS.data.doctree | Bin 175601 -> 175563 bytes docs/build/doctrees/pyFTS.distributed.doctree | Bin 26636 -> 46228 bytes docs/build/doctrees/pyFTS.doctree | Bin 4481 -> 4443 bytes docs/build/doctrees/pyFTS.hyperparam.doctree | Bin 13244 -> 13206 bytes docs/build/doctrees/pyFTS.models.doctree | Bin 224436 -> 224398 bytes .../doctrees/pyFTS.models.ensemble.doctree | Bin 58409 -> 58371 bytes .../doctrees/pyFTS.models.incremental.doctree | Bin 33983 -> 33945 bytes .../pyFTS.models.multivariate.doctree | Bin 153700 -> 153662 bytes .../pyFTS.models.nonstationary.doctree | Bin 144394 -> 144356 bytes .../doctrees/pyFTS.models.seasonal.doctree | Bin 128720 -> 128682 bytes .../build/doctrees/pyFTS.partitioners.doctree | Bin 101473 -> 101435 bytes .../doctrees/pyFTS.probabilistic.doctree | Bin 52157 -> 52119 bytes docs/build/doctrees/quickstart.doctree | Bin 32339 -> 32301 bytes docs/build/html/genindex.html | 36 +++- docs/build/html/objects.inv | Bin 6541 -> 6581 bytes docs/build/html/pyFTS.benchmarks.html | 24 +++ docs/build/html/pyFTS.common.html | 144 +++++++++++-- docs/build/html/pyFTS.distributed.html | 191 +++++++++++++++--- docs/build/html/searchindex.js | 2 +- 24 files changed, 343 insertions(+), 54 deletions(-) diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 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  • add_new_PWFLGR() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method) -
    • -
  • aggregate() (in module pyFTS.common.Transformations)
  • AllMethodEnsembleFTS (class in pyFTS.models.ensemble.ensemble)
    • @@ -202,6 +200,10 @@
  • (pyFTS.common.Transformations.BoxCox method)
  • (pyFTS.common.Transformations.Differential method) +
    • +
  • (pyFTS.common.Transformations.LinearTrend method) +
    • +
  • (pyFTS.common.Transformations.ROI method)
  • (pyFTS.common.Transformations.Scale method)
    • @@ -453,6 +455,8 @@
  • differential_offset() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)
  • distance() (in module pyFTS.partitioners.CMeans) +
    • +
  • distributed_model_train_test_time() (in module pyFTS.benchmarks.benchmarks)
  • distributed_predict() (in module pyFTS.distributed.spark)
    • @@ -855,6 +859,8 @@
  • generate_high_order_recurrent_flr() (in module pyFTS.common.FLR)
  • generate_indexed_flrs() (in module pyFTS.common.FLR) +
    • +
  • generate_indexes() (pyFTS.common.Transformations.LinearTrend method)
  • generate_lhs_flrg() (pyFTS.models.hofts.HighOrderFTS method) @@ -991,11 +997,11 @@
  • get_distribution_ahead_statistics() (in module pyFTS.benchmarks.Measures)
  • get_distribution_interquantile() (pyFTS.models.ensemble.ensemble.EnsembleFTS method) -
    • -
  • get_distribution_statistics() (in module pyFTS.benchmarks.Measures)
    • - +
      -
    • LinearSeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer) +
    • LinearTrend (class in pyFTS.common.Transformations)
    • ljung_box_test() (in module pyFTS.benchmarks.ResidualAnalysis)
      • @@ -1878,7 +1892,7 @@
    • rmse_interval() (in module pyFTS.benchmarks.Measures)
      • -
    • roi() (in module pyFTS.common.Transformations) +
    • ROI (class in pyFTS.common.Transformations)
    • run() (pyFTS.data.artificial.SignalEmulator method)
      • @@ -2011,8 +2025,6 @@
    • sliding_window_benchmarks2() (in module pyFTS.benchmarks.benchmarks)
    • smape() (in module pyFTS.benchmarks.Measures) -
      • -
    • smoothing() (in module pyFTS.common.Transformations)
    • SortedCollection (class in pyFTS.common.SortedCollection)
      • @@ -2056,6 +2068,8 @@
    • (pyFTS.benchmarks.knn.KNearestNeighbors method)
    • (pyFTS.benchmarks.quantreg.QuantileRegression method) +
      • +
    • (pyFTS.common.Transformations.LinearTrend method)
    • (pyFTS.common.fts.FTS method)
      • @@ -2125,6 +2139,8 @@
    • Transformation (class in pyFTS.common.Transformations)
    • trapmf() (in module pyFTS.common.Membership) +
      • +
    • trend() (pyFTS.common.Transformations.LinearTrend method)
    • TrendWeightedFLRG (class in pyFTS.models.cheng)
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zlei4j{niWL4>3g5+k`GK^-9?uqS;V12(EV%UIbR(_Jf)|hr@tPg@3~7MELH<0j3vb zNIRXSpQ7&vo=C;^w?3;EVu&&;04_1Tl>viqCWFh)Rp3naGFDFWz$LH?J4q&a!f_@$ zZ#gix!F$h4@cV>GiT*X|R*j3h)a^e{s|;^ix5-={`Ft~XU;C6$doGVnW3a62#&V|5 zfIOHDyue;~#y3;TNI8bUfx~=BylDJxCLQgX`J*DqlS~#~_EFup{g&l7_3h10ef?8C WkuYfA%y%Zl&iwwMH2w>Z|2_cH6;_e} diff --git a/docs/build/html/pyFTS.benchmarks.html b/docs/build/html/pyFTS.benchmarks.html index a9118b5..d2fa55f 100644 --- a/docs/build/html/pyFTS.benchmarks.html +++ b/docs/build/html/pyFTS.benchmarks.html @@ -160,6 +160,30 @@ pyFTS.benchmarks.benchmarks.compareModelsTable(original, models_fo, models_ho)¶
  • AdaptiveExpectation (class in pyFTS.common.Transformations)
    • +
    +
    +pyFTS.benchmarks.benchmarks.distributed_model_train_test_time(models, data, windowsize, train=0.8, **kwargs)¶
    +

    Assess the train and test times for a given list of configured models and save the results on a database.

    + +++ + + + + + +
    Parameters:
      +
    • models – A list of FTS models already configured, but not yet trained,
      • +
    • data – time series data, including train and test data
      • +
    • windowsize – Train/test data windows
      • +
    • train – Percent of data window that will be used to train the models
      • +
    • kwargs –
      • +
    +
    Returns:

    +
    +
    +
    pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods()¶
    diff --git a/docs/build/html/pyFTS.common.html b/docs/build/html/pyFTS.common.html index 4a23211..4576022 100644 --- a/docs/build/html/pyFTS.common.html +++ b/docs/build/html/pyFTS.common.html @@ -885,6 +885,8 @@ bisect but with a simpler API and support for key functions.

    class pyFTS.common.Transformations.BoxCox(plambda)¶

    Bases: pyFTS.common.Transformations.Transformation

    Box-Cox power transformation

    +

    y’(t) = log( y(t) ) +y(t) = exp( y’(t) )

    apply(data, param=None, **kwargs)¶
    @@ -940,6 +942,8 @@ bisect but with a simpler API and support for key functions.

    class pyFTS.common.Transformations.Differential(lag)¶

    Bases: pyFTS.common.Transformations.Transformation

    Differentiation data transform

    +

    y’(t) = y(t) - y(t-1) +y(t) = y(t-1) + y’(t)

    apply(data, param=None, **kwargs)¶
    @@ -990,6 +994,131 @@ bisect but with a simpler API and support for key functions.

    +
    +
    +class pyFTS.common.Transformations.LinearTrend(**kwargs)¶
    +

    Bases: pyFTS.common.Transformations.Transformation

    +

    Linear Trend. Estimate

    +

    y’(t) = y(t) - (a*t+b) +y(t) = y’(t) + (a*t+b)

    +
    +
    +apply(data, param=None, **kwargs)¶
    +

    Apply the transformation on input data

    + +++ + + + + + +
    Parameters:
      +
    • data – input data
      • +
    • param –
      • +
    • kwargs –
      • +
    +
    Returns:

    numpy array with transformed data

    +
    +
    + +
    +
    +generate_indexes(data, value, **kwargs)¶
    +
    + +
    +
    +increment(value, **kwargs)¶
    +
    + +
    +
    +inverse(data, param=None, **kwargs)¶
    +
    +++ + + + + + +
    Parameters:
      +
    • data – transformed data
      • +
    • param –
      • +
    • kwargs –
      • +
    +
    Returns:

    numpy array with inverse transformed data

    +
    +
    + +
    +
    +train(data, **kwargs)¶
    +
    + +
    +
    +trend(data)¶
    +
    + +
    + +
    +
    +class pyFTS.common.Transformations.ROI(**kwargs)¶
    +

    Bases: pyFTS.common.Transformations.Transformation

    +

    Return of Investment (ROI) transformation. Retrieved from Sadaei and Lee (2014) - Multilayer Stock +Forecasting Model Using Fuzzy Time Series

    +

    y’(t) = ( y(t) - y(t-1) ) / y(t-1) +y(t) = ( y(t-1) * y’(t) ) + y(t-1)

    +
    +
    +apply(data, param=None, **kwargs)¶
    +

    Apply the transformation on input data

    + +++ + + + + + +
    Parameters:
      +
    • data – input data
      • +
    • param –
      • +
    • kwargs –
      • +
    +
    Returns:

    numpy array with transformed data

    +
    +
    + +
    +
    +inverse(data, param=None, **kwargs)¶
    +
    +++ + + + + + +
    Parameters:
      +
    • data – transformed data
      • +
    • param –
      • +
    • kwargs –
      • +
    +
    Returns:

    numpy array with inverse transformed data

    +
    +
    + +
    +
    class pyFTS.common.Transformations.Scale(min=0, max=1)¶
    @@ -1100,21 +1229,6 @@ bisect but with a simpler API and support for key functions.

    pyFTS.common.Transformations.Z(original)¶
    -
    -
    -pyFTS.common.Transformations.aggregate(original, operation)¶
    -
    - -
    -
    -pyFTS.common.Transformations.roi(original)¶
    -
    - -
    -
    -pyFTS.common.Transformations.smoothing(original, lags)¶
    -
    -

    pyFTS.common.Util module¶

    diff --git a/docs/build/html/pyFTS.distributed.html b/docs/build/html/pyFTS.distributed.html index d7219d0..c14c6f3 100644 --- a/docs/build/html/pyFTS.distributed.html +++ b/docs/build/html/pyFTS.distributed.html @@ -120,34 +120,70 @@
    pyFTS.distributed.spark.create_multivariate_model(**parameters)¶
    -
    +

    From the dictionary of parameters, create a multivariate FTS model

    + +++ + + + + + +
    Parameters:parameters – dictionary of parameters
    Returns:multivariate FTS model
    +
    pyFTS.distributed.spark.create_spark_conf(**kwargs)¶
    -
    +

    Configure the Spark master node

    + +++ + + + + + +
    Parameters:kwargs –
    Returns:
    +
    pyFTS.distributed.spark.create_univariate_model(**parameters)¶
    -
    +

    From the dictionary of parameters, create an univariate FTS model

    + +++ + + + + + +
    Parameters:parameters – dictionary of parameters
    Returns:univariate FTS model
    +
    pyFTS.distributed.spark.distributed_predict(data, model, **kwargs)¶
    -
    +

    The main method for distributed forecasting with FTS models using Spark clusters.

    +

    It takes a trained FTS model and the test data, connect with the Spark cluster, +proceed the distributed forecasting and return the merged forecasted values.

    +
    - @@ -157,19 +193,22 @@
    pyFTS.distributed.spark.distributed_train(model, data, **kwargs)¶
    -
    Parameters:
      -
    • model –
      • -
    • data –
      • -
    • url –
      • +
    • model – an FTS trained model
      • +
    • data – test data
      • +
    • url – URL of the Spark master
    • app –
    Returns:

    +
    Returns:

    forecasted values
    +

    The main method for distributed training of FTS models using Spark clusters.

    +

    It takes an empty model and the train data, connect with the Spark cluster, proceed the +distributed training and return the learned model.

    +
    - @@ -179,18 +218,42 @@
    pyFTS.distributed.spark.get_clustered_partitioner(explanatory_variables, target_variable, **parameters)¶
    -
    +

    Return the UoD partitioner from the ‘shared_partitioner’ fuzzy sets, special case for +clustered multivariate FTS.

    +
    Parameters:
      -
    • model –
      • -
    • data –
      • -
    • url –
      • -
    • app –
      • +
    • model – An empty (non-trained) FTS model
      • +
    • data – train data
      • +
    • url – URL of the Spark master node
      • +
    • app – Application name
    Returns:

    +
    Returns:

    trained model
    +++ + + + + + +
    Parameters:
      +
    • explanatory_variables – the list with the names of the explanatory variables
      • +
    • target_variable – the name of the target variable
      • +
    +
    Returns:

    Partitioner object

    +
    +
    pyFTS.distributed.spark.get_partitioner(shared_partitioner, type='common', variables=[])¶
    -
    +

    Return the UoD partitioner from the ‘shared_partitioner’ fuzzy sets

    +
    - + - +
    Parameters:part –
    Parameters:
      +
    • shared_partitioner – the shared variable with the fuzzy sets
      • +
    • type – the type of the partitioner
      • +
    • variables – in case of a Multivariate FTS, the list of variables
      • +
    +
    Returns:
    Returns:

    Partitioner object

    +
    @@ -199,28 +262,78 @@
    pyFTS.distributed.spark.get_variables(**parameters)¶
    -
    +

    From the dictionary of parameters, return a tuple with the list of explanatory and target variables

    + +++ + + + + + +
    Parameters:parameters – dictionary of parameters
    Returns:a tuple with the list of explanatory and target variables
    +
    pyFTS.distributed.spark.share_parameters(model, context, data)¶
    -
    +

    Create a shared variable with a dictionary of the model parameters and hyperparameters

    + +++ + + + + + +
    Parameters:
      +
    • model – the FTS model to extract the parameters and hyperparameters
      • +
    • context – Spark context
      • +
    • data – dataset
      • +
    +
    Returns:

    the shared variable with the dictionary of parameters

    +
    +
    pyFTS.distributed.spark.slave_forecast_multivariate(data, **parameters)¶
    -
    +

    Receive test data, create a multivariate FTS model from the parameters and return the forecasted values

    + +++ + + + + + +
    Parameters:
      +
    • data – test data
      • +
    • parameters – dictionary of parameters
      • +
    +
    Returns:

    forecasted values from the data input

    +
    +
    pyFTS.distributed.spark.slave_forecast_univariate(data, **parameters)¶
    -
    +

    Receive test data, create an univariate FTS model from the parameters and return the forecasted values

    +
    - + - +
    Parameters:data –
    Parameters:
      +
    • data – test data
      • +
    • parameters – dictionary of parameters
      • +
    +
    Returns:
    Returns:

    forecasted values from the data input

    +
    @@ -229,18 +342,40 @@
    pyFTS.distributed.spark.slave_train_multivariate(data, **parameters)¶
    -
    +

    Receive train data, train a multivariate FTS model and return the learned rules

    + +++ + + + + + +
    Parameters:
      +
    • data – train data
      • +
    • parameters – dictionary of parameters
      • +
    +
    Returns:

    Key/value list of the learned rules

    +
    +
    pyFTS.distributed.spark.slave_train_univariate(data, **parameters)¶
    -
    +

    Receive train data, train an univariate FTS model and return the learned rules

    +
    - + - +
    Parameters:data –
    Parameters:
      +
    • data – train data
      • +
    • parameters – dictionary of parameters
      • +
    +
    Returns:
    Returns:

    Key/value list of the learned rules

    +
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