From 9a66da2d5a70335b533337647dd0314506bffd1d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= Date: Tue, 11 Dec 2018 18:27:18 -0200 Subject: [PATCH] Updating documentation --- docs/build/doctrees/environment.pickle | Bin 2225378 -> 2228331 bytes docs/build/doctrees/pyFTS.common.doctree | Bin 384282 -> 392396 bytes .../pyFTS.models.multivariate.doctree | Bin 107993 -> 113390 bytes .../build/html/_modules/pyFTS/common/fts.html | 29 ++++++++++- .../pyFTS/models/multivariate/cmvfts.html | 31 ++++++++---- docs/build/html/genindex.html | 14 +++++- docs/build/html/objects.inv | Bin 6072 -> 6092 bytes docs/build/html/pyFTS.common.html | 45 +++++++++++++++++- .../build/html/pyFTS.models.multivariate.html | 26 ++++++++++ docs/build/html/searchindex.js | 2 +- 10 files changed, 133 insertions(+), 14 deletions(-) diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 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zU)WTtd|~b4J*#feudP_KeEsIi9hJH7UM}ry&vk E2PB$4TmS$7 diff --git a/docs/build/html/_modules/pyFTS/common/fts.html b/docs/build/html/_modules/pyFTS/common/fts.html index 1580953..fd5ad2d 100644 --- a/docs/build/html/_modules/pyFTS/common/fts.html +++ b/docs/build/html/_modules/pyFTS/common/fts.html @@ -162,7 +162,7 @@ :param data: time series with minimal length to the order of the model - :keyword type: the forecasting type, one of these values: point(default), interval or distribution. + :keyword type: the forecasting type, one of these values: point(default), interval, distribution or multivariate. :keyword steps_ahead: The forecasting horizon, i. e., the number of steps ahead to forecast :keyword start: in the multi step forecasting, the index of the data where to start forecasting :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster @@ -204,6 +204,8 @@ ret = self.forecast_interval(ndata, **kwargs) elif type == 'distribution': ret = self.forecast_distribution(ndata, **kwargs) + elif type == 'multivariate': + ret = self.forecast_multivariate(ndata, **kwargs) elif steps_ahead > 1: if type == 'point': ret = self.forecast_ahead(ndata, steps_ahead, **kwargs) @@ -211,8 +213,10 @@ ret = self.forecast_ahead_interval(ndata, steps_ahead, **kwargs) elif type == 'distribution': ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs) + elif type == 'multivariate': + ret = self.forecast_ahead_multivariate(ndata, **kwargs) - if not ['point', 'interval', 'distribution'].__contains__(type): + if not ['point', 'interval', 'distribution', 'multivariate'].__contains__(type): raise ValueError('The argument \'type\' has an unknown value.') else: @@ -258,6 +262,16 @@ """ raise NotImplementedError('This model do not perform one step ahead distribution forecasts!') +

+
[docs] def forecast_ahead(self, data, steps, **kwargs): """ Point forecast n steps ahead @@ -307,6 +321,17 @@ """ raise NotImplementedError('This model do not perform multi step ahead distribution forecasts!')
+
[docs] def forecast_ahead_multivariate(self, data, steps, **kwargs): + """ + Multivariate forecast n step ahead + + :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model + :param steps: the number of steps ahead to forecast + :param kwargs: model specific parameters + :return: a Pandas Dataframe object representing the forecasted values for each variable + """ + raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')
+
[docs] def train(self, data, **kwargs): """ Method specific parameter fitting diff --git a/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html b/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html index bcb40d8..49d462a 100644 --- a/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html +++ b/docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html @@ -74,6 +74,7 @@

Source code for pyFTS.models.multivariate.cmvfts

 
 import numpy as np
+import pandas as pd
 from pyFTS.common import FuzzySet, FLR, fts, flrg
 from pyFTS.models import hofts
 from pyFTS.models.multivariate import mvfts, grid, common
@@ -129,24 +130,38 @@
         if self.model.is_high_order:
             self.model.order = self.order
 
-        if self.pre_fuzzyfy:
-            ndata = self.fuzzyfy(data)
-        else:
-            ndata = [self.format_data(k) for k in data.to_dict('records')]
+        ndata = self.check_data(data)
 
         self.model.train(ndata, fuzzyfied=self.pre_fuzzyfy)
 
         self.cluster.prune()
+
[docs] def check_data(self, data): + if self.pre_fuzzyfy: + ndata = self.fuzzyfy(data) + else: + ndata = [self.format_data(k) for k in data.to_dict('records')] + + return ndata
+
[docs] def forecast(self, ndata, **kwargs): - if self.pre_fuzzyfy: - ndata = self.fuzzyfy(ndata) - else: - ndata = [self.format_data(k) for k in ndata.to_dict('records')] + ndata = self.check_data(ndata) return self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
+
[docs] def forecast_multivariate(self, data, **kwargs): + + ndata = self.check_data(data) + + ret = {} + for var in self.explanatory_variables: + self.cluster.change_target_variable(var) + ret[var.name] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs) + + columns = ret.keys() + return pd.DataFrame(ret, columns=columns)
+ def __str__(self): """String representation of the model""" return str(self.model) diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 5d38257..5020778 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -322,6 +322,8 @@
  • (in module pyFTS.models.nonstationary.common)
  • +
  • check_data() (pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method) +
  • check_ignore_list() (in module pyFTS.benchmarks.Util)
  • check_replace_list() (in module pyFTS.benchmarks.Util) @@ -345,11 +347,11 @@
  • cluster_params (pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)
  • ClusteredMVFTS (class in pyFTS.models.multivariate.cmvfts) -
  • -
  • CMeansPartitioner (class in pyFTS.partitioners.CMeans)
  • +
  • forecast_ahead_multivariate() (pyFTS.common.fts.FTS method) +
  • forecast_distribution() (pyFTS.benchmarks.arima.ARIMA method)
  • +
  • forecast_multivariate() (pyFTS.common.fts.FTS method) + +
  • format_data() (pyFTS.models.multivariate.mvfts.MVFTS method) diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index cc547fc5f6e08d3c0fe595a6cd91752393f3bb7a..be439e4478ad593430b265285c500dde84dbeeff 100644 GIT binary patch delta 6028 zcmV;77jx*iFU&8Hc7I8e+c>hv@BJw%;?32Jo)_^hb1Sdy8JDNJWY4^q3j-n{30)*X z17xYxpMJ1#7579!K6G?h5`O>0o(TeAe);%qcb8xOmh!kv=-sdCZ}mxVD$?YC>}PNO za`)yJ`{|x$(J3Y3S&k%Wzg$cmJWwJ_L9sBwTmJgxk1{*%3V%{CS+Gch4`mh=jAwU$ zoj@-#9~l)9#e{x4QI^Qh8U0a`gcTPAVK-XIVkT3rXi)1myWQ9?T(G*jXQzT4NDG0or zWqXn!lERdd41WWc9>Rhn6~6k9C#HvBz*0$(FqkzdPekMyl^7jKBDK~t%bpW80wcAF z8s|8mKT*}M)wFk?kwkTI*UA4H7epr(N;R(v8sizMW}h@y$WT;YNJMs?@Z!l6*NeeE zn3843goao)e=k_#*s?StAlg1PL52m{mkAMJObT)kB!8uDwl!cixocz?sUb4W9CIA( zi5k`#6Oo`1kwpip<^_4liW7D6HJ8W;gY%+ciBOB~&Dt8=2!bI6XF8hk@d zMBxdVCnUlF3Q7tZHXrLAlTg>TM_&s;qC(GMdKPk|9at7C3!U?7JpZQ74}z!RK6YfX zOj1#_<$vdlt3rp?&Sosb85Twq+NO^q!E>5*T2#Duxnl*uB|BPLBhIv9{D{V3|q%r0r@GGidrTGdr=)y2lD6=-pV^a6!OEX3Tg!X 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    +
    +
    +forecast_ahead_multivariate(data, steps, **kwargs)[source]¶
    +

    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
    • +
    • kwargs – model specific parameters
    • +
    +
    Returns:

    a Pandas Dataframe object representing the forecasted values for each variable

    +
    +
    +
    forecast_distribution(data, **kwargs)[source]¶
    @@ -1709,6 +1731,27 @@ when the LHS pattern is identified on time t.

    +
    +
    +forecast_multivariate(data, **kwargs)[source]¶
    +

    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

    +
    +
    +
    fuzzy(data)[source]¶
    @@ -1846,7 +1889,7 @@ needed to forecast a single step ahead

    Parameters:
    • data – time series with minimal length to the order of the model
    • -
    • type – the forecasting type, one of these values: point(default), interval or distribution.
    • +
    • 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
    • start – in the multi step forecasting, the index of the data where to start forecasting
    • distributed – boolean, indicate if the forecasting procedure will be distributed in a dispy cluster
    • diff --git a/docs/build/html/pyFTS.models.multivariate.html b/docs/build/html/pyFTS.models.multivariate.html index ade8ac3..623320b 100644 --- a/docs/build/html/pyFTS.models.multivariate.html +++ b/docs/build/html/pyFTS.models.multivariate.html @@ -487,6 +487,11 @@ transformations and partitioners.

      class pyFTS.models.multivariate.cmvfts.ClusteredMVFTS(**kwargs)[source]¶

      Bases: pyFTS.models.multivariate.mvfts.MVFTS

      Meta model for multivariate, high order, clustered multivariate FTS

      +
      +
      +check_data(data)[source]¶
      +
      +
      cluster = None¶
      @@ -526,6 +531,27 @@ transformations and partitioners.

    +
    +
    +forecast_multivariate(data, **kwargs)[source]¶
    +

    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

    +
    +
    +
    fts_method = None¶
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  • [docs] def forecast_multivariate(self, data, **kwargs): + """ + Multivariate forecast one step ahead + + :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model + :param kwargs: model specific parameters + :return: a Pandas Dataframe object representing the forecasted values for each variable + """ + raise NotImplementedError('This model do not perform one step ahead multivariate forecasts!')

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