- Issue #3 - Code documentation with PEP 257 compliance

- Benchmarks refactoring and optimizations
 - Probabilistic package, with Kernel Density Estimation

benchmarks/Util.py

@@ -2,12 +2,68 @@
 Benchmark utility functions
 """
 
+import matplotlib as plt
+import matplotlib.cm as cmx
+import matplotlib.colors as pltcolors
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from checkbox_support.parsers.tests.test_modinfo import testMultipleModinfoParser
+from mpl_toolkits.mplot3d import Axes3D
+
+
 import numpy as np
 import pandas as pd
 from copy import deepcopy
 from pyFTS.common import Util
 
 
+def extract_measure(dataframe,measure,data_columns):
+    if not dataframe.empty:
+        tmp = dataframe[(dataframe.Measure == measure)][data_columns].to_dict(orient="records")[0]
+        ret = [k for k in tmp.values()]
+        return ret
+    else:
+        return None
+
+
+def find_best(dataframe, criteria, ascending):
+    models = dataframe.Model.unique()
+    orders = dataframe.Order.unique()
+    ret = {}
+    for m in models:
+        for o in orders:
+            mod = {}
+            df = dataframe[(dataframe.Model == m) & (dataframe.Order == o)].sort_values(by=criteria, ascending=ascending)
+            if not df.empty:
+                _key = str(m) + str(o)
+                best = df.loc[df.index[0]]
+                mod['Model'] = m
+                mod['Order'] = o
+                mod['Scheme'] = best["Scheme"]
+                mod['Partitions'] = best["Partitions"]
+
+                ret[_key] = mod
+
+    return ret
+
+
+def point_dataframe_sintetic_columns():
+    return ["Model", "Order", "Scheme", "Partitions", "Size", "RMSEAVG", "RMSESTD", "SMAPEAVG", "SMAPESTD", "UAVG",
+            "USTD", "TIMEAVG", "TIMESTD"]
+
+
+def point_dataframe_analytic_columns(experiments):
+    columns = [str(k) for k in np.arange(0, experiments)]
+    columns.insert(0, "Model")
+    columns.insert(1, "Order")
+    columns.insert(2, "Scheme")
+    columns.insert(3, "Partitions")
+    columns.insert(4, "Size")
+    columns.insert(5, "Measure")
+    return columns
+
+
 def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u):
     """
     Create a dataframe to store the benchmark results
@@ -32,9 +88,14 @@ def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, t
                 mfts = objs[k]
                 mod.append(mfts.shortname)
                 mod.append(mfts.order)
+                if not mfts.benchmark_only:
                     mod.append(mfts.partitioner.name)
                     mod.append(mfts.partitioner.partitions)
                     mod.append(len(mfts))
+                else:
+                    mod.append('-')
+                    mod.append('-')
+                    mod.append('-')
                 mod.append(np.round(np.nanmean(rmse[k]), 2))
                 mod.append(np.round(np.nanstd(rmse[k]), 2))
                 mod.append(np.round(np.nanmean(smape[k]), 2))
@@ -42,31 +103,202 @@ def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, t
                 mod.append(np.round(np.nanmean(u[k]), 2))
                 mod.append(np.round(np.nanstd(u[k]), 2))
                 mod.append(np.round(np.nanmean(times[k]), 4))
+                mod.append(np.round(np.nanstd(times[k]), 4))
                 ret.append(mod)
             except Exception as ex:
                 print("Erro ao salvar ", k)
                 print("Exceção ", ex)
 
-        columns = ["Model", "Order", "Scheme","Partitions", "Size", "RMSEAVG", "RMSESTD", "SMAPEAVG", "SMAPESTD", "UAVG", "USTD", "TIMEAVG"]
+        columns = point_dataframe_sintetic_columns()
     else:
         for k in sorted(objs.keys()):
             try:
                 mfts = objs[k]
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'RMSE']
+                n = mfts.shortname
+                o = mfts.order
+                if not mfts.benchmark_only:
+                    s = mfts.partitioner.name
+                    p = mfts.partitioner.partitions
+                    l = len(mfts)
+                else:
+                    s = '-'
+                    p = '-'
+                    l = '-'
+
+                tmp = [n, o, s, p, l, 'RMSE']
                 tmp.extend(rmse[k])
                 ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),  'SMAPE']
+                tmp = [n, o, s, p, l, 'SMAPE']
                 tmp.extend(smape[k])
                 ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),  'U']
+                tmp = [n, o, s, p, l, 'U']
                 tmp.extend(u[k])
                 ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),  'TIME']
+                tmp = [n, o, s, p, l, 'TIME']
                 tmp.extend(times[k])
                 ret.append(deepcopy(tmp))
             except Exception as ex:
                 print("Erro ao salvar ", k)
                 print("Exceção ", ex)
+        columns = point_dataframe_analytic_columns(experiments)
+    dat = pd.DataFrame(ret, columns=columns)
+    if save: dat.to_csv(Util.uniquefilename(file), sep=";", index=False)
+    return dat
+
+
+def cast_dataframe_to_sintetic_point(infile, outfile, experiments):
+    columns = point_dataframe_analytic_columns(experiments)
+    dat = pd.read_csv(infile, sep=";", usecols=columns)
+    models = dat.Model.unique()
+    orders = dat.Order.unique()
+    schemes = dat.Scheme.unique()
+    partitions = dat.Partitions.unique()
+
+    data_columns = analytical_data_columns(experiments)
+
+    ret = []
+
+    for m in models:
+        for o in orders:
+            for s in schemes:
+                for p in partitions:
+                    mod = []
+                    df = dat[(dat.Model == m) & (dat.Order == o) & (dat.Scheme == s) & (dat.Partitions == p)]
+                    if not df.empty:
+                        rmse = extract_measure(df, 'RMSE', data_columns)
+                        smape = extract_measure(df, 'SMAPE', data_columns)
+                        u = extract_measure(df, 'U', data_columns)
+                        times = extract_measure(df, 'TIME', data_columns)
+                        mod.append(m)
+                        mod.append(o)
+                        mod.append(s)
+                        mod.append(p)
+                        mod.append(extract_measure(df, 'RMSE', ['Size'])[0])
+                        mod.append(np.round(np.nanmean(rmse), 2))
+                        mod.append(np.round(np.nanstd(rmse), 2))
+                        mod.append(np.round(np.nanmean(smape), 2))
+                        mod.append(np.round(np.nanstd(smape), 2))
+                        mod.append(np.round(np.nanmean(u), 2))
+                        mod.append(np.round(np.nanstd(u), 2))
+                        mod.append(np.round(np.nanmean(times), 4))
+                        mod.append(np.round(np.nanstd(times), 4))
+                        ret.append(mod)
+
+    dat = pd.DataFrame(ret, columns=point_dataframe_sintetic_columns())
+    dat.to_csv(Util.uniquefilename(outfile), sep=";", index=False)
+
+
+def analytical_data_columns(experiments):
+    data_columns = [str(k) for k in np.arange(0, experiments)]
+    return data_columns
+
+
+def plot_dataframe_point(file_synthetic, file_analytic, experiments):
+
+    fig, axes = plt.subplots(nrows=4, ncols=1, figsize=[6, 8])
+
+    axes[0].set_title('RMSE')
+    axes[1].set_title('SMAPE')
+    axes[2].set_title('U Statistic')
+    axes[3].set_title('Execution Time')
+
+    dat_syn = pd.read_csv(file_synthetic, sep=";", usecols=point_dataframe_sintetic_columns())
+
+    bests = find_best(dat_syn, ['UAVG','RMSEAVG','USTD','RMSESTD'], [1,1,1,1])
+
+    dat_ana = pd.read_csv(file_analytic, sep=";", usecols=point_dataframe_analytic_columns(experiments))
+
+    data_columns = analytical_data_columns(experiments)
+
+    rmse = []
+    smape = []
+    u = []
+    times = []
+    labels = []
+
+    for b in bests.keys():
+        best = bests[b]
+        tmp = dat_ana[(dat_ana.Model == best["Model"]) & (dat_ana.Order == best["Order"])
+                & (dat_ana.Scheme == best["Scheme"]) & (dat_ana.Partitions == best["Partitions"])]
+        rmse.append( extract_measure(tmp,'RMSE',data_columns) )
+        smape.append(extract_measure(tmp, 'SMAPE', data_columns))
+        u.append(extract_measure(tmp, 'U', data_columns))
+        times.append(extract_measure(tmp, 'TIME', data_columns))
+        labels.append(best["Model"] + " " + str(best["Order"]))
+
+    axes[0].boxplot(rmse, labels=labels, showmeans=True)
+    axes[1].boxplot(smape, labels=labels, showmeans=True)
+    axes[2].boxplot(u, labels=labels, showmeans=True)
+    axes[3].boxplot(times, labels=labels, showmeans=True)
+
+    plt.show()
+
+
+
+def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times):
+    ret = []
+    if sintetic:
+        for k in sorted(objs.keys()):
+            mod = []
+            mfts = objs[k]
+            mod.append(mfts.shortname)
+            mod.append(mfts.order)
+            if not mfts.benchmark_only:
+                mod.append(mfts.partitioner.name)
+                mod.append(mfts.partitioner.partitions)
+                l = len(mfts)
+            else:
+                mod.append('-')
+                mod.append('-')
+                l = '-'
+            mod.append(round(np.nanmean(sharpness[k]), 2))
+            mod.append(round(np.nanstd(sharpness[k]), 2))
+            mod.append(round(np.nanmean(resolution[k]), 2))
+            mod.append(round(np.nanstd(resolution[k]), 2))
+            mod.append(round(np.nanmean(coverage[k]), 2))
+            mod.append(round(np.nanstd(coverage[k]), 2))
+            mod.append(round(np.nanmean(times[k]), 2))
+            mod.append(round(np.nanstd(times[k]), 2))
+            mod.append(l)
+            ret.append(mod)
+
+        columns = interval_dataframe_sintetic_columns()
+    else:
+        for k in sorted(objs.keys()):
+            try:
+                mfts = objs[k]
+                n = mfts.shortname
+                o = mfts.order
+                if not mfts.benchmark_only:
+                    s = mfts.partitioner.name
+                    p = mfts.partitioner.partitions
+                    l = len(mfts)
+                else:
+                    s = '-'
+                    p = '-'
+                    l = '-'
+
+                tmp = [n, o, s, p, l, 'Sharpness']
+                tmp.extend(sharpness[k])
+                ret.append(deepcopy(tmp))
+                tmp = [n, o, s, p, l, 'Resolution']
+                tmp.extend(resolution[k])
+                ret.append(deepcopy(tmp))
+                tmp = [n, o, s, p, l, 'Coverage']
+                tmp.extend(coverage[k])
+                ret.append(deepcopy(tmp))
+                tmp = [n, o, s, p, l, 'TIME']
+                tmp.extend(times[k])
+                ret.append(deepcopy(tmp))
+            except Exception as ex:
+                print("Erro ao salvar ", k)
+                print("Exceção ", ex)
+        columns = interval_dataframe_analytic_columns(experiments)
+    dat = pd.DataFrame(ret, columns=columns)
+    if save: dat.to_csv(Util.uniquefilename(file), sep=";")
+    return dat
+
+def interval_dataframe_analytic_columns(experiments):
     columns = [str(k) for k in np.arange(0, experiments)]
     columns.insert(0, "Model")
     columns.insert(1, "Order")
@@ -74,6 +306,112 @@ def save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, t
     columns.insert(3, "Partitions")
     columns.insert(4, "Size")
     columns.insert(5, "Measure")
+    return columns
+
+
+def interval_dataframe_sintetic_columns():
+    columns = ["Model", "Order", "Scheme", "Partitions", "SHARPAVG", "SHARPSTD", "RESAVG", "RESSTD", "COVAVG",
+               "COVSTD", "TIMEAVG", "TIMESTD", "SIZE"]
+    return columns
+
+
+def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic):
+    """
+    Save benchmark results for m-step ahead probabilistic forecasters 
+    :param experiments: 
+    :param file: 
+    :param objs: 
+    :param crps_interval: 
+    :param crps_distr: 
+    :param times1: 
+    :param times2: 
+    :param save: 
+    :param sintetic: 
+    :return: 
+    """
+    ret = []
+
+    if sintetic:
+
+        for k in sorted(objs.keys()):
+            try:
+                ret = []
+                for k in sorted(objs.keys()):
+                    try:
+                        mod = []
+                        mfts = objs[k]
+                        mod.append(mfts.shortname)
+                        mod.append(mfts.order)
+                        if not mfts.benchmark_only:
+                            mod.append(mfts.partitioner.name)
+                            mod.append(mfts.partitioner.partitions)
+                            l = len(mfts)
+                        else:
+                            mod.append('-')
+                            mod.append('-')
+                            l = '-'
+                        mod.append(np.round(np.nanmean(crps_interval[k]), 2))
+                        mod.append(np.round(np.nanstd(crps_interval[k]), 2))
+                        mod.append(np.round(np.nanmean(crps_distr[k]), 2))
+                        mod.append(np.round(np.nanstd(crps_distr[k]), 2))
+                        mod.append(l)
+                        mod.append(np.round(np.nanmean(times1[k]), 4))
+                        mod.append(np.round(np.nanmean(times2[k]), 4))
+                        ret.append(mod)
+                    except Exception as e:
+                        print('Erro: %s' % e)
+            except Exception as ex:
+                print("Erro ao salvar ", k)
+                print("Exceção ", ex)
+
+        columns = ahead_dataframe_sintetic_columns()
+    else:
+        for k in sorted(objs.keys()):
+            try:
+                mfts = objs[k]
+                n = mfts.shortname
+                o = mfts.order
+                if not mfts.benchmark_only:
+                    s = mfts.partitioner.name
+                    p = mfts.partitioner.partitions
+                    l = len(mfts)
+                else:
+                    s = '-'
+                    p = '-'
+                    l = '-'
+                tmp = [n, o, s, p, l, 'CRPS_Interval']
+                tmp.extend(crps_interval[k])
+                ret.append(deepcopy(tmp))
+                tmp = [n, o, s, p, l, 'CRPS_Distribution']
+                tmp.extend(crps_distr[k])
+                ret.append(deepcopy(tmp))
+                tmp = [n, o, s, p, l, 'TIME_Interval']
+                tmp.extend(times1[k])
+                ret.append(deepcopy(tmp))
+                tmp = [n, o, s, p, l, 'TIME_Distribution']
+                tmp.extend(times2[k])
+                ret.append(deepcopy(tmp))
+            except Exception as ex:
+                print("Erro ao salvar ", k)
+                print("Exceção ", ex)
+        columns = ahead_dataframe_analytic_columns(experiments)
     dat = pd.DataFrame(ret, columns=columns)
     if save: dat.to_csv(Util.uniquefilename(file), sep=";")
     return dat
+
+
+def ahead_dataframe_analytic_columns(experiments):
+    columns = [str(k) for k in np.arange(0, experiments)]
+    columns.insert(0, "Model")
+    columns.insert(1, "Order")
+    columns.insert(2, "Scheme")
+    columns.insert(3, "Partitions")
+    columns.insert(4, "Size")
+    columns.insert(5, "Measure")
+    return columns
+
+
+def ahead_dataframe_sintetic_columns():
+    columns = ["Model", "Order", "Scheme", "Partitions", "CRPS1AVG", "CRPS1STD", "CRPS2AVG", "CRPS2STD",
+               "SIZE", "TIME1AVG", "TIME2AVG"]
+    return columns

benchmarks/benchmarks.py

@@ -4,21 +4,24 @@
 """Benchmarks to FTS methods"""
 
 
+import datetime
+import time
+from copy import deepcopy
+
+import matplotlib as plt
+import matplotlib.cm as cmx
+import matplotlib.colors as pltcolors
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import time
-import datetime
-import matplotlib as plt
-import matplotlib.colors as pltcolors
-import matplotlib.cm as cmx
-import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
+
+from probabilistic import ProbabilityDistribution
+from pyFTS import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, ensemble, hwang
+from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, Util, quantreg
+from pyFTS.common import Transformations, Util
 # from sklearn.cross_validation import KFold
-from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM
+from pyFTS.partitioners import Grid
-from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util, quantreg
-from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
-from pyFTS import fts, song, chen, yu, ismailefendi, sadaei, hofts, hwang,  pwfts, ifts, cheng, ensemble, hwang
-from copy import deepcopy
 
 colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
           'cyan', 'blue', 'darkblue', 'purple', 'darkviolet']
@@ -369,62 +372,6 @@ def getProbabilityDistributionStatistics(pmfs, data):
         ret += "	\\\\ \n"
     return ret
 
-def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times):
-    ret = []
-    if sintetic:
-        for k in sorted(objs.keys()):
-            mod = []
-            mfts = objs[k]
-            mod.append(mfts.shortname)
-            mod.append(mfts.order)
-            mod.append(mfts.partitioner.name)
-            mod.append(mfts.partitioner.partitions)
-            mod.append(round(np.nanmean(sharpness[k]), 2))
-            mod.append(round(np.nanstd(sharpness[k]), 2))
-            mod.append(round(np.nanmean(resolution[k]), 2))
-            mod.append(round(np.nanstd(resolution[k]), 2))
-            mod.append(round(np.nanmean(coverage[k]), 2))
-            mod.append(round(np.nanstd(coverage[k]), 2))
-            mod.append(round(np.nanmean(times[k]), 2))
-            mod.append(round(np.nanstd(times[k]), 2))
-            mod.append(len(mfts))
-            ret.append(mod)
-
-        columns = ["Model", "Order", "Scheme", "Partitions", "SHARPAVG", "SHARPSTD", "RESAVG", "RESSTD", "COVAVG",
-                   "COVSTD", "TIMEAVG", "TIMESTD", "SIZE"]
-    else:
-        for k in sorted(objs.keys()):
-            try:
-                mfts = objs[k]
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),
-                       'Sharpness']
-                tmp.extend(sharpness[k])
-                ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),
-                       'Resolution']
-                tmp.extend(resolution[k])
-                ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),
-                       'Coverage']
-                tmp.extend(coverage[k])
-                ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),
-                       'TIME']
-                tmp.extend(times[k])
-                ret.append(deepcopy(tmp))
-            except Exception as ex:
-                print("Erro ao salvar ", k)
-                print("Exceção ", ex)
-        columns = [str(k) for k in np.arange(0, experiments)]
-        columns.insert(0, "Model")
-        columns.insert(1, "Order")
-        columns.insert(2, "Scheme")
-        columns.insert(3, "Partitions")
-        columns.insert(4, "Size")
-        columns.insert(5, "Measure")
-    dat = pd.DataFrame(ret, columns=columns)
-    if save: dat.to_csv(Util.uniquefilename(file), sep=";")
-    return dat
 
 
 def interval_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[Grid.GridPartitioner],
@@ -518,7 +465,7 @@ def interval_sliding_window(data, windowsize, train=0.8,models=None,partitioners
                                 coverage[_key].append(_cov)
                                 times[_key].append(_tdiff)
 
-    return save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times)
+    return Util.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times)
 
 
 def all_interval_forecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5],
@@ -637,80 +584,6 @@ def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]):
     ax.legend(handles0, labels0)
 
 
-def save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic):
-    """
-    Save benchmark results for m-step ahead probabilistic forecasters 
-    :param experiments: 
-    :param file: 
-    :param objs: 
-    :param crps_interval: 
-    :param crps_distr: 
-    :param times1: 
-    :param times2: 
-    :param save: 
-    :param sintetic: 
-    :return: 
-    """
-    ret = []
-
-    if sintetic:
-
-        for k in sorted(objs.keys()):
-            try:
-                ret = []
-                for k in sorted(objs.keys()):
-                    try:
-                        mod = []
-                        mfts = objs[k]
-                        mod.append(mfts.shortname)
-                        mod.append(mfts.order)
-                        mod.append(mfts.partitioner.name)
-                        mod.append(mfts.partitioner.partitions)
-                        mod.append(np.round(np.nanmean(crps_interval[k]), 2))
-                        mod.append(np.round(np.nanstd(crps_interval[k]), 2))
-                        mod.append(np.round(np.nanmean(crps_distr[k]), 2))
-                        mod.append(np.round(np.nanstd(crps_distr[k]), 2))
-                        mod.append(len(mfts))
-                        mod.append(np.round(np.nanmean(times1[k]), 4))
-                        mod.append(np.round(np.nanmean(times2[k]), 4))
-                        ret.append(mod)
-                    except Exception as e:
-                        print('Erro: %s' % e)
-            except Exception as ex:
-                print("Erro ao salvar ", k)
-                print("Exceção ", ex)
-
-        columns = ["Model", "Order", "Scheme", "Partitions", "CRPS1AVG", "CRPS1STD", "CRPS2AVG", "CRPS2STD",
-                   "SIZE", "TIME1AVG", "TIME2AVG"]
-    else:
-        for k in sorted(objs.keys()):
-            try:
-                mfts = objs[k]
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts), 'CRPS_Interval']
-                tmp.extend(crps_interval[k])
-                ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),  'CRPS_Distribution']
-                tmp.extend(crps_distr[k])
-                ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),  'TIME_Interval']
-                tmp.extend(times1[k])
-                ret.append(deepcopy(tmp))
-                tmp = [mfts.shortname, mfts.order, mfts.partitioner.name, mfts.partitioner.partitions, len(mfts),  'TIME_Distribution']
-                tmp.extend(times2[k])
-                ret.append(deepcopy(tmp))
-            except Exception as ex:
-                print("Erro ao salvar ", k)
-                print("Exceção ", ex)
-        columns = [str(k) for k in np.arange(0, experiments)]
-        columns.insert(0, "Model")
-        columns.insert(1, "Order")
-        columns.insert(2, "Scheme")
-        columns.insert(3, "Partitions")
-        columns.insert(4, "Size")
-        columns.insert(5, "Measure")
-    dat = pd.DataFrame(ret, columns=columns)
-    if save: dat.to_csv(Util.uniquefilename(file), sep=";")
-    return dat
 
 
 def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution = None, partitioners=[Grid.GridPartitioner],
@@ -806,7 +679,7 @@ def ahead_sliding_window(data, windowsize, train, steps, models=None, resolution
 
                                 if dump: print(_crps1, _crps2, _tdiff, _t1, _t2)
 
-    return save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic)
+    return Util.save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic)
 
 
 def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resolution = None, max_order=3,save=False, file=None, tam=[20, 5],
@@ -979,6 +852,7 @@ def plotCompared(original, forecasts, labels, title):
     ax.set_xlim([0, len(original)])
     ax.set_ylim([min(original), max(original)])
 
+
 def SelecaoSimples_MenorRMSE(original, parameters, modelo):
     ret = []
     errors = []

benchmarks/distributed_benchmarks.py

@@ -6,20 +6,16 @@ To enable a dispy cluster node:
 python3 /usr/local/bin/dispynode.py -i [local IP] -d
 """
 
-import random
+import datetime
+import time
+
 import dispy
 import dispy.httpd
-from copy import deepcopy
 import numpy as np
-import pandas as pd
+
-import time
+from pyFTS.benchmarks import benchmarks, Util as bUtil
-import datetime
+from pyFTS.common import Util
-import pyFTS
+from pyFTS.partitioners import Grid
-from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM
-from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution
-from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
-from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang,  pwfts, ifts
-from pyFTS.benchmarks import  benchmarks, parallel_benchmarks, Util as bUtil
 
 
 def run_point(mfts, partitioner, train_data, test_data, window_key=None, transformation=None, indexer=None):

benchmarks/parallel_benchmarks.py

@@ -2,19 +2,17 @@
 joblib Parallelized Benchmarks to FTS methods
 """
 
-from copy import deepcopy
+import datetime
-from joblib import Parallel, delayed
 import multiprocessing
+import time
+from copy import deepcopy
 
 import numpy as np
-import pandas as pd
+from joblib import Parallel, delayed
-import time
+
-import datetime
+from pyFTS.benchmarks import benchmarks, Util
-from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM
+from pyFTS.common import Util
-from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution
+from pyFTS.partitioners import Grid
-from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
-from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang,  pwfts, ifts
-from pyFTS.benchmarks import  benchmarks
 
 
 def run_point(mfts, partitioner, train_data, test_data, transformation=None, indexer=None):
@@ -140,7 +138,7 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
 
     print("Process Duration: {0}".format(_process_end - _process_start))
 
-    return benchmarks.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
+    return Util.save_dataframe_point(experiments, file, objs, rmse, save, sintetic, smape, times, u)
 
 
 def run_interval(mfts, partitioner, train_data, test_data, transformation=None, indexer=None):
@@ -267,7 +265,7 @@ def interval_sliding_window(data, windowsize, train=0.8, models=None, partitione
 
     print("Process Duration: {0}".format(_process_end - _process_start))
 
-    return benchmarks.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times)
+    return Util.save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, sintetic, times)
 
 
 def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, transformation=None, indexer=None):
@@ -397,4 +395,4 @@ def ahead_sliding_window(data, windowsize, train, steps,resolution, models=None,
 
     print("Process Duration: {0}".format(_process_end - _process_start))
 
-    return benchmarks.save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic)
+    return Util.save_dataframe_ahead(experiments, file, objs, crps_interval, crps_distr, times1, times2, save, sintetic)

probabilistic/ProbabilityDistribution.py

@@ -98,7 +98,6 @@ class ProbabilityDistribution(object):
         axis.set_xlabel('Universe of Discourse')
         axis.set_ylabel('Probability')
 
-
     def __str__(self):
         head = '|'
         body = '|'

probabilistic/kde.py

@@ -0,0 +1,25 @@
+"""
+Kernel Density Estimation 
+"""
+
+
+class KernelSmoothing(object):
+    """Kernel Density Estimation"""
+    def __init__(self,h, data, method="epanechnikov"):
+        self.h = h
+        self.data = data
+        self.method = method
+
+    def kernel(self, u):
+        if self.method == "epanechnikov":
+            return (3/4) * (1 - u**2)
+        elif self.method == "uniform":
+            return 0.5
+        elif self.method == "uniform":
+            return 0.5
+
+    def probability(self, x):
+        l = len(self.data)
+        p = sum([self.kernel((x - k)/self.h) for k in self.data]) / l*self.h
+
+        return p

tests/general.py

@@ -35,12 +35,15 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 
 #print(FCM.FCMPartitionerTrimf.__module__)
 
-#gauss = random.normal(0,1.0,1000)
+#gauss = random.normal(0,1.0,5000)
 #gauss_teste = random.normal(0,1.0,400)
 
 
-taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
+#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
-taiex = np.array(taiexpd["avg"][:5000])
+#taiex = np.array(taiexpd["avg"][:5000])
+
+#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
+#nasdaq = np.array(nasdaqpd["avg"][0:5000])
 
 #from statsmodels.tsa.arima_model import ARIMA as stats_arima
 from statsmodels.tsa.tsatools import lagmat
@@ -54,9 +57,12 @@ from statsmodels.tsa.tsatools import lagmat
 
 from pyFTS.benchmarks import distributed_benchmarks as bchmk
 #from pyFTS.benchmarks import parallel_benchmarks as bchmk
-#from pyFTS.benchmarks import benchmarks as bchmk
+from pyFTS.benchmarks import Util
 #from pyFTS.benchmarks import arima
 
+#Util.cast_dataframe_to_sintetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
+
+Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
 
 #tmp = arima.ARIMA("")
 #tmp.train(taiex[:1600],None,parameters=(2,0,1))
@@ -66,11 +72,11 @@ from pyFTS.benchmarks import distributed_benchmarks as bchmk
 
 #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
 
-bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
+#bchmk.point_sliding_window(gauss,2000,train=0.8, #models=[yu.WeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
+#                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(10,200,step=5), #transformation=diff,
+#                     partitions= np.arange(3,10,step=1), #transformation=diff,
-                     dump=True, save=True, file="experiments/taiex_point_distributed.csv",
+#                     dump=True, save=True, file="experiments/gauss_point_distributed.csv",
-                     nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
+#                     nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
 
 #bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])