Improvements on partitioners.Util

pyFTS/partitioners/Util.py

@@ -18,118 +18,15 @@ all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitio
 mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
 
 
-def sliding_window_simple_search(data, windowsize, model, partitions, orders, **kwargs):
+def plot_sets(data, sets, titles, size=[12, 10], save=False, file=None, axis=None):
-
-    _3d = len(orders) > 1
-    ret = []
-    errors = np.array([[0 for k in range(len(partitions))] for kk in range(len(orders))])
-    forecasted_best = []
-
-    figsize = kwargs.get('figsize', [10, 15])
-    fig = plt.figure(figsize=figsize)
-
-    plotforecasts = kwargs.get('plotforecasts',False)
-    if plotforecasts:
-        ax0 = fig.add_axes([0, 0.4, 0.9, 0.5])  # left, bottom, width, height
-        ax0.set_xlim([0, len(data)])
-        ax0.set_ylim([min(data) * 0.9, max(data) * 1.1])
-        ax0.set_title('Forecasts')
-        ax0.set_ylabel('F(T)')
-        ax0.set_xlabel('T')
-    min_rmse = 1000000.0
-    best = None
-
-    intervals = kwargs.get('intervals',False)
-    threshold = kwargs.get('threshold',0.5)
-
-    progressbar = kwargs.get('progressbar', None)
-
-    rng1 = enumerate(partitions, start=0)
-
-    if progressbar:
-        from tqdm import tqdm
-        rng1 = enumerate(tqdm(partitions), start=0)
-
-    for pc, p in rng1:
-        fs = Grid.GridPartitioner(data=data, npart=p)
-
-        rng2 = enumerate(orders, start=0)
-
-        if progressbar:
-            rng2 = enumerate(tqdm(orders), start=0)
-
-        for oc, o in rng2:
-            _error = []
-            for ct, train, test in Util.sliding_window(data, windowsize, 0.8, **kwargs):
-                fts = model("q = " + str(p) + " n = " + str(o), partitioner=fs)
-                fts.fit(train, order=o)
-                if not intervals:
-                    forecasted = fts.forecast(test)
-                    if not fts.has_seasonality:
-                        _error.append( Measures.rmse(np.array(test[o:]), np.array(forecasted[:-1])) )
-                    else:
-                        _error.append( Measures.rmse(np.array(test[o:]), np.array(forecasted)) )
-                    for kk in range(o):
-                        forecasted.insert(0, None)
-                    if plotforecasts: ax0.plot(forecasted, label=fts.name)
-                else:
-                    forecasted = fts.forecast_interval(test)
-                    _error.append( 1.0 - Measures.rmse_interval(np.array(test[o:]), np.array(forecasted[:-1])) )
-            error = np.nanmean(_error)
-            errors[oc, pc] = error
-            if (min_rmse - error) > threshold:
-                min_rmse = error
-                best = fts
-                forecasted_best = forecasted
-
-    # print(min_rmse)
-    if plotforecasts:
-        # handles0, labels0 = ax0.get_legend_handles_labels()
-        # ax0.legend(handles0, labels0)
-        elev = kwargs.get('elev', 30)
-        azim = kwargs.get('azim', 144)
-        ax0.plot(test, label="Original", linewidth=3.0, color="black")
-        if _3d: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
-    if not plotforecasts: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
-    # ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
-    if _3d:
-        ax1.set_title('Error Surface')
-        ax1.set_ylabel('Model order')
-        ax1.set_xlabel('Number of partitions')
-        ax1.set_zlabel('RMSE')
-        X, Y = np.meshgrid(partitions, orders)
-        surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
-    else:
-        ax1 = fig.add_axes([0, 1, 0.9, 0.9])
-        ax1.set_title('Error Curve')
-        ax1.set_ylabel('Number of partitions')
-        ax1.set_xlabel('RMSE')
-        ax0.plot(errors,partitions)
-    ret.append(best)
-    ret.append(forecasted_best)
-
-    # plt.tight_layout()
-
-    file = kwargs.get('file', None)
-    save = kwargs.get('save', False)
-
-    Util.show_and_save_image(fig, file, save)
-
-    return ret
-
-
-def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
     num = len(sets)
-    #fig = plt.figure(figsize=tam)
+
-    maxx = max(data)
+    if axis is None:
-    minx = min(data)
+        fig, axes = plt.subplots(nrows=num, ncols=1,figsize=size)
-    #h = 1/num
-    #print(h)
-    fig, axes = plt.subplots(nrows=num, ncols=1,figsize=tam)
     for k in np.arange(0,num):
         ticks = []
         x = []
-        ax = axes[k]
+        ax = axes[k] if axis is None else axis
         ax.set_title(titles[k])
         ax.set_ylim([0, 1.1])
         for key in sets[k].keys():
@@ -147,18 +44,32 @@ def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
         ax.xaxis.set_ticklabels(ticks)
         ax.xaxis.set_ticks(x)
 
-    plt.tight_layout()
+    if axis is None:
+        plt.tight_layout()
 
-    Util.show_and_save_image(fig, file, save)
+        Util.show_and_save_image(fig, file, save)
 
 
-def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None):
+def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None, axis=None):
     sets = [k.sets for k in objs]
     titles = [k.name for k in objs]
-    plot_sets(data, sets, titles, tam, save, file)
+    plot_sets(data, sets, titles, tam, save, file, axis)
 
 
-def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=False, file=None):
+def explore_partitioners(data, npart, methods=None, mf=None, transformation=None,
+                         size=[12, 10], save=False, file=None):
+    """
+    Create partitioners for the mf membership functions and npart partitions and show the partitioning images.
+    :data: Time series data
+    :npart: Maximum number of partitions of the universe of discourse
+    :methods: A list with the partitioning methods to be used
+    :mf: A list with the membership functions to be used
+    :transformation: a transformation to be used in partitioner
+    :size: list, the size of the output image [width, height]
+    :save: boolean, if the image will be saved on disk
+    :file: string, the file path to save the image
+    :return: the list of the built partitioners
+    """
     if methods is None:
         methods = all_methods
 
@@ -169,10 +80,10 @@ def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=
 
     for p in methods:
         for m in mf:
-            obj = p(data=data, npart=npart, func=m)
+            obj = p(data=data, npart=npart, func=m, transformation=transformation)
             obj.name = obj.name  + " - " + obj.membership_function.__name__
             objs.append(obj)
 
-    plot_partitioners(data, objs, tam, save, file)
+    plot_partitioners(data, objs, size, save, file)
 
     return objs