예제 #1
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    def _theta_plot_default(self):

        theta = self.theta
        nclasses = theta.shape[0]

        # create a plot data object and give it this data
        plot_data = ArrayPlotData()

        plot_data.set_data('classes', range(nclasses))

        # create the plot
        plot = Plot(plot_data)

        # --- plot theta samples
        if self.theta_samples is not None:
            self._plot_samples(plot, plot_data)

        # --- plot values of theta
        plots = self._plot_theta_values(plot, plot_data)

        # --- adjust plot appearance

        plot.aspect_ratio = 1.6 if is_display_small() else 1.7

        # adjust axis bounds
        y_high = theta.max()
        if self.theta_samples is not None:
            y_high = max(y_high, self.theta_samples.max())

        plot.range2d = DataRange2D(
            low  = (-0.2, 0.0),
            high = (nclasses-1+0.2, y_high*1.1)
        )

        # create new horizontal axis
        label_axis = self._create_increment_one_axis(
            plot, 0., nclasses, 'bottom')
        label_axis.title = 'True classes'
        self._add_index_axis(plot, label_axis)

        # label vertical axis
        plot.value_axis.title = 'Probability'

        # add legend
        legend = Legend(component=plot, plots=plots,
                        align="ur", border_padding=10)
        legend.tools.append(LegendTool(legend, drag_button="left"))
        legend.padding_right = -100
        plot.overlays.append(legend)

        container = VPlotContainer(width=plot.width + 100, halign='left')
        plot.padding_bottom = 50
        plot.padding_top = 10
        plot.padding_left = 0
        container.add(plot)
        container.bgcolor = 0xFFFFFF

        self.decorate_plot(container, theta)

        return container
예제 #2
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    def _theta_plot_default(self):

        theta = self.theta
        nclasses = theta.shape[0]

        # create a plot data object and give it this data
        plot_data = ArrayPlotData()

        plot_data.set_data('classes', list(range(nclasses)))

        # create the plot
        plot = Plot(plot_data)

        # --- plot theta samples
        if self.theta_samples is not None:
            self._plot_samples(plot, plot_data)

        # --- plot values of theta
        plots = self._plot_theta_values(plot, plot_data)

        # --- adjust plot appearance

        plot.aspect_ratio = 1.6 if is_display_small() else 1.7

        # adjust axis bounds
        y_high = theta.max()
        if self.theta_samples is not None:
            y_high = max(y_high, self.theta_samples.max())

        plot.range2d = DataRange2D(low=(-0.2, 0.0),
                                   high=(nclasses - 1 + 0.2, y_high * 1.1))

        # create new horizontal axis
        label_axis = self._create_increment_one_axis(plot, 0., nclasses,
                                                     'bottom')
        label_axis.title = 'True classes'
        self._add_index_axis(plot, label_axis)

        # label vertical axis
        plot.value_axis.title = 'Probability'

        # add legend
        legend = Legend(component=plot,
                        plots=plots,
                        align="ur",
                        border_padding=10)
        legend.tools.append(LegendTool(legend, drag_button="left"))
        legend.padding_right = -100
        plot.overlays.append(legend)

        container = VPlotContainer(width=plot.width + 100, halign='left')
        plot.padding_bottom = 50
        plot.padding_top = 10
        plot.padding_left = 0
        container.add(plot)
        container.bgcolor = 0xFFFFFF

        self.decorate_plot(container, theta)

        return container
예제 #3
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    def _theta_plot_default(self):
        """Create plot of theta parameters."""

        # We plot both the thetas and the samples from the posterior; if the
        # latter are not defined, the corresponding ArrayPlotData names
        # should be set to an empty list, so that they are not displayed
        theta = self.model.theta
        theta_len = theta.shape[0]

        # create the plot data
        if not self.theta_plot_data:
            self.theta_plot_data = ArrayPlotData()
            self._update_plot_data()

        # create the plot
        theta_plot = Plot(self.theta_plot_data)

        for idx in range(theta_len):
            # candle plot summarizing samples over the posterior
            theta_plot.candle_plot((_w_idx('index', idx),
                                    _w_idx('min', idx),
                                    _w_idx('barmin', idx),
                                    _w_idx('avg', idx),
                                    _w_idx('barmax', idx),
                                    _w_idx('max', idx)),
                                    color = get_annotator_color(idx),
                                    bar_line_color = "black",
                                    stem_color = "blue",
                                    center_color = "red",
                                    center_width = 2)

            # plot of raw samples
            theta_plot.plot((_w_idx('ysamples', idx),
                             _w_idx('xsamples', idx)),
                            type='scatter',
                            color='black',
                            marker='dot',
                            line_width=0.5,
                            marker_size=1)

            # plot current parameters
            theta_plot.plot((_w_idx('y', idx), _w_idx('x', idx)),
                            type='scatter',
                            color=get_annotator_color(idx),
                            marker='plus',
                            marker_size=8,
                            line_width=2)

        # adjust axis bounds
        theta_plot.range2d = self._compute_range2d()

        # remove horizontal grid and axis
        theta_plot.underlays = [theta_plot.x_grid, theta_plot.y_axis]

        # create new horizontal axis
        label_list = [str(i) for i in range(theta_len)]

        label_axis = LabelAxis(
            theta_plot,
            orientation = 'bottom',
            positions = range(1, theta_len+1),
            labels = label_list,
            label_rotation = 0
        )
        # use a FixedScale tick generator with a resolution of 1
        label_axis.tick_generator = ScalesTickGenerator(scale=FixedScale(1.))

        theta_plot.index_axis = label_axis
        theta_plot.underlays.append(label_axis)
        theta_plot.padding = 25
        theta_plot.padding_left = 40
        theta_plot.aspect_ratio = 1.0

        container = VPlotContainer()
        container.add(theta_plot)
        container.bgcolor = 0xFFFFFF

        self.decorate_plot(container, theta)
        self._set_title(theta_plot)

        return container
예제 #4
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    def _theta_plot_default(self):
        theta = self.theta
        nannotators = theta.shape[0]
        samples = self.theta_samples

        # plot data object
        plot_data = ArrayPlotData()

        # create the plot
        plot = Plot(plot_data)

        # --- plot theta as vertical dashed lines
        # add vertical lines extremes
        plot_data.set_data('line_extr', [0., 1.])

        for k in range(nannotators):
            name = self._theta_name(k)
            plot_data.set_data(name, [theta[k], theta[k]])

        plots = {}
        for k in range(nannotators):
            name = self._theta_name(k)
            line_plot = plot.plot(
                (name, 'line_extr'),
                line_width = 2.,
                color = get_annotator_color(k),
                line_style = 'dash',
                name = name
            )
            plots[name] = line_plot

        # --- plot samples as distributions
        if samples is not None:
            bins = np.linspace(0., 1., 100)
            max_hist = 0.
            for k in range(nannotators):
                name = self._theta_name(k) + '_distr_'
                hist, x = np.histogram(samples[:,k], bins=bins)
                hist = hist / float(hist.sum())
                max_hist = max(max_hist, hist.max())

                # make "bars" out of histogram values
                y = np.concatenate(([0], np.repeat(hist, 2), [0]))
                plot_data.set_data(name+'x', np.repeat(x, 2))
                plot_data.set_data(name+'y', y)

            for k in range(nannotators):
                name = self._theta_name(k) + '_distr_'
                plot.plot((name+'x', name+'y'),
                          line_width = 2.,
                          color = get_annotator_color(k)
                          )

        # --- adjust plot appearance

        plot.aspect_ratio = 1.6 if is_display_small() else 1.7
        plot.padding = [20,0,10,40]

        # adjust axis bounds
        x_low, x_high = theta.min(), theta.max()
        y_low, y_high = 0., 1.
        if samples is not None:
            x_high = max(x_high, samples.max())
            x_low = min(x_low, samples.min())
            y_high = max_hist

        plot.range2d = DataRange2D(
            low  = (max(x_low-0.05, 0.), y_low),
            high = (min(x_high*1.1, 1.), min(y_high*1.1, 1.))
        )

        # label axes
        plot.value_axis.title = 'Probability'
        plot.index_axis.title = 'Theta'

        # add legend
        legend = Legend(component=plot, plots=plots,
                        align="ul", padding=5)
        legend.tools.append(LegendTool(legend, drag_button="left"))
        plot.overlays.append(legend)

        container = VPlotContainer()
        container.add(plot)
        container.bgcolor = 0xFFFFFF

        self.decorate_plot(container, theta)
        self._set_title(plot)

        return container
예제 #5
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    def _theta_plot_default(self):
        """Create plot of theta parameters."""

        # We plot both the thetas and the samples from the posterior; if the
        # latter are not defined, the corresponding ArrayPlotData names
        # should be set to an empty list, so that they are not displayed
        theta = self.model.theta
        theta_len = theta.shape[0]

        # create the plot data
        if not self.theta_plot_data:
            self.theta_plot_data = ArrayPlotData()
            self._update_plot_data()

        # create the plot
        theta_plot = Plot(self.theta_plot_data)

        for idx in range(theta_len):
            # candle plot summarizing samples over the posterior
            theta_plot.candle_plot((_w_idx('index', idx),
                                    _w_idx('min', idx),
                                    _w_idx('barmin', idx),
                                    _w_idx('avg', idx),
                                    _w_idx('barmax', idx),
                                    _w_idx('max', idx)),
                                    color = get_annotator_color(idx),
                                    bar_line_color = "black",
                                    stem_color = "blue",
                                    center_color = "red",
                                    center_width = 2)

            # plot of raw samples
            theta_plot.plot((_w_idx('ysamples', idx),
                             _w_idx('xsamples', idx)),
                            type='scatter',
                            color='black',
                            marker='dot',
                            line_width=0.5,
                            marker_size=1)

            # plot current parameters
            theta_plot.plot((_w_idx('y', idx), _w_idx('x', idx)),
                            type='scatter',
                            color=get_annotator_color(idx),
                            marker='plus',
                            marker_size=8,
                            line_width=2)

        # adjust axis bounds
        theta_plot.range2d = self._compute_range2d()

        # remove horizontal grid and axis
        theta_plot.underlays = [theta_plot.x_grid, theta_plot.y_axis]

        # create new horizontal axis
        label_list = [str(i) for i in range(theta_len)]

        label_axis = LabelAxis(
            theta_plot,
            orientation = 'bottom',
            positions = list(range(1, theta_len+1)),
            labels = label_list,
            label_rotation = 0
        )
        # use a FixedScale tick generator with a resolution of 1
        label_axis.tick_generator = ScalesTickGenerator(scale=FixedScale(1.))

        theta_plot.index_axis = label_axis
        theta_plot.underlays.append(label_axis)
        theta_plot.padding = 25
        theta_plot.padding_left = 40
        theta_plot.aspect_ratio = 1.0

        container = VPlotContainer()
        container.add(theta_plot)
        container.bgcolor = 0xFFFFFF

        self.decorate_plot(container, theta)
        self._set_title(theta_plot)

        return container
예제 #6
0
    def _theta_plot_default(self):
        theta = self.theta
        nannotators = theta.shape[0]
        samples = self.theta_samples

        # plot data object
        plot_data = ArrayPlotData()

        # create the plot
        plot = Plot(plot_data)

        # --- plot theta as vertical dashed lines
        # add vertical lines extremes
        plot_data.set_data('line_extr', [0., 1.])

        for k in range(nannotators):
            name = self._theta_name(k)
            plot_data.set_data(name, [theta[k], theta[k]])

        plots = {}
        for k in range(nannotators):
            name = self._theta_name(k)
            line_plot = plot.plot(
                (name, 'line_extr'),
                line_width = 2.,
                color = get_annotator_color(k),
                line_style = 'dash',
                name = name
            )
            plots[name] = line_plot

        # --- plot samples as distributions
        if samples is not None:
            bins = np.linspace(0., 1., 100)
            max_hist = 0.
            for k in range(nannotators):
                name = self._theta_name(k) + '_distr_'
                hist, x = np.histogram(samples[:,k], bins=bins)
                hist = hist / float(hist.sum())
                max_hist = max(max_hist, hist.max())

                # make "bars" out of histogram values
                y = np.concatenate(([0], np.repeat(hist, 2), [0]))
                plot_data.set_data(name+'x', np.repeat(x, 2))
                plot_data.set_data(name+'y', y)

            for k in range(nannotators):
                name = self._theta_name(k) + '_distr_'
                plot.plot((name+'x', name+'y'),
                          line_width = 2.,
                          color = get_annotator_color(k)
                          )

        # --- adjust plot appearance

        plot.aspect_ratio = 1.6 if is_display_small() else 1.7
        plot.padding = [20,0,10,40]

        # adjust axis bounds
        x_low, x_high = theta.min(), theta.max()
        y_low, y_high = 0., 1.
        if samples is not None:
            x_high = max(x_high, samples.max())
            x_low = min(x_low, samples.min())
            y_high = max_hist

        plot.range2d = DataRange2D(
            low  = (max(x_low-0.05, 0.), y_low),
            high = (min(x_high*1.1, 1.), min(y_high*1.1, 1.))
        )

        # label axes
        plot.value_axis.title = 'Probability'
        plot.index_axis.title = 'Theta'

        # add legend
        legend = Legend(component=plot, plots=plots,
                        align="ul", padding=5)
        legend.tools.append(LegendTool(legend, drag_button="left"))
        plot.overlays.append(legend)

        container = VPlotContainer()
        container.add(plot)
        container.bgcolor = 0xFFFFFF

        self.decorate_plot(container, theta)
        self._set_title(plot)

        return container