Python models.TapTool方法代码示例

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import TapTool [as 别名]
def create_stock(cls, source):

        # xdr1 = DataRange1d(sources=[source.columns("x")])
        # ydr1 = DataRange1d(sources=[source.columns("y")])

        # plot1 = figure(title="Outliers", x_range=xdr1, y_range=ydr1, plot_width=650, plot_height=400)
        stock_plot = figure(title="", plot_width=650, plot_height=400)
        # stock_plot.tools.append(TapTool(plot=stock_plot))
        # stock_plot.line(x="x", y="values", size=12, color="blue", line_dash=[2, 4], source=source)
        return stock_plot
        # plot1.scatter(x="x", y="y", size="size", fill_color="red", source=source) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import TapTool [as 别名]
def _add_select_tools(current_src, other_src, param_plot, point_glyph):
    select_js_kwargs = {"current_src": current_src, "other_src": other_src}
    select_js_code = """
    // adapted from https://stackoverflow.com/a/44996422

    var chosen = current_src.selected.indices;
    if (typeof(chosen) == "number"){
        var chosen = [chosen]
    };

    var chosen_models = [];

    for (var i = 0; i < chosen.length; ++ i){
        chosen_models.push(current_src.data['model'][chosen[i]])
    };

    var chosen_models_indices = [];
    for (var i = 0; i < current_src.data['index'].length; ++ i){
        if (chosen_models.includes(current_src.data['model'][i])){
            chosen_models_indices.push(i)
        };
    };
    current_src.selected.indices = chosen_models_indices;
    current_src.change.emit();

    for (var i = 0; i < other_src.length; ++i){
        var chosen_models_indices = [];
        for (var j = 0; j < other_src[i].data['index'].length; ++ j){
            if (chosen_models.includes(other_src[i].data['model'][j])){
                chosen_models_indices.push(j)
            };
        };
        other_src[i].selected.indices = chosen_models_indices;
        other_src[i].change.emit();
    };

    """
    select_callback = CustomJS(args=select_js_kwargs, code=select_js_code)
    # point_glyph as only renderer assures that when a point is chosen
    # only that point's model is chosen
    # this makes it impossible to choose models based on clicking confidence bands
    tap = TapTool(renderers=[point_glyph], callback=select_callback)
    param_plot.tools.append(tap)
    boxselect = BoxSelectTool(renderers=[point_glyph], callback=select_callback)
    param_plot.tools.append(boxselect) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import TapTool [as 别名]
def visualize_self_attention_scores(tokens, scores, filename="/notebooks/embedding/self-attention.png",
                                    use_notebook=False):
    mean_prob = np.mean(scores)
    weighted_edges = []
    for idx_1, token_prob_dist_1 in enumerate(scores):
        for idx_2, el in enumerate(token_prob_dist_1):
            if idx_1 == idx_2 or el < mean_prob:
                weighted_edges.append((tokens[idx_1], tokens[idx_2], 0))
            else:
                weighted_edges.append((tokens[idx_1], tokens[idx_2], el))
    max_prob = np.max([el[2] for el in weighted_edges])
    weighted_edges = [(el[0], el[1], (el[2] - mean_prob) / (max_prob - mean_prob)) for el in weighted_edges]

    G = nx.Graph()
    G.add_nodes_from([el for el in tokens])
    G.add_weighted_edges_from(weighted_edges)

    plot = Plot(plot_width=500, plot_height=500,
                x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
    plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool())

    graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0, 0))

    graph_renderer.node_renderer.data_source.data['colors'] = Spectral8[:len(tokens)]
    graph_renderer.node_renderer.glyph = Circle(size=15, line_color=None, fill_color="colors")
    graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color="colors")
    graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color="grey")

    graph_renderer.edge_renderer.data_source.data["line_width"] = [G.get_edge_data(a, b)['weight'] * 3 for a, b in
                                                                   G.edges()]
    graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_width={'field': 'line_width'})
    graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color="grey", line_width=5)
    graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color="grey", line_width=5)

    graph_renderer.selection_policy = NodesAndLinkedEdges()
    graph_renderer.inspection_policy = EdgesAndLinkedNodes()

    plot.renderers.append(graph_renderer)

    x, y = zip(*graph_renderer.layout_provider.graph_layout.values())
    data = {'x': list(x), 'y': list(y), 'connectionNames': tokens}
    source = ColumnDataSource(data)
    labels = LabelSet(x='x', y='y', text='connectionNames', source=source, text_align='center')
    plot.renderers.append(labels)
    plot.add_tools(SaveTool())
    if use_notebook:
        output_notebook()
        show(plot)
    else:
        export_png(plot, filename)
        print("save @ " + filename)