def plot_3(data, ss, *args):
if len(data) <= <NUM_LIT:1>:<EOL><INDENT>warnings.warn("<STR_LIT>")<EOL>return None<EOL><DEDENT>scores = np.array([d['<STR_LIT>'] for d in data])<EOL>warped = np.array([ss.point_to_unit(d['<STR_LIT>']) for d in data])<EOL>X = TSNE(n_components=<NUM_LIT:2>).fit_transform(warped)<EOL>e_scores = np.exp(scores)<EOL>mine, maxe = np.min(e_scores), np.max(e_scores)<EOL>color = (e_scores - mine) / (maxe - mine)<EOL>mapped_colors = list(map(rgb2hex, cm.get_cmap('<STR_LIT>')(color)))<EOL>p = bk.figure(title='<STR_LIT>', tools=TOOLS)<EOL>df_params = nonconstant_parameters(data)<EOL>df_params['<STR_LIT>'] = scores<EOL>df_params['<STR_LIT:x>'] = X[:, <NUM_LIT:0>]<EOL>df_params['<STR_LIT:y>'] = X[:, <NUM_LIT:1>]<EOL>df_params['<STR_LIT>'] = mapped_colors<EOL>df_params['<STR_LIT>'] = <NUM_LIT:1><EOL>p.circle(<EOL>x='<STR_LIT:x>', y='<STR_LIT:y>', color='<STR_LIT>', radius='<STR_LIT>',<EOL>source=ColumnDataSource(data=df_params), fill_alpha=<NUM_LIT>,<EOL>line_color=None)<EOL>cp = p<EOL>hover = cp.select(dict(type=HoverTool))<EOL>format_tt = [(s, '<STR_LIT>' % s) for s in df_params.columns]<EOL>hover.tooltips = OrderedDict([("<STR_LIT:index>", "<STR_LIT>")] + format_tt)<EOL>xax, yax = p.axis<EOL>xax.axis_label = '<STR_LIT>'<EOL>yax.axis_label = '<STR_LIT>'<EOL>return p<EOL>
t-SNE embedding of the parameters, colored by score