def plot_3(data, ss, *args):
    """t-SNE embedding of the parameters, colored by score
    """

    if len(data) <= 1:
        warnings.warn("Only one datapoint. Could not compute t-SNE embedding.")
        return None

    scores = np.array([d['mean_test_score'] for d in data])
    # maps each parameters to a vector of floats
    warped = np.array([ss.point_to_unit(d['parameters']) for d in data])

    # Embed into 2 dimensions with t-SNE
    X = TSNE(n_components=2).fit_transform(warped)

    e_scores = np.exp(scores)
    mine, maxe = np.min(e_scores), np.max(e_scores)
    color = (e_scores - mine) / (maxe - mine)
    mapped_colors = list(map(rgb2hex, cm.get_cmap('RdBu_r')(color)))

    p = bk.figure(title='t-SNE (unsupervised)', tools=TOOLS)

    df_params = nonconstant_parameters(data)
    df_params['score'] = scores
    df_params['x'] = X[:, 0]
    df_params['y'] = X[:, 1]
    df_params['color'] = mapped_colors
    df_params['radius'] = 1
    p.circle(
        x='x', y='y', color='color', radius='radius',
        source=ColumnDataSource(data=df_params), fill_alpha=0.6,
        line_color=None)
    cp = p
    hover = cp.select(dict(type=HoverTool))
    format_tt = [(s, '@%s' % s) for s in df_params.columns]
    hover.tooltips = OrderedDict([("index", "$index")] + format_tt)

    xax, yax = p.axis
    xax.axis_label = 't-SNE coord 1'
    yax.axis_label = 't-SNE coord 2'
    return p
def plot_3(data, ss, *args):
    """t-SNE embedding of the parameters, colored by score
    """

    if len(data) <= 1:
        warnings.warn("Only one datapoint. Could not compute t-SNE embedding.")
        return None

    scores = np.array([d['mean_test_score'] for d in data])
    # maps each parameters to a vector of floats
    warped = np.array([ss.point_to_unit(d['parameters']) for d in data])

    # Embed into 2 dimensions with t-SNE
    X = TSNE(n_components=2).fit_transform(warped)

    e_scores = np.exp(scores)
    mine, maxe = np.min(e_scores), np.max(e_scores)
    color = (e_scores - mine) / (maxe - mine)
    mapped_colors = list(map(rgb2hex, cm.get_cmap('RdBu_r')(color)))

    p = bk.figure(title='t-SNE (unsupervised)', tools=TOOLS)

    df_params = nonconstant_parameters(data)
    df_params['score'] = scores
    df_params['x'] = X[:, 0]
    df_params['y'] = X[:, 1]
    df_params['color'] = mapped_colors
    df_params['radius'] = 1
    p.circle(
        x='x', y='y', color='color', radius='radius',
        source=ColumnDataSource(data=df_params), fill_alpha=0.6,
        line_color=None)
    cp = p
    hover = cp.select(dict(type=HoverTool))
    format_tt = [(s, '@%s' % s) for s in df_params.columns]
    hover.tooltips = OrderedDict([("index", "$index")] + format_tt)

    xax, yax = p.axis
    xax.axis_label = 't-SNE coord 1'
    yax.axis_label = 't-SNE coord 2'
    return p
def plot_3 ( data , ss , * args ) : if len ( data ) <= 1 : warnings . warn ( "Only one datapoint. Could not compute t-SNE embedding." ) return None scores = np . array ( [ d [ 'mean_test_score' ] for d in data ] ) # maps each parameters to a vector of floats warped = np . array ( [ ss . point_to_unit ( d [ 'parameters' ] ) for d in data ] ) # Embed into 2 dimensions with t-SNE X = TSNE ( n_components = 2 ) . fit_transform ( warped ) e_scores = np . exp ( scores ) mine , maxe = np . min ( e_scores ) , np . max ( e_scores ) color = ( e_scores - mine ) / ( maxe - mine ) mapped_colors = list ( map ( rgb2hex , cm . get_cmap ( 'RdBu_r' ) ( color ) ) ) p = bk . figure ( title = 't-SNE (unsupervised)' , tools = TOOLS ) df_params = nonconstant_parameters ( data ) df_params [ 'score' ] = scores df_params [ 'x' ] = X [ : , 0 ] df_params [ 'y' ] = X [ : , 1 ] df_params [ 'color' ] = mapped_colors df_params [ 'radius' ] = 1 p . circle ( x = 'x' , y = 'y' , color = 'color' , radius = 'radius' , source = ColumnDataSource ( data = df_params ) , fill_alpha = 0.6 , line_color = None ) cp = p hover = cp . select ( dict ( type = HoverTool ) ) format_tt = [ ( s , '@%s' % s ) for s in df_params . columns ] hover . tooltips = OrderedDict ( [ ( "index" , "$index" ) ] + format_tt ) xax , yax = p . axis xax . axis_label = 't-SNE coord 1' yax . axis_label = 't-SNE coord 2' return p
t-SNE embedding of the parameters, colored by score
t - SNE embedding of the parameters colored by score