dim_learning_rate = Real(low=1e-6, high=1e-2, prior='log-uniform')
dim_num_dense_layers = Integer(low=1, high=5)
dim_num_dense_nodes = Integer(low=5, high=512)
dim_activation = Categorical(categories=['relu', 'sigmoid'])

plot_evaluations(search_results)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-40-bf80cc7b140c> in <module>()
----> 1 plot_evaluations(search_results)

~/development/scikit-optimize/skopt/plots.py in plot_evaluations(result, bins, dimensions)
    402                     bins_ = bins
    403                 ax[i, i].hist(samples[:, j], bins=bins_,
--> 404                               range=space.dimensions[j].bounds)
    405 
    406             # lower triangle

~/anaconda3/envs/tf-test/lib/python3.6/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
   1890                     warnings.warn(msg % (label_namer, func.__name__),
   1891                                   RuntimeWarning, stacklevel=2)
-> 1892             return func(ax, *args, **kwargs)
   1893         pre_doc = inner.__doc__
   1894         if pre_doc is None:

~/anaconda3/envs/tf-test/lib/python3.6/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
   6190             # this will automatically overwrite bins,
   6191             # so that each histogram uses the same bins
-> 6192             m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
   6193             m = m.astype(float)  # causes problems later if it's an int
   6194             if mlast is None:

~/anaconda3/envs/tf-test/lib/python3.6/site-packages/numpy/lib/function_base.py in histogram(a, bins, range, normed, weights, density)
    794             for i in arange(0, len(a), BLOCK):
    795                 sa = sort(a[i:i+BLOCK])
--> 796                 n += np.r_[sa.searchsorted(bins[:-1], 'left'),
    797                            sa.searchsorted(bins[-1], 'right')]
    798         else:

TypeError: Cannot cast array data from dtype('float64') to dtype('<U32') according to the rule 'safe'

plot_evaluations(search_results, dimensions=['X_0', 'X_1', 'X_2'])

plot_objective(search_results, dimensions=['X_0', 'X_1', 'X_2'])


---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-43-f5cf47c2e09c> in <module>()
----> 1 plot_objective(search_results, dimensions=['X_0', 'X_1', 'X_2'])

~/development/scikit-optimize/skopt/plots.py in plot_objective(result, levels, n_points, n_samples, size, zscale, dimensions)
    341                 xi, yi, zi = partial_dependence(space, result.models[-1],
    342                                                 i, j,
--> 343                                                 rvs_transformed, n_points)
    344                 ax[i, j].contourf(xi, yi, zi, levels,
    345                                   locator=locator, cmap='viridis_r')

~/development/scikit-optimize/skopt/plots.py in partial_dependence(space, model, i, j, sample_points, n_samples, n_points)
    243 
    244         bounds = space.dimensions[i].bounds
--> 245         yi = np.linspace(bounds[0], bounds[1], n_points)
    246         yi_transformed = space.dimensions[i].transform(yi)
    247 

~/anaconda3/envs/tf-test/lib/python3.6/site-packages/numpy/core/function_base.py in linspace(start, stop, num, endpoint, retstep, dtype)
    106     # Convert float/complex array scalars to float, gh-3504
    107     # and make sure one can use variables that have an __array_interface__, gh-6634
--> 108     start = asanyarray(start) * 1.0
    109     stop  = asanyarray(stop)  * 1.0
    110 

TypeError: ufunc 'multiply' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32')