[plotting] latent plotting had dimension mix up in it

GPy/plotting/gpy_plot/gp_plots.py

@@ -393,7 +393,7 @@ def plot_f(self, plot_limits=None, fixed_inputs=None,
          apply_link, which_data_ycols, which_data_rows,
          visible_dims, levels, samples, 0,
          lower, upper, plot_data, plot_inducing,
-         plot_density, predict_kw, projection, legend)
+         plot_density, predict_kw, projection, legend, **kwargs)
 
 
 

GPy/plotting/gpy_plot/latent_plots.py

@@ -123,13 +123,16 @@ def plot_latent_inducing(self,
     :param kwargs: the kwargs for the scatter plots
     """
     input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
+    if input_3 is None: zlabel=None
+    else: zlabel = 'latent dimension %i' % input_3
+        
 
     if 'color' not in kwargs:
         kwargs['color'] = 'white'
     canvas, kwargs = pl().new_canvas(projection=projection,
                               xlabel='latent dimension %i' % input_1,
                               ylabel='latent dimension %i' % input_2,
-                              zlabel='latent dimension %i' % input_3, **kwargs)
+                              zlabel=zlabel, **kwargs)
     Z = self.Z.values
     labels = np.array(['inducing'] * Z.shape[0])
     scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
@@ -195,7 +198,7 @@ def plot_magnification(self, labels=None, which_indices=None,
         labels = np.ones(self.num_data)
         legend = False
     scatters = _plot_latent_scatter(canvas, X, which_indices, labels, marker, num_samples, projection='2d', **scatter_kwargs or {})
-    view = _plot_magnification(self, canvas, which_indices[:2], Xgrid, xmin, xmax, resolution, updates, mean, covariance, kern, **imshow_kwargs)
+    view = _plot_magnification(self, canvas, which_indices, Xgrid, xmin, xmax, resolution, updates, mean, covariance, kern, **imshow_kwargs)
     retval = pl().add_to_canvas(canvas, dict(scatter=scatters, imshow=view),
                            legend=legend,
                            )

GPy/plotting/gpy_plot/plot_util.py

@@ -131,6 +131,8 @@ def helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resol
         Xnew, x, y, xmin, xmax = x_frame2D(X[:,free_dims], plot_limits, resolution)
         Xgrid = np.zeros((Xnew.shape[0], self.input_dim))
         Xgrid[:,free_dims] = Xnew
+        #xmin = Xgrid.min(0)[free_dims]
+        #xmax = Xgrid.max(0)[free_dims]
         for i,v in fixed_inputs:
             Xgrid[:,i] = v
     else:
@@ -305,7 +307,7 @@ def get_free_dims(model, visible_dims, fixed_dims):
         visible_dims = np.arange(model.input_dim)
     dims = np.asanyarray(visible_dims)
     if fixed_dims is not None:
-        dims = np.setdiff1d(dims, fixed_dims)
+        dims = [dim for dim in dims if dim not in fixed_dims]
     return np.asanyarray([dim for dim in dims if dim is not None])
 
 
@@ -357,7 +359,7 @@ def x_frame2D(X,plot_limits=None,resolution=None):
     """
     assert X.shape[1]==2, "x_frame2D is defined for two-dimensional inputs"
     if plot_limits is None:
-        xmin, xmax = X.min(0),X.max(0)
+        xmin, xmax = X.min(0), X.max(0)
         xmin, xmax = xmin-0.075*(xmax-xmin), xmax+0.075*(xmax-xmin)
     elif len(plot_limits) == 2:
         xmin, xmax = plot_limits

GPy/testing/plotting_tests.py

@@ -311,26 +311,35 @@ def test_gplvm():
     from ..examples.dimensionality_reduction import _simulate_matern
     from ..kern import RBF
     from ..models import GPLVM
-    np.random.seed(11111)
-    import matplotlib
+    np.random.seed(12345)
     matplotlib.rcParams.update(matplotlib.rcParamsDefault)
     matplotlib.rcParams[u'figure.figsize'] = (4,3)
     matplotlib.rcParams[u'text.usetex'] = False
     Q = 3
-    _, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
-    Y = Ylist[0]
-    k = RBF(Q, ARD=True)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
+    # Define dataset 
+    N = 10
+    k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
+    k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
+    k3 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[0.1,0.1,10,10,10]), ARD=True)
+    X = np.random.normal(0, 1, (N, 5))
+    A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
+    B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
+    C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
+    
+    Y = np.vstack((A,B,C))
+    labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
+    
+    k = RBF(Q, ARD=True, lengthscale=2)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
     m = GPLVM(Y, Q, init="PCA", kernel=k)
-    m.likelihood.variance = .1
+    m.likelihood.variance = .001
     #m.optimize(messages=0)
-    labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
     np.random.seed(111)
-    m.plot_latent()
+    m.plot_latent(labels=labels)
     np.random.seed(111)
     m.plot_scatter(projection='3d', labels=labels)
     np.random.seed(111)
     m.plot_magnification(labels=labels)
-    m.plot_steepest_gradient_map(resolution=7)
+    m.plot_steepest_gradient_map(resolution=7, data_labels=labels)
     for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
         yield (do_test, )
 
@@ -338,31 +347,41 @@ def test_bayesian_gplvm():
     from ..examples.dimensionality_reduction import _simulate_matern
     from ..kern import RBF
     from ..models import BayesianGPLVM
-    import matplotlib
+    np.random.seed(12345)
     matplotlib.rcParams.update(matplotlib.rcParamsDefault)
     matplotlib.rcParams[u'figure.figsize'] = (4,3)
     matplotlib.rcParams[u'text.usetex'] = False
-    np.random.seed(111)
     Q = 3
-    _, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
-    Y = Ylist[0]
-    k = RBF(Q, ARD=True)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
-    # k = kern.RBF(Q, ARD=True, lengthscale=10.)
+    # Define dataset 
+    N = 10
+    k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
+    k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
+    k3 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[0.1,0.1,10,10,10]), ARD=True)
+    X = np.random.normal(0, 1, (N, 5))
+    A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
+    B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
+    C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
+    
+    Y = np.vstack((A,B,C))
+    labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
+    
+    k = RBF(Q, ARD=True, lengthscale=2)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
     m = BayesianGPLVM(Y, Q, init="PCA", kernel=k)
-    m.likelihood.variance = .1
+    m.likelihood.variance = .001
     #m.optimize(messages=0)
-    labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
     np.random.seed(111)
     m.plot_inducing(projection='2d')
     np.random.seed(111)
     m.plot_inducing(projection='3d')
     np.random.seed(111)
-    m.plot_scatter(projection='3d')
+    m.plot_latent(projection='2d', labels=labels)
+    np.random.seed(111)
+    m.plot_scatter(projection='3d', labels=labels)
     np.random.seed(111)
     m.plot_magnification(labels=labels)
     np.random.seed(111)
-    m.plot_steepest_gradient_map(resolution=7)
-    for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent_3d", "magnification", 'gradient']], extensions=extensions):
+    m.plot_steepest_gradient_map(resolution=7, data_labels=labels)
+    for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
         yield (do_test, )
 
 if __name__ == '__main__':