[testing] more restructuring, almost ready to ship, added some tests for testing with travis

GPy/plotting/gpy_plot/latent_plots.py

@@ -0,0 +1,107 @@
+#===============================================================================
+# Copyright (c) 2015, Max Zwiessele
+# All rights reserved.
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+# 
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# 
+# * Neither the name of GPy.plotting.gpy_plot.latent_plots nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#===============================================================================
+import numpy as np
+from . import pl
+from .plot_util import get_x_y_var, get_free_dims, get_which_data_ycols,\
+    get_which_data_rows, update_not_existing_kwargs, helper_predict_with_model,\
+    helper_for_plot_data
+
+def plot_prediction_fit(self, plot_limits=None,
+        which_data_rows='all', which_data_ycols='all', 
+        fixed_inputs=None, resolution=None,
+        plot_raw=False, apply_link=False, visible_dims=None,
+        predict_kw=None, scatter_kwargs=None, **plot_kwargs):
+    """
+    Plot the fit of the (Bayesian)GPLVM latent space prediction to the outputs.
+    This scatters two output dimensions against each other and a line
+    from the prediction in two dimensions between them.
+    
+    Give the Y_metadata in the predict_kw if you need it.
+    
+    :param which_data_rows: which of the training data to plot (default all)
+    :type which_data_rows: 'all' or a slice object to slice self.X, self.Y
+    :param array-like which_data_ycols: which columns of y to plot (array-like or list of ints)
+    :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
+    :type fixed_inputs: a list of tuples
+    :param int resolution: The resolution of the prediction [defaults are 1D:200, 2D:50]
+    :param bool plot_raw: plot the latent function (usually denoted f) only?
+    :param bool apply_link: whether to apply the link function of the GP to the raw prediction.
+    :param array-like visible_dims: which columns of the input X (!) to plot (array-like or list of ints)
+    :param dict predict_kw: the keyword arguments for the prediction. If you want to plot a specific kernel give dict(kern=<specific kernel>) in here
+    :param dict sactter_kwargs: kwargs for the scatter plot, specific for the plotting library you are using
+    :param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
+    """
+    canvas, kwargs = pl.get_new_canvas(plot_kwargs)
+    plots = _plot_prediction_fit(self, canvas, plot_limits, which_data_rows, which_data_ycols, 
+                                 fixed_inputs, resolution, plot_raw, 
+                                 apply_link, visible_dims,
+                                 predict_kw, scatter_kwargs, **kwargs)
+    return pl.show_canvas(canvas, plots)
+
+def _plot_prediction_fit(self, canvas, plot_limits=None,
+        which_data_rows='all', which_data_ycols='all', 
+        fixed_inputs=None, resolution=None,
+        plot_raw=False, apply_link=False, visible_dims=False,
+        predict_kw=None, scatter_kwargs=None, **plot_kwargs):
+    
+    ycols = get_which_data_ycols(self, which_data_ycols)
+    rows = get_which_data_rows(self, which_data_rows)
+
+    if visible_dims is None:
+        visible_dims = self.get_most_significant_input_dimensions()[:1]
+
+    X, _, Y, _, free_dims, Xgrid, _, _, _, _, resolution = helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resolution)
+    
+    plots = {}
+    
+    if len(free_dims)<2:
+        if len(free_dims)==1:
+            if scatter_kwargs is None:
+                scatter_kwargs = {}
+            update_not_existing_kwargs(scatter_kwargs, pl.defaults.data_y_1d)  # @UndefinedVariable
+            plots['output'] = pl.scatter(canvas, Y[rows, ycols[0]], Y[rows, ycols[1]],
+                                      c=X[rows, free_dims[0]],
+                                      **scatter_kwargs)
+            if predict_kw is None:
+                predict_kw = {}
+            mu, _, _ = helper_predict_with_model(self, Xgrid, plot_raw, 
+                                              apply_link, None, 
+                                              ycols, predict_kw)
+            update_not_existing_kwargs(plot_kwargs, pl.defaults.data_y_1d_plot)  # @UndefinedVariable
+            plots['output_fit'] = pl.plot(canvas, mu[:, 0], mu[:, 1], **plot_kwargs)
+        else:
+            pass #Nothing to plot!
+    else:
+        raise NotImplementedError("Cannot plot in more then one dimension.")
+    return plots
+    
+    
+    
+