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[plotly] last minute change for ipython notebook
This commit is contained in:
Max Zwiessele
parent
f4f6eb32c2
commit
e3617942d4
1 changed files with 36 additions and 39 deletions
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@ -1,21 +1,21 @@
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#===============================================================================
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# Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
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# All rights reserved.
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#
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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#
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# * Neither the name of GPy nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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@ -34,11 +34,8 @@ import itertools
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def in_ipynb():
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try:
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cfg = get_ipython().config
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if cfg['IPKernelApp']['parent_appname'] == 'ipython-notebook':
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return True
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else:
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return False
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get_ipython()
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return True
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except NameError:
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return False
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@ -54,11 +51,11 @@ def find_best_layout_for_subplots(num_subplots):
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def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, which_data_ycols, predict_kw, samples=0):
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"""
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Make the right decisions for prediction with a model
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Make the right decisions for prediction with a model
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based on the standard arguments of plotting.
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This is quite complex and will take a while to understand,
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so do not change anything in here lightly!!!
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so do not change anything in here lightly!!!
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"""
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# Put some standards into the predict_kw so that prediction is done automatically:
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if predict_kw is None:
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@ -77,7 +74,7 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
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predict_kw['Y_metadata']['output_index'] = Xgrid[:,-1:].astype(np.int)
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mu, _ = self.predict(Xgrid, **predict_kw)
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if percentiles is not None:
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percentiles = self.predict_quantiles(Xgrid, quantiles=percentiles, **predict_kw)
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else: percentiles = []
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@ -87,11 +84,11 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
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fsamples = fsamples[which_data_ycols] if fsamples.ndim == 3 else fsamples
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else:
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fsamples = None
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# Filter out the ycolums which we want to plot:
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retmu = mu[:, which_data_ycols]
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percs = [p[:, which_data_ycols] for p in percentiles]
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if plot_raw and apply_link:
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for i in range(len(which_data_ycols)):
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retmu[:, [i]] = self.likelihood.gp_link.transf(mu[:, [i]])
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@ -108,8 +105,8 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
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def helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resolution):
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"""
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Figure out the data, free_dims and create an Xgrid for
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the prediction.
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the prediction.
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This is only implemented for two dimensions for now!
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"""
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X, Xvar, Y = get_x_y_var(self)
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@ -119,7 +116,7 @@ def helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resoluti
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fixed_inputs = []
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fixed_dims = get_fixed_dims(self, fixed_inputs)
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free_dims = get_free_dims(self, visible_dims, fixed_dims)
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if len(free_dims) == 1:
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#define the frame on which to plot
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resolution = resolution or 200
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@ -139,7 +136,7 @@ def helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resoluti
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for i,v in fixed_inputs:
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Xgrid[:,i] = v
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else:
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raise TypeError("calculated free_dims {} from visible_dims {} and fixed_dims {} is neither 1D nor 2D".format(free_dims, visible_dims, fixed_dims))
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raise TypeError("calculated free_dims {} from visible_dims {} and fixed_dims {} is neither 1D nor 2D".format(free_dims, visible_dims, fixed_dims))
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return X, Xvar, Y, fixed_dims, free_dims, Xgrid, x, y, xmin, xmax, resolution
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def scatter_label_generator(labels, X, visible_dims, marker=None):
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@ -148,10 +145,10 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
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if not lab in ulabels:
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ulabels.append(lab)
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if marker is not None:
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marker = itertools.cycle(list(marker))
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marker = itertools.cycle(list(marker))
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else:
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m = None
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try:
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input_1, input_2, input_3 = visible_dims
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except:
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@ -175,12 +172,12 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
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this_label = 'class {!s}'.format(ul)
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else:
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this_label = ul
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if marker is not None:
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m = next(marker)
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index = np.nonzero(labels == ul)[0]
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if input_2 is None:
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x = X[index, input_1]
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y = np.zeros(index.size)
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@ -191,14 +188,14 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
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z = None
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else:
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x = X[index, input_1]
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y = X[index, input_2]
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y = X[index, input_2]
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z = X[index, input_3]
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yield x, y, z, this_label, index, m
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def subsample_X(X, labels, num_samples=1000):
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"""
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Stratified subsampling if labels are given.
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This means due to rounding errors you might get a little differences between the
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Stratified subsampling if labels are given.
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This means due to rounding errors you might get a little differences between the
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num_samples and the returned subsampled X.
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"""
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if X.shape[0] > num_samples:
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@ -258,13 +255,13 @@ def subsample_X(X, labels, num_samples=1000):
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# plt.show()
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#=======================================================================
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return X, labels
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def update_not_existing_kwargs(to_update, update_from):
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"""
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This function updates the keyword aguments from update_from in
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This function updates the keyword aguments from update_from in
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to_update, only if the keys are not set in to_update.
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This is used for updated kwargs from the default dicts.
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"""
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if to_update is None:
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@ -274,12 +271,12 @@ def update_not_existing_kwargs(to_update, update_from):
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def get_x_y_var(model):
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"""
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The the data from a model as
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X the inputs,
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The the data from a model as
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X the inputs,
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X_variance the variance of the inputs ([default: None])
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and Y the outputs
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:returns: (X, X_variance, Y)
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:returns: (X, X_variance, Y)
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"""
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if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
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X = model.X.mean.values
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@ -294,10 +291,10 @@ def get_x_y_var(model):
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def get_free_dims(model, visible_dims, fixed_dims):
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"""
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work out what the inputs are for plotting (1D or 2D)
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The visible dimensions are the dimensions, which are visible.
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the fixed_dims are the fixed dimensions for this.
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the fixed_dims are the fixed dimensions for this.
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The free_dims are then the visible dims without the fixed dims.
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"""
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if visible_dims is None:
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@ -306,7 +303,7 @@ def get_free_dims(model, visible_dims, fixed_dims):
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if fixed_dims is not None:
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dims = np.setdiff1d(dims, fixed_dims)
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return np.asanyarray([dim for dim in dims if dim is not None])
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def get_fixed_dims(model, fixed_inputs):
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"""
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@ -321,7 +318,7 @@ def get_which_data_ycols(model, which_data_ycols):
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if which_data_ycols == 'all' or which_data_ycols is None:
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return np.arange(model.output_dim)
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return which_data_ycols
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def get_which_data_rows(model, which_data_rows):
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"""
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Helper to get the data rows to plot.
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