Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/core/gp.py

@@ -516,8 +516,8 @@ class GP(Model):
     def plot(self, plot_limits=None, which_data_rows='all',
         which_data_ycols='all', fixed_inputs=[],
         levels=20, samples=0, fignum=None, ax=None, resolution=None,
-        plot_raw=False,
+        plot_raw=False, linecol=None,fillcol=None, Y_metadata=None,
-        linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx', predict_kw=None):
+        data_symbol='kx', predict_kw=None, plot_training_data=True):
         """
         Plot the posterior of the GP.
           - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@@ -554,6 +554,8 @@ class GP(Model):
         :type Y_metadata: dict
         :param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
         :type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
+        :param plot_training_data: whether or not to plot the training points
+        :type plot_training_data: boolean
         """
         assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
         from ..plotting.matplot_dep import models_plots
@@ -566,7 +568,85 @@ class GP(Model):
                                      which_data_ycols, fixed_inputs,
                                      levels, samples, fignum, ax, resolution,
                                      plot_raw=plot_raw, Y_metadata=Y_metadata,
-                                     data_symbol=data_symbol, predict_kw=predict_kw, **kw)
+                                     data_symbol=data_symbol, predict_kw=predict_kw, 
+                                     plot_training_data=plot_training_data, **kw)
+
+
+    def plot_data(self, which_data_rows='all',
+        which_data_ycols='all', visible_dims=None,
+        fignum=None, ax=None, data_symbol='kx'):
+        """
+        Plot the training data
+          - For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
+
+        Can plot only part of the data
+        using which_data_rows and which_data_ycols.
+
+        :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
+        :type plot_limits: np.array
+        :param which_data_rows: which of the training data to plot (default all)
+        :type which_data_rows: 'all' or a slice object to slice model.X, model.Y
+        :param which_data_ycols: when the data has several columns (independant outputs), only plot these
+        :type which_data_ycols: 'all' or a list of integers
+        :param visible_dims: an array specifying the input dimensions to plot (maximum two)
+        :type visible_dims: a numpy array
+        :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
+        :type resolution: int
+        :param levels: number of levels to plot in a contour plot.
+        :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
+        :type levels: int
+        :param samples: the number of a posteriori samples to plot
+        :type samples: int
+        :param fignum: figure to plot on.
+        :type fignum: figure number
+        :param ax: axes to plot on.
+        :type ax: axes handle
+        :param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
+        :type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
+        :param fillcol: color of fill [Tango.colorsHex['lightBlue']]
+        :type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
+        :param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
+        :type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
+        """
+        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
+        from ..plotting.matplot_dep import models_plots
+        kw = {}
+        return models_plots.plot_data(self, which_data_rows,
+                                     which_data_ycols, visible_dims,
+                                     fignum, ax, data_symbol, **kw)
+
+
+    def plot_fit_errorbars(self, which_data_rows='all',
+            which_data_ycols='all', fixed_inputs=[], fignum=None, ax=None,
+            linecol=None, data_symbol='kx', predict_kw=None, plot_training_data=True):
+
+        """
+        Plot the posterior error bars corresponding to the training data
+          - For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
+
+        Can plot only part of the data
+        using which_data_rows and which_data_ycols.
+
+        :param which_data_rows: which of the training data to plot (default all)
+        :type which_data_rows: 'all' or a slice object to slice model.X, model.Y
+        :param which_data_ycols: when the data has several columns (independant outputs), only plot these
+        :type which_data_rows: 'all' or a list of integers
+        :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
+        :type fixed_inputs: a list of tuples
+        :param fignum: figure to plot on.
+        :type fignum: figure number
+        :param ax: axes to plot on.
+        :type ax: axes handle
+        :param plot_training_data: whether or not to plot the training points
+        :type plot_training_data: boolean
+        """
+        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
+        from ..plotting.matplot_dep import models_plots
+        kw = {}
+        return models_plots.plot_fit_errorbars(self, which_data_rows, which_data_ycols, fixed_inputs,
+                                    fignum, ax, linecol, data_symbol,
+                                    predict_kw, plot_training_data, **kw)
+
 
     def plot_magnification(self, labels=None, which_indices=None,
                 resolution=50, ax=None, marker='o', s=40,

GPy/plotting/matplot_dep/base_plots.py

@@ -47,6 +47,27 @@ def gpplot(x, mu, lower, upper, edgecol='#3300FF', fillcol='#33CCFF', ax=None, f
     return plots
 
 
+def gperrors(x, mu, lower, upper, edgecol=None, ax=None, fignum=None, **kwargs):
+    _, axes = ax_default(fignum, ax)
+
+    mu = mu.flatten()
+    x = x.flatten()
+    lower = lower.flatten()
+    upper = upper.flatten()
+
+    plots = []
+
+    if edgecol is None:
+        edgecol='#3300FF'
+
+    if not 'alpha' in kwargs.keys():
+        kwargs['alpha'] = 0.3
+
+    plots.append(axes.errorbar(x,mu,yerr=np.vstack([mu-lower,upper-mu]),color=edgecol,**kwargs))
+    plots[-1][0].remove()
+    return plots
+
+
 def removeRightTicks(ax=None):
     ax = ax or pb.gca()
     for i, line in enumerate(ax.get_yticklines()):

GPy/plotting/matplot_dep/models_plots.py

@@ -3,19 +3,79 @@
 
 import numpy as np
 from . import Tango
-from base_plots import gpplot, x_frame1D, x_frame2D
+from base_plots import gpplot, x_frame1D, x_frame2D,gperrors
 from ...models.gp_coregionalized_regression import GPCoregionalizedRegression
 from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
 from scipy import sparse
 from ...core.parameterization.variational import VariationalPosterior
 from matplotlib import pyplot as plt
 
+
+def plot_data(model, which_data_rows='all',
+        which_data_ycols='all', visible_dims=None,
+        fignum=None, ax=None, data_symbol='kx',mew=1.5):
+    """
+    Plot the training data
+      - For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
+
+    Can plot only part of the data
+    using which_data_rows and which_data_ycols.
+
+    :param which_data_rows: which of the training data to plot (default all)
+    :type which_data_rows: 'all' or a slice object to slice model.X, model.Y
+    :param which_data_ycols: when the data has several columns (independant outputs), only plot these
+    :type which_data_rows: 'all' or a list of integers
+    :param visible_dims: an array specifying the input dimensions to plot (maximum two)
+    :type visible_dims: a numpy array
+    :param fignum: figure to plot on.
+    :type fignum: figure number
+    :param ax: axes to plot on.
+    :type ax: axes handle
+    """
+    #deal with optional arguments
+    if which_data_rows == 'all':
+        which_data_rows = slice(None)
+    if which_data_ycols == 'all':
+        which_data_ycols = np.arange(model.output_dim)
+
+    if ax is None:
+        fig = plt.figure(num=fignum)
+        ax = fig.add_subplot(111)
+
+    #data
+    X = model.X
+    Y = model.Y
+
+    #work out what the inputs are for plotting (1D or 2D)
+    if visible_dims is None:
+        visible_dims = np.arange(model.input_dim)
+    assert visible_dims.size <= 2, "Visible inputs cannot be larger than two"
+    free_dims = visible_dims
+    plots = {}
+    #one dimensional plotting
+    if len(free_dims) == 1:
+
+        for d in which_data_ycols:
+            plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=mew)
+
+    #2D plotting
+    elif len(free_dims) == 2:
+
+        for d in which_data_ycols:
+            plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40,
+            Y[which_data_rows, d], cmap=plt.cm.jet, vmin=Y.min(), vmax=Y.max(), linewidth=0.)
+
+    else:
+        raise NotImplementedError("Cannot define a frame with more than two input dimensions")
+    return plots
+
+
 def plot_fit(model, plot_limits=None, which_data_rows='all',
         which_data_ycols='all', fixed_inputs=[],
         levels=20, samples=0, fignum=None, ax=None, resolution=None,
         plot_raw=False,
         linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx',
-        apply_link=False, samples_f=0, plot_uncertain_inputs=True, predict_kw=None):
+        apply_link=False, samples_f=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
     """
     Plot the posterior of the GP.
       - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@@ -43,7 +103,6 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
     :type fignum: figure number
     :param ax: axes to plot on.
     :type ax: axes handle
-    :type output: integer (first output is 0)
     :param linecol: color of line to plot.
     :type linecol:
     :param fillcol: color of fill
@@ -52,6 +111,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
     :type apply_link: boolean
     :param samples_f: the number of posteriori f samples to plot p(f*|y)
     :type samples_f: int
+    :param plot_training_data: whether or not to plot the training points
+    :type plot_training_data: boolean
     """
     #deal with optional arguments
     if which_data_rows == 'all':
@@ -116,7 +177,11 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
         for d in which_data_ycols:
             plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
-            if not plot_raw: plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
+            #if not plot_raw: plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
+            if not plot_raw and plot_training_data:
+                plots['dataplot'] = plot_data(model=model, which_data_rows=which_data_rows,
+                visible_dims=free_dims, data_symbol=data_symbol, mew=1.5, ax=ax, fignum=fignum)
+
 
         #optionally plot some samples
         if samples: #NOTE not tested with fixed_inputs
@@ -196,7 +261,9 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         for d in which_data_ycols:
             m_d = m[:,d].reshape(resolution, resolution).T
             plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=plt.cm.jet)
-            if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=plt.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
+            #if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=plt.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
+            if not plot_raw and plot_training_data:
+                plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=plt.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
 
         #set the limits of the plot to some sensible values
         ax.set_xlim(xmin[0], xmax[0])
@@ -261,3 +328,83 @@ def fixed_inputs(model, non_fixed_inputs, fix_routine='median', as_list=True, X_
         return f_inputs
     else:
         return X
+
+
+def plot_fit_errorbars(model, which_data_rows='all',
+        which_data_ycols='all', fixed_inputs=[],
+        fignum=None, ax=None,
+        linecol='red', data_symbol='kx',
+        predict_kw=None, plot_training_data=True):
+
+    """
+    Plot the posterior error bars corresponding to the training data
+      - For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
+
+    Can plot only part of the data
+    using which_data_rows and which_data_ycols.
+
+    :param which_data_rows: which of the training data to plot (default all)
+    :type which_data_rows: 'all' or a slice object to slice model.X, model.Y
+    :param which_data_ycols: when the data has several columns (independant outputs), only plot these
+    :type which_data_rows: 'all' or a list of integers
+    :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
+    :type fixed_inputs: a list of tuples
+    :param fignum: figure to plot on.
+    :type fignum: figure number
+    :param ax: axes to plot on.
+    :type ax: axes handle
+    :param plot_training_data: whether or not to plot the training points
+    :type plot_training_data: boolean
+    """
+
+    #deal with optional arguments
+    if which_data_rows == 'all':
+        which_data_rows = slice(None)
+    if which_data_ycols == 'all':
+        which_data_ycols = np.arange(model.output_dim)
+
+    if ax is None:
+        fig = plt.figure(num=fignum)
+        ax = fig.add_subplot(111)
+
+    X = model.X
+    Y = model.Y
+
+    if predict_kw is None:
+        predict_kw = {}
+
+
+    #work out what the inputs are for plotting (1D or 2D)
+    fixed_dims = np.array([i for i,v in fixed_inputs])
+    free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims)
+    plots = {}
+
+    #one dimensional plotting
+    if len(free_dims) == 1:
+
+        m, v = model.predict(X, full_cov=False, Y_metadata=model.Y_metadata, **predict_kw)
+        fmu, fv = model._raw_predict(X, full_cov=False, **predict_kw)
+        lower, upper = model.likelihood.predictive_quantiles(fmu, fv, (2.5, 97.5), Y_metadata=model.Y_metadata)
+
+
+        for d in which_data_ycols:
+            plots['gperrors'] = gperrors(X, m[:, d], lower[:, d], upper[:, d], edgecol=linecol, ax=ax, fignum=fignum )
+            if plot_training_data:
+                plots['dataplot'] = plot_data(model=model, which_data_rows=which_data_rows,
+                visible_dims=free_dims, data_symbol=data_symbol, mew=1.5, ax=ax, fignum=fignum)
+
+
+        #set the limits of the plot to some sensible values
+        ymin, ymax = min(np.append(Y[which_data_rows, which_data_ycols].flatten(), lower)), max(np.append(Y[which_data_rows, which_data_ycols].flatten(), upper))
+        ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
+        ax.set_xlim(X[:,free_dims].min(), X[:,free_dims].max())
+        ax.set_ylim(ymin, ymax)
+
+
+    elif len(free_dims) == 2:
+        raise NotImplementedError("Not implemented yet")
+
+
+    else:
+        raise NotImplementedError("Cannot define a frame with more than two input dimensions")
+    return plots

GPy/util/linalg.py

@@ -7,48 +7,13 @@
 
 import numpy as np
 from scipy import linalg
-import types
+from scipy.linalg import lapack, blas
-import ctypes
+
-from ctypes import byref, c_char, c_int, c_double # TODO
-import scipy
-import warnings
-import os
 from .config import config
 import logging
 from . import linalg_cython
 
 
-_scipyversion = np.float64((scipy.__version__).split('.')[:2])
-_fix_dpotri_scipy_bug = True
-if np.all(_scipyversion >= np.array([0, 14])):
-    from scipy.linalg import lapack
-    _fix_dpotri_scipy_bug = False
-elif np.all(_scipyversion >= np.array([0, 12])):
-    #import scipy.linalg.lapack.clapack as lapack
-    from scipy.linalg import lapack
-else:
-    from scipy.linalg.lapack import flapack as lapack
-
-if config.getboolean('anaconda', 'installed') and config.getboolean('anaconda', 'MKL'):
-    try:
-        anaconda_path = str(config.get('anaconda', 'location'))
-        mkl_rt = ctypes.cdll.LoadLibrary(os.path.join(anaconda_path, 'DLLs', 'mkl_rt.dll'))
-        dsyrk = mkl_rt.dsyrk
-        dsyr = mkl_rt.dsyr
-        _blas_available = True
-        print('anaconda installed and mkl is loaded')
-    except:
-        _blas_available = False
-else:
-    try:
-        _blaslib = ctypes.cdll.LoadLibrary(np.core._dotblas.__file__) # @UndefinedVariable
-        dsyrk = _blaslib.dsyrk_
-        dsyr = _blaslib.dsyr_
-        _blas_available = True
-    except AttributeError as e:
-        _blas_available = False
-        warnings.warn("warning: caught this exception:" + str(e))
-
 def force_F_ordered_symmetric(A):
     """
     return a F ordered version of A, assuming A is symmetric
@@ -169,9 +134,6 @@ def dpotri(A, lower=1):
     :returns: A inverse
 
     """
-    if _fix_dpotri_scipy_bug:
-        assert lower==1, "scipy linalg behaviour is very weird. please use lower, fortran ordered arrays"
-        lower = 0
 
     A = force_F_ordered(A)
     R, info = lapack.dpotri(A, lower=lower) #needs to be zero here, seems to be a scipy bug
@@ -300,8 +262,8 @@ def pca(Y, input_dim):
     Z = linalg.svd(Y - Y.mean(axis=0), full_matrices=False)
     [X, W] = [Z[0][:, 0:input_dim], np.dot(np.diag(Z[1]), Z[2]).T[:, 0:input_dim]]
     v = X.std(axis=0)
-    X /= v;
+    X /= v
-    W *= v;
+    W *= v
     return X, W.T
 
 def ppca(Y, Q, iterations=100):
@@ -347,34 +309,15 @@ def tdot_blas(mat, out=None):
         out[:] = 0.0
 
     # # Call to DSYRK from BLAS
-    # If already in Fortran order (rare), and has the right sorts of strides I
-    # could avoid the copy. I also thought swapping to cblas API would allow use
-    # of C order. However, I tried that and had errors with large matrices:
-    # http://homepages.inf.ed.ac.uk/imurray2/code/tdot/tdot_broken.py
     mat = np.asfortranarray(mat)
-    TRANS = c_char('n'.encode('ascii'))
+    out = blas.dsyrk(alpha=1.0, a=mat, beta=0.0, c=out, overwrite_c=1,
-    N = c_int(mat.shape[0])
+                     trans=0, lower=0)
-    K = c_int(mat.shape[1])
-    LDA = c_int(mat.shape[0])
-    UPLO = c_char('l'.encode('ascii'))
-    ALPHA = c_double(1.0)
-    A = mat.ctypes.data_as(ctypes.c_void_p)
-    BETA = c_double(0.0)
-    C = out.ctypes.data_as(ctypes.c_void_p)
-    LDC = c_int(np.max(out.strides) // 8)
-    dsyrk(byref(UPLO), byref(TRANS), byref(N), byref(K),
-            byref(ALPHA), A, byref(LDA), byref(BETA), C, byref(LDC))
 
     symmetrify(out, upper=True)
-
-
     return np.ascontiguousarray(out)
 
 def tdot(*args, **kwargs):
-    if _blas_available:
+    return tdot_blas(*args, **kwargs)
-        return tdot_blas(*args, **kwargs)
-    else:
-        return tdot_numpy(*args, **kwargs)
 
 def DSYR_blas(A, x, alpha=1.):
     """
@@ -386,15 +329,7 @@ def DSYR_blas(A, x, alpha=1.):
     :param alpha: scalar
 
     """
-    N = c_int(A.shape[0])
+    A = blas.dsyr(lower=0, x=x, a=A, alpha=alpha, overwrite_a=True)
-    LDA = c_int(A.shape[0])
-    UPLO = c_char('l'.encode('ascii'))
-    ALPHA = c_double(alpha)
-    A_ = A.ctypes.data_as(ctypes.c_void_p)
-    x_ = x.ctypes.data_as(ctypes.c_void_p)
-    INCX = c_int(1)
-    dsyr(byref(UPLO), byref(N), byref(ALPHA),
-            x_, byref(INCX), A_, byref(LDA))
     symmetrify(A, upper=True)
 
 def DSYR_numpy(A, x, alpha=1.):
@@ -411,10 +346,8 @@ def DSYR_numpy(A, x, alpha=1.):
 
 
 def DSYR(*args, **kwargs):
-    if _blas_available:
+    return DSYR_blas(*args, **kwargs)
-        return DSYR_blas(*args, **kwargs)
+
-    else:
-        return DSYR_numpy(*args, **kwargs)
 
 def symmetrify(A, upper=False):
     """