Pushing the docs to 0.8/ for branch: 0.8.X, commit 4e391ec1cf7c0902bb7efc858931082d970ddd23

Author: skoptci

0.8/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: d0bab5cc18797a485150241ada3387d7
+tags: 645f666f9bcd5a90fca523b33c5a78b7

0.8/.nojekyll

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+"""
+===================================================
+Comparing initial sampling methods on integer space
+===================================================
+
+Holger Nahrstaedt 2020 Sigurd Carlsen October 2019
+
+.. currentmodule:: skopt
+
+When doing baysian optimization we often want to reserve some of the
+early part of the optimization to pure exploration. By default the
+optimizer suggests purely random samples for the first n_initial_points
+(10 by default). The downside to this is that there is no guarantee that
+these samples are spread out evenly across all the dimensions.
+
+Sampling methods as Latin hypercube, Sobol, Halton and Hammersly
+take advantage of the fact that we know beforehand how many random
+points we want to sample. Then these points can be "spread out" in
+such a way that each dimension is explored.
+
+See also the example on a real space
+:ref:`sphx_glr_auto_examples_initial_sampling_method.py`
+"""
+
+print(__doc__)
+import numpy as np
+np.random.seed(1234)
+import matplotlib.pyplot as plt
+from skopt.space import Space
+from skopt.sampler import Sobol
+from skopt.sampler import Lhs
+from skopt.sampler import Halton
+from skopt.sampler import Hammersly
+from skopt.sampler import Grid
+from scipy.spatial.distance import pdist
+
+#############################################################################
+
+def plot_searchspace(x, title):
+    fig, ax = plt.subplots()
+    plt.plot(np.array(x)[:, 0], np.array(x)[:, 1], 'bo', label='samples')
+    plt.plot(np.array(x)[:, 0], np.array(x)[:, 1], 'bs', markersize=40, alpha=0.5)
+    # ax.legend(loc="best", numpoints=1)
+    ax.set_xlabel("X1")
+    ax.set_xlim([0, 5])
+    ax.set_ylabel("X2")
+    ax.set_ylim([0, 5])
+    plt.title(title)
+    ax.grid(True)
+
+
+n_samples = 10
+space = Space([(0, 5), (0, 5)])
+
+#############################################################################
+# Random sampling
+# ---------------
+x = space.rvs(n_samples)
+plot_searchspace(x, "Random samples")
+pdist_data = []
+x_label = []
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("random")
+
+#############################################################################
+# Sobol
+# -----
+
+sobol = Sobol()
+x = sobol.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'Sobol')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("sobol")
+
+#############################################################################
+# Classic latin hypercube sampling
+# --------------------------------
+
+lhs = Lhs(lhs_type="classic", criterion=None)
+x = lhs.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'classic LHS')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("lhs")
+
+#############################################################################
+# Centered latin hypercube sampling
+# ---------------------------------
+
+lhs = Lhs(lhs_type="centered", criterion=None)
+x = lhs.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'centered LHS')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("center")
+
+#############################################################################
+# Maximin optimized hypercube sampling
+# ------------------------------------
+
+lhs = Lhs(criterion="maximin", iterations=10000)
+x = lhs.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'maximin LHS')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("maximin")
+
+#############################################################################
+# Correlation optimized hypercube sampling
+# ----------------------------------------
+
+lhs = Lhs(criterion="correlation", iterations=10000)
+x = lhs.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'correlation LHS')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("corr")
+
+#############################################################################
+# Ratio optimized hypercube sampling
+# ----------------------------------
+
+lhs = Lhs(criterion="ratio", iterations=10000)
+x = lhs.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'ratio LHS')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("ratio")
+
+#############################################################################
+# Halton sampling
+# ---------------
+
+halton = Halton()
+x = halton.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'Halton')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("halton")
+
+#############################################################################
+# Hammersly sampling
+# ------------------
+
+hammersly = Hammersly()
+x = hammersly.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'Hammersly')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("hammersly")
+
+#############################################################################
+# Grid sampling
+# -------------
+
+grid = Grid(border="include", use_full_layout=False)
+x = grid.generate(space.dimensions, n_samples)
+plot_searchspace(x, 'Grid')
+print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
+pdist_data.append(pdist(x).flatten())
+x_label.append("grid")
+
+#############################################################################
+# Pdist boxplot of all methods
+# ----------------------------
+#
+# This boxplot shows the distance between all generated points using
+# Euclidian distance. The higher the value, the better the sampling method.
+# It can be seen that random has the worst performance
+
+fig, ax = plt.subplots()
+ax.boxplot(pdist_data)
+plt.grid(True)
+plt.ylabel("pdist")
+_ = ax.set_ylim(0, 6)
+_ = ax.set_xticklabels(x_label, rotation=45, fontsize=8)