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handles import of pods correctly
This commit is contained in:
James Hensman
parent
d961f736f2
commit
e0d3633ec3
2 changed files with 32 additions and 7 deletions
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@ -6,12 +6,6 @@
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Gaussian Processes classification
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"""
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import GPy
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import pods
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try:
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from matplotlib import pyplot as plt
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except:
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pass
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default_seed = 10000
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@ -20,6 +14,8 @@ def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
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Run a Gaussian process classification on the three phase oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.oil()
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X = data['X']
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Xtest = data['Xtest']
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@ -55,6 +51,8 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -72,6 +70,7 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
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# Plot
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if plot:
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from matplotlib import pyplot as plt
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fig, axes = plt.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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@ -88,6 +87,8 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -108,6 +109,7 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
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# Plot
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if plot:
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from matplotlib import pyplot as plt
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fig, axes = plt.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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@ -124,6 +126,8 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -138,6 +142,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
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# Plot
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if plot:
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from matplotlib import pyplot as plt
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fig, axes = plt.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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@ -154,6 +159,8 @@ def toy_heaviside(seed=default_seed, optimize=True, plot=True):
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y.flatten() == -1] = 0
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@ -172,6 +179,7 @@ def toy_heaviside(seed=default_seed, optimize=True, plot=True):
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# Plot
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if plot:
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from matplotlib import pyplot as plt
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fig, axes = plt.subplots(2, 1)
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m.plot_f(ax=axes[0])
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m.plot(ax=axes[1])
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@ -191,6 +199,8 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
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:param kernel: kernel to use in the model
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:type kernel: a GPy kernel
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.crescent_data(seed=seed)
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Y = data['Y']
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Y[Y.flatten()==-1] = 0
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@ -10,10 +10,11 @@ except:
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pass
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import numpy as np
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import GPy
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import pods
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def olympic_marathon_men(optimize=True, plot=True):
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"""Run a standard Gaussian process regression on the Olympic marathon data."""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.olympic_marathon_men()
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# create simple GP Model
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@ -83,6 +84,8 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
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from the Mount Epomeo runs. Requires gpxpy to be installed on your system
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to load in the data.
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"""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.epomeo_gpx()
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num_data_list = []
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for Xpart in data['X']:
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@ -126,6 +129,8 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
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length_scales = np.linspace(0.1, 60., resolution)
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log_SNRs = np.linspace(-3., 4., resolution)
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)
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# data['Y'] = data['Y'][0::2, :]
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# data['X'] = data['X'][0::2, :]
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@ -206,6 +211,8 @@ def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.RBF):
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def olympic_100m_men(optimize=True, plot=True):
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"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.olympic_100m_men()
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# create simple GP Model
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@ -223,6 +230,8 @@ def olympic_100m_men(optimize=True, plot=True):
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def toy_rbf_1d(optimize=True, plot=True):
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"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.toy_rbf_1d()
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# create simple GP Model
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@ -237,6 +246,8 @@ def toy_rbf_1d(optimize=True, plot=True):
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def toy_rbf_1d_50(optimize=True, plot=True):
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"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.toy_rbf_1d_50()
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# create simple GP Model
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@ -352,6 +363,8 @@ def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, o
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def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
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"""Predict the location of a robot given wirelss signal strength readings."""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.robot_wireless()
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# create simple GP Model
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@ -376,6 +389,8 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
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def silhouette(max_iters=100, optimize=True, plot=True):
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"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
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try:import pods
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except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
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data = pods.datasets.silhouette()
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# create simple GP Model
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