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handles import of pods correctly

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This commit is contained in:
James Hensman 2014-10-16 17:00:17 +01:00
parent d961f736f2
commit e0d3633ec3
2 changed files with 32 additions and 7 deletions
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22
GPy/examples/classification.py
View file

@ -6,12 +6,6 @@
Gaussian Processes classification Gaussian Processes classification
""" """
import GPy import GPy
import pods
try:
from matplotlib import pyplot as plt
except:
pass
default_seed = 10000 default_seed = 10000
@ -20,6 +14,8 @@ def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
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. 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.
""" """
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.oil() data = pods.datasets.oil()
X = data['X'] X = data['X']
Xtest = data['Xtest'] Xtest = data['Xtest']
@ -55,6 +51,8 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
""" """
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -72,6 +70,7 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
# Plot # Plot
if plot: if plot:
from matplotlib import pyplot as plt
fig, axes = plt.subplots(2, 1) fig, axes = plt.subplots(2, 1)
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
@ -88,6 +87,8 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
""" """
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -108,6 +109,7 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
# Plot # Plot
if plot: if plot:
from matplotlib import pyplot as plt
fig, axes = plt.subplots(2, 1) fig, axes = plt.subplots(2, 1)
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
@ -124,6 +126,8 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
""" """
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -138,6 +142,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
# Plot # Plot
if plot: if plot:
from matplotlib import pyplot as plt
fig, axes = plt.subplots(2, 1) fig, axes = plt.subplots(2, 1)
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
@ -154,6 +159,8 @@ def toy_heaviside(seed=default_seed, optimize=True, plot=True):
""" """
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -172,6 +179,7 @@ def toy_heaviside(seed=default_seed, optimize=True, plot=True):
# Plot # Plot
if plot: if plot:
from matplotlib import pyplot as plt
fig, axes = plt.subplots(2, 1) fig, axes = plt.subplots(2, 1)
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
@ -191,6 +199,8 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
:param kernel: kernel to use in the model :param kernel: kernel to use in the model
:type kernel: a GPy kernel :type kernel: a GPy kernel
""" """
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.crescent_data(seed=seed) data = pods.datasets.crescent_data(seed=seed)
Y = data['Y'] Y = data['Y']
Y[Y.flatten()==-1] = 0 Y[Y.flatten()==-1] = 0

17
GPy/examples/regression.py
View file

@ -10,10 +10,11 @@ except:
pass pass
import numpy as np import numpy as np
import GPy import GPy
import pods
def olympic_marathon_men(optimize=True, plot=True): def olympic_marathon_men(optimize=True, plot=True):
"""Run a standard Gaussian process regression on the Olympic marathon data.""" """Run a standard Gaussian process regression on the Olympic marathon data."""
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.olympic_marathon_men() data = pods.datasets.olympic_marathon_men()
# create simple GP Model # create simple GP Model
@ -83,6 +84,8 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
from the Mount Epomeo runs. Requires gpxpy to be installed on your system from the Mount Epomeo runs. Requires gpxpy to be installed on your system
to load in the data. to load in the data.
""" """
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.epomeo_gpx() data = pods.datasets.epomeo_gpx()
num_data_list = [] num_data_list = []
for Xpart in data['X']: for Xpart in data['X']:
@ -126,6 +129,8 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
length_scales = np.linspace(0.1, 60., resolution) length_scales = np.linspace(0.1, 60., resolution)
log_SNRs = np.linspace(-3., 4., resolution) log_SNRs = np.linspace(-3., 4., resolution)
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number) data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)
# data['Y'] = data['Y'][0::2, :] # data['Y'] = data['Y'][0::2, :]
# data['X'] = data['X'][0::2, :] # data['X'] = data['X'][0::2, :]
@ -206,6 +211,8 @@ def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.RBF):
def olympic_100m_men(optimize=True, plot=True): def olympic_100m_men(optimize=True, plot=True):
"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data.""" """Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.olympic_100m_men() data = pods.datasets.olympic_100m_men()
# create simple GP Model # create simple GP Model
@ -223,6 +230,8 @@ def olympic_100m_men(optimize=True, plot=True):
def toy_rbf_1d(optimize=True, plot=True): def toy_rbf_1d(optimize=True, plot=True):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.""" """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.toy_rbf_1d() data = pods.datasets.toy_rbf_1d()
# create simple GP Model # create simple GP Model
@ -237,6 +246,8 @@ def toy_rbf_1d(optimize=True, plot=True):
def toy_rbf_1d_50(optimize=True, plot=True): def toy_rbf_1d_50(optimize=True, plot=True):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.""" """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.toy_rbf_1d_50() data = pods.datasets.toy_rbf_1d_50()
# create simple GP Model # create simple GP Model
@ -352,6 +363,8 @@ def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, o
def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True): def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
"""Predict the location of a robot given wirelss signal strength readings.""" """Predict the location of a robot given wirelss signal strength readings."""
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.robot_wireless() data = pods.datasets.robot_wireless()
# create simple GP Model # create simple GP Model
@ -376,6 +389,8 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
def silhouette(max_iters=100, optimize=True, plot=True): def silhouette(max_iters=100, optimize=True, plot=True):
"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper.""" """Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
data = pods.datasets.silhouette() data = pods.datasets.silhouette()
# create simple GP Model # create simple GP Model