def create(self, network_factory: bayesianpy.network.NetworkFactory):
network = network_factory.create()
#builder.create_cluster_variable(network, 5)
if not dk.empty(self._continuous):
for c_name in self._continuous.columns:
builder.create_continuous_variable(network, c_name)
if not dk.empty(self._discrete):
for d_name in self._discrete.columns:
builder.create_discrete_variable(network, self._discrete, d_name, blanks=self._blanks)
network = bayesianpy.network.remove_single_state_nodes(network)
return network
def create(self, network_factory):
network = network_factory.create()
cluster = builder.try_get_node(network, "Cluster")
if cluster is None:
cluster = builder.create_cluster_variable(
network,
self._latent_states,
variable_name=self._latent_variable_name)
if not dk.empty(self._continuous):
for c_name in self._continuous.columns:
self._logger.info("Pre-processing {} column".format(c_name))
c = builder.create_continuous_variable(network, c_name)
try:
builder.create_link(network, cluster, c)
except ValueError as e:
self._logger.warn(e)
if not dk.empty(self._discrete):
for d_name in self._discrete.columns:
if d_name in self._discrete_states:
states = self._discrete_states[str(d_name)]
else:
states = dk.compute(self._discrete[str(
d_name)].dropna().unique()).tolist()
try:
c = builder.create_discrete_variable(
network, self._discrete, str(d_name), states)
builder.create_link(network, cluster, c)
except BaseException as e:
self._logger.warn(e)
return network
def create(self, network_factory):
network = network_factory.create()
if not dk.empty(self._continuous):
for c_name in self._continuous.columns:
c = builder.create_continuous_variable(network, c_name)
if dk.empty(self._discrete):
for d_name in self._discrete.columns:
if d_name in self._discrete_states:
states = self._discrete_states[d_name]
else:
states = dk.compute(
self._discrete[d_name].dropna().unique()).tolist()
try:
c = builder.create_discrete_variable(
network, self._discrete, d_name, states)
except BaseException as e:
self._logger.warn(e)
parent_node = builder.try_get_node(network, self._parent_node)
if parent_node is None:
raise ValueError("Parent node: {} not recognised".format(
self._parent_node))
for node in network.getNodes():
if node == parent_node:
continue
builder.create_link(network, parent_node, node)
return network
def main():
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler())
logger.setLevel(logging.INFO)
bayesianpy.jni.attach(logger)
db_folder = bayesianpy.utils.get_path_to_parent_dir(__file__)
iris = pd.read_csv(os.path.join(db_folder, "data/iris.csv"),
index_col=False)
network = bayesianpy.network.create_network()
cluster = builder.create_cluster_variable(network, 4)
node = builder.create_multivariate_continuous_node(
network,
iris.drop('iris_class', axis=1).columns.tolist(), "joint")
builder.create_link(network, cluster, node)
class_variable = builder.create_discrete_variable(
network, iris, 'iris_class', iris['iris_class'].unique())
builder.create_link(network, cluster, class_variable)
head_variables = [
'sepal_length', 'sepal_width', 'petal_length', 'petal_width'
]
with bayesianpy.data.DataSet(iris, db_folder, logger) as dataset:
model = bayesianpy.model.NetworkModel(network, logger)
model.train(dataset)
queries = [
bayesianpy.model.QueryConditionalJointProbability(
head_variables=[v], tail_variables=['iris_class'])
for v in head_variables
]
(engine, _, _) = bayesianpy.model.InferenceEngine(network).create()
query = bayesianpy.model.SingleQuery(network, engine, logger)
results = query.query(queries, aslist=True)
jd = bayesianpy.visual.JointDistribution()
fig = plt.figure(figsize=(10, 10))
for i, r in enumerate(list(results)):
ax = fig.add_subplot(2, 2, i + 1)
jd.plot_distribution_with_variance(ax, iris,
queries[i].get_head_variables(),
r)
plt.show()
def create(self, network_factory: bayesianpy.network.NetworkFactory):
network = network_factory.create()
cluster = builder.create_cluster_variable(network, self._latent_states)
if not dk.empty(self._continuous):
for c_name in self._continuous.columns:
c = builder.create_discretised_variable(network, self._continuous, c_name, bin_count=self._bin_count,
mode=self._binning_mode, zero_crossing=self._zero_crossing)
builder.create_link(network, cluster, c)
if not dk.empty(self._discrete):
for d_name in self._discrete.columns:
states = dk.compute(self._discrete[d_name].dropna().unique())
c = builder.create_discrete_variable(network, self._discrete, d_name, states)
builder.create_link(network, cluster, c)
return network
def main():
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler())
logger.setLevel(logging.INFO)
bayesianpy.jni.attach(logger)
db_folder = bayesianpy.utils.get_path_to_parent_dir(__file__)
iris = pd.read_csv(os.path.join(db_folder, "data/iris.csv"),
index_col=False)
network = bayesianpy.network.create_network()
cluster = builder.create_cluster_variable(network, 4)
node = builder.create_multivariate_continuous_node(
network,
iris.drop('iris_class', axis=1).columns.tolist(), "joint")
builder.create_link(network, cluster, node)
class_variable = builder.create_discrete_variable(
network, iris, 'iris_class', iris['iris_class'].unique())
builder.create_link(network, cluster, class_variable)
jd = bayesianpy.visual.JointDistribution()
def plot(head_variables, results):
fig = plt.figure(figsize=(10, 10))
n = len(head_variables) - 1
total = n * (n + 1) / 2
k = 1
for i, hv in enumerate(head_variables):
for j in range(i + 1, len(head_variables)):
ax = fig.add_subplot(total / 2, 2, k)
jd.plot_distribution_with_covariance(
ax, iris, (head_variables[i], head_variables[j]), results)
k += 1
plt.show()
with bayesianpy.data.DataSet(iris, db_folder, logger) as dataset:
model = bayesianpy.model.NetworkModel(network, logger)
model.train(dataset)
head_variables = [
'sepal_length', 'sepal_width', 'petal_length', 'petal_width'
]
query_type_class = bayesianpy.model.QueryConditionalJointProbability(
head_variables=head_variables,
tail_variables=['iris_class', 'Cluster'])
(engine, _, _) = bayesianpy.model.InferenceEngine(network).create()
query = bayesianpy.model.Query(network, engine, logger)
results_class = query.execute([query_type_class])
plot(head_variables, results_class)
query_type_cluster = bayesianpy.model.QueryConditionalJointProbability(
head_variables=head_variables, tail_variables=['Cluster'])
results_cluster = query.execute([query_type_cluster])
plot(head_variables, results_cluster)
from bayesianpy.network import Builder as builder
import bayesianpy.network
nt = bayesianpy.network.create_network()
# where df is your dataframe
task = builder.create_discrete_variable(nt, df, 'task')
size = builder.create_continuous_variable(nt, 'size')
grasp_pose = builder.create_continuous_variable(nt, 'GraspPose')
builder.create_link(nt, size, grasp_pose)
builder.create_link(nt, task, grasp_pose)
for v in ['fill level', 'object shape', 'side graspable']:
va = builder.create_discrete_variable(nt, df, v)
builder.create_link(nt, va, grasp_pose)
builder.create_link(nt, task, va)
# write df to data store
with bayesianpy.data.DataSet(df, bayesianpy.utils.get_path_to_parent_dir(__file__), logger) as dataset:
model = bayesianpy.model.NetworkModel(nt, logger)
model.train(dataset)
# to query model multi-threaded
results = model.batch_query(dataset, [bayesianpy.model.QueryModelStatistics()], append_to_df=False)
def main():
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler())
logger.setLevel(logging.INFO)
bayesianpy.jni.attach(logger)
db_folder = bayesianpy.utils.get_path_to_parent_dir(__file__)
iris = pd.read_csv(os.path.join(db_folder, "data/iris.csv"),
index_col=False)
network = bayesianpy.network.create_network()
num_clusters = 3
cluster = builder.create_cluster_variable(network, num_clusters)
node = builder.create_multivariate_continuous_node(
network,
iris.drop('iris_class', axis=1).columns.tolist(), "joint")
builder.create_link(network, cluster, node)
class_variable = builder.create_discrete_variable(
network, iris, 'iris_class', iris['iris_class'].unique())
builder.create_link(network, cluster, class_variable)
train, test = train_test_split(iris, test_size=0.7)
# train the model and query the most likely states and probability of each latent state.
with bayesianpy.data.DataSet(iris, db_folder, logger) as dataset:
model = bayesianpy.model.NetworkModel(network, logger)
model.train(dataset.subset(train.index.tolist()))
test_subset = dataset.subset(test.index.tolist())
results = model.batch_query(
test_subset,
# creates columns Cluster$$Cluster0, Cluster$$Cluster1,
# Cluster$$Cluster2, as
# suffix is set to an empty string.
[
bayesianpy.model.QueryStateProbability("Cluster", suffix=""),
# creates column 'iris_class_maxlikelihood'
bayesianpy.model.QueryMostLikelyState("iris_class"),
# creates column 'Cluster_maxlikelihood'
bayesianpy.model.QueryMostLikelyState("Cluster")
])
cluster_accuracy = {}
# get a list of cluster accuracies, using the Bayes Server Confusion matrix class
# weighted by the Cluster accuracy.
with bayesianpy.data.DataSet(results, db_folder, logger) as resultset:
for c in range(num_clusters):
matrix = bayesianpy.jni.bayesServerAnalysis()\
.ConfusionMatrix.create(resultset.create_data_reader_command(), "iris_class",
"iris_class_maxlikelihood", "Cluster$$Cluster{}".format(c))
cluster_accuracy.update(
{'Cluster{}'.format(c): matrix.getAccuracy()})
# generate samples from the trained model, to give us some additional testing data.
samples = bayesianpy.model.Sampling(network).sample(num_samples=20).drop(
["Cluster", "iris_class"], axis=1)
reader = bayesianpy.data.DataFrameReader(samples)
inference = bayesianpy.model.InferenceEngine(network).create_engine()
evidence = bayesianpy.model.Evidence(network, inference)
query = bayesianpy.model.SingleQuery(network, inference, logger)
query_type = [bayesianpy.model.QueryStateProbability('Cluster', suffix="")]
# query the expected Cluster membership, and generate a wrapper for
# comparing the values, weighted by cluster membership.
while reader.read():
result = query.query(query_type,
evidence=evidence.apply(reader.to_dict()))
cv_results = []
for i, (key, value) in enumerate(result.items()):
n = bayesianpy.network.Discrete.fromstring(key)
weighting = cluster_accuracy[n.state]
cv_results.append(bayesianpy.jni.bayesServerAnalysis().
DefaultCrossValidationTestResult(
jp.JDouble(weighting),
jp.JObject(value, jp.java.lang.Object),
jp.java.lang.Double(jp.JDouble(value))))
score = bayesianpy.jni.bayesServerAnalysis().CrossValidation.combine(
jp.java.util.Arrays.asList(cv_results),
bayesianpy.jni.bayesServerAnalysis().CrossValidationCombineMethod.
WEIGHTED_AVERAGE)
# append the score on to the existing dataframe
samples.set_value(reader.get_index(), 'score', score)
variables = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
cmap = plt.cm.get_cmap('Blues')
fig = plt.figure(figsize=(10, 10))
k = 1
# plot!
for i, v in enumerate(variables):
for j in range(i + 1, len(variables)):
v1 = variables[j]
ax = fig.add_subplot(3, 2, k)
ax.set_title("{} vs {}".format(v, v1))
ax.scatter(x=iris[v].tolist(),
y=iris[v1].tolist(),
facecolors='none',
alpha=0.1)
h = ax.scatter(x=samples[v].tolist(),
y=samples[v1].tolist(),
c=samples['score'].tolist(),
vmin=samples.score.min(),
vmax=samples.score.max(),
cmap=cmap)
k += 1
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(h, cax=cbar_ax)
plt.show()
