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 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)
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)
# manually build the network, leaving out the 'iris-class' variable
network = bayesianpy.network.create_network()
cluster = builder.create_cluster_variable(network, 4)
node = builder.create_multivariate_continuous_node(network,
iris.columns.tolist(),
"joint")
builder.create_link(network, cluster, node)
with bayesianpy.data.DataSet(iris.drop('iris_class', axis=1), db_folder,
logger) as dataset:
# build the 'normal' model on two of the classes
model = bayesianpy.model.NetworkModel(network, logger)
subset = dataset.subset(
iris[(iris.iris_class == "Iris-versicolor") |
(iris.iris_class == "Iris-virginica")].index.tolist())
model.train(subset)
variables = [
'sepal_length', 'sepal_width', 'petal_length', 'petal_width'
]
# query the trained model on all the data, including the Iris-setosa class
# get the loglikelihood value for the whole model on each individual sample,
# the lower the loglikelihood value the less likely the data point has been
# generated by the model.
results = model.batch_query(dataset,
[bayesianpy.model.QueryModelStatistics()])
cmap = plt.cm.get_cmap('Blues_r')
fig = plt.figure(figsize=(10, 10))
k = 1
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))
h = ax.scatter(x=iris[v].tolist(),
y=iris[v1].tolist(),
c=results['loglikelihood'].tolist(),
vmin=results.loglikelihood.min(),
vmax=results.loglikelihood.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()
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()