e2f021a5e4444befdb9e5926b96bc96c408faa85,examples/acp_classification_tree.py,,,#,26
Before Change
// Define models
// -----------------------------------------------------------------------------
models = { "ACP-RandomSubSampler" : AggregatedCp(
IcpClassifier(
ProbEstClassifierNc(
DecisionTreeClassifier())),
RandomSubSampler()),
"ACP-CrossSampler" : AggregatedCp(
IcpClassifier(
ProbEstClassifierNc(
DecisionTreeClassifier())),
CrossSampler()),
"ACP-BootstrapSampler" : AggregatedCp(
IcpClassifier(
ProbEstClassifierNc(
DecisionTreeClassifier())),
BootstrapSampler()),
"CCP" : CrossConformalClassifier(
IcpClassifier(
ProbEstClassifierNc(
DecisionTreeClassifier()))),
"BCP" : BootstrapConformalClassifier(
IcpClassifier(
ProbEstClassifierNc(
DecisionTreeClassifier())))
}
// -----------------------------------------------------------------------------
// Train, predict and evaluate
// -----------------------------------------------------------------------------
for name, model in models.iteritems():
model.fit(data.data[train, :], data.target[train])
prediction = model.predict(data.data[test, :], significance=significance)
table = np.hstack((prediction, truth))
df = pd.DataFrame(table, columns=columns)
print("\n{}".format(name))
print("Error rate: {}".format(class_mean_errors(prediction,
truth,
After Change
// Define models
// -----------------------------------------------------------------------------
models = { "ACP-RandomSubSampler" : AggregatedCp(
IcpClassifier(
ClassifierNc(
ClassifierAdapter(DecisionTreeClassifier()))),
RandomSubSampler()),
"ACP-CrossSampler" : AggregatedCp(
IcpClassifier(
ClassifierNc(
ClassifierAdapter(DecisionTreeClassifier()))),
CrossSampler()),
"ACP-BootstrapSampler" : AggregatedCp(
IcpClassifier(
ClassifierNc(
ClassifierAdapter(DecisionTreeClassifier()))),
BootstrapSampler()),
"CCP" : CrossConformalClassifier(
IcpClassifier(
ClassifierNc(
ClassifierAdapter(DecisionTreeClassifier())))),
"BCP" : BootstrapConformalClassifier(
IcpClassifier(
ClassifierNc(
ClassifierAdapter(DecisionTreeClassifier()))))
}
// -----------------------------------------------------------------------------
// Train, predict and evaluate
// -----------------------------------------------------------------------------
for name, model in models.iteritems():
model.fit(data.data[train, :], data.target[train])
prediction = model.predict(data.data[test, :], significance=significance)
table = np.hstack((prediction, truth))
df = pd.DataFrame(table, columns=columns)
print("\n{}".format(name))
print("Error rate: {}".format(class_mean_errors(prediction,
truth,
In pattern: SUPERPATTERN
Frequency: 5
Non-data size: 3
Instances
Project Name: donlnz/nonconformist
Commit Name: e2f021a5e4444befdb9e5926b96bc96c408faa85
Time: 2016-09-09
Author: henrik.linusson@gmail.com
File Name: examples/acp_classification_tree.py
Class Name:
Method Name: