@Override
public Nonconformity fit(DataFrame x, Vector y) {
Objects.requireNonNull(x, "Input data is required.");
Objects.requireNonNull(y, "Input target is required.");
Check.argument(x.rows() == y.size(), "The size of input data and input target don't match");
Classifier probabilityEstimator = classifier.fit(x, y);
Check.state(
probabilityEstimator != null
&& probabilityEstimator
.getCharacteristics()
.contains(ClassifierCharacteristic.ESTIMATOR),
"The produced classifier can't estimate probabilities");
return new ProbabilityEstimateNonconformity(probabilityEstimator, errorFunction);
}
@Override
public DoubleArray estimate(DataFrame x, Vector y) {
Objects.requireNonNull(x, "Input data required.");
Objects.requireNonNull(y, "Input target required.");
Check.argument(x.rows() == y.size(), "The size of input data and input target don't match.");
return errorFunction.apply(classifier.estimate(x), y, classifier.getClasses());
}
@Override
public double estimate(Vector example, Object label) {
Objects.requireNonNull(example, "Require an example.");
int trueClassIndex = classifier.getClasses().loc().indexOf(label);
Check.argument(trueClassIndex >= 0, "illegal label %s", label);
return errorFunction.apply(classifier.estimate(example), trueClassIndex);
}
@Override
public InductiveConformalClassifier fit(DataFrame x, Vector y) {
Objects.requireNonNull(x, "Input data is required.");
Objects.requireNonNull(y, "Input target is required.");
Check.argument(x.rows() == y.size(), "The size of input data and input target don't match.");
return new InductiveConformalClassifier(learner.fit(x, y), Vectors.unique(y));
}
@Override
public DataFrame parse(Supplier<? extends DataFrame.Builder> supplier) {
Check.state(url != null, "No database provided");
Check.state(query != null, "No query provided");
try {
Connection connection = DriverManager.getConnection(url, properties);
PreparedStatement stmt = connection.prepareStatement(query);
ResultSet resultSet = stmt.executeQuery();
ObjectIndex.Builder index = new ObjectIndex.Builder();
if (header != null) {
header.forEach(index::add);
} else {
ResultSetMetaData metaData = resultSet.getMetaData();
for (int i = 0; i < metaData.getColumnCount(); i++) {
// index starts with 1
String columnLabel = metaData.getColumnLabel(i + 1);
Object remappedColumnLabel = headerReMap.get(columnLabel);
if (remappedColumnLabel != null) {
index.add(remappedColumnLabel);
} else {
index.add(columnLabel);
}
}
}
SqlEntryReader entryReader = new SqlEntryReader(resultSet);
DataFrame.Builder builder = supplier.get();
List<Class<?>> columnTypes;
if (types != null) {
columnTypes = types;
} else {
columnTypes = entryReader.getTypes();
}
columnTypes.stream().map(VectorType::of).forEach(builder::add);
builder.readAll(entryReader);
builder.setColumnIndex(index.build());
return builder.build();
} catch (SQLException e) {
throw new EntryReaderException(e);
}
}
