Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/79#discussion_r116510491
--- Diff: core/src/main/java/hivemall/regression/GeneralRegressionUDTF.java
---
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package hivemall.regression;
+
+import hivemall.annotations.Since;
+import hivemall.model.FeatureValue;
+import hivemall.optimizer.LossFunctions;
+import hivemall.optimizer.LossFunctions.LossFunction;
+import hivemall.optimizer.Optimizer;
+import hivemall.optimizer.OptimizerOptions;
+
+import java.util.Map;
+
+import javax.annotation.Nonnull;
+
+import org.apache.commons.cli.CommandLine;
+import org.apache.commons.cli.Options;
+import org.apache.hadoop.hive.ql.exec.Description;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
+import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
+
+/**
+ * A general regression class with replaceable optimization functions.
+ */
+@Description(name = "train_regression",
+ value = "_FUNC_(list<string|int|bigint> features, double label [,
const string options])"
+ + " - Returns a relation consists of <string|int|bigint
feature, float weight>",
+ extended = "Build a prediction model by a generic regressor")
+@Since(version = "0.5-rc.1")
+public final class GeneralRegressionUDTF extends RegressionBaseUDTF {
+
+ @Nonnull
+ private final Map<String, String> optimizerOptions;
+ private Optimizer optimizer;
+ private LossFunction lossFunction;
+
+ public GeneralRegressionUDTF() {
+ super(true); // This enables new model interfaces
+ this.optimizerOptions = OptimizerOptions.create();
+ }
+
+ @Override
+ public StructObjectInspector initialize(ObjectInspector[] argOIs)
throws UDFArgumentException {
+ if (argOIs.length != 2 && argOIs.length != 3) {
+ throw new UDFArgumentException(this.getClass().getSimpleName()
+ + " takes 2 or 3 arguments: List<Text|Int|BitInt>
features, float target "
+ + "[, constant string options]");
+ }
+
+ StructObjectInspector outputOI = super.initialize(argOIs);
+
+ if (lossFunction.forBinaryClassification()) {
+ throw new UDFArgumentException("The loss function `" +
lossFunction + "` is not for regression");
+ }
+ if (is_mini_batch) {
+ throw new UDFArgumentException("_FUNC_ does not currently
support `-mini_batch` option");
+ }
+
+ try {
+ this.optimizer = createOptimizer(optimizerOptions);
+ } catch (Throwable e) {
+ throw new UDFArgumentException(e.getMessage());
+ }
+
+ return outputOI;
+ }
+
+ @Override
+ protected Options getOptions() {
+ Options opts = super.getOptions();
+ opts.addOption("loss", "loss_function", true,
+ "Loss function [default: SquaredLoss, QuantileLoss,
EpsilonInsensitiveLoss]");
+ OptimizerOptions.setup(opts);
+ return opts;
+ }
+
+ @Override
+ protected CommandLine processOptions(ObjectInspector[] argOIs) throws
UDFArgumentException {
+ CommandLine cl = super.processOptions(argOIs);
+ try {
+ if (cl.hasOption("loss_function")) {
+ this.lossFunction =
LossFunctions.getLossFunction(cl.getOptionValue("loss_function"));
+ } else {
+ this.lossFunction =
LossFunctions.getLossFunction("SquaredLoss");
+ }
+ } catch (Throwable e) {
+ throw new UDFArgumentException(e.getMessage());
+ }
+ OptimizerOptions.propcessOptions(cl, optimizerOptions);
+ return cl;
+ }
+
+ @Override
+ protected final void checkTargetValue(final float target) throws
UDFArgumentException {
--- End diff --
@takuti Maybe for logistic regression that is actually a classifier taking
0/1 values. @maropu is not expert of machine learning algorithm.
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