Github user njayaram2 commented on a diff in the pull request:
https://github.com/apache/incubator-madlib/pull/149#discussion_r128102125
--- Diff: src/modules/convex/mlp_igd.cpp ---
@@ -0,0 +1,236 @@
+/*
+ * 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.
+ *
+ * @file mlp_igd.cpp
+ *
+ * @brief Multilayer Perceptron functions
+ *
+ *//*
----------------------------------------------------------------------- */
+#include <boost/lexical_cast.hpp>
+
+#include <dbconnector/dbconnector.hpp>
+#include <modules/shared/HandleTraits.hpp>
+
+#include "mlp_igd.hpp"
+
+#include "task/mlp.hpp"
+#include "algo/igd.hpp"
+#include "algo/loss.hpp"
+
+#include "type/tuple.hpp"
+#include "type/model.hpp"
+#include "type/state.hpp"
+
+namespace madlib {
+
+namespace modules {
+
+namespace convex {
+
+// These 2 classes contain public static methods that can be called
+typedef IGD<MLPIGDState<MutableArrayHandle<double> >,
MLPIGDState<ArrayHandle<double> >,
+ MLP<MLPModel<MutableArrayHandle<double> >, MLPTuple > >
MLPIGDAlgorithm;
+
+typedef Loss<MLPIGDState<MutableArrayHandle<double> >,
MLPIGDState<ArrayHandle<double> >,
+ MLP<MLPModel<MutableArrayHandle<double> >, MLPTuple > >
MLPLossAlgorithm;
+
+typedef MLP<MLPModel<MutableArrayHandle<double> >,MLPTuple> MLPTask;
+
+/**
+ * @brief Perform the multilayer perceptron transition step
+ *
+ * Called for each tuple.
+ */
+AnyType
+mlp_igd_transition::run(AnyType &args) {
+ // For the first tuple: args[0] is nothing more than a marker that
+ // indicates that we should do some initial operations.
+ // For other tuples: args[0] holds the computation state until last
tuple
+ MLPIGDState<MutableArrayHandle<double> > state = args[0];
+
+ // initilize the state if first tuple
+ if (state.algo.numRows == 0) {
+ if (!args[3].isNull()) {
+ MLPIGDState<ArrayHandle<double> > previousState = args[3];
+
+ state.allocate(*this, previousState.task.numberOfStages,
+ previousState.task.numbersOfUnits);
+ state = previousState;
+ } else {
+ // configuration parameters
+ ArrayHandle<double> numbersOfUnits =
args[4].getAs<ArrayHandle<double> >();
+
+ double stepsize = args[5].getAs<double>();
+
+ state.allocate(*this, numbersOfUnits.size() - 1,
+ reinterpret_cast<const double
*>(numbersOfUnits.ptr()));
+ state.task.stepsize = stepsize;
+
+
+ int activation = args[6].getAs<int>();
+
+ int is_classification = args[7].getAs<int>();
+ state.task.model.initialize(is_classification, activation);
+ }
+
+ // resetting in either case
+ state.reset();
+ }
+
+ // meta data
+ const uint16_t N = state.task.numberOfStages;
+ const double *n = state.task.numbersOfUnits;
+
+ // tuple
+ MappedColumnVector indVar;
+ MappedColumnVector depVar;
+ try {
+ // an exception is raised in the backend if args[2] contains nulls
+ MappedColumnVector x = args[1].getAs<MappedColumnVector>();
+ // x is a const reference, we can only rebind to change its pointer
+ indVar.rebind(x.memoryHandle(), x.size());
+ MappedColumnVector y = args[2].getAs<MappedColumnVector>();
+ depVar.rebind(y.memoryHandle(), y.size());
+
+ } catch (const ArrayWithNullException &e) {
+ return args[0];
+ }
+ MLPTuple tuple;
+ tuple.indVar.rebind(indVar.memoryHandle(), indVar.size());
+ tuple.depVar.rebind(depVar.memoryHandle(), depVar.size());
+
+ // Now do the transition step
+ MLPIGDAlgorithm::transition(state, tuple);
+ MLPLossAlgorithm::transition(state, tuple);
+ state.algo.numRows ++;
+
+ return state;
+}
+
+/**
+ * @brief Perform the perliminary aggregation function: Merge transition
states
+ */
+AnyType
+mlp_igd_merge::run(AnyType &args) {
+ MLPIGDState<MutableArrayHandle<double> > stateLeft = args[0];
+ MLPIGDState<ArrayHandle<double> > stateRight = args[1];
+
+ // We first handle the trivial case where this function is called with
one
+ // of the states being the initial state
+ if (stateLeft.algo.numRows == 0) { return stateRight; }
+ else if (stateRight.algo.numRows == 0) { return stateLeft; }
+
+ // Merge states together
+ MLPIGDAlgorithm::merge(stateLeft, stateRight);
+ MLPLossAlgorithm::merge(stateLeft, stateRight);
+ // The following numRows update, cannot be put above, because the model
+ // averaging depends on their original values
+ stateLeft.algo.numRows += stateRight.algo.numRows;
+
+ return stateLeft;
+}
+
+/**
+ * @brief Perform the multilayer perceptron final step
+ */
+AnyType
+mlp_igd_final::run(AnyType &args) {
+ // We request a mutable object. Depending on the backend, this might
perform
+ // a deep copy.
+ MLPIGDState<MutableArrayHandle<double> > state = args[0];
+
+ // Aggregates that haven't seen any data just return Null.
+ if (state.algo.numRows == 0) { return Null(); }
+
+ // finalizing
+ MLPIGDAlgorithm::final(state);
+
+ // Return the mean loss
+ state.algo.loss =
state.algo.loss/static_cast<double>(state.algo.numRows);
+
+ // for stepsize tuning
+ std::stringstream debug;
+ debug << "loss: " << state.algo.loss;
+ warning(debug.str());
--- End diff --
Remove debug statements, or change it to `debug` instead of `warning`.
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