[Example] Add ALS application

examples/CMakeLists.txt

@@ -126,3 +126,9 @@ add_executable(TriangleCounting triangle_counting.cpp)
 target_link_libraries(TriangleCounting ${husky})
 target_link_libraries(TriangleCounting ${HUSKY_EXTERNAL_LIB})
 husky_default_properties(TriangleCounting)
+
+# Alternating Least Squares
+add_executable(ALS als.cpp)
+target_link_libraries(ALS ${husky})
+target_link_libraries(ALS ${HUSKY_EXTERNAL_LIB})
+husky_default_properties(ALS)

examples/als.cpp

@@ -0,0 +1,287 @@
+// Copyright 2015 Husky Team
+//
+// Licensed 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.
+
+#include <algorithm>
+#include <random>
+#include <string>
+#include <vector>
+
+#include "boost/tokenizer.hpp"
+#include <Eigen/Dense>
+
+#include "core/executor.hpp"
+#include "core/objlist.hpp"
+#include "core/utils.hpp"
+#include "io/input/inputformat_store.hpp"
+#include "io/input/line_inputformat.hpp"
+#include "io/hdfs_manager.hpp"
+#include "lib/aggregator_factory.hpp"
+
+#include "lib/vector.hpp"
+
+typedef Eigen::VectorXd VectorT;
+typedef Eigen::MatrixXd MatrixT;
+
+const int MAGIC = 50000000;
+typedef Eigen::VectorXd VectorT;
+typedef Eigen::MatrixXd MatrixT;
+typedef std::pair<double, VectorT> FactorMsgT;
+
+/// Any class that subclasses it can be able to do ALS
+/// TODO need some machanism to "remember" the deduplicated requests
+class ALSNode{
+public:
+    ALSNode() {}
+    ALSNode(int id) : key(id) {
+        ever_active = false;
+    }
+
+    static void set_rank(int _rank) {
+        rank = _rank;
+    }
+    static void set_lambda(double _lambda) {
+        lambda = _lambda;
+    }
+    static void set_iter(int _iter) {
+        iter = _iter;
+    }
+
+    typedef int KeyT;
+    KeyT key;
+    bool active;
+    bool ever_active;
+    std::vector<int> nbs;
+    std::vector<double> obs;
+    VectorT factors;
+
+    static int rank;  // Default is 20
+    static double lambda;  // Default is 0.01
+    static int iter;  // Default is 10
+
+    virtual KeyT const & id() const {
+        return key;
+    }
+
+    void broadcast(husky::PushChannel<FactorMsgT, ALSNode>& ch) {
+        ever_active = true;
+        for(int i = 0; i < nbs.size(); i++) {
+            ch.push(FactorMsgT(obs[i], factors), nbs[i]);
+        }
+    }
+
+    friend husky::BinStream& operator<<(husky::BinStream& stream, const ALSNode& node) {
+        stream << node.key << node.active << node.ever_active << node.nbs << node.obs << node.factors;
+        return stream;
+    }
+
+    friend husky::BinStream& operator>>(husky::BinStream& stream, ALSNode& node) {
+        stream >> node.key >> node.active >> node.ever_active >> node.nbs >> node.obs >> node.factors;
+        return stream;
+    }
+};
+
+int ALSNode::rank = 20;
+double ALSNode::lambda = 0.01;
+int ALSNode::iter = 10;
+
+class UserItemRatingObject {
+   public:
+    using KeyT = int;
+    explicit UserItemRatingObject(int _user, int _item, double _rating) : user(_user), item(_item), rating(_rating) {
+		std::mt19937 rng;
+		rng.seed(std::random_device()());
+		std::uniform_int_distribution<std::mt19937::result_type> dist(1, 1 << 31);
+		objid = dist(rng);
+    }
+    KeyT objid;
+    int user;
+    int item;
+    double rating;
+    const KeyT& id() const { return objid; }
+};
+
+
+// TODO: Serialization for VectorT (Eigen)
+void als() {
+    // Prepare ObjList for later ALS
+    int num_worker = husky::Context::get_num_workers();
+    int worker_id = husky::Context::get_global_tid();
+    auto& als_list = husky::ObjListStore::create_objlist<ALSNode>("als-node");
+    auto& ac = husky::lib::AggregatorFactory::get_channel();
+    // Parse Data
+    auto& tmp_store = husky::ObjListStore::create_objlist<UserItemRatingObject>("tmp");  // Intented to be stayed at local
+    husky::lib::Aggregator<int> max_user_index_agg(0, [](int&a, const int& b) {a = std::max(a, b);});
+    husky::lib::Aggregator<int> max_item_index_agg(0, [](int&a, const int& b) {a = std::max(a, b);});
+    husky::lib::Aggregator<int> num_rating_agg(0, [](int&a, const int& b) {a += b;});
+    auto& infmt = husky::io::InputFormatStore::create_line_inputformat();
+    std::string url = husky::Context::get_param("input");
+    husky::LOG_I << url;
+    infmt.set_input(url);
+    std::function<void(boost::string_ref)> parser = [&](boost::string_ref chunk) {
+        if (chunk.empty())
+            return;
+        boost::char_separator<char> sep(" \t");
+        boost::tokenizer<boost::char_separator<char>> tok(chunk, sep);
+
+        auto it = tok.begin();
+        int user = std::stoi(*it);
+        it++;
+        int item = std::stoi(*it)+MAGIC;
+        it++;
+        double rating = std::stof(*it);
+
+        max_user_index_agg.update(user);
+        max_item_index_agg.update(item);
+        tmp_store.add_object(UserItemRatingObject(user, item, rating));
+    };
+    husky::load(infmt, {&ac}, parser);
+    husky::LOG_I << "Finished Loading From HDFS";
+    // loadData into ALS Obj
+    int num_node = std::max(max_item_index_agg.get_value(), max_user_index_agg.get_value());
+    husky::LOG_I << "num_node: " << std::to_string(num_node);
+    for (int i = worker_id; i <= num_node; i += num_worker) {
+        als_list.add_object(ALSNode(i));
+    }
+    husky::LOG_I << "Created als_list";
+    husky::globalize(als_list);
+    husky::LOG_I << "Balanced als_list";
+    husky::PushChannel<std::pair<int, double>, ALSNode>& data_push_channel = 
+        husky::ChannelStore::create_push_channel<std::pair<int, double>>(tmp_store, als_list);
+    husky::list_execute(tmp_store, {}, {&data_push_channel}, [&](UserItemRatingObject& obj) {
+        data_push_channel.push(std::pair<int, double>(obj.item, obj.rating), obj.user);
+        data_push_channel.push(std::pair<int, double>(obj.user, obj.rating), obj.item);
+    });
+    husky::LOG_I << "Pushed Data from tmp_store to als_list";
+    husky::list_execute(als_list, {&data_push_channel}, {}, [&](ALSNode& node){
+        auto& vec_msg = data_push_channel.get(node);
+        for (auto& msg : vec_msg) {
+            node.nbs.push_back(msg.first);
+            node.obs.push_back(msg.second);
+        }
+    });
+    husky::LOG_I << "als_list received data";
+    husky::list_execute(als_list, {}, {}, [&](ALSNode& node){
+        if (node.nbs.size() == 0) als_list.delete_object(&node);
+    });
+    husky::LOG_I << "deleted useless obj in als_list";
+    husky::balance(als_list);
+    husky::LOG_I << "blanced after deletion in als_list";
+    // Set the parameter for training
+    int num_iter = 10;
+    // Initiate ALS list
+    auto& factors_push_channel = husky::ChannelStore::create_push_channel<FactorMsgT>(als_list, als_list);
+    husky::list_execute(als_list, {}, {&factors_push_channel}, [&](ALSNode& node) {
+		node.factors.resize(node.rank);
+		node.factors.setRandom();
+		if(node.key < MAGIC) {
+			node.broadcast(factors_push_channel);
+			node.active = false;
+		} else {
+			node.active = true;
+		}
+		return;
+    });
+    husky::LOG_I << "Initilizated als_list";
+    // Train ALS
+    for (int iter_train = 0; iter_train < num_iter; iter_train++) {
+        husky::list_execute(als_list, {&factors_push_channel}, {&factors_push_channel}, [&] (ALSNode & node) {
+            // If I'm type 0 and I'm initing, send msg
+            if(node.active == false) {
+                node.active = true;
+                return;
+            } else {
+                MatrixT sum_mat;
+                VectorT sum_vec = VectorT::Zero(node.rank);
+                auto & recv_data = factors_push_channel.get(node);
+                if (recv_data.size() == 0) return;
+                for(int i = 0; i < recv_data.size(); i++) {
+                    double rating = recv_data[i].first;
+                    auto & other_factors = recv_data[i].second;
+                    assert(other_factors.size() == node.rank);
+                    if(sum_mat.size() == 0) {
+                        sum_mat.resize(node.rank, node.rank);
+                        sum_mat.triangularView<Eigen::Upper>() = other_factors * other_factors.transpose();
+                    } else {
+                        sum_mat.triangularView<Eigen::Upper>() += other_factors * other_factors.transpose();
+                    }
+                    sum_vec += other_factors * rating;
+                }
+                // husky::LOG_I << "regularization";
+                double regularization = node.lambda*node.nbs.size()*(node.key < MAGIC);
+                for(int i = 0; i < sum_mat.rows(); ++i) 
+                    sum_mat(i,i) += regularization; 
+                // TODO
+                // husky::LOG_I << "added regularization";
+                node.factors = sum_mat.selfadjointView<Eigen::Upper>().ldlt().solve(sum_vec);
+                node.broadcast(factors_push_channel);
+                node.active = false;
+            }
+        });
+    }
+    husky::LOG_I << "Trained als_list";
+    // Show The result, get the rmse
+    auto rmse_agg = husky::lib::Aggregator<double>(0.0, [](double& a, const double& b){ a += b; });
+    husky::list_execute(als_list, {&factors_push_channel}, {&ac}, [&](ALSNode& node){
+		if(not node.active) return;
+		MatrixT sum_mat;
+		VectorT sum_vec = VectorT::Zero(node.rank);
+        auto & recv_data = factors_push_channel.get(node);
+        if (recv_data.size() == 0) return;
+        for(int i=0; i<recv_data.size(); i++) {
+            double rating = recv_data[i].first;
+            auto & X = recv_data[i].second;
+			assert(X.size() == node.rank);
+			MatrixT XtX(node.rank, node.rank);
+			if(sum_mat.size() == 0) {
+				sum_mat.resize(node.rank, node.rank);
+				sum_mat.triangularView<Eigen::Upper>() = X * X.transpose();
+			} else {
+				sum_mat.triangularView<Eigen::Upper>() += X * X.transpose();
+			}
+			sum_vec += X * rating;
+		}
+		node.factors = sum_mat.selfadjointView<Eigen::Upper>().ldlt().solve(sum_vec);
+
+		for(int i = 0; i < recv_data.size(); i++) {
+            double rating = recv_data[i].first;
+            auto & X = recv_data[i].second;
+			double pred = X.dot(node.factors);
+			pred = std::max(1., pred);
+			pred = std::min(5., pred);
+			double loss = pred - rating;
+			loss *= loss;
+			rmse_agg.update(loss);
+            num_rating_agg.update(1);
+		}
+    });
+    int num_rating = num_rating_agg.get_value();
+	if (worker_id == 0) {
+		double rmse = rmse_agg.get_value();
+		rmse = sqrt(rmse/num_rating);
+        husky::LOG_I << "num_rating: " << std::to_string(num_rating);
+        husky::LOG_I << std::to_string(rmse);
+	}
+}
+
+int main(int argc, char** argv) {
+    std::vector<std::string> args;
+    args.push_back("hdfs_namenode");
+    args.push_back("hdfs_namenode_port");
+    args.push_back("input");
+    if (husky::init_with_args(argc, argv, args)) {
+        husky::run_job(als);
+        return 0;
+    }
+    return 1;
+}