iu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-08-18 18:41:03--  http://mtg.upf.edu/static/datasets/last.fm/lastfm-dataset-360K.tar.gz\n",
      "Resolving mtg.upf.edu (mtg.upf.edu)... 84.89.139.55\n",
      "Connecting to mtg.upf.edu (mtg.upf.edu)|84.89.139.55|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 569202935 (543M) [application/x-gzip]\n",
      "Saving to: ‘lastfm-dataset-360K.tar.gz’\n",
      "\n",
      "lastfm-dataset-360K 100%[===================>] 542.83M  8.87MB/s    in 74s     \n",
      "\n",
      "2020-08-18 18:42:20 (7.30 MB/s) - ‘lastfm-dataset-360K.tar.gz’ saved [569202935/569202935]\n",
      "\n",
      "gzip: lastfm-dataset-360K.tar.gz: No such file or directory\n",
      "tar: lastfm-dataset-360K.tar: Cannot open: No such file or directory\n",
      "tar: Error is not recoverable: exiting now\n",
      "Collecting implicit\n",
      "  Using cached implicit-0.4.2.tar.gz (1.1 MB)\n",
      "Requirement already satisfied: numpy in /home/reisei88/anaconda3/lib/python3.6/site-packages (from implicit) (1.19.0)\n",
      "Requirement already satisfied: scipy>=0.16 in /home/reisei88/anaconda3/lib/python3.6/site-packages (from implicit) (1.4.1)\n",
      "Requirement already satisfied: tqdm>=4.27 in /home/reisei88/anaconda3/lib/python3.6/site-packages (from implicit) (4.47.0)\n",
      "Building wheels for collected packages: implicit\n",
      "  Building wheel for implicit (setup.py) ... \u001b[?25lerror\n",
      "\u001b[31m  ERROR: Command errored out with exit status 1:\n",
      "   command: /home/reisei88/anaconda3/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-u14zlcn5/implicit/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-u14zlcn5/implicit/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d /tmp/pip-wheel-lfxuo8d9\n",
      "       cwd: /tmp/pip-install-u14zlcn5/implicit/\n",
      "  Complete output (122 lines):\n",
      "  running bdist_wheel\n",
      "  running build\n",
      "  running build_py\n",
      "  creating build\n",
      "  creating build/lib.linux-x86_64-3.6\n",
      "  creating build/lib.linux-x86_64-3.6/implicit\n",
      "  copying implicit/__init__.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "  copying implicit/als.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "  copying implicit/nearest_neighbours.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "  copying implicit/utils.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "  copying implicit/approximate_als.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "  creating build/lib.linux-x86_64-3.6/implicit/cuda\n",
      "  copying implicit/cuda/__init__.py -> build/lib.linux-x86_64-3.6/implicit/cuda\n",
      "  creating build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  copying implicit/datasets/lastfm.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  copying implicit/datasets/__init__.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  copying implicit/datasets/sketchfab.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  copying implicit/datasets/million_song_dataset.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  copying implicit/datasets/_download.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  copying implicit/datasets/movielens.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  copying implicit/datasets/reddit.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "  running build_ext\n",
      "  building 'implicit._als' extension\n",
      "  creating build/temp.linux-x86_64-3.6\n",
      "  creating build/temp.linux-x86_64-3.6/implicit\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -I/home/reisei88/anaconda3/include/python3.6m -c implicit/_als.cpp -o build/temp.linux-x86_64-3.6/implicit/_als.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/_als.o -o build/lib.linux-x86_64-3.6/implicit/_als.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "  building 'implicit._nearest_neighbours' extension\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/_nearest_neighbours.cpp -o build/temp.linux-x86_64-3.6/implicit/_nearest_neighbours.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/_nearest_neighbours.o -o build/lib.linux-x86_64-3.6/implicit/_nearest_neighbours.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "  building 'implicit.bpr' extension\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/bpr.cpp -o build/temp.linux-x86_64-3.6/implicit/bpr.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/bpr.o -o build/lib.linux-x86_64-3.6/implicit/bpr.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "  building 'implicit.lmf' extension\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -I/home/reisei88/anaconda3/include/python3.6m -c implicit/lmf.cpp -o build/temp.linux-x86_64-3.6/implicit/lmf.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/lmf.o -o build/lib.linux-x86_64-3.6/implicit/lmf.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "  building 'implicit.evaluation' extension\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -I/home/reisei88/anaconda3/include/python3.6m -c implicit/evaluation.cpp -o build/temp.linux-x86_64-3.6/implicit/evaluation.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  implicit/evaluation.cpp: In function 'PyObject* __pyx_pf_8implicit_10evaluation_10ranking_metrics_at_k(PyObject*, PyObject*, PyObject*, PyObject*, int, PyObject*, int)':\n",
      "  implicit/evaluation.cpp:4815:52: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
      "                                     if (((__pyx_t_26 < __pyx_t_20) != 0)) {\n",
      "                                           ~~~~~~~~~~~^~~~~~~~~~~~\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/evaluation.o -o build/lib.linux-x86_64-3.6/implicit/evaluation.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "  building 'implicit.recommender_base' extension\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/recommender_base.cpp -o build/temp.linux-x86_64-3.6/implicit/recommender_base.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/topnc.cpp -o build/temp.linux-x86_64-3.6/implicit/topnc.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/recommender_base.o build/temp.linux-x86_64-3.6/implicit/topnc.o -o build/lib.linux-x86_64-3.6/implicit/recommender_base.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "  building 'implicit.cuda._cuda' extension\n",
      "  creating build/temp.linux-x86_64-3.6/implicit/cuda\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/_cuda.cpp -o build/temp.linux-x86_64-3.6/implicit/cuda/_cuda.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "  cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "  /usr/local/cuda-10.1/bin/nvcc -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/als.cu -o build/temp.linux-x86_64-3.6/implicit/cuda/als.o -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 --ptxas-options=-v -O2 -c --compiler-options '-fPIC'\n",
      "  ptxas info    : 38 bytes gmem\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf' for 'sm_30'\n",
      "  ptxas info    : Function properties for _ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 32 registers, 128 bytes smem, 400 bytes cmem[0]\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit20l2_regularize_kernelEifPf' for 'sm_30'\n",
      "  ptxas info    : Function properties for _ZN8implicit20l2_regularize_kernelEifPf\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 5 registers, 336 bytes cmem[0]\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i' for 'sm_30'\n",
      "  ptxas info    : Function properties for _ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i\n",
      "      8 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 38 registers, 128 bytes smem, 388 bytes cmem[0], 24 bytes cmem[2]\n",
      "  ptxas info    : 38 bytes gmem\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf' for 'sm_50'\n",
      "  ptxas info    : Function properties for _ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 32 registers, 128 bytes smem, 400 bytes cmem[0]\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit20l2_regularize_kernelEifPf' for 'sm_50'\n",
      "  ptxas info    : Function properties for _ZN8implicit20l2_regularize_kernelEifPf\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 4 registers, 336 bytes cmem[0]\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i' for 'sm_50'\n",
      "  ptxas info    : Function properties for _ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i\n",
      "      8 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 40 registers, 128 bytes smem, 388 bytes cmem[0], 20 bytes cmem[2]\n",
      "  ptxas info    : 38 bytes gmem\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf' for 'sm_60'\n",
      "  ptxas info    : Function properties for _ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 32 registers, 128 bytes smem, 400 bytes cmem[0]\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit20l2_regularize_kernelEifPf' for 'sm_60'\n",
      "  ptxas info    : Function properties for _ZN8implicit20l2_regularize_kernelEifPf\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 4 registers, 336 bytes cmem[0]\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i' for 'sm_60'\n",
      "  ptxas info    : Function properties for _ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i\n",
      "      8 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 40 registers, 128 bytes smem, 388 bytes cmem[0], 20 bytes cmem[2]\n",
      "  /usr/local/cuda-10.1/bin/nvcc -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/bpr.cu -o build/temp.linux-x86_64-3.6/implicit/cuda/bpr.o -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 --ptxas-options=-v -O2 -c --compiler-options '-fPIC'\n",
      "  ptxas info    : 0 bytes gmem\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_' for 'sm_30'\n",
      "  ptxas info    : Function properties for _ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 25 registers, 144 bytes smem, 416 bytes cmem[0], 32 bytes cmem[2]\n",
      "  ptxas info    : 0 bytes gmem\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_' for 'sm_50'\n",
      "  ptxas info    : Function properties for _ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 29 registers, 144 bytes smem, 416 bytes cmem[0], 32 bytes cmem[2]\n",
      "  ptxas info    : 0 bytes gmem\n",
      "  ptxas info    : Compiling entry function '_ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_' for 'sm_60'\n",
      "  ptxas info    : Function properties for _ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_\n",
      "      0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "  ptxas info    : Used 28 registers, 144 bytes smem, 416 bytes cmem[0], 32 bytes cmem[2]\n",
      "  /usr/local/cuda-10.1/bin/nvcc -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/matrix.cu -o build/temp.linux-x86_64-3.6/implicit/cuda/matrix.o -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 --ptxas-options=-v -O2 -c --compiler-options '-fPIC'\n",
      "  ptxas info    : 0 bytes gmem\n",
      "  ptxas info    : 0 bytes gmem\n",
      "  ptxas info    : 0 bytes gmem\n",
      "  /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/cuda/_cuda.o build/temp.linux-x86_64-3.6/implicit/cuda/als.o build/temp.linux-x86_64-3.6/implicit/cuda/bpr.o build/temp.linux-x86_64-3.6/implicit/cuda/matrix.o -L/usr/local/cuda-10.1/lib64 -lcudart -lcublas -lcurand -o build/lib.linux-x86_64-3.6/implicit/cuda/_cuda.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "  /home/reisei88/anaconda3/bin/../lib/gcc/x86_64-conda_cos6-linux-gnu/7.5.0/../../../../x86_64-conda_cos6-linux-gnu/bin/ld: cannot find -lcublas\n",
      "  collect2: error: ld returned 1 exit status\n",
      "  error: command '/home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++' failed with exit status 1\n",
      "  ----------------------------------------\u001b[0m\n",
      "\u001b[31m  ERROR: Failed building wheel for implicit\u001b[0m\n",
      "\u001b[?25h  Running setup.py clean for implicit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed to build implicit\n",
      "\u001b[33mDEPRECATION: Could not build wheels for implicit which do not use PEP 517. pip will fall back to legacy 'setup.py install' for these. pip 21.0 will remove support for this functionality. A possible replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/issues/8368.\u001b[0m\n",
      "Installing collected packages: implicit\n",
      "    Running setup.py install for implicit ... \u001b[?25lerror\n",
      "\u001b[31m    ERROR: Command errored out with exit status 1:\n",
      "     command: /home/reisei88/anaconda3/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-u14zlcn5/implicit/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-u14zlcn5/implicit/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-cpse6gm5/install-record.txt --single-version-externally-managed --compile --install-headers /home/reisei88/anaconda3/include/python3.6m/implicit\n",
      "         cwd: /tmp/pip-install-u14zlcn5/implicit/\n",
      "    Complete output (122 lines):\n",
      "    running install\n",
      "    running build\n",
      "    running build_py\n",
      "    creating build\n",
      "    creating build/lib.linux-x86_64-3.6\n",
      "    creating build/lib.linux-x86_64-3.6/implicit\n",
      "    copying implicit/__init__.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "    copying implicit/als.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "    copying implicit/nearest_neighbours.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "    copying implicit/utils.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "    copying implicit/approximate_als.py -> build/lib.linux-x86_64-3.6/implicit\n",
      "    creating build/lib.linux-x86_64-3.6/implicit/cuda\n",
      "    copying implicit/cuda/__init__.py -> build/lib.linux-x86_64-3.6/implicit/cuda\n",
      "    creating build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    copying implicit/datasets/lastfm.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    copying implicit/datasets/__init__.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    copying implicit/datasets/sketchfab.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    copying implicit/datasets/million_song_dataset.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    copying implicit/datasets/_download.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    copying implicit/datasets/movielens.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    copying implicit/datasets/reddit.py -> build/lib.linux-x86_64-3.6/implicit/datasets\n",
      "    running build_ext\n",
      "    building 'implicit._als' extension\n",
      "    creating build/temp.linux-x86_64-3.6\n",
      "    creating build/temp.linux-x86_64-3.6/implicit\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -I/home/reisei88/anaconda3/include/python3.6m -c implicit/_als.cpp -o build/temp.linux-x86_64-3.6/implicit/_als.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/_als.o -o build/lib.linux-x86_64-3.6/implicit/_als.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "    building 'implicit._nearest_neighbours' extension\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/_nearest_neighbours.cpp -o build/temp.linux-x86_64-3.6/implicit/_nearest_neighbours.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/_nearest_neighbours.o -o build/lib.linux-x86_64-3.6/implicit/_nearest_neighbours.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "    building 'implicit.bpr' extension\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/bpr.cpp -o build/temp.linux-x86_64-3.6/implicit/bpr.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/bpr.o -o build/lib.linux-x86_64-3.6/implicit/bpr.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "    building 'implicit.lmf' extension\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -I/home/reisei88/anaconda3/include/python3.6m -c implicit/lmf.cpp -o build/temp.linux-x86_64-3.6/implicit/lmf.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/lmf.o -o build/lib.linux-x86_64-3.6/implicit/lmf.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "    building 'implicit.evaluation' extension\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -I/home/reisei88/anaconda3/include/python3.6m -c implicit/evaluation.cpp -o build/temp.linux-x86_64-3.6/implicit/evaluation.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    implicit/evaluation.cpp: In function 'PyObject* __pyx_pf_8implicit_10evaluation_10ranking_metrics_at_k(PyObject*, PyObject*, PyObject*, PyObject*, int, PyObject*, int)':\n",
      "    implicit/evaluation.cpp:4815:52: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]\n",
      "                                       if (((__pyx_t_26 < __pyx_t_20) != 0)) {\n",
      "                                             ~~~~~~~~~~~^~~~~~~~~~~~\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/evaluation.o -o build/lib.linux-x86_64-3.6/implicit/evaluation.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "    building 'implicit.recommender_base' extension\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/recommender_base.cpp -o build/temp.linux-x86_64-3.6/implicit/recommender_base.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit -I/home/reisei88/anaconda3/include/python3.6m -c implicit/topnc.cpp -o build/temp.linux-x86_64-3.6/implicit/topnc.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/recommender_base.o build/temp.linux-x86_64-3.6/implicit/topnc.o -o build/lib.linux-x86_64-3.6/implicit/recommender_base.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "    building 'implicit.cuda._cuda' extension\n",
      "    creating build/temp.linux-x86_64-3.6/implicit/cuda\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc -DNDEBUG -fwrapv -O2 -Wall -Wstrict-prototypes -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include -fPIC -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/_cuda.cpp -o build/temp.linux-x86_64-3.6/implicit/cuda/_cuda.o -Wno-unused-function -Wno-maybe-uninitialized -O3 -ffast-math -fopenmp -std=c++11\n",
      "    cc1plus: warning: command line option '-Wstrict-prototypes' is valid for C/ObjC but not for C++\n",
      "    /usr/local/cuda-10.1/bin/nvcc -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/als.cu -o build/temp.linux-x86_64-3.6/implicit/cuda/als.o -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 --ptxas-options=-v -O2 -c --compiler-options '-fPIC'\n",
      "    ptxas info    : 38 bytes gmem\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf' for 'sm_30'\n",
      "    ptxas info    : Function properties for _ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 32 registers, 128 bytes smem, 400 bytes cmem[0]\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit20l2_regularize_kernelEifPf' for 'sm_30'\n",
      "    ptxas info    : Function properties for _ZN8implicit20l2_regularize_kernelEifPf\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 5 registers, 336 bytes cmem[0]\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i' for 'sm_30'\n",
      "    ptxas info    : Function properties for _ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i\n",
      "        8 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 38 registers, 128 bytes smem, 388 bytes cmem[0], 24 bytes cmem[2]\n",
      "    ptxas info    : 38 bytes gmem\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf' for 'sm_50'\n",
      "    ptxas info    : Function properties for _ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 32 registers, 128 bytes smem, 400 bytes cmem[0]\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit20l2_regularize_kernelEifPf' for 'sm_50'\n",
      "    ptxas info    : Function properties for _ZN8implicit20l2_regularize_kernelEifPf\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 4 registers, 336 bytes cmem[0]\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i' for 'sm_50'\n",
      "    ptxas info    : Function properties for _ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i\n",
      "        8 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 40 registers, 128 bytes smem, 388 bytes cmem[0], 20 bytes cmem[2]\n",
      "    ptxas info    : 38 bytes gmem\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf' for 'sm_60'\n",
      "    ptxas info    : Function properties for _ZN8implicit21calculate_loss_kernelEiiiPKfS1_S1_PKiS3_S1_fPf\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 32 registers, 128 bytes smem, 400 bytes cmem[0]\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit20l2_regularize_kernelEifPf' for 'sm_60'\n",
      "    ptxas info    : Function properties for _ZN8implicit20l2_regularize_kernelEifPf\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 4 registers, 336 bytes cmem[0]\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i' for 'sm_60'\n",
      "    ptxas info    : Function properties for _ZN8implicit23least_squares_cg_kernelEiiiPfPKfS2_PKiS4_S2_i\n",
      "        8 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 40 registers, 128 bytes smem, 388 bytes cmem[0], 20 bytes cmem[2]\n",
      "    /usr/local/cuda-10.1/bin/nvcc -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/bpr.cu -o build/temp.linux-x86_64-3.6/implicit/cuda/bpr.o -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 --ptxas-options=-v -O2 -c --compiler-options '-fPIC'\n",
      "    ptxas info    : 0 bytes gmem\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_' for 'sm_30'\n",
      "    ptxas info    : Function properties for _ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 25 registers, 144 bytes smem, 416 bytes cmem[0], 32 bytes cmem[2]\n",
      "    ptxas info    : 0 bytes gmem\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_' for 'sm_50'\n",
      "    ptxas info    : Function properties for _ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 29 registers, 144 bytes smem, 416 bytes cmem[0], 32 bytes cmem[2]\n",
      "    ptxas info    : 0 bytes gmem\n",
      "    ptxas info    : Compiling entry function '_ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_' for 'sm_60'\n",
      "    ptxas info    : Function properties for _ZN8implicit17bpr_update_kernelEiPjS0_PiS1_S1_iPfS2_ffbS1_\n",
      "        0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n",
      "    ptxas info    : Used 28 registers, 144 bytes smem, 416 bytes cmem[0], 32 bytes cmem[2]\n",
      "    /usr/local/cuda-10.1/bin/nvcc -Iimplicit/cuda -I/usr/local/cuda-10.1/include -I. -I/home/reisei88/anaconda3/include/python3.6m -c implicit/cuda/matrix.cu -o build/temp.linux-x86_64-3.6/implicit/cuda/matrix.o -gencode=arch=compute_30,code=sm_30 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 --ptxas-options=-v -O2 -c --compiler-options '-fPIC'\n",
      "    ptxas info    : 0 bytes gmem\n",
      "    ptxas info    : 0 bytes gmem\n",
      "    ptxas info    : 0 bytes gmem\n",
      "    /home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++ -pthread -shared -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,-rpath,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/home/reisei88/anaconda3/lib -Wl,-rpath-link,/home/reisei88/anaconda3/lib -L/home/reisei88/anaconda3/lib -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/reisei88/anaconda3/include -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /home/reisei88/anaconda3/include build/temp.linux-x86_64-3.6/implicit/cuda/_cuda.o build/temp.linux-x86_64-3.6/implicit/cuda/als.o build/temp.linux-x86_64-3.6/implicit/cuda/bpr.o build/temp.linux-x86_64-3.6/implicit/cuda/matrix.o -L/usr/local/cuda-10.1/lib64 -lcudart -lcublas -lcurand -o build/lib.linux-x86_64-3.6/implicit/cuda/_cuda.cpython-36m-x86_64-linux-gnu.so -fopenmp -std=c++11\n",
      "    /home/reisei88/anaconda3/bin/../lib/gcc/x86_64-conda_cos6-linux-gnu/7.5.0/../../../../x86_64-conda_cos6-linux-gnu/bin/ld: cannot find -lcublas\n",
      "    collect2: error: ld returned 1 exit status\n",
      "    error: command '/home/reisei88/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++' failed with exit status 1\n",
      "    ----------------------------------------\u001b[0m\n",
      "\u001b[31mERROR: Command errored out with exit status 1: /home/reisei88/anaconda3/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-u14zlcn5/implicit/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-u14zlcn5/implicit/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-cpse6gm5/install-record.txt --single-version-externally-managed --compile --install-headers /home/reisei88/anaconda3/include/python3.6m/implicit Check the logs for full command output.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "# 1) 작업디렉토리 생성\n",
    "! mkdir -p ~/aiffel/recommendata_iu/data\n",
    "\n",
    "# 2) wget으로 데이터 다운로드 (주의) 오래걸립니다. \n",
    "! wget http://mtg.upf.edu/static/datasets/last.fm/lastfm-dataset-360K.tar.gz\n",
    "\n",
    "# 3) 다운받은 데이터를 작업디렉토리로 이동\n",
    "! mv lastfm-dataset-360K.tar.gz ~/aiffel/recommendata_iu/data\n",
    "\n",
    "# 4) gzip으로 압축된 압축을 해제하면 tar 파일이 하나 나옵니다. \n",
    "! gunzip lastfm-dataset-360K.tar.gz\n",
    "\n",
    "# 5) tar 압축을 다시 해제하면 우리가 사용할 최종 데이터 파일이 나옵니다. \n",
    "! tar -xvf lastfm-dataset-360K.tar\n",
    "\n",
    "# 6) 필요 라이브러리 설치\n",
    "! pip install implicit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tar (child): lastfm-dataset-360K.tar.gz: Cannot open: No such file or directory\r\n",
      "tar (child): Error is not recoverable: exiting now\r\n",
      "tar: Child returned status 2\r\n",
      "tar: Error is not recoverable: exiting now\r\n"
     ]
    }
   ],
   "source": [
    "! tar -xvzf lastfm-dataset-360K.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "more: stat of /home/reisei88/aiffel/recommendata_iu/data/lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv failed: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "! more ~/aiffel/recommendata_iu/data/lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 파일 열기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-c14a199e1e1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'~'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/aiffel/recommendata_iu/data/lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mcol_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'artist_MBID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'artist'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'play'\u001b[0m\u001b[0;34m]\u001b[0m   \u001b[0;31m# 임의로 지정한 컬럼명\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\t'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mcol_names\u001b[0m\u001b[0;34m)\u001b[0m      \u001b[0;31m# sep='\\t'로 주어야 tsv를 열 수 있습니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'str'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "fname = os.getenv('~') + '/aiffel/recommendata_iu/data/lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv'\n",
    "col_names = ['user_id', 'artist_MBID', 'artist', 'play']   # 임의로 지정한 컬럼명\n",
    "data = pd.read_csv(fname, sep='\\t', names= col_names)      # sep='\\t'로 주어야 tsv를 열 수 있습니다.  \n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-4956ac6ce49e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 사용하는 컬럼만 남겨줍니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0musing_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'artist'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'play'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0musing_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 사용하는 컬럼만 남겨줍니다.\n",
    "using_cols = ['user_id', 'artist', 'play']\n",
    "data = data[using_cols]\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-4066ea5b3b96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 검색을 쉽게하기 위해 아티스트 문자열을 소문자로 바꿔줍시다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "data['artist'] = data['artist'].str.lower() # 검색을 쉽게하기 위해 아티스트 문자열을 소문자로 바꿔줍시다.\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-74f623ac3198>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcondition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcondition\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "condition = (data['user_id']== data.loc[0, 'user_id'])\n",
    "data.loc[condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-0eaceda2115d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 유저 수\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 유저 수\n",
    "data['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-02e073ad6563>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 아티스트 수\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 아티스트 수\n",
    "data['artist'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-db0f8dfb4af0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 인기 많은 아티스트\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0martist_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0martist_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 인기 많은 아티스트\n",
    "artist_count = data.groupby('artist')['user_id'].count()\n",
    "artist_count.sort_values(ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-02e073ad6563>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 아티스트 수\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 아티스트 수\n",
    "data['artist'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-db0f8dfb4af0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 인기 많은 아티스트\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0martist_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0martist_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 인기 많은 아티스트\n",
    "artist_count = data.groupby('artist')['user_id'].count()\n",
    "artist_count.sort_values(ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-31ed021404ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 유저별 몇 명의 아티스트를 듣고 있는지에 대한 통계\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0muser_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0muser_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 유저별 몇 명의 아티스트를 듣고 있는지에 대한 통계\n",
    "user_count = data.groupby('user_id')['artist'].count()\n",
    "user_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-cd6ead0ae64d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 유저별 play횟수 중앙값에 대한 통계\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0muser_median\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'play'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0muser_median\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 유저별 play횟수 중앙값에 대한 통계\n",
    "user_median = data.groupby('user_id')['play'].median()\n",
    "user_median.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-5ad661e14d31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmy_playlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'zimin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'artist'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmy_favorite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'play'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zimin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# user_id에 'zimin'이라는 데이터가 없다면\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_playlist\u001b[0m\u001b[0;34m)\u001b[0m                           \u001b[0;31m# 위에 임의로 만든 my_favorite 데이터를 추가해 줍니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 본인이 좋아하시는 아티스트 데이터로 바꿔서 추가하셔도 됩니다! 단, 이름은 꼭 데이터셋에 있는 것과 동일하게 맞춰주세요. \n",
    "my_favorite = ['black eyed peas' , 'maroon5' ,'jason mraz' ,'coldplay' ,'beyoncé']\n",
    "\n",
    "# 'zimin'이라는 user_id가 위 아티스트의 노래를 30회씩 들었다고 가정하겠습니다.\n",
    "my_playlist = pd.DataFrame({'user_id': ['zimin']*5, 'artist': my_favorite, 'play':[30]*5})\n",
    "\n",
    "if not data.isin({'user_id':['zimin']})['user_id'].any():  # user_id에 'zimin'이라는 데이터가 없다면\n",
    "    data = data.append(my_playlist)                           # 위에 임의로 만든 my_favorite 데이터를 추가해 줍니다. \n",
    "\n",
    "data.tail(10)       # 잘 추가되었는지 확인해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-fd810f1b0996>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 고유한 유저, 아티스트를 찾아내는 코드\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0muser_unique\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0martist_unique\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# 유저, 아티스트 indexing 하는 코드 idx는 index의 약자입니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 고유한 유저, 아티스트를 찾아내는 코드\n",
    "user_unique = data['user_id'].unique()\n",
    "artist_unique = data['artist'].unique()\n",
    "\n",
    "# 유저, 아티스트 indexing 하는 코드 idx는 index의 약자입니다.\n",
    "user_to_idx = {v:k for k,v in enumerate(user_unique)}\n",
    "artist_to_idx = {v:k for k,v in enumerate(artist_unique)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'user_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-6581f6fc9bbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 인덱싱이 잘 되었는지 확인해 봅니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zimin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m    \u001b[0;31m# 358869명의 유저 중 마지막으로 추가된 유저이니 358868이 나와야 합니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'black eyed peas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'user_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "# 인덱싱이 잘 되었는지 확인해 봅니다. \n",
    "print(user_to_idx['zimin'])    # 358869명의 유저 중 마지막으로 추가된 유저이니 358868이 나와야 합니다. \n",
    "print(artist_to_idx['black eyed peas'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-166d2255afbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# user_to_idx.get을 통해 user_id 컬럼의 모든 값을 인덱싱한 Series를 구해 봅시다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# 혹시 정상적으로 인덱싱되지 않은 row가 있다면 인덱스가 NaN이 될 테니 dropna()로 제거합니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtemp_user_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_to_idx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_user_data\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m   \u001b[0;31m# 모든 row가 정상적으로 인덱싱되었다면\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'user_id column indexing OK!!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# indexing을 통해 데이터 컬럼 내 값을 바꾸는 코드\n",
    "# dictionary 자료형의 get 함수는 https://wikidocs.net/16 을 참고하세요.\n",
    "\n",
    "# user_to_idx.get을 통해 user_id 컬럼의 모든 값을 인덱싱한 Series를 구해 봅시다. \n",
    "# 혹시 정상적으로 인덱싱되지 않은 row가 있다면 인덱스가 NaN이 될 테니 dropna()로 제거합니다. \n",
    "temp_user_data = data['user_id'].map(user_to_idx.get).dropna()\n",
    "if len(temp_user_data) == len(data):   # 모든 row가 정상적으로 인덱싱되었다면\n",
    "    print('user_id column indexing OK!!')\n",
    "    data['user_id'] = temp_user_data   # data['user_id']을 인덱싱된 Series로 교체해 줍니다. \n",
    "else:\n",
    "    print('user_id column indexing Fail!!')\n",
    "\n",
    "# artist_to_idx을 통해 artist 컬럼도 동일한 방식으로 인덱싱해 줍니다. \n",
    "temp_artist_data = data['artist'].map(artist_to_idx.get).dropna()\n",
    "if len(temp_artist_data) == len(data):\n",
    "    print('artist column indexing OK!!')\n",
    "    data['artist'] = temp_artist_data\n",
    "else:\n",
    "    print('artist column indexing Fail!!')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "https://orill.tistory.com/entry/Explicit-vs-Implicit-Feedback-Datasets?category=1066301"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-ba8edbe1fbef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 1회만 play한 데이터의 비율을 보는 코드\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0monly_one\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'play'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monly_one\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{one},{all_data}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Ratio of only_one over all data is {one/all_data:.2%}'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# f-format에 대한 설명은 https://bit.ly/2DTLqYU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# 1회만 play한 데이터의 비율을 보는 코드\n",
    "only_one = data[data['play']<2]\n",
    "one, all_data = len(only_one), len(data)\n",
    "print(f'{one},{all_data}')\n",
    "print(f'Ratio of only_one over all data is {one/all_data:.2%}')  # f-format에 대한 설명은 https://bit.ly/2DTLqYU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "우리가 앞으로 만들어갈 모델에서는 암묵적 데이터의 해석을 위해 다음과 같은 규칙을 적용할 것입니다.\n",
    "\n",
    "한 번이라도 들었으면 선호한다고 판단한다.\n",
    "많이 재생한 아티스트에 대해 가중치를 주어서 더 확실히 좋아한다고 판단한다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7-5. Matrix Factorization(MF)\n",
    "데이터가 준비되었으니 이제 모델을 만들 차례입니다!\n",
    "\n",
    "아래 그림의 왼쪽에서 보는 것처럼, 우리는 m명의 사용자들이 n명의 아티스트에 대해 평가한 데이터를 포함한 (m,n) 사이즈의 평가행렬(Rating Matrix)를 만들 것입니다. 행렬 중 일부는 데이터가 채워져 있지만, 나머지 부분은 데이터가 비어 있습니다. 우리가 앞에서 살펴보았던 추천시스템의 협업 필터링(Collaborative Filtering)이란 결국은 이런 평가행렬을 전제로 하는 것입니다. 만약 우리가 이 평가행렬의 비어 있는 부분을 포함한 완벽한 정보를 얻을 수 만 있다면 (모든 사용자의 모든 아이템에 대한 선호도를 다 파악한 셈이니) 그야말로 완벽한 추천이 가능해질 것입니다.\n",
    "\n",
    "우리는 추천 시스템의 다양한 모델 중 Matrix Factorization(MF, 행렬분해) 모델을 사용하겠습니다. MF 모델은 2006년 Netflix에서 백만달러의 상금을 걸고 개최한 자사 추천시스템의 성능을 10% 이상 향상시키는 챌린지를 계기로 알려지게 되었습니다. 기본 아이디어는 아래 그림과 같습니다. (m,n) 사이즈의 행렬 R을 (m,k) 사이즈의 행렬 P와 (k,n) 사이즈의 행렬 Q로 분해한다면 R이란 그저 P와 Q의 행렬곱으로 표현 가능할 수 있다는 간단한 아이디어입니다. 대체로 k는 m이나 n보다 훨씬 작은 값이기 때문에 계산량 측면으로도 훨씬 유리해집니다. 하지만 아이디어의 단순함에도 불구하고 MF 모델은 성능이 준수하고 Scalability가 좋아서 많이 사용되는 모델입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://arxiv.org/pdf/1610.05838.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아래 그림을 다시한번 봅시다. 위에 소개한 MF 모델의 개요도와 크게 다를 것은 없습니다. 단지, MF 모델을 사용자에게 영화를 추천하는 모델에 대입해서 그려 보았다는 정도의 차이가 있습니다. 위 그림 방식으로 말하자면 m=4, n=5, k=2인 MF 모델이 되겠군요.\n",
    "\n",
    "위에서 MF 모델은 큰 평가행렬 R을 두개의 Feature Matrix P와 Q로 분해한다고 하였습니다.\n",
    "무엇의 Feature일까요? (m, k) 사이즈의 Feature Matrix P는 k 차원의 벡터를 사용자 수만큼 모아놓은 행렬입니다. 그렇다면 아래 그림에서 첫번째 벡터 \n",
    "P\n",
    "0\n",
    "=(1, 0.1)은 바로 빨간 모자를 쓴 첫번째 사용자의 특성(Feature) 벡터가 됩니다.\n",
    "같은 방식으로 Q 행렬의 첫번째 벡터 \n",
    "Q\n",
    "0\n",
    "=(0.9, -0.2)는 해리포터 영화의 특성 벡터가 되겠지요. MF 모델은 이 두 벡터를 내적해서 얻어지는 0.88이 바로 \n",
    "R\n",
    "0\n",
    ",\n",
    "0\n",
    " 으로 정의되는 사용자의 영화 선호도로 보는 모델입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://developers.google.com/machine-learning/recommendation/collaborative/matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델의 목표는 모든 유저와 아이템에 대해 k-dimension의 벡터를 잘 만드는 것입니다.\n",
    "벡터를 잘 만드는 기준은 유저i의 벡터(\n",
    "U\n",
    "i\n",
    ")와 아이템j의 벡터(\n",
    "I\n",
    "j\n",
    ")를 내적했을 때 유저i가 아이템j에 대해 평가한 수치(\n",
    "M\n",
    "i\n",
    "j\n",
    ")와 비슷한지 입니다.\n",
    "U\n",
    "i\n",
    "⋅\n",
    "I\n",
    "j\n",
    "=\n",
    "M\n",
    "i\n",
    "j\n",
    "MF에도 다양한 변형이 있습니다. 이번에 사용할 모델은"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://yifanhu.net/PUB/cf.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "논문에서 제안한 모델을 사용할 것입니다.\n",
    "\n",
    "이 모델의 경우 이전 스텝에서 제가 'black eyed peas'를 play했다는 데이터를 추가했기 때문에 저의 벡터와 black eyed peas의 벡터를 곱했을 때 1에 가까워야 모델이 잘 학습하는 것을 목표로 합니다.\n",
    "\n",
    "오해할 수 있는데, 제가 black eyed peas를 5번 들었어도 모델이 근사하고자 하는 것이 아티스트를 선호하는지 안 하는지를 맞추는 것이기 때문에 두 벡터를 곱했을 때 5에 가까워지는 대신 1에 가까워져야 합니다. 이를 통해서 제가 들어보지 못한 queen에 대해서도 저의 벡터와 queen의 벡터를 곱하여 수치를 예상할 수 있습니다.\n",
    "\n",
    "만약 목표가 유저의 재생횟수를 맞춰야 하는 것(혹은 별점을 맞추는 것)이라면 그에 맞는 다른 모델을 사용해야 합니다.\n",
    "\n",
    "구조가 거의 똑같고 모델의 목표가 재생횟수를 맞춰지는 쪽으로 학습(유저와 아이템의 벡터를 만들기)하면 됩니다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://towardsdatascience.com/recommendation-system-matrix-factorization-d61978660b4b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7-6. CSR(Compressed Sparse Row) Matrix\n",
    "\n",
    "\n",
    "유저 X 아이템 평가행렬을 다시 생각해 봅시다. 유저는 36만 명이고 아티스트는 29만 명입니다. 이를 행렬로 표현하고 행렬의 각 원소에 정수 한 개 (1byte)가 들어간다면 36만 * 29만 * 1byte \n",
    "≈\n",
    " 97GB가 필요합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "참고로 여러분들이 사용하고 계실 컴퓨터의 메모리는 많아야 16GB일 테니 97GB나 되는 거대한 행렬을 메모리에 올려 놓고 작업한다는 것은 불가능할 것입니다. 이런 경우의 좋은 대안이 되는 것이 CSR(Compressed Sparse Row) Matrix입니다. 다음 스텝에서 수행할 모델 학습의 input으로 사용할 데이터 타입을 CSR Matrix로 할 것입니다.\n",
    "CSR Matrix는 Sparse한 matrix에서 0이 아닌 유효한 데이터로 채워지는 데이터의 값과 좌표 정보로만으로 구성하여 메모리 사용량을 최소화하면서도 Sparse한 matrix와 동일한 행렬을 표현할 수 있도록 하는 데이터 구조입니다. CSR에 대한 자세한 설명은 다음 링크를 참고해 주세요. 혹시 설명이 부족하면 그 아래 StackOverflow의 설명이 보완적인 역할을 해줄 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://lovit.github.io/nlp/machine%20learning/2018/04/09/sparse_mtarix_handling/#csr-matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/53254104/cant-understand-scipy-sparse-csr-matrix-example/62118005#62118005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7-7. MF 모델 학습하기\n",
    "이전 스텝에서 설명한 Matrix Factorization 모델을 implicit 패키지를 사용하여 학습해 봅시다.\n",
    "\n",
    "implicit 패키지는 이전 스텝에서 설명한 암묵적(implicit) dataset을 사용하는 다양한 모델을 굉장히 빠르게 학습할 수 있는 패키지입니다.\n",
    "이 패키지에 구현된 als(AlternatingLeastSquares) 모델을 사용하겠습니다. Matrix Factorization에서 쪼개진 두 Feature Matrix를 한꺼번에 훈련하는 것은 잘 수렴하지 않기 때문에, 한쪽을 고정시키고 다른 쪽을 학습하는 방식을 번갈아 수행하는 AlternatingLeastSquares 방식이 효과적인 것으로 알려져 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'implicit'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-8d632a8df206>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mimplicit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mals\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAlternatingLeastSquares\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# implicit 라이브러리에서 권장하고 있는 부분입니다. 학습 내용과는 무관합니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'implicit'"
     ]
    }
   ],
   "source": [
    "from implicit.als import AlternatingLeastSquares\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# implicit 라이브러리에서 권장하고 있는 부분입니다. 학습 내용과는 무관합니다.\n",
    "os.environ['OPENBLAS_NUM_THREADS']='1'\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "os.environ['MKL_NUM_THREADS']='1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'AlternatingLeastSquares' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-a7583ff360c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Implicit AlternatingLeastSquares 모델의 선언\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mals_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAlternatingLeastSquares\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfactors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregularization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_gpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'AlternatingLeastSquares' is not defined"
     ]
    }
   ],
   "source": [
    "# Implicit AlternatingLeastSquares 모델의 선언\n",
    "als_model = AlternatingLeastSquares(factors=100, regularization=0.01, use_gpu=False, iterations=15, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'AlternatingLeastSquares' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-a7583ff360c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Implicit AlternatingLeastSquares 모델의 선언\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mals_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAlternatingLeastSquares\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfactors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregularization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_gpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'AlternatingLeastSquares' is not defined"
     ]
    }
   ],
   "source": [
    "# Implicit AlternatingLeastSquares 모델의 선언\n",
    "als_model = AlternatingLeastSquares(factors=100, regularization=0.01, use_gpu=False, iterations=15, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'csr_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-efaf3390bbd4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# als 모델은 input으로 (item X user 꼴의 matrix를 받기 때문에 Transpose해줍니다.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcsr_data_transpose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsr_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcsr_data_transpose\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'csr_data' is not defined"
     ]
    }
   ],
   "source": [
    "# als 모델은 input으로 (item X user 꼴의 matrix를 받기 때문에 Transpose해줍니다.)\n",
    "csr_data_transpose = csr_data.T\n",
    "csr_data_transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'als_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-4d3b0eec9961>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 모델 훈련\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsr_data_transpose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'als_model' is not defined"
     ]
    }
   ],
   "source": [
    "# 모델 훈련\n",
    "als_model.fit(csr_data_transpose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'user_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-4d9075035a1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mzimin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblack_eyed_peas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muser_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zimin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martist_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'black eyed peas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mzimin_vector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblack_eyed_peas_vector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_factors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mzimin\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_factors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mblack_eyed_peas\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'슝=3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'user_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "zimin, black_eyed_peas = user_to_idx['zimin'], artist_to_idx['black eyed peas']\n",
    "zimin_vector, black_eyed_peas_vector = als_model.user_factors[zimin], als_model.item_factors[black_eyed_peas]\n",
    "\n",
    "print('슝=3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'zimin_vector' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-3dad8c69e6be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mzimin_vector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'zimin_vector' is not defined"
     ]
    }
   ],
   "source": [
    "zimin_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'black_eyed_peas_vector' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-c1171ecff5d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mblack_eyed_peas_vector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'black_eyed_peas_vector' is not defined"
     ]
    }
   ],
   "source": [
    "black_eyed_peas_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-b02d24aa5bab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# zimin과 black_eyed_peas를 내적하는 코드\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzimin_vector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblack_eyed_peas_vector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "# zimin과 black_eyed_peas를 내적하는 코드\n",
    "np.dot(zimin_vector, black_eyed_peas_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artist_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-9a08dd7aa196>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mqueen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0martist_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'queen'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mqueen_vector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_factors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mqueen\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzimin_vector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqueen_vector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'artist_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "queen = artist_to_idx['queen']\n",
    "queen_vector = als_model.item_factors[queen]\n",
    "np.dot(zimin_vector, queen_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7-8. 비슷한 아티스트 찾기 + 유저에게 추천하기\n",
    "비슷한 아티스트 찾기\n",
    "AlternatingLeastSquares 클래스에 구현되어 있는 similar_items 메서드를 통하여 비슷한 아티스트를 찾습니다. 처음으로는 제가 좋아하는 coldplay로 찾아보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artist_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-b215531d34ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfavorite_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'coldplay'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0martist_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0martist_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfavorite_artist\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0msimilar_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilar_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msimilar_artist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'artist_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "favorite_artist = 'coldplay'\n",
    "artist_id = artist_to_idx[favorite_artist]\n",
    "similar_artist = als_model.similar_items(artist_id, N=15)\n",
    "similar_artist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(아티스트의 id, 유사도) Tuple 로 반환하고 있습니다. 아티스트의 id를 다시 아티스트의 이름으로 매핑시켜 주겠습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artist_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-eb1979f33a94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#artist_to_idx 를 뒤집어, index로부터 artist 이름을 얻는 dict를 생성합니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0midx_to_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mk\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martist_to_idx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0midx_to_artist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msimilar_artist\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'artist_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "#artist_to_idx 를 뒤집어, index로부터 artist 이름을 얻는 dict를 생성합니다. \n",
    "idx_to_artist = {v:k for k,v in artist_to_idx.items()}\n",
    "[idx_to_artist[i[0]] for i in similar_artist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "몇 번 더 반복해서 확인하기 위해 위의 코드를 함수로 만들고 확인해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "슝=3\n"
     ]
    }
   ],
   "source": [
    "def get_similar_artist(artist_name: str):\n",
    "    artist_id = artist_to_idx[artist_name]\n",
    "    similar_artist = als_model.similar_items(artist_id)\n",
    "    similar_artist = [idx_to_artist[i[0]] for i in similar_artist]\n",
    "    return similar_artist\n",
    "\n",
    "print(\"슝=3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "저는 힙합도 좋아하기 때문에 2pac으로도 확인해 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artist_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-7a1f42126620>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_similar_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2pac'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-35-b4aad4c46de7>\u001b[0m in \u001b[0;36mget_similar_artist\u001b[0;34m(artist_name)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_similar_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0martist_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0martist_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0martist_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0msimilar_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilar_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0msimilar_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0midx_to_artist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msimilar_artist\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msimilar_artist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'artist_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "get_similar_artist('2pac')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "우와! 스눕독, 닥터 드레, 노토리우스, 나스, 제이지 같은 전설들이 추천됩니다. 힙합의 경우 마니아들이 많아서 더 잘 추천되는 것으로 생각됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artist_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-69a3ff989450>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_similar_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lady gaga'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-35-b4aad4c46de7>\u001b[0m in \u001b[0;36mget_similar_artist\u001b[0;34m(artist_name)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_similar_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0martist_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0martist_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0martist_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0msimilar_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimilar_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0msimilar_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0midx_to_artist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msimilar_artist\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msimilar_artist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'artist_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "get_similar_artist('lady gaga')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "lady gaga의 경우 여자 아티스트들이 추천되고 있습니다.\n",
    "\n",
    "유저에게 아티스트 추천하기\n",
    "AlternatingLeastSquares 클래스에 구현되어 있는 recommend 메서드를 통하여 제가 좋아할 만한 아티스트를 추천 받습니다. filter_already_liked_items 는 유저가 이미 평가한 아이템은 제외하는 Argument입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'user_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-e3be7590529d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muser_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zimin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# recommend에서는 user*item CSR Matrix를 받습니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0martist_recommended\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecommend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcsr_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter_already_liked_items\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0martist_recommended\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'user_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "user = user_to_idx['zimin']\n",
    "# recommend에서는 user*item CSR Matrix를 받습니다.\n",
    "artist_recommended = als_model.recommend(user, csr_data, N=20, filter_already_liked_items=True)\n",
    "artist_recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artist_recommended' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-da74b7c781ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0midx_to_artist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martist_recommended\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'artist_recommended' is not defined"
     ]
    }
   ],
   "source": [
    "[idx_to_artist[i[0]] for i in artist_recommended]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rihanna를 추천해주고 있네요. 모델은 왜 rihanna를 추천해줬을까요? AlternatingLeastSquares 클래스에 구현된 explain 메소드를 사용하면 제가 기록을 남긴 데이터 중 이 추천에 기여한 정도를 확인할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artist_to_idx' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-0d618723e152>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrihanna\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0martist_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rihanna'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mexplain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mals_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcsr_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitemid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrihanna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'artist_to_idx' is not defined"
     ]
    }
   ],
   "source": [
    "rihanna = artist_to_idx['rihanna']\n",
    "explain = als_model.explain(user, csr_data, itemid=rihanna)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 method는 추천한 콘텐츠의 점수에 기여한 다른 콘텐츠와 기여도(합이 콘텐츠의 점수가 됩니다.)를 반환합니다. 어떤 아티스트들이 이 추천에 얼마나 기여하고 있는 걸까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'explain' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-80a0b6b45835>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx_to_artist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexplain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'explain' is not defined"
     ]
    }
   ],
   "source": [
    "[(idx_to_artist[i[0]], i[1]) for i in explain[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "beyoncé와 black eyed peas가 가장 크게 기여 했네요. 둘 다 흑인 아티스트이고 beyoncé의 경우는 같은 성별이라는 점도 크게 작용하지 않았을까 싶습니다.\n",
    "\n",
    "눈치채신 분도 있겠지만 결과가 조금 이상합니다. filter_already_liked_items=True로 했는데 앞에 maroon 5는 제가 이미 평가한 아이템이 나오고 있습니다.이것은 implicit 버전 0.4.2에서 생긴 버그입니다. 코드는 사람이 만들기 때문에 이렇게 실수할 가능성이 있습니다.\n",
    "오픈 소스 생태계에서는 누구든 이런 문제점을 지적하고 Pull Request를 통하여 수정을 제안할 수 있습니다.\n",
    "많이들 사용하는 Tensorflow나 numpy, pandas도 여러 작은 버그들이 있습니다. 이런 유명한 패키지의 오류를 고쳐서 Pull Request를 하여 Contribute 한다면 좋은 이력이 되기도 합니다.\n",
    "\n",
    "모델이 추천한 20개 중 몇 개를 실제로 선호하는지 계산하여 모델의 객관적인 지표를 만들 수 있을 것 같습니다. 자세한 방법은 이쪽을 참고해주세요.\n",
    "\n",
    "마무리\n",
    "후 고생 많으셨습니다!\n",
    "추천시스템에서 Baseline으로 많이 사용되는 MF를 통해 유저에게 아티스트를 추천해보았습니다. 이제 여러분은 추천시스템 관련 task에서 기본 모델을 만드실 수 있을 겁니다.\n",
    "\n",
    "그러나 우리가 만든 모델은 몇 가지 아쉬운 점이 있습니다.\n",
    "\n",
    "유저, 아티스트에 대한 Meta 정보를 반영하기 쉽지 않습니다. 연령대별로 음악취향이 굉장히 다르지 않을까요?\n",
    "유저가 언제 play 했는지에 반영하기 쉽지 않습니다. 10년 전에 즐겨듣던 아티스트와 지금 즐겨듣는 아티스트를 비교해보세요.\n",
    "이러한 이유와 딥러닝의 발전으로 MF 이외의 모델 구조도 많이 연구/ 사용되고 있습니다. 하지만 어떤 추천 모델도 핵심은 MF와 비슷합니다.\n",
    "유저와 아이템에 대한 벡터를 잘 학습하여 취향에 맞게(유저에 맞게) 아이템을 보여주거나(Retrieval) 걸러내는(Filtering) 역할입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 프로젝트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이전 스텝에서 배운 MF 모델 학습 방법을 토대로, 내가 좋아할만한 영화 추천 시스템을 제작해 보겠습니다.\n",
    "이번에 활용할 데이터셋은 추천시스템의 MNIST라고 부를만한 Movielens 데이터입니다.\n",
    "\n",
    "유저가 영화에 대해 평점을 매긴 데이터가 데이터 크기 별로 있습니다. MovieLens 1M Dataset 사용을 권장합니다.\n",
    "별점 데이터는 대표적인 explicit 데이터입니다. 하지만 implicit 데이터로 간주하고 테스트해볼 수 있습니다.\n",
    "별점을 시청횟수로 해석해서 생각하겠습니다.\n",
    "또한 유저가 3점 미만으로 준 데이터는 선호하지 않는다고 가정하고 제외하겠습니다.\n",
    "MovieLens 1M Dataset 데이터셋의 경우는 크기가 작아서 아래와 같이 직접 다운 받고 압축해제 하겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) wget으로 데이터 다운로드\n",
    "! wget http://files.grouplens.org/datasets/movielens/ml-1m.zip\n",
    "\n",
    "2) 다운받은 데이터를 작업디렉토리로 이동\n",
    "! mv ml-1m.zip ~/aiffel/recommendata_iu/data\n",
    "\n",
    "3) gzip으로 압축된 압축을 해제하면 tar 파일이 하나 나옵니다. \n",
    "! unzip ml-1m.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/reisei88/aiffel/recommendata_iu/data/ml-1m/ratings.dat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-886907b249f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mrating_file_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'HOME'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/aiffel/recommendata_iu/data/ml-1m/ratings.dat'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mratings_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'movie_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rating'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mratings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrating_file_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'::'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mratings_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'python'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0morginal_data_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mratings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1022\u001b[0m                                  ' \"c\", \"python\", or' ' \"python-fwf\")'.format(\n\u001b[1;32m   1023\u001b[0m                                      engine=engine))\n\u001b[0;32m-> 1024\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, **kwds)\u001b[0m\n\u001b[1;32m   2075\u001b[0m         f, handles = _get_handle(f, mode, encoding=self.encoding,\n\u001b[1;32m   2076\u001b[0m                                  \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompression\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2077\u001b[0;31m                                  memory_map=self.memory_map)\n\u001b[0m\u001b[1;32m   2078\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2079\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# Python 3 and no explicit encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'replace'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m             \u001b[0;31m# Python 3 and binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/reisei88/aiffel/recommendata_iu/data/ml-1m/ratings.dat'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "rating_file_path=os.getenv('HOME') + '/aiffel/recommendata_iu/data/ml-1m/ratings.dat'\n",
    "ratings_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_csv(rating_file_path, sep='::', names=ratings_cols, engine='python')\n",
    "orginal_data_size = len(ratings)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ratings' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-f196e6e01404>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 3점 이상만 남깁니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mratings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mratings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mratings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rating'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mfiltered_data_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'orginal_data_size: {orginal_data_size}, filtered_data_size: {filtered_data_size}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ratings' is not defined"
     ]
    }
   ],
   "source": [
    "# 3점 이상만 남깁니다.\n",
    "ratings = ratings[ratings['rating']>=3]\n",
    "filtered_data_size = len(ratings)\n",
    "\n",
    "print(f'orginal_data_size: {orginal_data_size}, filtered_data_size: {filtered_data_size}')\n",
    "print(f'Ratio of Remaining Data is {filtered_data_size / orginal_data_size:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ratings' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-742393b2ca78>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# rating 컬럼의 이름을 count로 바꿉니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mratings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'rating'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'ratings' is not defined"
     ]
    }
   ],
   "source": [
    "# rating 컬럼의 이름을 count로 바꿉니다.\n",
    "ratings.rename(columns={'rating':'count'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/reisei88/aiffel/recommendata_iu/data/ml-1m/movies.dat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-ed1f2f40b6d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmovie_file_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'HOME'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/aiffel/recommendata_iu/data/ml-1m/movies.dat'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'movie_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'title'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'genre'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmovies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmovie_file_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'::'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'python'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mmovies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1022\u001b[0m                                  ' \"c\", \"python\", or' ' \"python-fwf\")'.format(\n\u001b[1;32m   1023\u001b[0m                                      engine=engine))\n\u001b[0;32m-> 1024\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, **kwds)\u001b[0m\n\u001b[1;32m   2075\u001b[0m         f, handles = _get_handle(f, mode, encoding=self.encoding,\n\u001b[1;32m   2076\u001b[0m                                  \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompression\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2077\u001b[0;31m                                  memory_map=self.memory_map)\n\u001b[0m\u001b[1;32m   2078\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2079\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# Python 3 and no explicit encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'replace'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m             \u001b[0;31m# Python 3 and binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/reisei88/aiffel/recommendata_iu/data/ml-1m/movies.dat'"
     ]
    }
   ],
   "source": [
    "# 영화 제목을 보기 위해 메타 데이터를 읽어옵니다.\n",
    "movie_file_path=os.getenv('HOME') + '/aiffel/recommendata_iu/data/ml-1m/movies.dat'\n",
    "cols = ['movie_id', 'title', 'genre'] \n",
    "movies = pd.read_csv(movie_file_path, sep='::', names=cols, engine='python')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) 분석해 봅시다.\n",
    "\n",
    "- ratings에 있는 유니크한 영화 개수\n",
    "- rating에 있는 유니크한 사용자 수\n",
    "- 가장 인기있는 영화 30개(인기순)\n",
    "\n",
    "3) 내가 선호하는 영화를 5가지 골라서 rating에 추가해 줍시다.\n",
    "\n",
    "4) CSR matrix를 직접 만들어 봅시다.\n",
    "\n",
    "5) als_model = AlternatingLeastSquares 모델을 직접 구성하여 훈련시켜 봅시다.\n",
    "\n",
    "6) 내가 선호하는 5가지 영화 중 하나와 그 외의 영화 하나를 골라 훈련된 모델이 예측한 나의 선호도를 파악해 보세요.\n",
    "\n",
    "7) 내가 좋아하는 영화와 비슷한 영화를 추천받아 봅시다.\n",
    "\n",
    "8) 내가 가장 좋아할 만한 영화들을 추천받아 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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