diff --git a/test.ipynb b/test.ipynb index 138c081..a919fa7 100644 --- a/test.ipynb +++ b/test.ipynb @@ -2,84 +2,203 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 46, "source": [ "# This Notebook is created with VS Code on Windows\r\n", "# Create python virtual environment\r\n", - "#!python -m venv .venv\r\n", + "!python -m venv .venv\r\n", "# If you want to use it on macOS/Linux\r\n", "# You may need to run sudo apt-get install python3-venv first\r\n", "#python3 -m venv .venv\r\n", "\r\n", "# Install Python Packages\r\n", "!pip install --user --upgrade pip\r\n", + "!pip install --upgrade setuptools\r\n", "!pip install --user seaborn\r\n", "!pip install --user numpy\r\n", "!pip install --user pandas\r\n", "!pip install --user matplotlib\r\n", "!pip install --user plotly\r\n", - "!pip install --user nbformat\r\n" + "!pip install --user nbformat\r\n", + "!pip install --user surprise\r\n" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Requirement already satisfied: pip in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (21.2.4)\n", + "Requirement already satisfied: pip in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (21.2.4)\n", "Requirement already satisfied: seaborn in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (0.11.2)\n", "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (3.3.4)\n", - "Requirement already satisfied: numpy>=1.15 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (1.20.1)\n", + "Requirement already satisfied: numpy>=1.15 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (1.19.5)\n", "Requirement already satisfied: scipy>=1.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (1.6.1)\n", "Requirement already satisfied: pandas>=0.23 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from seaborn) (1.2.2)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n", - "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n", "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (8.1.0)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib>=2.2->seaborn) (2.4.6)\n", - "Requirement already satisfied: six in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.15.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n", + "Requirement already satisfied: six in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.15.0)\n", "Requirement already satisfied: pytz>=2017.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.1)\n", - "Requirement already satisfied: numpy in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (1.20.1)\n", + "Requirement already satisfied: numpy in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (1.19.5)\n", "Requirement already satisfied: pandas in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (1.2.2)\n", - "Requirement already satisfied: numpy>=1.16.5 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas) (1.20.1)\n", "Requirement already satisfied: pytz>=2017.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas) (2021.1)\n", - "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas) (2.8.1)\n", - "Requirement already satisfied: six>=1.5 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from pandas) (2.8.1)\n", + "Requirement already satisfied: numpy>=1.16.5 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from pandas) (1.19.5)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n", "Requirement already satisfied: matplotlib in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (3.3.4)\n", - "Requirement already satisfied: numpy>=1.15 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.20.1)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (2.4.6)\n", - "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (2.8.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.3.1)\n", - "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (8.1.0)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (0.10.0)\n", - "Requirement already satisfied: six in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.3.1)\n", + "Requirement already satisfied: numpy>=1.15 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (1.19.5)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib) (2.8.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from matplotlib) (8.1.0)\n", + "Requirement already satisfied: six in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n", "Requirement already satisfied: plotly in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (5.3.0)\n", - "Requirement already satisfied: six in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from plotly) (1.15.0)\n", + "Requirement already satisfied: six in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from plotly) (1.15.0)\n", "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from plotly) (8.0.1)\n", - "Collecting nbformat\n", - " Downloading nbformat-5.1.3-py3-none-any.whl (178 kB)\n", - "Requirement already satisfied: ipython-genutils in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (0.2.0)\n", + "Requirement already satisfied: nbformat in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (5.1.3)\n", "Requirement already satisfied: traitlets>=4.1 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (5.0.5)\n", - "Collecting jsonschema!=2.5.0,>=2.4\n", - " Downloading jsonschema-3.2.0-py2.py3-none-any.whl (56 kB)\n", + "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (3.2.0)\n", + "Requirement already satisfied: ipython-genutils in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (0.2.0)\n", "Requirement already satisfied: jupyter-core in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from nbformat) (4.7.1)\n", - "Requirement already satisfied: six>=1.11.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (1.15.0)\n", - "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (19.3.0)\n", + "Requirement already satisfied: six>=1.11.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (1.15.0)\n", "Requirement already satisfied: setuptools in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (49.2.1)\n", - "Collecting pyrsistent>=0.14.0\n", - " Downloading pyrsistent-0.18.0-cp38-cp38-win_amd64.whl (62 kB)\n", + "Requirement already satisfied: pyrsistent>=0.14.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (0.18.0)\n", + "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat) (21.2.0)\n", "Requirement already satisfied: pywin32>=1.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from jupyter-core->nbformat) (300)\n", - "Installing collected packages: pyrsistent, jsonschema, nbformat\n", - "Successfully installed jsonschema-3.2.0 nbformat-5.1.3 pyrsistent-0.18.0\n" + "Collecting surprise\n", + " Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n", + "Collecting scikit-surprise\n", + " Downloading scikit-surprise-1.1.1.tar.gz (11.8 MB)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-surprise->surprise) (1.0.1)\n", + "Requirement already satisfied: numpy>=1.11.2 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-surprise->surprise) (1.19.5)\n", + "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-surprise->surprise) (1.6.1)\n", + "Requirement already satisfied: six>=1.10.0 in c:\\users\\oli\\appdata\\roaming\\python\\python38\\site-packages (from scikit-surprise->surprise) (1.15.0)\n", + "Building wheels for collected packages: scikit-surprise\n", + " Building wheel for scikit-surprise (setup.py): started\n", + " Building wheel for scikit-surprise (setup.py): finished with status 'error'\n", + " Running setup.py clean for scikit-surprise\n", + "Failed to build scikit-surprise\n", + "Installing collected packages: scikit-surprise, surprise\n", + " Running setup.py install for scikit-surprise: started\n", + " Running setup.py install for scikit-surprise: finished with status 'error'\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ - " WARNING: The script jsonschema.exe is installed in 'C:\\Users\\Oli\\AppData\\Roaming\\Python\\Python38\\Scripts' which is not on PATH.\n", - " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n", - " WARNING: The script jupyter-trust.exe is installed in 'C:\\Users\\Oli\\AppData\\Roaming\\Python\\Python38\\Scripts' which is not on PATH.\n", - " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n" + " ERROR: Command errored out with exit status 1:\n", + " command: 'c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d 'C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-wheel-dz4_1ymq'\n", + " cwd: C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-install-jibnz200\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\n", + " Complete output (49 lines):\n", + " running bdist_wheel\n", + " running build\n", + " running build_py\n", + " creating build\n", + " creating build\\lib.win-amd64-3.8\n", + " creating build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\accuracy.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\builtin_datasets.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\dataset.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\dump.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\reader.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\trainset.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\utils.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\__main__.py -> build\\lib.win-amd64-3.8\\surprise\n", + " creating build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\search.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\split.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\validation.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " creating build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\algo_base.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\baseline_only.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\knns.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\predictions.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\random_pred.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " running egg_info\n", + " writing scikit_surprise.egg-info\\PKG-INFO\n", + " writing dependency_links to scikit_surprise.egg-info\\dependency_links.txt\n", + " writing entry points to scikit_surprise.egg-info\\entry_points.txt\n", + " writing requirements to scikit_surprise.egg-info\\requires.txt\n", + " writing top-level names to scikit_surprise.egg-info\\top_level.txt\n", + " reading manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " writing manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n", + " copying surprise\\similarities.c -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\similarities.pyx -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\prediction_algorithms\\co_clustering.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\matrix_factorization.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\optimize_baselines.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\slope_one.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\co_clustering.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\matrix_factorization.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\optimize_baselines.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\slope_one.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " running build_ext\n", + " building 'surprise.similarities' extension\n", + " error: Microsoft Visual C++ 14.0 is required. Get it with \"Build Tools for Visual Studio\": https://visualstudio.microsoft.com/downloads/\n", + " ----------------------------------------\n", + " ERROR: Failed building wheel for scikit-surprise\n", + " ERROR: Command errored out with exit status 1:\n", + " command: 'c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-record-ivcio2vz\\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\\Users\\Oli\\AppData\\Roaming\\Python\\Python38\\Include\\scikit-surprise'\n", + " cwd: C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-install-jibnz200\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\n", + " Complete output (49 lines):\n", + " running install\n", + " running build\n", + " running build_py\n", + " creating build\n", + " creating build\\lib.win-amd64-3.8\n", + " creating build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\accuracy.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\builtin_datasets.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\dataset.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\dump.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\reader.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\trainset.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\utils.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\__main__.py -> build\\lib.win-amd64-3.8\\surprise\n", + " creating build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\search.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\split.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\validation.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " copying surprise\\model_selection\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\model_selection\n", + " creating build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\algo_base.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\baseline_only.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\knns.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\predictions.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\random_pred.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\__init__.py -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " running egg_info\n", + " writing scikit_surprise.egg-info\\PKG-INFO\n", + " writing dependency_links to scikit_surprise.egg-info\\dependency_links.txt\n", + " writing entry points to scikit_surprise.egg-info\\entry_points.txt\n", + " writing requirements to scikit_surprise.egg-info\\requires.txt\n", + " writing top-level names to scikit_surprise.egg-info\\top_level.txt\n", + " reading manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n", + " reading manifest template 'MANIFEST.in'\n", + " writing manifest file 'scikit_surprise.egg-info\\SOURCES.txt'\n", + " copying surprise\\similarities.c -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\similarities.pyx -> build\\lib.win-amd64-3.8\\surprise\n", + " copying surprise\\prediction_algorithms\\co_clustering.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\matrix_factorization.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\optimize_baselines.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\slope_one.c -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\co_clustering.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\matrix_factorization.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\optimize_baselines.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " copying surprise\\prediction_algorithms\\slope_one.pyx -> build\\lib.win-amd64-3.8\\surprise\\prediction_algorithms\n", + " running build_ext\n", + " building 'surprise.similarities' extension\n", + " error: Microsoft Visual C++ 14.0 is required. Get it with \"Build Tools for Visual Studio\": https://visualstudio.microsoft.com/downloads/\n", + " ----------------------------------------\n", + "ERROR: Command errored out with exit status 1: 'c:\\users\\oli\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Oli\\\\AppData\\\\Local\\\\Temp\\\\pip-install-jibnz200\\\\scikit-surprise_07dc192d3cf347769fae216d5b05a944\\\\setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\Oli\\AppData\\Local\\Temp\\pip-record-ivcio2vz\\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\\Users\\Oli\\AppData\\Roaming\\Python\\Python38\\Include\\scikit-surprise' Check the logs for full command output.\n" ] } ], @@ -87,10 +206,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "source": [ - "import numpy as np # linear algebra\r\n", - "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\r\n", + "import numpy as np # maths\r\n", + "import pandas as pd # data processing\r\n", "import matplotlib.pyplot as plt\r\n", "import seaborn as sns\r\n", "import os\r\n", @@ -137,12 +256,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "source": [ "# Import Tables\r\n", - "reviews = pd.read_csv('./ml-latest-small/ratings.csv', names=['userId', 'movieId', 'rating', 'timestamp'], delimiter=',', engine='python')\r\n", - "movies = pd.read_csv('./ml-latest-small/movies.csv', names=['movieId', 'title', 'genres'], delimiter=',', engine='python')\r\n", - "users = pd.read_csv('./ml-latest-small/users.csv', names=['userId', 'gender', 'age', 'occupation', 'zip'], delimiter='::', engine='python')\r\n", + "reviews = pd.read_csv('./ml-1m/ratings.dat', names=['userId', 'movieId', 'rating', 'timestamp'], delimiter='::', engine='python')\r\n", + "movies = pd.read_csv('./ml-1m/movies.dat', names=['movieId', 'title', 'genres'], delimiter='::', engine='python')\r\n", + "users = pd.read_csv('./ml-1m/users.dat', names=['userId', 'gender', 'age', 'occupation', 'zip'], delimiter='::', engine='python')\r\n", "\r\n", "# Print Table shape\r\n", "print('Reviews shape:', reviews.shape)\r\n", @@ -154,9 +273,9 @@ "output_type": "stream", "name": "stdout", "text": [ - "Reviews shape: (100836, 4)\n", - "Users shape: (610, 5)\n", - "Movies shape: (9742, 3)\n" + "Reviews shape: (1000209, 4)\n", + "Users shape: (6040, 5)\n", + "Movies shape: (3883, 3)\n" ] } ], @@ -164,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "source": [ "# Drop unused Attributes\r\n", "reviews.drop(['timestamp'], axis=1, inplace=True) # Time\r\n", @@ -178,9 +297,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "source": [ - "# print movie table\r\n", + "# Print movie table\r\n", "movies.head()" ], "outputs": [ @@ -188,19 +307,19 @@ "output_type": "execute_result", "data": { "text/plain": [ - " movieId title \\\n", - "0 1 Toy Story (1995) \n", - "1 2 Jumanji (1995) \n", - "2 3 Grumpier Old Men (1995) \n", - "3 4 Waiting to Exhale (1995) \n", - "4 5 Father of the Bride Part II (1995) \n", + " movieId title genres \\\n", + "0 1 Toy Story (1995) Animation|Children's|Comedy \n", + "1 2 Jumanji (1995) Adventure|Children's|Fantasy \n", + "2 3 Grumpier Old Men (1995) Comedy|Romance \n", + "3 4 Waiting to Exhale (1995) Comedy|Drama \n", + "4 5 Father of the Bride Part II (1995) Comedy \n", "\n", - " genres release_year \n", - "0 Adventure|Animation|Children|Comedy|Fantasy 1995 \n", - "1 Adventure|Children|Fantasy 1995 \n", - "2 Comedy|Romance 1995 \n", - "3 Comedy|Drama|Romance 1995 \n", - "4 Comedy 1995 " + " release_year \n", + "0 1995 \n", + "1 1995 \n", + "2 1995 \n", + "3 1995 \n", + "4 1995 " ], "text/html": [ "
\n", @@ -232,14 +351,14 @@ " 0\n", " 1\n", " Toy Story (1995)\n", - " Adventure|Animation|Children|Comedy|Fantasy\n", + " Animation|Children's|Comedy\n", " 1995\n", " \n", " \n", " 1\n", " 2\n", " Jumanji (1995)\n", - " Adventure|Children|Fantasy\n", + " Adventure|Children's|Fantasy\n", " 1995\n", " \n", " \n", @@ -253,7 +372,7 @@ " 3\n", " 4\n", " Waiting to Exhale (1995)\n", - " Comedy|Drama|Romance\n", + " Comedy|Drama\n", " 1995\n", " \n", " \n", @@ -269,16 +388,16 @@ ] }, "metadata": {}, - "execution_count": 26 + "execution_count": 6 } ], "metadata": {} }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "source": [ - "# changed feature values based on README_users.txt\r\n", + "# Changed feature values based on README_users.txt\r\n", "\r\n", "ages_map = {1: 'Under 18',\r\n", " 18: '18 - 24',\r\n", @@ -321,16 +440,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "source": [ + "# Plot age kategories\r\n", + "\r\n", "age_reindex = ['Under 18', '18 - 24', '25 - 34', '35 - 44', '45 - 49', '50 - 55', '56+']\r\n", - "\r\n", "age_counts = users['age'].value_counts().reindex(age_reindex)\r\n", - "\r\n", "sns.barplot(x=age_counts.values,\r\n", " y=age_counts.index,\r\n", " palette='magma').set_title(\r\n", - " 'Users age', fontsize=24)\r\n", + " 'Users age', fontsize=12)\r\n", "\r\n", "plt.show()" ], @@ -341,13 +460,1214 @@ "text/plain": [ "
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" }, "metadata": {} } ], "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "# Plot gender of users\r\n", + "gender_counts = users['gender'].value_counts()\r\n", + "colors1 = ['lightblue', 'pink']\r\n", + "pie = go.Pie(labels=gender_counts.index,\r\n", + " values=gender_counts.values,\r\n", + " marker=dict(colors=colors1),\r\n", + " hole=0.5)\r\n", + "layout = go.Layout(title='Gender Users', font=dict(size=12), legend=dict(orientation='h'))\r\n", + "\r\n", + "fig = go.Figure(data=[pie], layout=layout)\r\n", + "py.iplot(fig)" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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4123555Bug's Life, A (1998)Animation|Children's|Comedy1998FemaleUnder 18K-12 student
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American Beauty (1999)715
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" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 44, + "source": [ + "# Print movie / user sum\r\n", + "n_movies = final_df['movieId'].nunique()\r\n", + "n_users = final_df['userId'].nunique()\r\n", + "\r\n", + "print('Number of movies:', n_movies)\r\n", + "print('Number of users:', n_users) " + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of movies: 3706\n", + "Number of users: 6040\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 45, + "source": [ + "# implement SVD with Python SurPRISE, a Python Recommendation Framework\r\n", + "\r\n", + "from surprise import Reader, Dataset, SVD, SVDpp\r\n", + "from surprise import accuracy\r\n", + "\r\n", + "reader = Reader(rating_scale=(1, 5))\r\n", + "dataset = Dataset.load_from_df(final_df[['userId', 'movieId', 'rating']], reader=reader)\r\n", + "\r\n", + "svd = SVD(n_factors=50)\r\n", + "svd_plusplus = SVDpp(n_factors=50)\r\n", + "\r\n", + "# train with SVD\r\n", + "trainset = dataset.build_full_trainset()\r\n", + "svd.fit(trainset)\r\n", + "# train with SVD++, ATTENTION this take a LONG TIME\r\n", + "# svd_plusplus.fit(trainset)\r\n" + ], + "outputs": [ + { + "output_type": "error", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'surprise'", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# implement SVD with Python SurPRISE, a Python Recommendation Framework\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msurprise\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mReader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSVD\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSVDpp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msurprise\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'surprise'" + ] + } + ], + "metadata": {} } ], "metadata": {