{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting Started\n",
"\n",
"This notebook gets you started with a brief nDCG evaluation with LensKit for Python.\n",
"\n",
"This notebook is also available on [Google Collaboratory](https://colab.research.google.com/drive/1ym040cKkQf85epu80VtIkMXy3LpfYQky?usp=sharing) and [nbviewer](https://nbviewer.jupyter.org/github/lenskit/lkpy/blob/master/doc/GettingStarted.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We first import the LensKit components we need:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from lenskit.als import BiasedMFScorer\n",
"from lenskit.batch import recommend\n",
"from lenskit.data import ItemListCollection, UserIDKey, load_movielens\n",
"from lenskit.knn import ItemKNNScorer\n",
"from lenskit.metrics import NDCG, RBP, RecipRank, RunAnalysis\n",
"from lenskit.pipeline import topn_pipeline\n",
"from lenskit.splitting import SampleFrac, crossfold_users"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And Pandas is very useful, as is Seaborn for plotting:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `pyprojroot` package makes it easy to find input data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from pyprojroot.here import here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading Data\n",
"\n",
"We're going to use the ML-100K data set:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" user_id | \n",
" item_id | \n",
" rating | \n",
" timestamp | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 1 | \n",
" 1 | \n",
" 5.0 | \n",
" 874965758 | \n",
"
\n",
" \n",
" | 1 | \n",
" 1 | \n",
" 2 | \n",
" 3.0 | \n",
" 876893171 | \n",
"
\n",
" \n",
" | 2 | \n",
" 1 | \n",
" 3 | \n",
" 4.0 | \n",
" 878542960 | \n",
"
\n",
" \n",
" | 3 | \n",
" 1 | \n",
" 4 | \n",
" 3.0 | \n",
" 876893119 | \n",
"
\n",
" \n",
" | 4 | \n",
" 1 | \n",
" 5 | \n",
" 3.0 | \n",
" 889751712 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" NDCG | \n",
" RBP | \n",
" RecipRank | \n",
"
\n",
" \n",
" | model | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | ALS | \n",
" 0.125894 | \n",
" 0.088839 | \n",
" 0.19244 | \n",
"
\n",
" \n",
" | II | \n",
" 0.095334 | \n",
" 0.036945 | \n",
" 0.10837 | \n",
"
\n",
" \n",
"
\n",
"