683 lines
71 KiB
Plaintext
683 lines
71 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Building train and test sets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import scipy.sparse as sparse\n",
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"import time\n",
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"import random\n",
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"import evaluation_measures as ev\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import os\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"import helpers\n",
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"\n",
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"os.makedirs('./Datasets/', exist_ok = True)\n",
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"\n",
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"helpers.download_movielens_100k_dataset()\n",
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"\n",
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"df=pd.read_csv('./Datasets/ml-100k/u.data',delimiter='\\t', header=None)\n",
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"df.columns=['user', 'item', 'rating', 'timestamp']\n",
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"\n",
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"train, test = train_test_split(df, test_size=0.2, random_state=30)\n",
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"\n",
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"train.to_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None, index=False)\n",
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"test.to_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None, index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Interactions properties"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### How data looks like?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>user</th>\n",
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" <th>item</th>\n",
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" <th>rating</th>\n",
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" <th>timestamp</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>196</td>\n",
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" <td>242</td>\n",
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" <td>3</td>\n",
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" <td>881250949</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>186</td>\n",
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" <td>302</td>\n",
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" <td>3</td>\n",
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" <td>891717742</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>22</td>\n",
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" <td>377</td>\n",
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" <td>1</td>\n",
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" <td>878887116</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>244</td>\n",
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" <td>51</td>\n",
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" <td>2</td>\n",
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" <td>880606923</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>166</td>\n",
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" <td>346</td>\n",
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" <td>1</td>\n",
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" <td>886397596</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" user item rating timestamp\n",
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"0 196 242 3 881250949\n",
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"1 186 302 3 891717742\n",
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"2 22 377 1 878887116\n",
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"3 244 51 2 880606923\n",
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"4 166 346 1 886397596"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df[:5]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Sample properties"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"We have 943 users, 1682 items and 100000 ratings.\n",
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"\n",
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"Average number of ratings per user is 106.04. \n",
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"\n",
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"Average number of ratings per item is 59.453.\n",
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"\n",
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"Data sparsity (% of missing entries) is 6.3047%.\n"
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]
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}
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],
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"source": [
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"users, items, ratings=len(set(df['user'])), len(set(df['item'])), len(df)\n",
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"\n",
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"print('We have {} users, {} items and {} ratings.\\n'.format(users, items, ratings))\n",
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"\n",
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"print('Average number of ratings per user is {}. \\n'.format(round(ratings/users,2)))\n",
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"print('Average number of ratings per item is {}.\\n'.format(round(ratings/items,4)))\n",
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"print('Data sparsity (% of missing entries) is {}%.'.format(round(100*ratings/(users*items),4)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAHvCAYAAACsfXllAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeZgcZbn+8fvJAoKgLIaYBGQQWUJImCSNBBGMC8vJJohL+ImAeBL0wEEE0ShCAqIGgSPimkQQUFlUDpgFkUWQXZgJCQdDFIID2QghrNmAwPP7o2omXVM9PZ3MTL01qe/nuvqa7qrq6rufqUn66ap6y9xdAAAAAACE0CN0AAAAAABAcdGUAgAAAACCoSkFAAAAAARDUwoAAAAACIamFAAAAAAQDE0pAAAAACAYmlIAyIiZTTEzj28jQ+fpbsxsJzP7npk9amavmdnbcS1fDp2tq5jZVWXbTF3oPAAAdAWaUgCdruxDdPPtoBqeM75s+SkZxEQ3YmZ9JTVK+rakeknbSbKgoTaDmY2Mv5yYQpMJAECkV+gAAArh+5I+HjoEurVzJNXF9++X9FtJKyS5pDcDZdocIyVNju/fLakpVBAAAPKCphRAFj5mZp9w9ztCB0G3NSr++ZKkI9x9bcgwWXH3kySdFDgGAABdisN3AXSl8sbh+8FSYEuwW/zzn0VpSAEAKAqaUgBdabGkm+L7B5rZp0KGQbe2Vfzz9aApAABAp6MpBdDVviPp7fj+hWbWc3NXVDYQ0t0dXdbM7m5eJn7cw8xOjqc/b2ZrzOxxM/uOmW3f6rnvNbPvmtljZvaqmb1iZveY2Wc34z2NMrM/mdkSM3s9/nmdmR28Cet4j5mdY2b3mtlzZvaGma2MH3/DzLZr5/lNcS2a4sfvMLPTzew+M1sRj3J796a+t7L1b2tmXzOzu+J8r8c1vs/MvmVm727jeS2jFZdN/kiFgbRGbmKek8qee1I8rWRmvzKzp+LffWK9Fjk0Hv33r2a2LH4fa8zs32Z2vZmNrfKaU+L3Mbls8l0V3svdrZ5XdfTdeOCkxABhZvY+M7vUzBbG+V42swfM7L/MrKbTdszsGDObE//+18fbyG8tHrSsUg3bWM8nzez3Zva0ma2N17XUzOab2R/iTDvXkqmN9SfqZmZ9zOwCM/u/+G/zVTNrNLNJZrbNJqz3qLj2T1o00vNaM1sUT/twO8/d5O2rxkw1j8Rcy7Lx3/l/mdntZrY83p5Xx7/rR8zsCjP7jJltVen5Zes52Mx+YWYL4m1tvZk9a2Y3mNnodp5bafvdx8wuM7Mn4t9f1W0MwBbC3blx48atU2+KBp9xSQvjx1eVTTupjeeML1tmSjvrvXsTMlRcVtEgM83LbCfpjrLHrW9zJe0YP+9gSc9XWfaSKpmmlC03UtLPqqznLUmTa3ifJ0l6tcp6XNJzkg6uso6meLkmSXtIerzCOtqteRvrHiFpaTv5XlB0nmi1elW7jdzETCeVb4+SJknaUG29kn5dY5Y/S3pXB97L3a2ed1XZvLoK6x1ZNn+KpKMUnXfb1vpvk7R1ldr0lvT7Ks/fIOms1jWssJ5tJM2u8T2f0Qn/1twtaaikJVVe50lJe7Szvj6S7qwh868k9e6s7avG91p1W9jE7WbPuB61/H7q23iNd0q6vobnz5a0fRvraL39nqDotI/W60htY9y4cduybgx0BCALUyQdp+gQzClmdq27vxE2UsKvFY0OfL+iD+TPSdpd0qnxz6GSLjOzyZL+ouh9/ErSfZLekHSopAmKBo87y8xu9fYHdfqqpKMVNWS/kvSYpG0VNRXHKjqSZYqZrXL3n1ZagZl9VdJl8cO1kv4o6QFJqyS9J17XOEl9Jd1hZge6+4IqmbaW9L+SBsXv7UZJyxR9UO/bzvuplG+opL8qalAk6VFJ10p6VtJ7JX1W0iGSdpY028yOcPe7y1ZxvaR58f3mw8D/oWjve7nHNzVbmc8pqtMrkq5WdNmZtyQdEE9rto2iQ4f/JulhSYskrVFUm70lfUHSTvG6rlH0uy3X/F7Gx68pSedWyP5CB95LvaSzFV0qZ5qkB+PMJUlfVtREHK5oJOPz2ljHdEmfie+vV9TcPKioJiVJX5J0iaJtrZrvS2reS7Zc0WjJ/5C0WtGXQB9Q9AXPYbW/varerWjbHaCo8b5Z0ouS9okzvy9+zTvNrN7dX229AjPbSdF73TOeND9e51OKjvbYX1GTOSBeZy+1PwhVrdtXZszMJP1BUT2kaLv8o6SnFY1kvaOkgZI+qmibqrSOrRV9kTcinrRI0g2SnojX8QFFDebeiraDm83scHd/u8Lqmh2iaNt8S9IViv49Xq/od/jcZrxVAN1J6K6YGzduW95NG7/dXlg27fKy6adXeE7IPaUu6dsVlumjjXv5Nij68Pa8pCEVlv1C2bpuaeM1p7R6zccl7VJhuaMVfbBzRY3P+yosUypb5tFKy8TLjVHUOLukh9pYpqlVrq91wjbQQ8k9rpdJ6lFhuXPLlnlW0js6+ruvIdtJrd7vE5L6t/OcQyXtUGX+O5Xcw/iRGraBkTVkvaps+boK80e2ei/PSNqrwnIfLNteXlSFvaWKvphpXs9KSftXWKauwvZyUqtlekp6OZ7XVGkbb/U3tm8Hfpet96idWmGZ7STdVbbMT9pY103x/LclfbWNZbZT9MVU87qO6oztq8b3WnVbqHVZRf92NM+bJalnlfXsJ2nnCtN/VLaOH0rqVWGZ3ooa8eblvlzD9rtc0n4drRU3bty6341zSgFk5XuKGixJOsfaOc8xY39x99TowO6+UlLzXsqeivZunObuj1VY9jeKDoeTokvgtHckygZJn3P35yus62ZJl8YPt5X0lQrPP0/RnprXJI1x92crvYi7z5Y0NX54kJl9qJ1cN7n7j9pZphZjFO1xlaSHFDW6qb0k7v5dSXPih7tJOr4TXntTuKTx7r6s6kLu97r7y1Xmr1G096x5G/9C50XcJMe7+5OtJ7r7w4r2ZEnRnrAPVnju18run+buqT3Q7t6k9vcO9lG051KS/lRpGy9b30p3X9jO+mp1vbv/rMJrrFb0pVfz3tEvmdkO5cuY2TBt3Lv9I3f/cRt5m9fVvJfzzHYy1bR9ZewDZfevdPe32lrQ3Re4+6ryaWbWT9J/xQ//192/4e4bKjz3TUn/qWgPrNR+rSTpFK9+NAeALRRNKYBMuPsKbTzUdBdJZwSM01rFw2Nj95fdX6Hqhy3eF//cWhsPAWzLX9z9H1XmX6boMDZJOqZ8hpntqI2HRl7n7kvbea3flt0/op1lf9LO/FqVj7R8sbt7lWWnlt3PeoTme919fmesyN1fk/R/8cODOmOdm+hRd7+3yvy/lt3fr3yGmb1DG7eNZYoO76zIo0OsU1/MlFnX1ut0sUvbmhH/+9P8d7CNokNqyzV/ieDV1hOv6yVJt8QPD4sPZW1Lp21fnaj8kkqD2lyqbZ/VxtGwL6m2YNyYNn8Zslc7AzQ9o2jPLYAC4pxSAFm6WNFev50kfd3Mfu7uLwbOJEl/rzJvRdn9xkp7+9pYdsd2XvPOajPd/Tkze0LReWx7m9m73b1578wh2vil4ltm1vr8xdZ6l90fWGW5txSdU9cZmvfEuaTb21n2AW081zDrZq5aE5cQNx+flfRJRXvN+yrKbBUW37VT0m2ah9qZX/7lRevt8wBt3E7uaWc7l6LD34dUmuHur5jZw4q2gU+Y2U2Kvuy4N25SusIris7XrOav2riH70BF5/k2OzT++bKkD0anXVa1ddnP9ys6RLeSmrevDN2n6IuDbSRNjr/kurrSESBtOLTs/q41/PtTvq0NVHRId8Vc7Xx5BWALRlMKIDPxh9WLJF2k6PC+SZK+ETaVpGhgoLaUXxez2nKtl31HO8s+1c785mX2V9T0vFcbDxmsK1vmK6p8eG9bqjXLq9x9/Sasq5p+8c/n4j2IbXL3t81skaLGaCcz28qzGwirvb3MkiQzG6xo4Ke9alzvuzY70eZrb5Ckattn/7L7T6t97S1zqqIvXt6l6LDYoyWtMbO/K2q
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"text/plain": [
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"<Figure size 1152x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"items_per_user=df.groupby(['user']).count()['rating']\n",
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"\n",
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"plt.figure(figsize=(16,8))\n",
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"plt.hist(items_per_user, bins=100)\n",
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"\n",
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"# Let's add median\n",
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"t=items_per_user.median()\n",
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"plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
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"plt.text(t*1.1, plt.ylim()[1]*0.9, 'Median: {:.0f}'.format(t))\n",
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"\n",
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"# Let's add also some percentiles\n",
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"t=items_per_user.quantile(0.25)\n",
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"plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
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"plt.text(t*1.1, plt.ylim()[1]*0.95, '25% quantile: {:.0f}'.format(t))\n",
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"\n",
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"t=items_per_user.quantile(0.75)\n",
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"plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
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"plt.text(t*1.05, plt.ylim()[1]*0.95, '75% quantile: {:.0f}'.format(t))\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('Number of ratings per user', fontsize=30)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x576 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"items_per_user=df.groupby(['item']).count()['rating']\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(16,8))\n",
|
|||
|
|
"plt.hist(items_per_user, bins=100)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Let's add median\n",
|
|||
|
|
"t=items_per_user.median()\n",
|
|||
|
|
"plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
|
|||
|
|
"plt.text(t*1.1, plt.ylim()[1]*0.9, 'Median: {:.0f}'.format(t))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Let's add also some percentiles\n",
|
|||
|
|
"t=items_per_user.quantile(0.25)\n",
|
|||
|
|
"plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
|
|||
|
|
"plt.text(t*1.1, plt.ylim()[1]*0.95, '25% quantile: {:.0f}'.format(t))\n",
|
|||
|
|
"\n",
|
|||
|
|
"t=items_per_user.quantile(0.75)\n",
|
|||
|
|
"plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
|
|||
|
|
"plt.text(t*1.05, plt.ylim()[1]*0.95, '75% quantile: {:.0f}'.format(t))\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('Number of ratings per item', fontsize=30)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"rating\n",
|
|||
|
|
"1 0.06110\n",
|
|||
|
|
"2 0.11370\n",
|
|||
|
|
"3 0.27145\n",
|
|||
|
|
"4 0.34174\n",
|
|||
|
|
"5 0.21201\n",
|
|||
|
|
"Name: user, dtype: float64"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df.groupby(['rating']).count()['user']/len(df)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Item attributes"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"genres = pd.read_csv('./Datasets/ml-100k/u.genre', sep='|', header=None,\n",
|
|||
|
|
" encoding='latin-1')\n",
|
|||
|
|
"genres=dict(zip(genres[1], genres[0]))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{0: 'unknown',\n",
|
|||
|
|
" 1: 'Action',\n",
|
|||
|
|
" 2: 'Adventure',\n",
|
|||
|
|
" 3: 'Animation',\n",
|
|||
|
|
" 4: \"Children's\",\n",
|
|||
|
|
" 5: 'Comedy',\n",
|
|||
|
|
" 6: 'Crime',\n",
|
|||
|
|
" 7: 'Documentary',\n",
|
|||
|
|
" 8: 'Drama',\n",
|
|||
|
|
" 9: 'Fantasy',\n",
|
|||
|
|
" 10: 'Film-Noir',\n",
|
|||
|
|
" 11: 'Horror',\n",
|
|||
|
|
" 12: 'Musical',\n",
|
|||
|
|
" 13: 'Mystery',\n",
|
|||
|
|
" 14: 'Romance',\n",
|
|||
|
|
" 15: 'Sci-Fi',\n",
|
|||
|
|
" 16: 'Thriller',\n",
|
|||
|
|
" 17: 'War',\n",
|
|||
|
|
" 18: 'Western'}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"genres"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"movies = pd.read_csv('./Datasets/ml-100k/u.item', sep='|', encoding='latin-1', header=None)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <th>5</th>\n",
|
|||
|
|
" <th>6</th>\n",
|
|||
|
|
" <th>7</th>\n",
|
|||
|
|
" <th>8</th>\n",
|
|||
|
|
" <th>9</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>14</th>\n",
|
|||
|
|
" <th>15</th>\n",
|
|||
|
|
" <th>16</th>\n",
|
|||
|
|
" <th>17</th>\n",
|
|||
|
|
" <th>18</th>\n",
|
|||
|
|
" <th>19</th>\n",
|
|||
|
|
" <th>20</th>\n",
|
|||
|
|
" <th>21</th>\n",
|
|||
|
|
" <th>22</th>\n",
|
|||
|
|
" <th>23</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>Toy Story (1995)</td>\n",
|
|||
|
|
" <td>01-Jan-1995</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>GoldenEye (1995)</td>\n",
|
|||
|
|
" <td>01-Jan-1995</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>Four Rooms (1995)</td>\n",
|
|||
|
|
" <td>01-Jan-1995</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>3 rows × 24 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" 0 1 2 3 \\\n",
|
|||
|
|
"0 1 Toy Story (1995) 01-Jan-1995 NaN \n",
|
|||
|
|
"1 2 GoldenEye (1995) 01-Jan-1995 NaN \n",
|
|||
|
|
"2 3 Four Rooms (1995) 01-Jan-1995 NaN \n",
|
|||
|
|
"\n",
|
|||
|
|
" 4 5 6 7 8 9 ... \\\n",
|
|||
|
|
"0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 0 0 0 1 1 ... \n",
|
|||
|
|
"1 http://us.imdb.com/M/title-exact?GoldenEye%20(... 0 1 1 0 0 ... \n",
|
|||
|
|
"2 http://us.imdb.com/M/title-exact?Four%20Rooms%... 0 0 0 0 0 ... \n",
|
|||
|
|
"\n",
|
|||
|
|
" 14 15 16 17 18 19 20 21 22 23 \n",
|
|||
|
|
"0 0 0 0 0 0 0 0 0 0 0 \n",
|
|||
|
|
"1 0 0 0 0 0 0 0 1 0 0 \n",
|
|||
|
|
"2 0 0 0 0 0 0 0 1 0 0 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[3 rows x 24 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"movies[:3]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for i in range(19):\n",
|
|||
|
|
" movies[i+5]=movies[i+5].apply(lambda x: genres[i] if x==1 else '')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"movies['genre']=movies.iloc[:, 5:].apply(lambda x: ', '.join(x[x!='']), axis = 1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"movies=movies[[0,1,'genre']]\n",
|
|||
|
|
"movies.columns=['id', 'title', 'genres']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>id</th>\n",
|
|||
|
|
" <th>title</th>\n",
|
|||
|
|
" <th>genres</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>Toy Story (1995)</td>\n",
|
|||
|
|
" <td>Animation, Children's, Comedy</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>GoldenEye (1995)</td>\n",
|
|||
|
|
" <td>Action, Adventure, Thriller</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>Four Rooms (1995)</td>\n",
|
|||
|
|
" <td>Thriller</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>Get Shorty (1995)</td>\n",
|
|||
|
|
" <td>Action, Comedy, Drama</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>Copycat (1995)</td>\n",
|
|||
|
|
" <td>Crime, Drama, Thriller</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" id title genres\n",
|
|||
|
|
"0 1 Toy Story (1995) Animation, Children's, Comedy\n",
|
|||
|
|
"1 2 GoldenEye (1995) Action, Adventure, Thriller\n",
|
|||
|
|
"2 3 Four Rooms (1995) Thriller\n",
|
|||
|
|
"3 4 Get Shorty (1995) Action, Comedy, Drama\n",
|
|||
|
|
"4 5 Copycat (1995) Crime, Drama, Thriller"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"movies.to_csv('./Datasets/ml-100k/movies.csv', index=False)\n",
|
|||
|
|
"movies[:5]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Toy example"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import os\n",
|
|||
|
|
"os.makedirs('./Datasets/toy-example/', exist_ok = True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"toy_train=pd.DataFrame([[0,0,3,0], [0,10,4,0], [0,40,5,0], [0,70,4,0],\n",
|
|||
|
|
" [10,10,1,0], [10,20,2,0], [10,30,3,0],\n",
|
|||
|
|
" [20,30,5,0], [20,50,3,0], [20,60,4,0]])\n",
|
|||
|
|
"toy_test=pd.DataFrame([[0,60,3,0],\n",
|
|||
|
|
" [10,40,5,0],\n",
|
|||
|
|
" [20,0,5,0], [20,20,4,0], [20,70,2,0]])\n",
|
|||
|
|
"\n",
|
|||
|
|
"toy_train.to_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, index=False)\n",
|
|||
|
|
"toy_test.to_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, index=False)"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.6.9"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 4
|
|||
|
|
}
|