sagemaker/test_another_instance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import os \n",
    "\n",
    "s3_resource = boto3.resource('s3')\n",
    "bucket = s3_resource.Bucket('anime-recommendation-system') \n",
    "for obj in bucket.objects.filter(Prefix = 'model'):\n",
    "    if not os.path.exists(os.path.dirname(obj.key)):\n",
    "        os.makedirs(os.path.dirname(obj.key))\n",
    "    bucket.download_file(obj.key, obj.key) # save to same path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkContext\n",
    "from pyspark.mllib.recommendation import MatrixFactorizationModel\n",
    "\n",
    "m = MatrixFactorizationModel.load(SparkContext.getOrCreate(), 'model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_rating = m.predict(2485327, 59)\n",
    "print(predicted_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "productID = 1575\n",
    "K = 10\n",
    "topKuser= m.recommendUsers(productID,K)\n",
    "for i in topKuser:\n",
    "    print(i)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}