music_ratings_learner.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "music_ratings_learner.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "4XAtsLhTHudu",
        "colab_type": "code",
        "outputId": "61163e0e-0136-444f-8b24-f26440d992c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2193
        }
      },
      "cell_type": "code",
      "source": [
        "# all config/downloads to use fastai\n",
        "!pip install pandas --upgrade\n",
        "!pip install plotly --upgrade\n",
        "!pip install fastai==0.7.0\n",
        "!pip install torchtext==0.2.3\n",
        "!pip install torch\n",
        "!pip install torchvision\n",
        "!pip install Pillow>=4.1.1\n",
        "!pip install image\n",
        "!pip install matplotlib\n",
        "from fastai.imports import *\n",
        "from fastai.transforms import *\n",
        "from fastai.conv_learner import *\n",
        "from fastai.model import *\n",
        "from fastai.dataset import *\n",
        "from fastai.sgdr import *\n",
        "from fastai.plots import *"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tv_PL4FaLD61",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zWXX43fpLEiM",
        "colab_type": "code",
        "outputId": "bfb18d94-220d-4e52-fb1c-46f5a75334f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "c9y8UtWxHxNy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from fastai.learner import *\n",
        "from fastai.column_data import *\n",
        "import copy\n",
        "from torch.autograd import Variable as V"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zZnMZo5eKXRO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Train a simple initial model with fastai on the unfiltered, but normalized data set"
      ]
    },
    {
      "metadata": {
        "id": "VDzgsbTlK2d0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "PATH = \"/content/drive/My Drive/fastai/\"\n",
        "df_scaled = pd.read_csv(PATH + 'music_ratings_normalized.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WEEwGBeiKZ5p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "val_idxs = get_cv_idxs(len(df_scaled))\n",
        "wd=2e-2\n",
        "n_factors = 20"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DRiCJJ_uKdQd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# train cf model\n",
        "cf = CollabFilterDataset.from_csv(PATH, 'music_ratings_normalized.csv', 'userID', 'artistID', 'weight')\n",
        "learn = cf.get_learner(n_factors, val_idxs, 64, opt_fn=optim.Adam)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L_78XpgcKzyx",
        "colab_type": "code",
        "outputId": "bc42c895-e753-434e-f97b-69d0347f5db1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "learn.fit(1e-2, 3, wds=wd, cycle_len=1, cycle_mult=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "321887c63b6a4f128ff864d7ee4073b8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Epoch', max=7, style=ProgressStyle(description_width='initial…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "epoch      trn_loss   val_loss   \n",
            "    0      291.585878 296.731119\n",
            "    1      97.410441  101.435788\n",
            "    2      52.691627  97.794665 \n",
            " 43%|████▎     | 500/1161 [00:04<00:05, 123.40it/s, loss=61.5]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-70-18dbbf42949e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_len\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_mult\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, lrs, n_cycle, wds, **kwargs)\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwarm_up\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/usr/local/lib/python3.6/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[0;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp16\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0mswa_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswa_model\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_swa\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswa_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswa_start\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             swa_eval_freq=swa_eval_freq, **kwargs)\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\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/usr/local/lib/python3.6/dist-packages/fastai/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    127\u001b[0m             \u001b[0mbatch_num\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\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--> 129\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_stepper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\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    130\u001b[0m             \u001b[0mavg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mavg_mom\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mavg_mom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0mdebias_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mavg_mom\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mbatch_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/model.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, xs, y, epoch)\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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     51\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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---> 52\u001b[0;31m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mraw_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\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     53\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_scale\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32massert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_scale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_fn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxtra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mmse_loss\u001b[0;34m(input, target, size_average, reduce)\u001b[0m\n\u001b[1;32m   1280\u001b[0m     \"\"\"\n\u001b[1;32m   1281\u001b[0m     return _pointwise_loss(lambda a, b: (a - b) ** 2, torch._C._nn.mse_loss,\n\u001b[0;32m-> 1282\u001b[0;31m                            input, target, size_average, reduce)\n\u001b[0m\u001b[1;32m   1283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36m_pointwise_loss\u001b[0;34m(lambd, lambd_optimized, input, target, size_average, reduce)\u001b[0m\n\u001b[1;32m   1246\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1247\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[0;32m-> 1248\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlambd_optimized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\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   1249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "YsHVoeK9s-40",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "??fit\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TM8Oav0CLtXd",
        "colab_type": "code",
        "outputId": "a2f4b0bb-0e50-4fff-988e-53aa43adde0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.sqrt(50.65)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.116881339463234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "cHZxpBOlLw5q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The average error on the unfiltered data set was about 7 points with the weights ranging around 0 and 80. This is the baseline model, and I will try to filter data and build a model to predict better than this baseline model."
      ]
    },
    {
      "metadata": {
        "id": "9BxtR0fCOTP2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now, another model can be tried on a data set that has been filtered to improve the density of the set"
      ]
    },
    {
      "metadata": {
        "id": "nW1akUo_OZTQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "filtered_df = pd.read_csv(PATH + 'music_ratings_filtscale.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UOMfitqtOjy7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "val_idxs = get_cv_idxs(len(filtered_df))\n",
        "wd=1e-5\n",
        "n_factors = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JBGQykoDOp3m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cf = CollabFilterDataset.from_csv(PATH, 'music_ratings_filtscale.csv', 'userID', 'artistID', 'weight')\n",
        "learn = cf.get_learner(n_factors, val_idxs, 32, opt_fn=optim.Adam)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EjlnNL2iOvLv",
        "colab_type": "code",
        "outputId": "9e799908-167e-421f-8e2e-05edb33d5fc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1255
        }
      },
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "learn.fit(.08, 3, wds=wd, cycle_len=1, cycle_mult=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-c08272ab0b83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m.08\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_len\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_mult\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[0;32m/usr/local/lib/python3.6/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, lrs, n_cycle, wds, **kwargs)\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwarm_up\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/usr/local/lib/python3.6/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[0;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp16\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0mswa_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswa_model\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_swa\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswa_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswa_start\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             swa_eval_freq=swa_eval_freq, **kwargs)\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\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/usr/local/lib/python3.6/dist-packages/fastai/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mall_val\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mval_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIterBatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\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    127\u001b[0m             \u001b[0mbatch_num\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\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/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1036\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1037\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminiters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mminiters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1038\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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   1039\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1040\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\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[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36mclose\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0;31m# decrement instance pos and remove from internal set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decr_instances\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[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m         \u001b[0;31m# GUI mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m_decr_instances\u001b[0;34m(cls, instance)\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_instances\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m                 \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexit\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    497\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: nocover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tqdm/_monitor.py\u001b[0m in \u001b[0;36mexit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexit\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[1;32m     51\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwas_killed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\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---> 52\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\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     53\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\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     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\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   1057\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   1058\u001b[0m             \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# already determined that the C code is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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   1073\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\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   1074\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\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;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "YdlYtzvsPAfk",
        "colab_type": "code",
        "outputId": "395a6a01-e524-4ddd-d56c-c52c2fd2ee66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.sqrt(26.66)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.163332257370234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "6wNT8IAOPDuf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "As can be seen, the loss on the model has now decreased to an average of 5.16, where it was initially 7 with the less dense data set."
      ]
    },
    {
      "metadata": {
        "id": "JltZycXUZRAr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Now that a decent model has been created, it can be built by scratch and compared to this previous model**"
      ]
    },
    {
      "metadata": {
        "id": "obG1sMKXiV2b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# create a custom class to handle data sets for the recommendation data\n",
        "from torch.utils import data\n",
        "\n",
        "\n",
        "# method for shuffling data in a data frame so that ordering\n",
        "# is random during training\n",
        "def shuffle_data(path, filename):\n",
        "  df = pd.read_csv(path + filename)\n",
        "  df = df.sample(frac=1)\n",
        "  df.to_csv(path + filename, index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6ZXPouZMIyJL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# create the class that implements the recommendation model\n",
        "from torch.autograd import Variable as V\n",
        "\n",
        "class Recommender(nn.Module):\n",
        "  def __init__(self, num_users, num_artists, num_factors):\n",
        "    super().__init__()\n",
        "    self.u = nn.Embedding(num_users, num_factors)\n",
        "    self.a = nn.Embedding(num_artists, num_factors)\n",
        "    self.u.weight.data.uniform_(-.01, .01)\n",
        "    self.a.weight.data.uniform_(-.01, .01)\n",
        "    self.ub = nn.Embedding(num_users, 1)\n",
        "    self.ab = nn.Embedding(num_artists, 1)\n",
        "    self.ub.weight.data.uniform_(-.01, .01)\n",
        "    self.ab.weight.data.uniform_(-.01, .01)\n",
        "    \n",
        "  def forward(self, cats, conts):\n",
        "    users, artists = cats[:,0],cats[:,1]\n",
        "    us, art = self.u(users), self.a(artists)\n",
        "    dp = (us*art).sum(1)\n",
        "    dpb = dp + self.ub(users).squeeze() + self.ab(artists).squeeze()\n",
        "    return dpb\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZhaXU8qIaz-p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# read in data frame created by EDA notebook\n",
        "contig_df = pd.read_csv(PATH + \"music_ratings_contig.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1LMFYw7eiO33",
        "colab_type": "code",
        "outputId": "f4f6aa8b-fb0e-4c24-8047-15e67411f205",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# separate music data into separate training and testing files\n",
        "shuffle_data(PATH, \"music_ratings_contig.csv\")\n",
        "train_amt = .7\n",
        "data_size = len(contig_df)\n",
        "train_size = int(train_amt*data_size)\n",
        "test_size = data_size - train_size\n",
        "\n",
        "# get separate data frames with train and test data\n",
        "train_data = contig_df.iloc[:train_size, :]\n",
        "test_data = contig_df.iloc[train_size:, :]\n",
        "print(f\"{train_size} -> {len(train_data)}\")\n",
        "print(f\"{test_size} -> {len(test_data)}\")\n",
        "\n",
        "# write these train and test data to separate csv file\n",
        "train_data.to_csv(PATH + \"music_train.csv\", index=False)\n",
        "test_data.to_csv(PATH + \"music_test.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49998 -> 49998\n",
            "21428 -> 21428\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HpolwHsRavjl",
        "colab_type": "code",
        "outputId": "3f47bab0-dbdc-4ff1-aa31-b6f323d63bde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# find number of artists and users being used1\n",
        "num_users = contig_df.loc[:, \"userID\"].nunique()\n",
        "num_artists = contig_df.loc[:, \"artistID\"].nunique()\n",
        "print(num_users)\n",
        "print(num_artists)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1881\n",
            "2828\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bnrYC1prbkf_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class RecDataset(data.Dataset):\n",
        "  'Create custom class for pytorch data set'\n",
        "  def __init__(self, path, file_name):\n",
        "        'Initialization of data frame'\n",
        "        self.data = pd.read_csv(path + file_name)\n",
        "\n",
        "  def __len__(self):\n",
        "        'find total number of samples'\n",
        "        return self.data.shape[0]\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "        'Generates one sample of data'\n",
        "        # get data sample from the data set\n",
        "        return (np.array(self.data.loc[index, :]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ENECiaeKbtt9",
        "colab_type": "code",
        "outputId": "36f58165-78ae-4dbd-99df-8bfe8b851302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "cell_type": "code",
      "source": [
        "# check that data set class works properly\n",
        "full_data = RecDataset(PATH, 'music_ratings_contig.csv')\n",
        "print(full_data[0])\n",
        "print(full_data[1])\n",
        "print(full_data[2])\n",
        "print(full_data[3])\n",
        "print(full_data[4])\n",
        "full_data.data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 91.      124.       -0.14043]\n",
            "[1154.       425.        -0.19586]\n",
            "[1294.      1587.        -0.16284]\n",
            "[1445.      1815.         0.01665]\n",
            "[1039.        70.        -0.00717]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>userID</th>\n",
              "      <th>artistID</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>91</td>\n",
              "      <td>124</td>\n",
              "      <td>-0.140434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1154</td>\n",
              "      <td>425</td>\n",
              "      <td>-0.195860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1294</td>\n",
              "      <td>1587</td>\n",
              "      <td>-0.162840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1445</td>\n",
              "      <td>1815</td>\n",
              "      <td>0.016647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1039</td>\n",
              "      <td>70</td>\n",
              "      <td>-0.007175</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userID  artistID    weight\n",
              "0      91       124 -0.140434\n",
              "1    1154       425 -0.195860\n",
              "2    1294      1587 -0.162840\n",
              "3    1445      1815  0.016647\n",
              "4    1039        70 -0.007175"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "OgjqWJtdb2rE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_loader(path, filename, bs):\n",
        "  \"\"\"method for getting a data loader from a csv file\"\"\"\n",
        "  shuffle_data(path, filename)\n",
        "  data_set = RecDataset(path, filename)\n",
        "  data_loader = data.DataLoader(data_set, batch_size=bs, num_workers=2)\n",
        "  return data_loader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qdKGqffXbzJ-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"# create data loaders for training and test data\n",
        "train_loader = get_loader(PATH, \"music_train.csv\", 32)\n",
        "#next(iter(train_loader))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X-2IuQ0JbIW3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# declare the size of the embeddings to be used\n",
        "num_factors = 40\n",
        "wd = 1e-3\n",
        "criterion = nn.MSELoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kj6IhBjMbTG4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Find the optimal learning rate with which to begin training"
      ]
    },
    {
      "metadata": {
        "id": "xN3jbPZNbSR_",
        "colab_type": "code",
        "outputId": "07b34962-12df-40e1-8b0b-8321975598d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "losses = []\n",
        "lrs = []\n",
        "\n",
        "lr = 1e-6\n",
        "practice_rec_model = Recommender(num_users, num_artists, num_factors)\n",
        "opt = optim.SGD(practice_rec_model.parameters(), lr, weight_decay=wd, momentum=0.9)\n",
        "while(lr <= 1):\n",
        "  test_data = next(iter(train_loader))\n",
        "  inputs = test_data[:, :2].long()\n",
        "  expected = V(test_data[:, 2].float())\n",
        "  \n",
        "  opt.zero_grad()\n",
        "\n",
        "  # run model\n",
        "  outputs = practice_rec_model(inputs, None)\n",
        "  loss = criterion(outputs, expected)\n",
        "  loss.backward()\n",
        "  opt.step()\n",
        "  losses.append(loss.data[0])\n",
        "  lrs.append(lr)\n",
        "  \n",
        "  # go to the next learning rate\n",
        "  lr *= 1.25\n",
        "  \n",
        "  # change the learning rate in the optimizer\n",
        "  for param_group in opt.param_groups:\n",
        "        param_group['lr'] = lr\n",
        "  \n",
        "plt.plot(lrs, losses)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fadbe418470>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JCTHkjR7C0ZN1nDh1xuk4JkTsPnyKZ986QPKgGL53cz6x0bYYirlwVvQD5HOT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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "S0FQ8NyTcL6G",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "From this test, it can be seen that the optimal learning rate before loss starts increasing is around .1 so this will be the learning rate that will be used to begin training"
      ]
    },
    {
      "metadata": {
        "id": "Slx4fqGquMm7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "training_loss = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QP9pG_vIcH2b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# declare training constants\n",
        "n_epoch = 3\n",
        "lr = .1\n",
        "\n",
        "# declare loss criterion for the model\n",
        "criterion = nn.MSELoss()\n",
        "\n",
        "# create model and optimizer\n",
        "recommender_model = Recommender(num_users, num_artists, num_factors).cuda()\n",
        "opt = optim.SGD(recommender_model.parameters(), lr, weight_decay=wd, momentum=0.9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4gkSge4ucdMe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Create training loop to train the recommender model on test data**"
      ]
    },
    {
      "metadata": {
        "id": "nWmmH0z0cS0a",
        "colab_type": "code",
        "outputId": "d3d11701-bda1-4b92-c09f-bf9779f83aa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "train_loader = get_loader(PATH, \"music_train.csv\", 32)\n",
        "for epoch in range(n_epoch):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, train_data in enumerate(train_loader):\n",
        "        # get the inputs\n",
        "        inputs = train_data.long().cuda()\n",
        "        actual_out = V(train_data[:, 2].float()).cuda()\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        opt.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = recommender_model.forward(inputs, None)\n",
        "        loss = criterion(outputs, actual_out)\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.data[0]\n",
        "        \n",
        "        #input()\n",
        "        if i % (len(train_loader)) == (len(train_loader) - 1):    # print every 2000 mini-batches\n",
        "            training_loss.append((running_loss/len(train_loader)))\n",
        "            print(f\"[{epoch + 1}, {i + 1}] loss: {running_loss/len(train_loader)}\")\n",
        "            #epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "    \n",
        "\n",
        "print('Finished Training')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 1563] loss: 0.9988490516434275\n",
            "[2, 1563] loss: 0.9849060274036726\n",
            "[3, 1563] loss: 0.984226661279943\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LbGg-Zqedl-S",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Continue training while reducing the learning rate to arrive at an optimal solution"
      ]
    },
    {
      "metadata": {
        "id": "2fI5bH1qciCV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# decrease the learning rate then train model again\n",
        "lr = .001\n",
        "opt = optim.SGD(recommender_model.parameters(), lr, weight_decay=wd, momentum=0.9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EdtnJRBNdyfH",
        "colab_type": "code",
        "outputId": "9218a6ac-604e-4ce4-c14a-d31c479a2ef7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "train_loader = get_loader(PATH, \"music_train.csv\", 32)\n",
        "for epoch in range(n_epoch):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, train_data in enumerate(train_loader):\n",
        "        # get the inputs\n",
        "        inputs = train_data.long().cuda()\n",
        "        actual_out = V(train_data[:, 2].float()).cuda()\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        opt.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = recommender_model.forward(inputs, None)\n",
        "        loss = criterion(outputs, actual_out)\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.data[0]\n",
        "        #input()\n",
        "        if i % (len(train_loader)) == (len(train_loader) - 1):    # print every 2000 mini-batches\n",
        "            training_loss.append((running_loss/len(train_loader)))\n",
        "            print(f\"[{epoch + 1}, {i + 1}] loss: {running_loss/len(train_loader)}\")\n",
        "            #epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "    \n",
        "\n",
        "print('Finished Training')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 1563] loss: 0.7607860005682934\n",
            "[2, 1563] loss: 0.7595202427903239\n",
            "[3, 1563] loss: 0.7582918316684782\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3qqJN5Dsv0_n",
        "colab_type": "code",
        "outputId": "60f9463d-a1a2-4d0c-e153-d8b35bde59c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "plt.title(\"Recommender System Training\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE Loss\")\n",
        "plt.plot(training_loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd6a508a400>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mAcuAM4EzzGznQnc/qpV9LyNsOYT6AcvD1yOBj9x9NYCZvQqMc/ebCa6owt3f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\nRKRZKhIiItIsFQkREWnW/wfGaTEoLKjrEgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Ut-IMga2d0NE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# decrease the learning rate then train model again\n",
        "n_epoch = 6\n",
        "lr =5e-3\n",
        "opt = optim.SGD(recommender_model.parameters(), lr, weight_decay=wd, momentum=0.9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IPdk35kCeaW7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_loader = get_loader(PATH, \"music_train.csv\", 32)\n",
        "for epoch in range(n_epoch):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, train_data in enumerate(train_loader):\n",
        "        # get the inputs\n",
        "        inputs = train_data.long().cuda()\n",
        "        actual_out = V(train_data[:, 2].float()).cuda()\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        opt.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = recommender_model.forward(inputs, None)\n",
        "        loss = criterion(outputs, actual_out)\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.data[0]\n",
        "        #input()\n",
        "        if i % (len(train_loader)) == (len(train_loader) - 1):    # print every 2000 mini-batches\n",
        "            print(f\"[{epoch + 1}, {i + 1}] loss: {running_loss/len(train_loader)}\")\n",
        "            #epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "    \n",
        "\n",
        "print('Finished Training')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t1OOA0u0ez6a",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The model seems to have converged around a loss of .3, so now the validation error can be considered"
      ]
    },
    {
      "metadata": {
        "id": "Y194VYLpebI5",
        "colab_type": "code",
        "outputId": "8f736663-c4c2-4176-d575-b5ba98d9d57e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_loader = get_loader(PATH, 'music_test.csv', 32)\n",
        "for i, test_data in enumerate(test_loader):\n",
        "    inputs = test_data.long().cuda()\n",
        "    actual_out = V(test_data[:, 2].float()).cuda()\n",
        "    # get prediction from recommender\n",
        "    pred = recommender_model(inputs, None)\n",
        "    loss = criterion(pred, actual_out)\n",
        "    running_loss += loss.data[0]\n",
        "\n",
        "print(f\"Validation MSE: {running_loss/len(test_loader)}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation MSE: 0.9591402612376346\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NlW5jRb4z6sf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# save the model to be used later\n",
        "torch.save(recommender_model.state_dict(), PATH + \"custom_model\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "562oTFAGRvAM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# find number of artists and users being used1\n",
        "MODEL_PATH = \"/content/drive/My Drive/fastai/custom_model\"\n",
        "\n",
        "num_users = contig_df.loc[:, \"userID\"].nunique()\n",
        "num_artists = contig_df.loc[:, \"artistID\"].nunique()\n",
        "rec_model = Recommender(num_users, num_artists, 40)\n",
        "rec_model.load_state_dict(torch.load(MODEL_PATH))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KdGcm55OSJz8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get dictionaries that contain index mappings to study embeddings\n",
        "import pickle\n",
        "user_to_index_dict = pickle.load(open(PATH + \"user_to_index_dict.txt\", \"rb\"))\n",
        "index_to_user_dict = pickle.load(open(PATH + \"index_to_user_dict.txt\", \"rb\"))\n",
        "artist_to_index_dict = pickle.load(open(PATH + \"artist_to_index_dict.txt\", \"rb\"))\n",
        "index_to_artist_dict = pickle.load(open(PATH + \"index_to_artist_dict.txt\", \"rb\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wpxW-e9PeRAX",
        "colab_type": "code",
        "outputId": "cef87037-c60e-4075-9700-fb91f6701445",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# get the dataframe of artists mapped to IDs\n",
        "import pandas as pd\n",
        "artist_df = pd.read_csv(PATH + 'artists_cleaned.csv')\n",
        "artist_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>MALICE MIZER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Diary of Dreams</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Carpathian Forest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Moi dix Mois</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Bella Morte</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id                name\n",
              "0   1        MALICE MIZER\n",
              "1   2     Diary of Dreams\n",
              "2   3   Carpathian Forest\n",
              "3   4        Moi dix Mois\n",
              "4   5         Bella Morte"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "o_9jXKf_BbdJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get data from artist embeddings into a numpy array\n",
        "np_artist_embedding = rec_model.ab.weight.data.numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n_OMymmAfI5d",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "np_embed = np.array(np_artist_embedding, copy=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CPZJRUJ6fPP-",
        "colab_type": "code",
        "outputId": "97452e7f-8ab9-4784-8ff9-a6ab899f7f9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "np_embed.sort(axis=0)\n",
        "np_embed"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.11609],\n",
              "       [-0.10077],\n",
              "       [-0.08861],\n",
              "       ...,\n",
              "       [ 0.58359],\n",
              "       [ 0.82891],\n",
              "       [ 0.95887]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "fx5ze30ECKpM",
        "colab_type": "code",
        "outputId": "5ec3efea-7ae3-4c55-e608-95edc0775167",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "# find the 5 largest values to see which artists they are for\n",
        "best_indices = np.argsort(np_artist_embedding, axis=0)[-5:, :]\n",
        "best_indices"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[320],\n",
              "       [ 92],\n",
              "       [330],\n",
              "       [156],\n",
              "       [ 19]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "metadata": {
        "id": "8RL1pPBUDZ8u",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# take indices and convert to the indices for embedding\n",
        "for i in range(best_indices.shape[0]):\n",
        "  best_indices[i, :] = index_to_artist_dict[best_indices[i, :].item()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CIHL_o4Wbvg9",
        "colab_type": "code",
        "outputId": "1f0b2881-4ff0-4bbd-e6ec-3b8622ea8349",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "best_indices"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[498],\n",
              "       [203],\n",
              "       [511],\n",
              "       [289],\n",
              "       [ 72]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "metadata": {
        "id": "t6HRK-ppdROE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sorted_embeddings = np.sort(np_artist_embedding, axis=0)\n",
        "best_embeddings = sorted_embeddings[-5:, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LzE_RWtQcVe-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "best_artists = []\n",
        "for i in range(5):\n",
        "  artist = best_indices[i, :].item()\n",
        "  artist_name = artist_df.loc[artist_df[\"id\"] == artist].iloc[:, 1].item()\n",
        "  artist_embedding = best_embeddings[i, :].item()\n",
        "  best_artists.append((artist_name, artist_embedding))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qBmMhcREdyrv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "best_artists = [i for i in reversed(best_artists)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SZOAUEX5e0C1",
        "colab_type": "code",
        "outputId": "42b64121-a5a9-41dd-d2dd-2a0a6022f3cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "best_artists"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(' Depeche Mode', 0.9588698744773865),\n",
              " (' Britney Spears', 0.8289055228233337),\n",
              " (' U2', 0.5835899710655212),\n",
              " (' Blur', 0.503320038318634),\n",
              " (' Paramore', 0.4168234169483185)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "metadata": {
        "id": "MM2e87FGhvnO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# basic matrix factorization model without bias term\n",
        "class Recommender(nn.Module):\n",
        "  def __init__(self, num_users, num_artists, num_factors):\n",
        "    super().__init__()\n",
        "    self.u = nn.Embedding(num_users, num_factors)\n",
        "    self.a = nn.Embedding(num_artists, num_factors)\n",
        "    self.u.weight.data.uniform_(-.01, .01)\n",
        "    self.a.weight.data.uniform_(-.01, .01)\n",
        "    \n",
        "  def forward(self, cats, conts):\n",
        "    users, artists = cats[:,0], cats[:,1]\n",
        "    us, art = self.u(users), self.a(artists)\n",
        "    return (us*art).sum(1)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}