Machine_Learning_III.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Machine_Learning_III.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "_E8o0HY-jBMF",
        "Q5uWLL4IF3Aa",
        "VpGF6W88kbTD"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfgdD7ZLTrgT"
      },
      "source": [
        "# Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3MVJHRFTd0n",
        "outputId": "836824ff-ce15-4737-c2f8-ccfd189852df"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "import torch\n",
        "drive.mount('/content/drive')\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim"
      ],
      "execution_count": 515,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vu_xqilcT0s8"
      },
      "source": [
        "# Files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAoocua8Tk-x"
      },
      "source": [
        "# train\n",
        "pos_train = pd.read_csv(\"drive/MyDrive/IMDb/train/positive_train_full.csv\", index_col=0)\n",
        "pos_train[\"Encoded\"] = 0\n",
        "pos_train[\"Encoded\"] = pos_train[\"Encoded\"].astype(object)\n",
        "\n",
        "neg_train = pd.read_csv(\"drive/MyDrive/IMDb/train/negative_train_full.csv\", index_col=0)\n",
        "neg_train[\"Encoded\"] = 0\n",
        "neg_train[\"Encoded\"] = neg_train[\"Encoded\"].astype(object)\n",
        "\n",
        "\n",
        "# test\n",
        "pos_test = pd.read_csv(\"drive/MyDrive/IMDb/test/positive_test_full.csv\", index_col=0)\n",
        "pos_test[\"Encoded\"] = 0\n",
        "pos_test[\"Encoded\"] = pos_test[\"Encoded\"].astype(object)\n",
        "\n",
        "neg_test = pd.read_csv(\"drive/MyDrive/IMDb/test/negative_test_full.csv\", index_col=0)\n",
        "neg_test[\"Encoded\"] = 0\n",
        "neg_test[\"Encoded\"] = neg_test[\"Encoded\"].astype(object)\n",
        "\n"
      ],
      "execution_count": 516,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vca0GRA4baVu"
      },
      "source": [
        "new_vocab = \"\"\n",
        "dataframes = [neg_train, pos_train, pos_test, neg_test]\n",
        "\n",
        "def remove_punc(review):\n",
        "  #print(type(review))\n",
        "  punc = '''!()-[]{};:'\"\\,<>./?@#$%^&*_~'''\n",
        "  for character in review:\n",
        "    if(character in punc):\n",
        "      review = review.replace(character, \"\")\n",
        "\n",
        "  return review\n",
        "\n",
        "for df in dataframes:\n",
        "  for i,entry in df.iterrows():\n",
        "    review = entry[\"Review\"]\n",
        "    review = remove_punc(review)\n",
        "    #print(review)\n",
        "    review_processed = review.lower().split(\" \")\n",
        "    #print(review_processed)\n",
        "    for word in review_processed:\n",
        "      new_vocab += \" \" + word\n"
      ],
      "execution_count": 517,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ik4d-U-fphyq"
      },
      "source": [
        "# Tokenizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8M7EdH_NbaOB"
      },
      "source": [
        "class WhiteSpaceTokenizer:\n",
        "  \"\"\"\n",
        "  DESCRIPTION: This class is used to tokenize text. Textual data cannot be directly represented in tensors, so it\n",
        "  is necessary to assign token ids to all of the words in a vocabulary\n",
        "\n",
        "  INPUT: This object instantiation requires no input\n",
        "  OUTPUT: The object instantiation returns an initialized WhiteSpaceTokenizer with the following callable methods:\n",
        "    1. __len__(): returns the number of words in a vocabulary\n",
        "    2. tokenize(text): This method returns a list of words/phrases using whitespace as the delimeter\n",
        "      text: a string that requires tokenization\n",
        "    3. fit(text): This method generates and assigns token ids for all words/phrases in text\n",
        "      text: a string that requires tokenization\n",
        "    4. encode(text): This method iterates through the token2id dictionary and extracts the token ids\n",
        "      text: a string that requires tokenization\n",
        "    5. decode(text): This method iterates through the token2id dictionary and extracts the tokens\n",
        "  \"\"\"\n",
        "  def __init__(self, max_size: int = 300000000000000, unk_token: str = \"[UNK]\"):\n",
        "    self.token2id = {}\n",
        "    self.id2token = {}\n",
        "    self.max_size = max_size\n",
        "    self.unk_token_id = 1  # 0 is reserved for padding token, 1 is the tokenid for unseen words\n",
        "    self.unk_token = unk_token\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.token2id)\n",
        "  \n",
        "  def tokenize(self, text):\n",
        "    # use python's split() as the built-in whitespace tokenizer, use lower-case letters\n",
        "    return text.lower().split(\" \")\n",
        "  \n",
        "  def fit(self, text):\n",
        "    # Initialize token2id with the unknown id\n",
        "    self.token2id = {self.unk_token: self.unk_token_id}\n",
        "    #print(\"INCOMING TEXT: \", text)\n",
        "    # for each word in the text input\n",
        "    for token in self.tokenize(text):\n",
        "      #print(\"TOKEN: \", token)\n",
        "      # if there are less tokens than the max size and the current token is not in token2id dictionary\n",
        "      if (token not in self.token2id):\n",
        "        # add the current token to the token2id dictionary\n",
        "        self.token2id[token] = (len(self.token2id) + 1)\n",
        "      #print(\"Current token2id: \", self.token2id)\n",
        "    # id2token is reverse mapping\n",
        "    self.id2token = {int(v): k for k, v in self.token2id.items()}\n",
        "\n",
        "  def encode(self, text):   \n",
        "    token_ids = []\n",
        "    # for each entity seperated by whitespace in the input text\n",
        "    for token in self.tokenize(text):\n",
        "      if(token in self.token2id):\n",
        "        # collect and append the token of that word\n",
        "        token_ids.append(self.token2id[token])\n",
        "      else:\n",
        "        token_ids.append(self.unk_token_id)\n",
        "    return token_ids\n",
        "\n",
        "  def decode(self, token_ids):\n",
        "    tokens = []\n",
        "    for token_id in token_ids:\n",
        "      if (token_id > 1) and (token_id not in self.id2token):\n",
        "        # token_id=1 is unknown\n",
        "        token = \"N/A\"\n",
        "      else:\n",
        "        token = self.id2token[token_id]\n",
        "      tokens.append(token)\n",
        "    return tokens"
      ],
      "execution_count": 518,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6DOFmpCfNQ-"
      },
      "source": [
        "tokenizer = WhiteSpaceTokenizer()\n",
        "tokenizer.fit(new_vocab)"
      ],
      "execution_count": 519,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVa7J-_wix-l"
      },
      "source": [
        "def remove_punc(review):\n",
        "  punc = '''!()-[]{};:'\"\\,<>./?@#$%^&*_~'''\n",
        "  for character in review:\n",
        "    if(character in punc):\n",
        "      review = review.replace(character, \"\")\n",
        "\n",
        "  return review\n",
        "\n",
        "hmr = 0\n",
        "for entry in neg_train.iterrows():\n",
        "  review = entry[1][\"Review\"]\n",
        "  review = remove_punc(review)\n",
        "  tokenized_review = tokenizer.encode(review)\n",
        "  \n",
        "  if(1 in tokenized_review):\n",
        "    print(\"ERROR\")\n",
        "\n",
        "  neg_train.at[hmr, \"Encoded\"]= tokenized_review\n",
        "  hmr += 1\n",
        "\n",
        "hmr = 0\n",
        "for entry in pos_train.iterrows():\n",
        "  review = entry[1][\"Review\"]\n",
        "  review = remove_punc(review)\n",
        "  tokenized_review = tokenizer.encode(review)\n",
        "  if(1 in tokenized_review):\n",
        "    print(\"ERROR\")\n",
        "  pos_train.at[hmr, \"Encoded\"]= tokenized_review\n",
        "  hmr += 1\n",
        "\n",
        "hmr = 0\n",
        "for entry in neg_test.iterrows():\n",
        "  review = entry[1][\"Review\"]\n",
        "  review = remove_punc(review)\n",
        "  tokenized_review = tokenizer.encode(review)\n",
        "  if(1 in tokenized_review):\n",
        "    print(\"ERROR\")\n",
        "  neg_test.at[hmr, \"Encoded\"]= tokenized_review\n",
        "  hmr += 1\n",
        "\n",
        "hmr = 0\n",
        "for entry in pos_test.iterrows():\n",
        "  review = entry[1][\"Review\"]\n",
        "  review = remove_punc(review)\n",
        "  tokenized_review = tokenizer.encode(review)\n",
        "  if(1 in tokenized_review):\n",
        "    print(\"ERROR\")\n",
        "  pos_test.at[hmr, \"Encoded\"]= tokenized_review\n",
        "  hmr += 1"
      ],
      "execution_count": 520,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "Qa5qf1FgjHHZ",
        "outputId": "e7195e31-a92f-4b50-c8f0-afbfc366da5a"
      },
      "source": [
        "neg_train.head()"
      ],
      "execution_count": 521,
      "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>ReviewID</th>\n",
              "      <th>titleid</th>\n",
              "      <th>titletype</th>\n",
              "      <th>primarytitle</th>\n",
              "      <th>originaltitle</th>\n",
              "      <th>isadult</th>\n",
              "      <th>startyear</th>\n",
              "      <th>endyear</th>\n",
              "      <th>runtimeminutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>Review</th>\n",
              "      <th>Score</th>\n",
              "      <th>Encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>tt0064354</td>\n",
              "      <td>movie</td>\n",
              "      <td>Futz</td>\n",
              "      <td>Futz</td>\n",
              "      <td>False</td>\n",
              "      <td>1969.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.0</td>\n",
              "      <td>[\"'Comedy'\"]</td>\n",
              "      <td>Story of a man who has unnatural feelings for ...</td>\n",
              "      <td>3</td>\n",
              "      <td>[3, 4, 5, 6, 7, 8, 9, 10, 11, 5, 12, 13, 14, 1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>tt0100680</td>\n",
              "      <td>movie</td>\n",
              "      <td>Stanley &amp; Iris</td>\n",
              "      <td>Stanley &amp; Iris</td>\n",
              "      <td>False</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>[\"'Drama'\", \"'Romance'\"]</td>\n",
              "      <td>Robert DeNiro plays the most unbelievably inte...</td>\n",
              "      <td>1</td>\n",
              "      <td>[93, 94, 95, 34, 96, 97, 98, 99, 4, 100, 43, 1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>tt0100680</td>\n",
              "      <td>movie</td>\n",
              "      <td>Stanley &amp; Iris</td>\n",
              "      <td>Stanley &amp; Iris</td>\n",
              "      <td>False</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>[\"'Drama'\", \"'Romance'\"]</td>\n",
              "      <td>I saw the capsule comment said \"great acting.\"...</td>\n",
              "      <td>1</td>\n",
              "      <td>[163, 164, 34, 165, 166, 167, 81, 168, 124, 16...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>tt0100680</td>\n",
              "      <td>movie</td>\n",
              "      <td>Stanley &amp; Iris</td>\n",
              "      <td>Stanley &amp; Iris</td>\n",
              "      <td>False</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>104.0</td>\n",
              "      <td>[\"'Drama'\", \"'Romance'\"]</td>\n",
              "      <td>If I had not read Pat Barker's 'Union Street' ...</td>\n",
              "      <td>4</td>\n",
              "      <td>[225, 163, 226, 116, 227, 228, 229, 230, 231, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>tt0047200</td>\n",
              "      <td>movie</td>\n",
              "      <td>The Mad Magician</td>\n",
              "      <td>The Mad Magician</td>\n",
              "      <td>False</td>\n",
              "      <td>1954.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72.0</td>\n",
              "      <td>[\"'Horror'\", \"'Mystery'\", \"'Thriller'\"]</td>\n",
              "      <td>This fanciful horror flick has Vincent Price p...</td>\n",
              "      <td>4</td>\n",
              "      <td>[101, 366, 367, 368, 8, 369, 370, 371, 5, 372,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ReviewID  ...                                            Encoded\n",
              "0         0  ...  [3, 4, 5, 6, 7, 8, 9, 10, 11, 5, 12, 13, 14, 1...\n",
              "1         1  ...  [93, 94, 95, 34, 96, 97, 98, 99, 4, 100, 43, 1...\n",
              "2         2  ...  [163, 164, 34, 165, 166, 167, 81, 168, 124, 16...\n",
              "3         3  ...  [225, 163, 226, 116, 227, 228, 229, 230, 231, ...\n",
              "4         4  ...  [101, 366, 367, 368, 8, 369, 370, 371, 5, 372,...\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 521
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LVIud-mq41k"
      },
      "source": [
        "# PyTorch "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1S5u5gJhwtA"
      },
      "source": [
        "from torch.nn.utils.rnn import pad_sequence\n"
      ],
      "execution_count": 522,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qP7PEOr-f22I"
      },
      "source": [
        "## Padding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0S60LDVgItM"
      },
      "source": [
        "# train\n",
        "training_data = [neg_train, pos_train]\n",
        "training_seq = []\n",
        "target_train = []\n",
        "for df in training_data:\n",
        "  #print(df.shape)\n",
        "  for i,row in df.iterrows():\n",
        "    #print(row[\"Encoded\"])\n",
        "    training_seq.append(torch.FloatTensor(row[\"Encoded\"]))\n",
        "    #print(torch.FloatTensor(row[\"Encoded\"]))\n",
        "    #print(torch.FloatTensor([row[\"Score\"]]))\n",
        "    target_train.append(torch.FloatTensor([row[\"Score\"]]))\n",
        "\n",
        "padded_train = pad_sequence(training_seq, batch_first=True) \n",
        "\n",
        "# test\n",
        "testing_data = [neg_test, pos_test]\n",
        "testing_seq = []\n",
        "target_test = []\n",
        "for df in testing_data:\n",
        "  for i, row in df.iterrows():\n",
        "    testing_seq.append(torch.FloatTensor(row[\"Encoded\"]))\n",
        "    target_test.append(torch.FloatTensor([row[\"Score\"]]))\n",
        "\n",
        "padded_test = pad_sequence(testing_seq, batch_first=True) "
      ],
      "execution_count": 523,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oeJJmPrlglp",
        "outputId": "d48479a2-9b70-4ebd-89f5-ab15e1229475"
      },
      "source": [
        "len(padded_test[10])"
      ],
      "execution_count": 524,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2278"
            ]
          },
          "metadata": {},
          "execution_count": 524
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-QZ08B0liKP",
        "outputId": "196a197d-9a14-4942-e368-c266b7964ed7"
      },
      "source": [
        "len(padded_train[1])"
      ],
      "execution_count": 525,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2470"
            ]
          },
          "metadata": {},
          "execution_count": 525
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbVmvmyQ-BUA",
        "outputId": "bba4b6de-522d-4611-c38a-d2bae4d9e13b"
      },
      "source": [
        "2470 - 2278\n"
      ],
      "execution_count": 526,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "192"
            ]
          },
          "metadata": {},
          "execution_count": 526
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScmmrKjTiWen"
      },
      "source": [
        "# Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMiWHV0mnJZF"
      },
      "source": [
        "class MLDataset:\n",
        "  def __init__(self, xs, ys):\n",
        "    self.xs = xs\n",
        "    self.ys = ys\n",
        "    \n",
        "  def __getitem__(self, i):\n",
        "    return self.xs[i], self.ys[i]\n",
        "    \n",
        "  def __len__(self):\n",
        "    return len(self.xs)"
      ],
      "execution_count": 527,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbzG6-MEraIm"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataset = MLDataset(padded_train, target_train)\n",
        "test_dataset = MLDataset(padded_test, target_test)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size = 2048)\n",
        "test_loader = DataLoader(test_dataset, batch_size = 2048)"
      ],
      "execution_count": 528,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRaBXeA3vf3W"
      },
      "source": [
        "# MLP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-ftt2aAxs5R"
      },
      "source": [
        "import torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
      ],
      "execution_count": 600,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q0JWqV8Jwya",
        "outputId": "74d18a73-132c-4ee7-86c5-aedc52e08213"
      },
      "source": [
        "print(device)\n",
        "meta_diffs_save = []"
      ],
      "execution_count": 601,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaxKuDkZ76g_"
      },
      "source": [
        "vocab_size = len(tokenizer.token2id)"
      ],
      "execution_count": 602,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nu_HHFH-mIPZ"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "class MLP(nn.Module):\n",
        "  def __init__(self, maxlen):\n",
        "    super().__init__()\n",
        "    self.layers = nn.Sequential(nn.Flatten(), nn.Linear(maxlen, 2048), \n",
        "                                nn.LeakyReLU(), \n",
        "                                nn.Linear(2048, 1024),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(1024),\n",
        "                                nn.Linear(1024,1024) ,\n",
        "                                nn.LeakyReLU(), \n",
        "                                nn.BatchNorm1d(1024),\n",
        "                                nn.Linear(1024, 512),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(512),\n",
        "                                nn.Linear(512, 64),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(64),\n",
        "                                nn.Linear(64, 12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(12),\n",
        "                                nn.Linear(12,12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(12),\n",
        "                                nn.Linear(12, 1),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(1)\n",
        "\n",
        "                                )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.layers(x)"
      ],
      "execution_count": 603,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NxOTgsv9VLS",
        "outputId": "312158c2-821b-4123-d23d-ff49185efca4"
      },
      "source": [
        "samp = padded_test[0]\n",
        "print(len(samp))\n",
        "n = nn.functional.pad(samp, (1,191), \"constant\", 0)\n",
        "len(n)"
      ],
      "execution_count": 604,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2278\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2470"
            ]
          },
          "metadata": {},
          "execution_count": 604
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjdvfH5ov5TL",
        "outputId": "bc234475-72d5-4011-96cf-001b1b819449"
      },
      "source": [
        "input_size = len(padded_train[0])\n",
        "print(input_size)\n",
        "\n",
        "mlp = MLP(input_size)\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "torch.manual_seed(50)\n",
        "\n",
        "pred_analysis = {}\n",
        "\n",
        "optimizer = torch.optim.Adam(mlp.parameters(), lr=1e-7)\n",
        "\n",
        "results = []\n",
        "\n",
        "for epoch in range(0,100):\n",
        "  print(\"Epoch: \", epoch)\n",
        "  # train\n",
        "  tr_loss = 0.0\n",
        "\n",
        "  for x, y in train_loader:\n",
        "    mlp = mlp.to(device)\n",
        "    x = x.to(device)\n",
        "    output = mlp(x)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(output, y)\n",
        "    loss.backward()\n",
        "\n",
        "    # step\n",
        "    optimizer.step()\n",
        "\n",
        "    # update loss \n",
        "    tr_loss += loss.item()\n",
        "\n",
        "    \n",
        "  print(tr_loss)\n",
        "\n",
        "  # test\n",
        "  test_loss = 0.0\n",
        "  mlp.eval()\n",
        "\n",
        "  yhat = []\n",
        "  yreal = []\n",
        "  hmr = 0\n",
        "  for x, y in test_loader:\n",
        "    x = x.to(device)\n",
        "    x = nn.functional.pad(x, (1,191), \"constant\", 0)\n",
        "    test_out = mlp(x)\n",
        "\n",
        "    y = y.to(device)\n",
        "    #if(epoch == 99)\n",
        "    #print(test_out.shape[0])\n",
        "    bsize = test_out.shape[0]\n",
        "\n",
        "    if(epoch == 99):\n",
        "      \n",
        "      for i in range(bsize):\n",
        "        yhat.append(test_out[i].tolist()[0])\n",
        "        yreal.append(y[i].tolist()[0])\n",
        "\n",
        "\n",
        "    #print(test_out[0].tolist()[0])\n",
        "    #print(nn.Flatten(y))\n",
        "    #kill = 3/0\n",
        "    loss = loss_fn(test_out, y)\n",
        "\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    test_loss += loss.item()\n",
        "    hmr += 1\n",
        "\n",
        "  print(test_loss)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  new_row = {\"Epoch\":epoch, \"Training_Loss\":tr_loss, \"Test_Loss\":test_loss}\n",
        "  results.append(new_row)\n"
      ],
      "execution_count": 605,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2470\n",
            "Epoch:  0\n",
            "584.7458624839783\n",
            "529.2398238182068\n",
            "Epoch:  1\n",
            "600.7728652954102\n",
            "529.2309160232544\n",
            "Epoch:  2\n",
            "600.7665853500366\n",
            "529.2107877731323\n",
            "Epoch:  3\n",
            "600.755832195282\n",
            "529.1785440444946\n",
            "Epoch:  4\n",
            "600.7404165267944\n",
            "529.1296935081482\n",
            "Epoch:  5\n",
            "600.7148008346558\n",
            "529.0574750900269\n",
            "Epoch:  6\n",
            "600.6752595901489\n",
            "528.9499325752258\n",
            "Epoch:  7\n",
            "600.6176328659058\n",
            "528.7850799560547\n",
            "Epoch:  8\n",
            "600.5348958969116\n",
            "528.5293941497803\n",
            "Epoch:  9\n",
            "600.4031329154968\n",
            "528.1298170089722\n",
            "Epoch:  10\n",
            "600.1814651489258\n",
            "527.4764623641968\n",
            "Epoch:  11\n",
            "599.8141160011292\n",
            "526.4477353096008\n",
            "Epoch:  12\n",
            "599.2290425300598\n",
            "524.9209685325623\n",
            "Epoch:  13\n",
            "598.3495659828186\n",
            "522.8008489608765\n",
            "Epoch:  14\n",
            "597.0553550720215\n",
            "520.0390448570251\n",
            "Epoch:  15\n",
            "595.3121132850647\n",
            "516.6791181564331\n",
            "Epoch:  16\n",
            "593.1176686286926\n",
            "512.7882180213928\n",
            "Epoch:  17\n",
            "590.4817323684692\n",
            "508.4492726325989\n",
            "Epoch:  18\n",
            "587.4623899459839\n",
            "503.7621383666992\n",
            "Epoch:  19\n",
            "584.1399345397949\n",
            "498.8259906768799\n",
            "Epoch:  20\n",
            "580.5568318367004\n",
            "493.6982264518738\n",
            "Epoch:  21\n",
            "576.7631540298462\n",
            "488.4141454696655\n",
            "Epoch:  22\n",
            "572.7829041481018\n",
            "483.0266637802124\n",
            "Epoch:  23\n",
            "568.6439652442932\n",
            "477.56215715408325\n",
            "Epoch:  24\n",
            "564.3644886016846\n",
            "472.05060482025146\n",
            "Epoch:  25\n",
            "559.9549016952515\n",
            "466.5006003379822\n",
            "Epoch:  26\n",
            "555.4236674308777\n",
            "460.9211959838867\n",
            "Epoch:  27\n",
            "550.7759552001953\n",
            "455.33513593673706\n",
            "Epoch:  28\n",
            "546.0184845924377\n",
            "449.7470645904541\n",
            "Epoch:  29\n",
            "541.1639204025269\n",
            "444.1497344970703\n",
            "Epoch:  30\n",
            "536.2301788330078\n",
            "438.5538954734802\n",
            "Epoch:  31\n",
            "531.2271242141724\n",
            "432.96874952316284\n",
            "Epoch:  32\n",
            "526.1615400314331\n",
            "427.40966844558716\n",
            "Epoch:  33\n",
            "521.0446043014526\n",
            "421.8833522796631\n",
            "Epoch:  34\n",
            "515.8805928230286\n",
            "416.3934528827667\n",
            "Epoch:  35\n",
            "510.66712856292725\n",
            "410.9431037902832\n",
            "Epoch:  36\n",
            "505.41054248809814\n",
            "405.5418484210968\n",
            "Epoch:  37\n",
            "500.1132082939148\n",
            "400.1985034942627\n",
            "Epoch:  38\n",
            "494.7775435447693\n",
            "394.92208528518677\n",
            "Epoch:  39\n",
            "489.407039642334\n",
            "389.7134099006653\n",
            "Epoch:  40\n",
            "484.0091676712036\n",
            "384.58009672164917\n",
            "Epoch:  41\n",
            "478.5853080749512\n",
            "379.5255913734436\n",
            "Epoch:  42\n",
            "473.1415090560913\n",
            "374.5529365539551\n",
            "Epoch:  43\n",
            "467.67766761779785\n",
            "369.67185068130493\n",
            "Epoch:  44\n",
            "462.20364332199097\n",
            "364.8901298046112\n",
            "Epoch:  45\n",
            "456.7208604812622\n",
            "360.210382938385\n",
            "Epoch:  46\n",
            "451.2302508354187\n",
            "355.6394052505493\n",
            "Epoch:  47\n",
            "445.7352395057678\n",
            "351.1859486103058\n",
            "Epoch:  48\n",
            "440.24187898635864\n",
            "346.85573744773865\n",
            "Epoch:  49\n",
            "434.7533006668091\n",
            "342.65278673171997\n",
            "Epoch:  50\n",
            "429.28056144714355\n",
            "338.5810658931732\n",
            "Epoch:  51\n",
            "423.8260407447815\n",
            "334.6476626396179\n",
            "Epoch:  52\n",
            "418.3959903717041\n",
            "330.86115550994873\n",
            "Epoch:  53\n",
            "412.99431467056274\n",
            "327.22749042510986\n",
            "Epoch:  54\n",
            "407.62380814552307\n",
            "323.7489848136902\n",
            "Epoch:  55\n",
            "402.28744888305664\n",
            "320.432382106781\n",
            "Epoch:  56\n",
            "396.9934592247009\n",
            "317.285945892334\n",
            "Epoch:  57\n",
            "391.74399423599243\n",
            "314.31546545028687\n",
            "Epoch:  58\n",
            "386.54404950141907\n",
            "311.5255470275879\n",
            "Epoch:  59\n",
            "381.39690041542053\n",
            "308.9192910194397\n",
            "Epoch:  60\n",
            "376.3089771270752\n",
            "306.50279569625854\n",
            "Epoch:  61\n",
            "371.28917717933655\n",
            "304.28064727783203\n",
            "Epoch:  62\n",
            "366.33427023887634\n",
            "302.2576847076416\n",
            "Epoch:  63\n",
            "361.45062351226807\n",
            "300.4403476715088\n",
            "Epoch:  64\n",
            "356.6413064002991\n",
            "298.8319754600525\n",
            "Epoch:  65\n",
            "351.9122920036316\n",
            "297.4350986480713\n",
            "Epoch:  66\n",
            "347.2650125026703\n",
            "296.2535538673401\n",
            "Epoch:  67\n",
            "342.706255197525\n",
            "295.28962326049805\n",
            "Epoch:  68\n",
            "338.2404992580414\n",
            "294.5462484359741\n",
            "Epoch:  69\n",
            "333.87191438674927\n",
            "294.02660608291626\n",
            "Epoch:  70\n",
            "329.6034162044525\n",
            "293.7300672531128\n",
            "Epoch:  71\n",
            "325.43804383277893\n",
            "293.65588092803955\n",
            "Epoch:  72\n",
            "321.37529015541077\n",
            "293.8046541213989\n",
            "Epoch:  73\n",
            "317.4189546108246\n",
            "294.17621326446533\n",
            "Epoch:  74\n",
            "313.57072472572327\n",
            "294.77036571502686\n",
            "Epoch:  75\n",
            "309.83275270462036\n",
            "295.5860986709595\n",
            "Epoch:  76\n",
            "306.2057538032532\n",
            "296.61999225616455\n",
            "Epoch:  77\n",
            "302.69240713119507\n",
            "297.86725330352783\n",
            "Epoch:  78\n",
            "299.2923309803009\n",
            "299.3242120742798\n",
            "Epoch:  79\n",
            "296.00631189346313\n",
            "300.98804473876953\n",
            "Epoch:  80\n",
            "292.8342881202698\n",
            "302.85033226013184\n",
            "Epoch:  81\n",
            "289.77630710601807\n",
            "304.90404891967773\n",
            "Epoch:  82\n",
            "286.8331880569458\n",
            "307.14118003845215\n",
            "Epoch:  83\n",
            "284.0036783218384\n",
            "309.55383110046387\n",
            "Epoch:  84\n",
            "281.28448820114136\n",
            "312.1306209564209\n",
            "Epoch:  85\n",
            "278.6734232902527\n",
            "314.862509727478\n",
            "Epoch:  86\n",
            "276.1687870025635\n",
            "317.73995780944824\n",
            "Epoch:  87\n",
            "273.76981568336487\n",
            "320.7484130859375\n",
            "Epoch:  88\n",
            "271.4751331806183\n",
            "323.87521171569824\n",
            "Epoch:  89\n",
            "269.2796277999878\n",
            "327.1086368560791\n",
            "Epoch:  90\n",
            "267.18025183677673\n",
            "330.43320655822754\n",
            "Epoch:  91\n",
            "265.1740734577179\n",
            "333.8327045440674\n",
            "Epoch:  92\n",
            "263.25905680656433\n",
            "337.29115104675293\n",
            "Epoch:  93\n",
            "261.4316017627716\n",
            "340.7921390533447\n",
            "Epoch:  94\n",
            "259.68553161621094\n",
            "344.3198127746582\n",
            "Epoch:  95\n",
            "258.01714539527893\n",
            "347.8560428619385\n",
            "Epoch:  96\n",
            "256.4227318763733\n",
            "351.38243103027344\n",
            "Epoch:  97\n",
            "254.89791083335876\n",
            "354.879358291626\n",
            "Epoch:  98\n",
            "253.43731880187988\n",
            "358.32830238342285\n",
            "Epoch:  99\n",
            "252.03635931015015\n",
            "361.709659576416\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98Fr_OdCpgye",
        "outputId": "d21d72d5-0e47-45bf-fc0c-ab5c26dc95c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "difference = []\n",
        "\n",
        "mean_diffs = [[],[],[],[],[],[],[],[],[],[]]\n",
        "print(len(mean_diffs))\n",
        "for i in range(len(yhat)):\n",
        "  d = yhat[i] - float(yreal[i])\n",
        "  difference.append(d)\n",
        "  #print(int(yreal[i]))\n",
        "  mean_diffs[(int(yreal[i])-1)].append(d)\n",
        "\n",
        "final_mean_diffs = []\n",
        "\n",
        "for i in range(len(mean_diffs)):\n",
        "  try:\n",
        "    print(sum(mean_diffs[i])/ len(mean_diffs[i]))\n",
        "    avgd = sum(mean_diffs[i])/ len(mean_diffs[i])\n",
        "    final_mean_diffs.append(avgd)\n",
        "  except:\n",
        "    print(\"\", end= \"\")\n",
        "    #final_mean_diffs.append(0)\n",
        "\n",
        "print(final_mean_diffs)\n",
        "\n",
        "xs = [1,2,3,4,7,8,9,10]\n",
        "\n",
        "plt.title(\"Model A: MLP\")\n",
        "plt.scatter(yreal, difference)\n",
        "plt.show()"
      ],
      "execution_count": 606,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "3.9826509320969907\n",
            "3.653916366837294\n",
            "3.310600705026272\n",
            "2.4934723858620824\n",
            "-0.755933402099695\n",
            "-1.8862142715813583\n",
            "-3.1357301495327436\n",
            "-4.795108168139063\n",
            "[3.9826509320969907, 3.653916366837294, 3.310600705026272, 2.4934723858620824, -0.755933402099695, -1.8862142715813583, -3.1357301495327436, -4.795108168139063]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGcL_qRXxMiM",
        "outputId": "34b55b35-9776-4cfd-8bd6-b34fddb677c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(xs, final_mean_diffs)\n",
        "plt.title(\"Average Error Commited Per True Rating Model A (MLP)\")\n",
        "plt.xlabel(\"True Rating\")\n",
        "plt.ylabel(\"Average Error\")\n",
        "plt.show()\n",
        "meta_diffs_save.append(final_mean_diffs)\n"
      ],
      "execution_count": 607,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzVP2fmRqe4O",
        "outputId": "096f8fbe-da96-4894-922c-de18fca32d2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "yhat[0]"
      ],
      "execution_count": 552,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7926719188690186"
            ]
          },
          "metadata": {},
          "execution_count": 552
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wN8cyWvOqgSA",
        "outputId": "d2327820-77a7-4498-b38d-1dbf346b11e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "yreal[0]"
      ],
      "execution_count": 553,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0"
            ]
          },
          "metadata": {},
          "execution_count": 553
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9VrJE-iBdAC"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nLdTIFr7wbq7",
        "outputId": "57db174d-9278-4344-9833-bf6298841bd3"
      },
      "source": [
        "df = pd.DataFrame(results)\n",
        "df.head()"
      ],
      "execution_count": 160,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1093.888800</td>\n",
              "      <td>1036.243434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1111.788595</td>\n",
              "      <td>1008.816912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1078.789608</td>\n",
              "      <td>973.026877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1036.123724</td>\n",
              "      <td>933.233231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>990.980523</td>\n",
              "      <td>894.327656</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Epoch  Training_Loss    Test_Loss\n",
              "0      0    1093.888800  1036.243434\n",
              "1      1    1111.788595  1008.816912\n",
              "2      2    1078.789608   973.026877\n",
              "3      3    1036.123724   933.233231\n",
              "4      4     990.980523   894.327656"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "yjCHF_EleMdm",
        "outputId": "ec67928b-74a1-4a6b-c4ef-cdf1b1208b09"
      },
      "source": [
        "df.sort_values(by = \"Training_Loss\")"
      ],
      "execution_count": 162,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>99</td>\n",
              "      <td>270.789808</td>\n",
              "      <td>314.674212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>98</td>\n",
              "      <td>271.500530</td>\n",
              "      <td>315.104280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>97</td>\n",
              "      <td>272.330869</td>\n",
              "      <td>315.498740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>96</td>\n",
              "      <td>273.280838</td>\n",
              "      <td>315.859610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>95</td>\n",
              "      <td>274.370808</td>\n",
              "      <td>316.178562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>990.980523</td>\n",
              "      <td>894.327656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1036.123724</td>\n",
              "      <td>933.233231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1078.789608</td>\n",
              "      <td>973.026877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1093.888800</td>\n",
              "      <td>1036.243434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1111.788595</td>\n",
              "      <td>1008.816912</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Epoch  Training_Loss    Test_Loss\n",
              "99     99     270.789808   314.674212\n",
              "98     98     271.500530   315.104280\n",
              "97     97     272.330869   315.498740\n",
              "96     96     273.280838   315.859610\n",
              "95     95     274.370808   316.178562\n",
              "..    ...            ...          ...\n",
              "4       4     990.980523   894.327656\n",
              "3       3    1036.123724   933.233231\n",
              "2       2    1078.789608   973.026877\n",
              "0       0    1093.888800  1036.243434\n",
              "1       1    1111.788595  1008.816912\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "FigVPwbWBWcf",
        "outputId": "d2d8d253-6013-4bf8-9b84-484cfaa864eb"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.plot(df[\"Epoch\"], df[\"Training_Loss\"]/1024, color = \"dodgerblue\")\n",
        "plt.plot(df[\"Epoch\"], df[\"Test_Loss\"]/1024, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 300 Epochs For MLP\")\n",
        "plt.legend([\"Train\", \"Validation\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Agp4jzDWDVbo",
        "outputId": "04afd5e4-06d4-4c81-8503-1aad40d1ccd6"
      },
      "source": [
        "\n",
        "plt.plot(df[\"Epoch\"], df[\"Training_Loss\"]/512, color = \"dodgerblue\")\n",
        "plt.plot(df[\"Epoch\"], df[\"Test_Loss\"]/512, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 100 Epochs For MLP\")\n",
        "plt.legend([\"Train\", \"Test\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82gjcBFRlnFU"
      },
      "source": [
        "df3 = df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCprS-aXGkB5"
      },
      "source": [
        "# RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UvltCkuABtCa",
        "outputId": "6d96d3f6-665f-4fe8-a846-95cf190ae0b0"
      },
      "source": [
        "#rnn = nn.RNN(input_size = 2470, hidden_size= 128, num_layers=1 , batch_first=True )\n",
        "\n",
        "\n",
        "class RNN(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    #self.layers = nn.Sequential(nn.RNN(input_size = 2470, hidden_size= 128, num_layers=1 , batch_first=True ), nn.Linear(128, 1))\n",
        "    self.rnn_layer = nn.RNN(input_size = 2470, hidden_size= 512, num_layers=1 , batch_first=True, bidirectional = True )\n",
        "    self.rnn2 =  nn.RNN(input_size = 1024, hidden_size= 256, num_layers=1 , batch_first=True, bidirectional = True )\n",
        "    self.rnn3 = nn.RNN(input_size = 512, hidden_size = 128, num_layers=1 , batch_first=True, bidirectional = True)\n",
        "\n",
        "    \"\"\"\n",
        "                                nn.Linear(128,128),\n",
        "                                nn.Dropout(p=0.01),\n",
        "                                nn.LeakyReLU(negative_slope=0.01),\n",
        "                                #nn.BatchNorm1d(512),\n",
        "                                nn.Linear(128, 64),\n",
        "                                nn.Dropout(p=0.01),\n",
        "                                nn.LeakyReLU(negative_slope=1),\n",
        "                                nn.Linear(64,12),\n",
        "                                nn.Dropout(p=0.01),\n",
        "                                nn.LeakyReLU(negative_slope=0.01),\n",
        "                                \"\"\"\n",
        "\n",
        "    self.layers = nn.Sequential( nn.Linear(256,12),\n",
        "                                nn.Dropout(p=0.01),\n",
        "                                nn.LeakyReLU(negative_slope=0.01), \n",
        "                                \n",
        "                                nn.Linear(12,1),\n",
        "                                nn.Dropout(p=0.01),\n",
        "                                nn.LeakyReLU(negative_slope=0.01))\n",
        "    \"\"\",\n",
        "                                #nn.BatchNorm1d(256),\n",
        "                                nn.Linear(256, 128),\n",
        "                                nn.LeakyReLU(),\n",
        "                                #nn.BatchNorm1d(128),\n",
        "                                nn.Linear(128, 64),\n",
        "                                nn.LeakyReLU(),\n",
        "                                #nn.BatchNorm1d(64),\n",
        "                                nn.Linear(64, 12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                #nn.BatchNorm1d(12),\n",
        "                                nn.Linear(12,12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(12, 1),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(1))\"\"\"\n",
        "\n",
        "  def forward(self, x):\n",
        "    hidden_states, _ = self.rnn_layer(x)\n",
        "    last_hidden_state = hidden_states[:, -1, :] # Last hidden state\n",
        "\n",
        "    last_hidden_state2 = torch.reshape(last_hidden_state, (last_hidden_state.shape[0], 1 , last_hidden_state.shape[1]))\n",
        "    hidden_states, _ = self.rnn2(last_hidden_state2)\n",
        "\n",
        "    last_hidden_state = hidden_states[:, -1, :]\n",
        "\n",
        "    last_hidden_state3 = torch.reshape(last_hidden_state, (last_hidden_state.shape[0], 1 , last_hidden_state.shape[1]))\n",
        "    hidden_states, _ = self.rnn3(last_hidden_state3)\n",
        "    last_hidden_state = hidden_states[:, -1, :]\n",
        "\n",
        "    out = self.layers(last_hidden_state)\n",
        "    #out = self.leaky1(self.linear1(self.dropout(last_hidden_state)))\n",
        "    #out = self.leaky1(self.linear2(out))\n",
        "    #out = self.leaky1(self.linear3(out))\n",
        "\n",
        "    return out, last_hidden_state\n",
        "\n",
        "rnn = RNN()\n",
        "\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "torch.manual_seed(50)\n",
        "\n",
        "optimizer = torch.optim.Adam(rnn.parameters(), lr=1e-6)\n",
        "\n",
        "results2 = []\n",
        "\n",
        "for epoch in range(0,100):\n",
        "  print(\"Epoch: \", epoch)\n",
        "  # train\n",
        "  tr_loss = 0.0\n",
        "  rnn.train()\n",
        "\n",
        "  for x, y in train_loader:\n",
        "    rnn = rnn.to(device)\n",
        "    x = x.to(device)\n",
        "    #print(x.shape)\n",
        "    #print(x.shape[0])\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    output, hidden = rnn(x)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(output, y)\n",
        "    loss.backward()\n",
        "\n",
        "    # step\n",
        "    optimizer.step()\n",
        "\n",
        "    # update loss \n",
        "    tr_loss += loss.item()\n",
        "\n",
        "    \n",
        "  print(tr_loss)\n",
        "\n",
        "  # test\n",
        "  test_loss = 0.0\n",
        "  rnn.eval()\n",
        "\n",
        "  yhat = []\n",
        "  yreal = []\n",
        "\n",
        "  for x, y in test_loader:\n",
        "    x = x.to(device)\n",
        "    x = nn.functional.pad(x, (1,191), \"constant\", 0)\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    test_out,hidden = rnn(x)\n",
        "\n",
        "    bsize = test_out.shape[0]\n",
        "\n",
        "    if(epoch == 99):\n",
        "      \n",
        "      for i in range(bsize):\n",
        "        yhat.append(test_out[i].tolist()[0])\n",
        "        yreal.append(y[i].tolist()[0])\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(test_out, y)\n",
        "    #loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    test_loss += loss.item()\n",
        "\n",
        "  print(test_loss)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  new_row = {\"Epoch\":epoch, \"Training_Loss\":tr_loss, \"Test_Loss\":test_loss}\n",
        "  results2.append(new_row)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "for x, y in train_loader:\n",
        "  print(x[0])\n",
        "  print(y[0])\n",
        "  print(x.shape)\n",
        "  x = torch.reshape(x,(512,1, 2470))\n",
        "  print(x.shape)\n",
        "  out, h_n = rnn(x) \n",
        "  break\n",
        "\"\"\""
      ],
      "execution_count": 608,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0\n",
            "572.0655217170715\n",
            "503.7477517127991\n",
            "Epoch:  1\n",
            "572.0327110290527\n",
            "503.714515209198\n",
            "Epoch:  2\n",
            "571.9793992042542\n",
            "503.6626362800598\n",
            "Epoch:  3\n",
            "571.8761925697327\n",
            "503.5749535560608\n",
            "Epoch:  4\n",
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            "Epoch:  5\n",
            "571.3820042610168\n",
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            "Epoch:  6\n",
            "570.7743048667908\n",
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            "Epoch:  7\n",
            "569.6705160140991\n",
            "501.6960196495056\n",
            "Epoch:  8\n",
            "567.7986164093018\n",
            "500.2032194137573\n",
            "Epoch:  9\n",
            "564.7942667007446\n",
            "497.96184730529785\n",
            "Epoch:  10\n",
            "560.6344299316406\n",
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            "Epoch:  11\n",
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            "Epoch:  12\n",
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            "Epoch:  13\n",
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            "Epoch:  14\n",
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            "Epoch:  15\n",
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            "Epoch:  16\n",
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            "Epoch:  97\n",
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            "Epoch:  98\n",
            "225.60310912132263\n",
            "198.76332235336304\n",
            "Epoch:  99\n",
            "224.14688634872437\n",
            "198.013521194458\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nfor x, y in train_loader:\\n  print(x[0])\\n  print(y[0])\\n  print(x.shape)\\n  x = torch.reshape(x,(512,1, 2470))\\n  print(x.shape)\\n  out, h_n = rnn(x) \\n  break\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 608
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7Nluw5sy6Cr",
        "outputId": "a2ec6d94-d881-4a0a-fc74-ab79ae44e031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "difference = []\n",
        "\n",
        "mean_diffs = [[],[],[],[],[],[],[],[],[],[]]\n",
        "print(len(mean_diffs))\n",
        "for i in range(len(yhat)):\n",
        "  d = yhat[i] - float(yreal[i])\n",
        "  difference.append(d)\n",
        "  #print(int(yreal[i]))\n",
        "  mean_diffs[(int(yreal[i])-1)].append(d)\n",
        "\n",
        "final_mean_diffs = []\n",
        "\n",
        "for i in range(len(mean_diffs)):\n",
        "  try:\n",
        "    print(sum(mean_diffs[i])/ len(mean_diffs[i]))\n",
        "    avgd = sum(mean_diffs[i])/ len(mean_diffs[i])\n",
        "    final_mean_diffs.append(avgd)\n",
        "  except:\n",
        "    print(\"\", end= \"\")\n",
        "    #final_mean_diffs.append(0)\n",
        "\n",
        "print(final_mean_diffs)\n",
        "\n",
        "xs = [1,2,3,4,7,8,9,10]\n",
        "\n",
        "plt.title(\"Model A: MLP\")\n",
        "plt.scatter(yreal, difference)\n",
        "plt.show()"
      ],
      "execution_count": 609,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "2.3268760812087916\n",
            "1.3422035079329662\n",
            "0.3566266908649383\n",
            "-0.6377309439983042\n",
            "-3.6113215139618626\n",
            "-4.616145386556873\n",
            "-5.624743058015635\n",
            "-6.640417375862343\n",
            "[2.3268760812087916, 1.3422035079329662, 0.3566266908649383, -0.6377309439983042, -3.6113215139618626, -4.616145386556873, -5.624743058015635, -6.640417375862343]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEq-Re4ry7tM",
        "outputId": "615bf321-2a99-49c6-9207-bb750e376335",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(xs, final_mean_diffs)\n",
        "plt.title(\"Average Error Commited Per True Rating Model B (RNN)\")\n",
        "plt.xlabel(\"True Rating\")\n",
        "plt.ylabel(\"Average Error\")\n",
        "plt.show()\n",
        "meta_diffs_save.append(final_mean_diffs)"
      ],
      "execution_count": 610,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dsJMnhe2IvhZ",
        "outputId": "9c28b058-f0e6-4498-acd2-9e8617b02af1"
      },
      "source": [
        "df2 = pd.DataFrame(results2)\n",
        "df2.head()"
      ],
      "execution_count": 209,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>572.065522</td>\n",
              "      <td>503.747752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>572.032711</td>\n",
              "      <td>503.714515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>571.979399</td>\n",
              "      <td>503.662636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>571.876193</td>\n",
              "      <td>503.574954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>571.702729</td>\n",
              "      <td>503.414956</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Epoch  Training_Loss   Test_Loss\n",
              "0      0     572.065522  503.747752\n",
              "1      1     572.032711  503.714515\n",
              "2      2     571.979399  503.662636\n",
              "3      3     571.876193  503.574954\n",
              "4      4     571.702729  503.414956"
            ]
          },
          "metadata": {},
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "7Ckrh_-vfgTJ",
        "outputId": "8da2caa1-72da-4319-9f30-a6438421c4e1"
      },
      "source": [
        "df2.sort_values(by = \"Training_Loss\")"
      ],
      "execution_count": 210,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>99</td>\n",
              "      <td>224.146886</td>\n",
              "      <td>198.013521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>98</td>\n",
              "      <td>225.603109</td>\n",
              "      <td>198.763322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>97</td>\n",
              "      <td>226.522400</td>\n",
              "      <td>199.533224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>96</td>\n",
              "      <td>227.566948</td>\n",
              "      <td>200.325632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>95</td>\n",
              "      <td>228.370713</td>\n",
              "      <td>201.133093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>571.702729</td>\n",
              "      <td>503.414956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>571.876193</td>\n",
              "      <td>503.574954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>571.979399</td>\n",
              "      <td>503.662636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>572.032711</td>\n",
              "      <td>503.714515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>572.065522</td>\n",
              "      <td>503.747752</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Epoch  Training_Loss   Test_Loss\n",
              "99     99     224.146886  198.013521\n",
              "98     98     225.603109  198.763322\n",
              "97     97     226.522400  199.533224\n",
              "96     96     227.566948  200.325632\n",
              "95     95     228.370713  201.133093\n",
              "..    ...            ...         ...\n",
              "4       4     571.702729  503.414956\n",
              "3       3     571.876193  503.574954\n",
              "2       2     571.979399  503.662636\n",
              "1       1     572.032711  503.714515\n",
              "0       0     572.065522  503.747752\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "O_dIhad8UrW1",
        "outputId": "ca76d95b-609d-4b35-c056-44545f2e6603"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "batch_size = 2048\n",
        "plt.plot(df2[\"Epoch\"], df2[\"Training_Loss\"]/batch_size, color = \"dodgerblue\")\n",
        "plt.plot(df2[\"Epoch\"], df2[\"Test_Loss\"]/batch_size, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 100 Epochs For RNN\")\n",
        "plt.legend([\"Train\", \"Validation\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_E8o0HY-jBMF"
      },
      "source": [
        "# LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HGt28G1vU--G",
        "outputId": "331f3682-0349-4bd6-9634-399ffaaaa83f"
      },
      "source": [
        "#rnn = nn.RNN(input_size = 2470, hidden_size= 128, num_layers=1 , batch_first=True )\n",
        "from torch.autograd import Variable \n",
        "\n",
        "class RNN(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.num_layers = 2\n",
        "    self.hidden_size = 512\n",
        "    #self.layers = nn.Sequential(nn.RNN(input_size = 2470, hidden_size= 128, num_layers=1 , batch_first=True ), nn.Linear(128, 1))\n",
        "    self.rnn_layer = nn.LSTM(input_size = 2470, hidden_size= 512, num_layers=1 , batch_first=True, bidirectional = True)\n",
        "    \n",
        "\n",
        "    self.lrelu = nn.LeakyReLU()\n",
        "    self.fc_1 =  nn.Linear(512, 128)\n",
        "    self.fc = nn.Linear(128, 1)\n",
        "\n",
        "    self.layers = nn.Sequential( nn.Linear(512,64),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01), \n",
        "                                \n",
        "                                nn.Linear(64,12),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01),\n",
        "                                nn.Linear(12,1),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01))\n",
        "\n",
        "    self.layers2 = nn.Sequential(nn.Linear(512, 64),\n",
        "                                nn.LeakyReLU(),\n",
        "                                #nn.BatchNorm1d(512),\n",
        "                                nn.Linear(64, 64),\n",
        "                                nn.LeakyReLU(),\n",
        "                                #nn.BatchNorm1d(512),\n",
        "                                nn.Linear(64,12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                #nn.BatchNorm1d(512),\n",
        "                                nn.Linear(12, 12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(12, 1),\n",
        "                                nn.LeakyReLU()\n",
        "                                #nn.BatchNorm1d(1)\n",
        "                                )\n",
        "\n",
        "\n",
        "  def forward(self, x):\n",
        "    #print(\"X: \", x.shape)\n",
        "    h_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) #hidden state\n",
        "    c_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) #internal state\n",
        "    h_0 = h_0.to(device)\n",
        "    c_0 = c_0.to(device)\n",
        "\n",
        "    # Propagate input through LSTM\n",
        "    output, (hn, cn) = self.rnn_layer(x, (h_0,c_0))\n",
        "    #print(\"O3: \", output.shape)\n",
        "    #hn = hn.view(-1, self.hidden_size)\n",
        "    out = hn\n",
        "    out = self.layers(hn)\n",
        "    #out = self.lrelu(hn)\n",
        "    #print(out.shape)\n",
        "    #out = self.fc_1(out)\n",
        "    #out = self.lrelu(out)\n",
        "    #out = self.fc(out)\n",
        "    #print(\"O2: \", out.shape)\n",
        "    return out\n",
        "\n",
        "    \"\"\"\n",
        "    hidden_states, _ = self.rnn_layer(x)\n",
        "    last_hidden_state = hidden_states[:, -1, :] # Last hidden state\n",
        "    out = self.linear1(self.dropout(last_hidden_state))\n",
        "    out = self.leaky1(self.linear2(out))\n",
        "    #out = self.leaky1(self.linear3(out))\n",
        "\n",
        "    return out, last_hidden_state\n",
        "    \"\"\"\n",
        "\n",
        "rnn = RNN()\n",
        "\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "torch.manual_seed(50)\n",
        "\n",
        "optimizer = torch.optim.Adam(rnn.parameters(), lr=1e-5)\n",
        "\n",
        "results3 = []\n",
        "\n",
        "for epoch in range(0, 100):\n",
        "  print(\"Epoch: \", epoch)\n",
        "  # train\n",
        "  tr_loss = 0.0\n",
        "  rnn.train()\n",
        "\n",
        "  for x, y in train_loader:\n",
        "    rnn = rnn.to(device)\n",
        "    x = x.to(device)\n",
        "    #print(x.shape)\n",
        "    #print(x.shape[0])\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    output = rnn(x)\n",
        "    #print(\"O: \", output.shape)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(output, y)\n",
        "    loss.backward()\n",
        "\n",
        "    # step\n",
        "    optimizer.step()\n",
        "\n",
        "    # update loss \n",
        "    tr_loss += loss.item()\n",
        "\n",
        "    \n",
        "  print(tr_loss)\n",
        "\n",
        "  # test\n",
        "  test_loss = 0.0\n",
        "  rnn.eval()\n",
        "\n",
        "  yhat = []\n",
        "  yreal = []\n",
        "\n",
        "  for x, y in test_loader:\n",
        "    x = x.to(device)\n",
        "    x = nn.functional.pad(x, (1,191), \"constant\", 0)\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    test_out,hidden = rnn(x)\n",
        "\n",
        "    if(epoch == 99):\n",
        "      for i in range(bsize):\n",
        "        yhat.append(test_out[i].tolist()[0])\n",
        "        yreal.append(y[i].tolist()[0])\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(test_out, y)\n",
        "    #loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    test_loss += loss.item()\n",
        "\n",
        "  print(test_loss)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  new_row = {\"Epoch\":epoch, \"Training_Loss\":tr_loss, \"Test_Loss\":test_loss}\n",
        "  results3.append(new_row)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "for x, y in train_loader:\n",
        "  print(x[0])\n",
        "  print(y[0])\n",
        "  print(x.shape)\n",
        "  x = torch.reshape(x,(512,1, 2470))\n",
        "  print(x.shape)\n",
        "  out, h_n = rnn(x) \n",
        "  break\n",
        "\"\"\""
      ],
      "execution_count": 611,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([2048, 1])) that is different to the input size (torch.Size([2, 2048, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([424, 1])) that is different to the input size (torch.Size([2, 424, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "571.9984316825867\n",
            "503.6975955963135\n",
            "Epoch:  1\n",
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            "503.6766128540039\n",
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            "162.7976565361023\n",
            "151.80762577056885\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nfor x, y in train_loader:\\n  print(x[0])\\n  print(y[0])\\n  print(x.shape)\\n  x = torch.reshape(x,(512,1, 2470))\\n  print(x.shape)\\n  out, h_n = rnn(x) \\n  break\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 611
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUszzL1l0Hna",
        "outputId": "7600011e-2dba-437f-c9a1-b4b50903b745",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "difference = []\n",
        "\n",
        "mean_diffs = [[],[],[],[],[],[],[],[],[],[]]\n",
        "print(len(mean_diffs))\n",
        "for i in range(len(yhat)):\n",
        "  d = yhat[i] - float(yreal[i])\n",
        "  difference.append(d)\n",
        "  #print(int(yreal[i]))\n",
        "  mean_diffs[(int(yreal[i])-1)].append(d)\n",
        "\n",
        "final_mean_diffs = []\n",
        "\n",
        "for i in range(len(mean_diffs)):\n",
        "  try:\n",
        "    print(sum(mean_diffs[i])/ len(mean_diffs[i]))\n",
        "    avgd = sum(mean_diffs[i])/ len(mean_diffs[i])\n",
        "    final_mean_diffs.append(avgd)\n",
        "  except:\n",
        "    print(\"\", end= \"\")\n",
        "    #final_mean_diffs.append(0)\n",
        "\n",
        "print(final_mean_diffs)\n",
        "\n",
        "xs = [1,2,3,4,7,8,9,10]\n",
        "\n",
        "plt.title(\"Model A: MLP\")\n",
        "plt.scatter(yreal, difference)\n",
        "plt.show()"
      ],
      "execution_count": 612,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "5.15652894005037\n",
            "4.172389991095617\n",
            "3.189596166873114\n",
            "2.212981247612859\n",
            "-0.7778056215130116\n",
            "-1.7688372725759531\n",
            "-2.786206452916357\n",
            "-3.7930758493953873\n",
            "[5.15652894005037, 4.172389991095617, 3.189596166873114, 2.212981247612859, -0.7778056215130116, -1.7688372725759531, -2.786206452916357, -3.7930758493953873]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FpWo6Ile0JcO",
        "outputId": "5813948e-5248-4706-cf9e-25aa851f3401",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(xs, final_mean_diffs)\n",
        "plt.title(\"Average Error Commited Per True Rating Model C (LSTM-1)\")\n",
        "plt.xlabel(\"True Rating\")\n",
        "plt.ylabel(\"Average Error\")\n",
        "plt.show()\n",
        "meta_diffs_save.append(final_mean_diffs)"
      ],
      "execution_count": 613,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQH64zQw1bd_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6DqtL2EpkM2W",
        "outputId": "11b035ff-2daa-4857-f177-74b7a345f3d8"
      },
      "source": [
        "df3 = pd.DataFrame(results3)\n",
        "df3.head()"
      ],
      "execution_count": 204,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>571.998432</td>\n",
              "      <td>503.697596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>571.975327</td>\n",
              "      <td>503.676613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>571.944840</td>\n",
              "      <td>503.649272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>571.907541</td>\n",
              "      <td>503.613916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>571.848269</td>\n",
              "      <td>503.536860</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Epoch  Training_Loss   Test_Loss\n",
              "0      0     571.998432  503.697596\n",
              "1      1     571.975327  503.676613\n",
              "2      2     571.944840  503.649272\n",
              "3      3     571.907541  503.613916\n",
              "4      4     571.848269  503.536860"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "CT5rBLLEkRbi",
        "outputId": "259dac76-c1c1-4390-9f24-72816f161ec5"
      },
      "source": [
        "df3.sort_values(by = \"Training_Loss\")"
      ],
      "execution_count": 205,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>76</td>\n",
              "      <td>155.839399</td>\n",
              "      <td>142.923285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>77</td>\n",
              "      <td>155.923751</td>\n",
              "      <td>143.386395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>75</td>\n",
              "      <td>156.236891</td>\n",
              "      <td>142.871583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>78</td>\n",
              "      <td>156.446977</td>\n",
              "      <td>144.258665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>74</td>\n",
              "      <td>157.099553</td>\n",
              "      <td>143.252933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>571.848269</td>\n",
              "      <td>503.536860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>571.907541</td>\n",
              "      <td>503.613916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>571.944840</td>\n",
              "      <td>503.649272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>571.975327</td>\n",
              "      <td>503.676613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>571.998432</td>\n",
              "      <td>503.697596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Epoch  Training_Loss   Test_Loss\n",
              "76     76     155.839399  142.923285\n",
              "77     77     155.923751  143.386395\n",
              "75     75     156.236891  142.871583\n",
              "78     78     156.446977  144.258665\n",
              "74     74     157.099553  143.252933\n",
              "..    ...            ...         ...\n",
              "4       4     571.848269  503.536860\n",
              "3       3     571.907541  503.613916\n",
              "2       2     571.944840  503.649272\n",
              "1       1     571.975327  503.676613\n",
              "0       0     571.998432  503.697596\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cZU5gCeelYLK",
        "outputId": "d28e938a-159b-47e0-fbbf-c4188885df72"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "batch_size = 2048\n",
        "plt.plot(df3[\"Epoch\"], df3[\"Training_Loss\"]/batch_size, color = \"dodgerblue\")\n",
        "plt.plot(df3[\"Epoch\"], df3[\"Test_Loss\"]/batch_size, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 100 Epochs For LSTM-1\")\n",
        "plt.legend([\"Train\", \"Validation\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5uWLL4IF3Aa"
      },
      "source": [
        "# LSTM - 2 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7-9iJ1IH3F6"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class LSTM1(nn.ModuleList):\n",
        "  def __init__(self):\n",
        "    super(LSTM1, self).__init__()\n",
        "    # Number of samples per time step\n",
        "    self.batch_size = 1024\n",
        "    \n",
        "    # Dimension of weight vectors\n",
        "    self.hidden_dim = 1\n",
        "    \n",
        "    # Dimension of embedded tensor\n",
        "    self.embedding_dim = 1235\n",
        "    \n",
        "    # The vocabulary size\n",
        "    self.input_size = 2470\n",
        "    \n",
        "    # Number of time steps\n",
        "    self.sequence_len = 2\n",
        "    \n",
        "    # Initialize embedding layer\n",
        "    self.embedding = nn.Embedding(self.input_size, self.embedding_dim, padding_idx=0)\n",
        "    \n",
        "    # Initialize LSTM Cell\n",
        "    self.lstm_cell = nn.LSTMCell(self.embedding_dim, self.hidden_dim)\n",
        "  def forward(self, x):\n",
        "\t\n",
        "    # Creation of cell state and hidden state\n",
        "    hidden_state = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    cell_state = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    \n",
        "    hidden_state = hidden_state.to(device)\n",
        "    cell_state = cell_state.to(device)\n",
        "    \n",
        "    # Weights initialization\n",
        "    torch.nn.init.xavier_normal_(hidden_state)\n",
        "    torch.nn.init.xavier_normal_(cell_state)\n",
        "    \n",
        "    # From idx-token to embedded tensors\n",
        "    #print(\"X Shape: \", x.shape)\n",
        "    #out = self.embedding(x)\n",
        "    out = x\n",
        "    #print(\"Out Shape: \", out.shape)\n",
        "    \n",
        "    # Prepare the shape for LSTMCell\n",
        "    out = out.view(self.sequence_len, x.size(0), -1)\n",
        "    #print(\"Out Shape 2: \", out.shape)\n",
        "    \n",
        "    # Unfolding LSTM\n",
        "    for i in range(self.sequence_len):\n",
        "      hidden_state, cell_state = self.lstm_cell(out[i], (hidden_state, cell_state))\n",
        "      \n",
        "    return hidden_state"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4M_oLERQaXh"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3MEWvvzQaCu"
      },
      "source": [
        "# LSTM 2\n",
        "class LSTM2(nn.ModuleList):\n",
        "  def __init__(self):\n",
        "    super(LSTM2, self).__init__()\n",
        "    # Number of samples per time step\n",
        "    self.batch_size = 2048\n",
        "    \n",
        "    # Dimension of weight vectors\n",
        "    self.hidden_dim = 128\n",
        "    \n",
        "    # Dimension of embedded tensor\n",
        "    self.embedding_dim = 1235\n",
        "    \n",
        "    # The vocabulary size\n",
        "    self.input_size = 2470\n",
        "    \n",
        "    # Number of time steps\n",
        "    self.sequence_len = 2\n",
        "    \n",
        "    # Initialize embedding layer\n",
        "    self.embedding = nn.Embedding(self.input_size, self.embedding_dim, padding_idx=0)\n",
        "    \n",
        "    # Initialize LSTM Cell\n",
        "    self.lstm_cell = nn.LSTMCell(self.embedding_dim, self.hidden_dim)\n",
        "\n",
        "\n",
        "    # Initialize LSTM Cell for the first layer\n",
        "    self.lstm_cell_1 = nn.LSTMCell(self.embedding_dim, self.hidden_dim)\n",
        "\n",
        "    # Initialize LSTM Cell for the second layer\n",
        "    self.lstm_cell_2 = nn.LSTMCell(self.hidden_dim, self.hidden_dim)\n",
        "\n",
        "    self.fully_connected = nn.Linear(128, 64)\n",
        "    self.lrelu = nn.LeakyReLU(negative_slope=2)\n",
        "    self.nrelu = nn.LeakyReLU(negative_slope=2)\n",
        "    self.fc2 = nn.Linear(64, 1)\n",
        "    self.fc3 = nn.Linear(12, 1)\n",
        "\n",
        "\n",
        "    self.layers = nn.Sequential( nn.Linear(128,64),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01), \n",
        "                                \n",
        "                                nn.Linear(64,12),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01),\n",
        "                                nn.Linear(12,1),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01))\n",
        "\n",
        "\n",
        "  def forward(self, x):\n",
        "\t\n",
        "    # batch_size x hidden_size\n",
        "    hidden_state = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    cell_state = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    hidden_state_2 = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    cell_state_2 = torch.zeros(x.size(0), self.hidden_dim)\n",
        "\n",
        "    hidden_state = hidden_state.to(device)\n",
        "    cell_state = cell_state.to(device)\n",
        "    hidden_state_2 = hidden_state_2.to(device)\n",
        "    cell_state_2 = cell_state_2.to(device)\n",
        "    \n",
        "    # weights initialization\n",
        "    torch.nn.init.xavier_normal_(hidden_state)\n",
        "    torch.nn.init.xavier_normal_(cell_state)\n",
        "    torch.nn.init.xavier_normal_(hidden_state_2)\n",
        "    torch.nn.init.xavier_normal_(cell_state_2)\n",
        "    \n",
        "    # From idx to embedding\n",
        "    #out = self.embedding(x)\n",
        "    out = x\n",
        "    \n",
        "    # Prepare the shape for LSTMCell\n",
        "    out = out.view(self.sequence_len, x.size(0), -1)\n",
        "    \n",
        "    # Unfolding LSTM\n",
        "    # Last hidden_state will be used to feed the fully connected neural net\n",
        "    for i in range(self.sequence_len):\n",
        "      hidden_state, cell_state = self.lstm_cell_1(out[i], (hidden_state, cell_state))\n",
        "      hidden_state_2, cell_state_2 = self.lstm_cell_2(hidden_state, (hidden_state_2, cell_state_2))\n",
        "      \n",
        "    # Last hidden state is passed through a fully connected neural net\n",
        "    #print(hidden_state_2.shape)\n",
        "    out = self.layers(hidden_state_2)\n",
        "    #out = self.fully_connected(hidden_state_2)\t\n",
        "    #out = self.lrelu(out)\n",
        "    #out = self.fc2(out)\n",
        "    #out = self.nrelu(out)\n",
        "    #out = self.fc3(out)    \n",
        "    return out"
      ],
      "execution_count": 402,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZE_OFJbzHJC-",
        "outputId": "c291fae5-c0af-43d9-822b-a8b4b5ce876a"
      },
      "source": [
        "#rnn = nn.RNN(input_size = 2470, hidden_size= 128, num_layers=1 , batch_first=True )\n",
        "from torch.autograd import Variable \n",
        "\n",
        "rnn = LSTM2()\n",
        "\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "torch.manual_seed(50)\n",
        "\n",
        "optimizer = torch.optim.Adam(rnn.parameters(), lr=1e-5)\n",
        "\n",
        "results4 = []\n",
        "\n",
        "for epoch in range(0,150):\n",
        "  print(\"Epoch: \", epoch)\n",
        "  # train\n",
        "  tr_loss = 0.0\n",
        "  rnn.train()\n",
        "\n",
        "  for x, y in train_loader:\n",
        "    rnn = rnn.to(device)\n",
        "    x = x.to(device)\n",
        "    #print(x.shape)\n",
        "    #print(x.shape[0])\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "    #x = x.long()\n",
        "\n",
        "    output = rnn(x)\n",
        "    #print(\"O: \", output.shape)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(output, y)\n",
        "    loss.backward()\n",
        "\n",
        "    # step\n",
        "    optimizer.step()\n",
        "\n",
        "    # update loss \n",
        "    tr_loss += loss.item()\n",
        "\n",
        "    \n",
        "  print(tr_loss)\n",
        "\n",
        "  # test\n",
        "  test_loss = 0.0\n",
        "  rnn.eval()\n",
        "\n",
        "  \n",
        "  for x, y in test_loader:\n",
        "    x = x.to(device)\n",
        "    x = nn.functional.pad(x, (1,191), \"constant\", 0)\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    test_out = rnn(x)\n",
        "\n",
        "    bsize = test_out.shape[0]\n",
        "\n",
        "    \n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(test_out, y)\n",
        "    #loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    test_loss += loss.item()\n",
        "\n",
        "  print(test_loss)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  new_row = {\"Epoch\":epoch, \"Training_Loss\":tr_loss, \"Test_Loss\":test_loss}\n",
        "  results4.append(new_row)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "for x, y in train_loader:\n",
        "  print(x[0])\n",
        "  print(y[0])\n",
        "  print(x.shape)\n",
        "  x = torch.reshape(x,(512,1, 2470))\n",
        "  print(x.shape)\n",
        "  out, h_n = rnn(x) \n",
        "  break\n",
        "\"\"\""
      ],
      "execution_count": 403,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0\n",
            "571.722011089325\n",
            "503.45845890045166\n",
            "Epoch:  1\n",
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            "503.454270362854\n",
            "Epoch:  2\n",
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            "Epoch:  3\n",
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            "Epoch:  4\n",
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            "Epoch:  5\n",
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            "Epoch:  6\n",
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            "Epoch:  7\n",
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            "503.15785217285156\n",
            "Epoch:  8\n",
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            "Epoch:  9\n",
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            "Epoch:  10\n",
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            "Epoch:  11\n",
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            "Epoch:  12\n",
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            "Epoch:  13\n",
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            "Epoch:  14\n",
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            "Epoch:  16\n",
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            "Epoch:  20\n",
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            "197.04187107086182\n",
            "177.84512186050415\n",
            "Epoch:  103\n",
            "194.12765836715698\n",
            "175.56934213638306\n",
            "Epoch:  104\n",
            "192.06610441207886\n",
            "173.710111618042\n",
            "Epoch:  105\n",
            "189.4467215538025\n",
            "171.6153907775879\n",
            "Epoch:  106\n",
            "186.5795660018921\n",
            "169.82101440429688\n",
            "Epoch:  107\n",
            "184.3298282623291\n",
            "167.88086318969727\n",
            "Epoch:  108\n",
            "181.49696445465088\n",
            "166.3337516784668\n",
            "Epoch:  109\n",
            "179.77135848999023\n",
            "164.72046375274658\n",
            "Epoch:  110\n",
            "177.46753025054932\n",
            "163.58650588989258\n",
            "Epoch:  111\n",
            "175.62179279327393\n",
            "162.49197006225586\n",
            "Epoch:  112\n",
            "174.09564018249512\n",
            "161.6080493927002\n",
            "Epoch:  113\n",
            "173.01132583618164\n",
            "160.91040325164795\n",
            "Epoch:  114\n",
            "171.7557716369629\n",
            "160.25859260559082\n",
            "Epoch:  115\n",
            "171.08059787750244\n",
            "160.3054027557373\n",
            "Epoch:  116\n",
            "170.42687797546387\n",
            "160.3614912033081\n",
            "Epoch:  117\n",
            "170.11708641052246\n",
            "160.76641750335693\n",
            "Epoch:  118\n",
            "170.15155220031738\n",
            "161.48901081085205\n",
            "Epoch:  119\n",
            "170.01956176757812\n",
            "161.960355758667\n",
            "Epoch:  120\n",
            "170.6645164489746\n",
            "162.79490756988525\n",
            "Epoch:  121\n",
            "170.7803201675415\n",
            "163.716233253479\n",
            "Epoch:  122\n",
            "171.43160247802734\n",
            "164.67172050476074\n",
            "Epoch:  123\n",
            "172.353928565979\n",
            "166.025315284729\n",
            "Epoch:  124\n",
            "172.8288893699646\n",
            "166.80849266052246\n",
            "Epoch:  125\n",
            "173.6707797050476\n",
            "167.94941997528076\n",
            "Epoch:  126\n",
            "173.99197006225586\n",
            "168.56100797653198\n",
            "Epoch:  127\n",
            "175.02589511871338\n",
            "169.4061942100525\n",
            "Epoch:  128\n",
            "175.3162236213684\n",
            "170.161479473114\n",
            "Epoch:  129\n",
            "175.76977348327637\n",
            "170.38001680374146\n",
            "Epoch:  130\n",
            "175.81024980545044\n",
            "170.71614933013916\n",
            "Epoch:  131\n",
            "176.1757345199585\n",
            "170.69278240203857\n",
            "Epoch:  132\n",
            "175.58451461791992\n",
            "170.32604551315308\n",
            "Epoch:  133\n",
            "175.32099723815918\n",
            "169.7543535232544\n",
            "Epoch:  134\n",
            "174.77670192718506\n",
            "169.01478719711304\n",
            "Epoch:  135\n",
            "173.65043210983276\n",
            "167.8123016357422\n",
            "Epoch:  136\n",
            "172.4005994796753\n",
            "166.5698366165161\n",
            "Epoch:  137\n",
            "171.3225121498108\n",
            "165.13156366348267\n",
            "Epoch:  138\n",
            "169.88104963302612\n",
            "163.60654306411743\n",
            "Epoch:  139\n",
            "168.39473247528076\n",
            "161.90813159942627\n",
            "Epoch:  140\n",
            "167.17293691635132\n",
            "160.31593322753906\n",
            "Epoch:  141\n",
            "165.52213525772095\n",
            "158.65560913085938\n",
            "Epoch:  142\n",
            "164.162531375885\n",
            "157.07805728912354\n",
            "Epoch:  143\n",
            "162.80950164794922\n",
            "155.39267826080322\n",
            "Epoch:  144\n",
            "161.8890781402588\n",
            "154.19393253326416\n",
            "Epoch:  145\n",
            "161.03460025787354\n",
            "153.07304096221924\n",
            "Epoch:  146\n",
            "160.12223720550537\n",
            "152.00028324127197\n",
            "Epoch:  147\n",
            "159.45288276672363\n",
            "151.11540699005127\n",
            "Epoch:  148\n",
            "159.13409805297852\n",
            "150.43212890625\n",
            "Epoch:  149\n",
            "159.08238697052002\n",
            "149.78326416015625\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nfor x, y in train_loader:\\n  print(x[0])\\n  print(y[0])\\n  print(x.shape)\\n  x = torch.reshape(x,(512,1, 2470))\\n  print(x.shape)\\n  out, h_n = rnn(x) \\n  break\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 403
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7EVJB3hI8g0"
      },
      "source": [
        "#device = \"cuda\""
      ],
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Sa_V7TlkLNEa",
        "outputId": "7e353b1c-71b9-4ec3-a1d2-d4174569a2b1"
      },
      "source": [
        "df4 = pd.DataFrame(results4)\n",
        "df4.head()\n",
        "\n"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>571.946454</td>\n",
              "      <td>503.657888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>571.942441</td>\n",
              "      <td>503.654031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>571.937607</td>\n",
              "      <td>503.649617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>571.932324</td>\n",
              "      <td>503.645019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>571.926931</td>\n",
              "      <td>503.640352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Epoch  Training_Loss   Test_Loss\n",
              "0      0     571.946454  503.657888\n",
              "1      1     571.942441  503.654031\n",
              "2      2     571.937607  503.649617\n",
              "3      3     571.932324  503.645019\n",
              "4      4     571.926931  503.640352"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9IjVfDA-TZPs",
        "outputId": "766797b5-0c5f-44c6-a153-40b929d5779c"
      },
      "source": [
        "df4.sort_values(by = \"Training_Loss\")"
      ],
      "execution_count": 202,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>99</td>\n",
              "      <td>201.262475</td>\n",
              "      <td>181.954710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>98</td>\n",
              "      <td>202.715850</td>\n",
              "      <td>182.935975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>97</td>\n",
              "      <td>203.208334</td>\n",
              "      <td>183.822592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>96</td>\n",
              "      <td>204.007402</td>\n",
              "      <td>184.609153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>95</td>\n",
              "      <td>204.837796</td>\n",
              "      <td>185.394363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>571.926931</td>\n",
              "      <td>503.640352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>571.932324</td>\n",
              "      <td>503.645019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>571.937607</td>\n",
              "      <td>503.649617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>571.942441</td>\n",
              "      <td>503.654031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>571.946454</td>\n",
              "      <td>503.657888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Epoch  Training_Loss   Test_Loss\n",
              "99     99     201.262475  181.954710\n",
              "98     98     202.715850  182.935975\n",
              "97     97     203.208334  183.822592\n",
              "96     96     204.007402  184.609153\n",
              "95     95     204.837796  185.394363\n",
              "..    ...            ...         ...\n",
              "4       4     571.926931  503.640352\n",
              "3       3     571.932324  503.645019\n",
              "2       2     571.937607  503.649617\n",
              "1       1     571.942441  503.654031\n",
              "0       0     571.946454  503.657888\n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gA4k6nGITaEc",
        "outputId": "318ad7e3-3b52-44c1-cf40-f39fb63d0943"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "batch_size = 2048\n",
        "\n",
        "plt.plot(df4[\"Epoch\"], df4[\"Training_Loss\"]/batch_size, color = \"dodgerblue\")\n",
        "plt.plot(df4[\"Epoch\"], df4[\"Test_Loss\"]/batch_size, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 100 Epochs For LSTM-2\")\n",
        "plt.legend([\"Train\", \"Validation\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpGF6W88kbTD"
      },
      "source": [
        "# GRU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "riYA71okUBuI"
      },
      "source": [
        "class GRUNet(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim, n_layers, drop_prob=0.1):\n",
        "        super(GRUNet, self).__init__()\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.n_layers = n_layers\n",
        "        \n",
        "        self.gru = nn.GRU(input_dim, hidden_dim, n_layers, batch_first=True, dropout=drop_prob)\n",
        "\n",
        "        self.layers = nn.Sequential(nn.Linear(hidden_dim, 128),\n",
        "                                    nn.LeakyReLU(),\n",
        "                                    nn.Linear(128, 70),\n",
        "                                    nn.LeakyReLU(),\n",
        "                                    nn.Linear(70, 64),\n",
        "                                    nn.LeakyReLU(),\n",
        "                                    nn.Linear(64, 32),\n",
        "                                    nn.Linear(32, 16),\n",
        "                                    nn.Linear(16,1)\n",
        "                                    )\n",
        "        self.layers3 = nn.Sequential(nn.Linear(1024, 64), nn.LeakyReLU(), nn.Linear(64,64), nn.LeakyReLU(), nn.Linear(64,1), nn.LeakyReLU())\n",
        "        self.layers2 = nn.Sequential(nn.Flatten(),\n",
        "                                nn.Linear(1024, 512),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(512),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(512, 64),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(64),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(64, 12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(12),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(12,12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(12),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(12, 10),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(10,5),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(5,5),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(5,1),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(1)\n",
        "\n",
        "                                )\n",
        "        \"\"\"\n",
        "        self.fc = nn.Linear(hidden_dim, 128)\n",
        "        self.relu = nn.LeakyReLU(negative_slope = 0.01)\n",
        "        self.fc_bn = nn.BatchNorm1d(hidden_dim)\n",
        "        self.fc2 = nn.Linear(128, 70)\n",
        "        self.fc2_bn = nn.BatchNorm1d(512)\n",
        "        self.fc3 = nn.Linear(70, 64)\n",
        "        self.fc3_bn = nn.BatchNorm1d(128)\n",
        "        self.fc4 = nn.Linear(64, 32)\n",
        "        self.fc4_bn = nn.BatchNorm1d(64)\n",
        "        self.fc5 = nn.Linear(32, 16)\n",
        "        self.fc5_bn = nn.BatchNorm1d(32)\n",
        "        self.fc6 = nn.Linear(16,1)\n",
        "        \"\"\"\n",
        "\n",
        "\n",
        "        \n",
        "    def forward(self, x, h):\n",
        "        out, h = self.gru(x, h)\n",
        "        out = self.layers2(out)\n",
        "        #out = self.fc(self.relu(self.fc_bn(out[:,-1])))\n",
        "        #out = self.fc2(self.relu(self.fc2_bn(out)))\n",
        "        #out = self.fc3(self.relu(self.fc3_bn(out)))\n",
        "        #out = self.fc4(self.relu(self.fc4_bn(out)))\n",
        "        #out = self.fc5(self.relu(self.fc5_bn(out)))\n",
        "        #out = self.fc6(self.relu(out))\n",
        "        return out, h\n",
        "    \n",
        "    def init_hidden(self, batch_size):\n",
        "        weight = next(self.parameters()).data\n",
        "        hidden = weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().to(device)\n",
        "        return hidden"
      ],
      "execution_count": 614,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gskg3R5QkeKc",
        "outputId": "67a383fc-ff20-4e9e-fb99-c6beadfc355a"
      },
      "source": [
        "rnn = GRUNet(2470, 1024, 2, 1)\n",
        "\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "torch.manual_seed(40)\n",
        "\n",
        "optimizer = torch.optim.Adam(rnn.parameters(), lr=1e-3, weight_decay=1e-4)\n",
        "\n",
        "results5 = []\n",
        "\n",
        "for epoch in range(0,100):\n",
        "  print(\"Epoch: \", epoch)\n",
        "  # train\n",
        "  tr_loss = 0.0\n",
        "  rnn.train()\n",
        "\n",
        "  for x, y in train_loader:\n",
        "    rnn = rnn.to(device)\n",
        "    x = x.to(device)\n",
        "    #print(x.shape)\n",
        "    #print(x.shape[0])\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "    #x = x.long()\n",
        "    #kill = 3/0\n",
        "\n",
        "\n",
        "    h = rnn.init_hidden(x.shape[0])\n",
        "    h = h.data\n",
        "\n",
        "    output,h = rnn(x, h)\n",
        "    #print(\"O: \", output.shape)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(output, y)\n",
        "    loss.backward()\n",
        "\n",
        "    # step\n",
        "    optimizer.step()\n",
        "\n",
        "    # update loss \n",
        "    tr_loss += loss.item()\n",
        "\n",
        "    \n",
        "  print(tr_loss)\n",
        "\n",
        "  # test\n",
        "  test_loss = 0.0\n",
        "  rnn.eval()\n",
        "\n",
        "  yhat = []\n",
        "  yreal = []\n",
        "\n",
        "  for x, y in test_loader:\n",
        "    x = x.to(device)\n",
        "    x = nn.functional.pad(x, (1,191), \"constant\", 0)\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    h = rnn.init_hidden(x.shape[0])\n",
        "    h = h.data\n",
        "\n",
        "    test_out,h = rnn(x,h)\n",
        "    bsize = test_out.shape[0]\n",
        "\n",
        "    if(epoch == 99):\n",
        "      \n",
        "      for i in range(bsize):\n",
        "        yhat.append(test_out[i].tolist()[0])\n",
        "        yreal.append(y[i].tolist()[0])\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(test_out, y)\n",
        "    #loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    test_loss += loss.item()\n",
        "\n",
        "  print(test_loss)\n",
        "\n",
        "\n",
        "  new_row = {\"Epoch\":epoch, \"Training_Loss\":tr_loss, \"Test_Loss\":test_loss}\n",
        "  results5.append(new_row)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 619,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:65: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1\n",
            "  \"num_layers={}\".format(dropout, num_layers))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0\n",
            "571.2991209030151\n",
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            "Epoch:  1\n",
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            "Epoch:  10\n",
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            "Epoch:  11\n",
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            "Epoch:  98\n",
            "214.63727593421936\n",
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            "Epoch:  99\n",
            "212.99410486221313\n",
            "189.34536933898926\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzxJg8fs4DlQ",
        "outputId": "32b68c58-148f-4dea-ebe3-c06f8f875128",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "difference = []\n",
        "\n",
        "mean_diffs = [[],[],[],[],[],[],[],[],[],[]]\n",
        "print(len(mean_diffs))\n",
        "print(len(yhat))\n",
        "for i in range(len(yhat)):\n",
        "  d = yhat[i] - float(yreal[i])\n",
        "  difference.append(d)\n",
        "  #print(int(yreal[i]))\n",
        "  mean_diffs[(int(yreal[i])-1)].append(d)\n",
        "\n",
        "final_mean_diffs = []\n",
        "\n",
        "for i in range(len(mean_diffs)):\n",
        "  try:\n",
        "    print(sum(mean_diffs[i])/ len(mean_diffs[i]))\n",
        "    avgd = sum(mean_diffs[i])/ len(mean_diffs[i])\n",
        "    final_mean_diffs.append(avgd)\n",
        "  except:\n",
        "    print(\"\", end= \"\")\n",
        "    #final_mean_diffs.append(0)\n",
        "\n",
        "print(final_mean_diffs)\n",
        "\n",
        "xs = [1,2,3,4,7,8,9,10]\n",
        "\n",
        "plt.title(\"Model F: GRU\")\n",
        "plt.scatter(yreal, difference)\n",
        "plt.show()\n"
      ],
      "execution_count": 620,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "24232\n",
            "2.5502254335303935\n",
            "1.5507912597482252\n",
            "0.5512687110000124\n",
            "-0.4498274745253503\n",
            "-3.442553816239039\n",
            "-4.442064516836574\n",
            "-5.44343492243741\n",
            "-6.442504580965197\n",
            "[2.5502254335303935, 1.5507912597482252, 0.5512687110000124, -0.4498274745253503, -3.442553816239039, -4.442064516836574, -5.44343492243741, -6.442504580965197]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYeKAgk14ENF",
        "outputId": "03e38f13-9698-4c13-de20-91e753ed61b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(xs, final_mean_diffs)\n",
        "plt.title(\"Average Error Commited Per True Rating Model F (GRU-2)\")\n",
        "plt.xlabel(\"True Rating\")\n",
        "plt.ylabel(\"Average Error\")\n",
        "plt.show()\n",
        "meta_diffs_save.append(final_mean_diffs)\n"
      ],
      "execution_count": 621,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqYjMmqTlUTx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3e3803b5-4689-4e5c-9094-909e2b9bbd3f"
      },
      "source": [
        "df5 = pd.DataFrame(results5)\n",
        "df5.head()\n"
      ],
      "execution_count": 338,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>570.626933</td>\n",
              "      <td>500.797830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>566.529037</td>\n",
              "      <td>496.825865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>561.678892</td>\n",
              "      <td>492.462719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>556.439290</td>\n",
              "      <td>488.313070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>551.002907</td>\n",
              "      <td>484.302573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Epoch  Training_Loss   Test_Loss\n",
              "0      0     570.626933  500.797830\n",
              "1      1     566.529037  496.825865\n",
              "2      2     561.678892  492.462719\n",
              "3      3     556.439290  488.313070\n",
              "4      4     551.002907  484.302573"
            ]
          },
          "metadata": {},
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOk6T0B-p1kw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a214ef46-550d-433c-bedd-081fef34bd2a"
      },
      "source": [
        "df5.sort_values(by = \"Training_Loss\").head()"
      ],
      "execution_count": 339,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>176</td>\n",
              "      <td>153.723663</td>\n",
              "      <td>147.409767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>178</td>\n",
              "      <td>153.737676</td>\n",
              "      <td>148.111499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>177</td>\n",
              "      <td>153.739826</td>\n",
              "      <td>147.742430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>175</td>\n",
              "      <td>153.759667</td>\n",
              "      <td>147.138999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>167</td>\n",
              "      <td>153.798573</td>\n",
              "      <td>146.313527</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Epoch  Training_Loss   Test_Loss\n",
              "176    176     153.723663  147.409767\n",
              "178    178     153.737676  148.111499\n",
              "177    177     153.739826  147.742430\n",
              "175    175     153.759667  147.138999\n",
              "167    167     153.798573  146.313527"
            ]
          },
          "metadata": {},
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfQQWM0y1dEH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a2bc62f3-44d7-4ae9-8b31-789ef2eed7dd"
      },
      "source": [
        "df5.sort_values(by = \"Test_Loss\").head()"
      ],
      "execution_count": 340,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>168</td>\n",
              "      <td>153.807118</td>\n",
              "      <td>146.269155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>169</td>\n",
              "      <td>153.833398</td>\n",
              "      <td>146.284165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>167</td>\n",
              "      <td>153.798573</td>\n",
              "      <td>146.313527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>170</td>\n",
              "      <td>153.869457</td>\n",
              "      <td>146.335006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>166</td>\n",
              "      <td>153.834127</td>\n",
              "      <td>146.400681</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Epoch  Training_Loss   Test_Loss\n",
              "168    168     153.807118  146.269155\n",
              "169    169     153.833398  146.284165\n",
              "167    167     153.798573  146.313527\n",
              "170    170     153.869457  146.335006\n",
              "166    166     153.834127  146.400681"
            ]
          },
          "metadata": {},
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjC6GfHnobfm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "1b7fc94f-d0cf-4b2d-db11-7a3d527b3faf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Training_Loss\"]/2048, color = \"dodgerblue\")\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Test_Loss\"]/2048, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 200 Epochs For GRU\")\n",
        "plt.legend([\"Train\", \"Test\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e88VRJYEpz8R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3c516a3f-67f4-42de-8363-7bb3843d7861"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Training_Loss\"]/2048, color = \"dodgerblue\")\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Test_Loss\"]/2048, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 100 Epochs For GRU\")\n",
        "plt.legend([\"Train\", \"Test\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vgeza9NjR3LP"
      },
      "source": [
        "# GRU-2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRQjVsa-L5Cy",
        "outputId": "2a861336-6eed-4b7f-d403-2d7149eed942",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "class GRUNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(GRUNet, self).__init__()\n",
        "        #self.hidden_dim = hidden_dim\n",
        "        #self.n_layers = n_layers\n",
        "        \n",
        "        self.gru  = nn.GRU(2470, 256, 1, batch_first=True, dropout= 0.2 )\n",
        "        self.gru2 = nn.GRU(256, 256, 1, batch_first=True, dropout= 0.2 )\n",
        "\n",
        "        self.layers = nn.Sequential(nn.Linear(256, 128),\n",
        "                                    nn.LeakyReLU(),\n",
        "                                    nn.Linear(128, 70),\n",
        "                                    nn.LeakyReLU(),\n",
        "                                    nn.Linear(70, 64),\n",
        "                                    nn.LeakyReLU(),\n",
        "                                    nn.Linear(64, 32),\n",
        "                                    nn.Linear(32, 16),\n",
        "                                    nn.Linear(16,1)\n",
        "                                    )\n",
        "        self.layers3 = nn.Sequential(nn.Linear(256, 64), nn.LeakyReLU(), nn.Linear(64,64), nn.LeakyReLU(), nn.Linear(64,1), nn.LeakyReLU())\n",
        "        self.layers2 = nn.Sequential(nn.Flatten(),\n",
        "                                nn.Linear(256, 200),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(200),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(200, 64),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(64),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(64, 12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(12),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(12,12),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(12),\n",
        "                                nn.Dropout(p = 0.2),\n",
        "                                nn.Linear(12, 10),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(10,5),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(5,5),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.Linear(5,1),\n",
        "                                nn.LeakyReLU(),\n",
        "                                nn.BatchNorm1d(1)\n",
        "\n",
        "                                )\n",
        "        \"\"\"\n",
        "        self.fc = nn.Linear(hidden_dim, 128)\n",
        "        self.relu = nn.LeakyReLU(negative_slope = 0.01)\n",
        "        self.fc_bn = nn.BatchNorm1d(hidden_dim)\n",
        "        self.fc2 = nn.Linear(128, 70)\n",
        "        self.fc2_bn = nn.BatchNorm1d(512)\n",
        "        self.fc3 = nn.Linear(70, 64)\n",
        "        self.fc3_bn = nn.BatchNorm1d(128)\n",
        "        self.fc4 = nn.Linear(64, 32)\n",
        "        self.fc4_bn = nn.BatchNorm1d(64)\n",
        "        self.fc5 = nn.Linear(32, 16)\n",
        "        self.fc5_bn = nn.BatchNorm1d(32)\n",
        "        self.fc6 = nn.Linear(16,1)\n",
        "        \"\"\"\n",
        "\n",
        "\n",
        "        \n",
        "    def forward(self, x, h):\n",
        "        out, h = self.gru(x, h)\n",
        "        #print(out.shape)\n",
        "        #print(h.shape)\n",
        "        #print(out.shape)\n",
        "        #print(h.shape)\n",
        "        out, h = self.gru2(out, h)\n",
        "        #print(out.shape)\n",
        "        #print(h.shape)\n",
        "        #kill = 3/0\n",
        "        out = self.layers2(out)\n",
        "        #out = self.fc(self.relu(self.fc_bn(out[:,-1])))\n",
        "        #out = self.fc2(self.relu(self.fc2_bn(out)))\n",
        "        #out = self.fc3(self.relu(self.fc3_bn(out)))\n",
        "        #out = self.fc4(self.relu(self.fc4_bn(out)))\n",
        "        #out = self.fc5(self.relu(self.fc5_bn(out)))\n",
        "        #out = self.fc6(self.relu(out))\n",
        "        return out, h\n",
        "    \n",
        "    def init_hidden(self, batch_size):\n",
        "        weight = next(self.parameters()).data\n",
        "        hidden = weight.new(1, batch_size, 256).zero_().to(device)\n",
        "        return hidden\n",
        "\n",
        "rnn = GRUNet()\n",
        "\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "torch.manual_seed(40)\n",
        "\n",
        "optimizer = torch.optim.Adam(rnn.parameters(), lr=1e-3,  weight_decay=1e-5)\n",
        "\n",
        "results5 = []\n",
        "\n",
        "for epoch in range(0,100):\n",
        "  print(\"Epoch: \", epoch)\n",
        "  # train\n",
        "  tr_loss = 0.0\n",
        "  rnn.train()\n",
        "\n",
        "  for x, y in train_loader:\n",
        "    rnn = rnn.to(device)\n",
        "    x = x.to(device)\n",
        "    #print(x.shape)\n",
        "    #print(x.shape[0])\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "    #x = x.long()\n",
        "    #kill = 3/0\n",
        "\n",
        "\n",
        "    h = rnn.init_hidden(x.shape[0])\n",
        "    h = h.data\n",
        "\n",
        "    output,h = rnn(x, h)\n",
        "    #print(\"O: \", output.shape)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(output, y)\n",
        "    loss.backward()\n",
        "\n",
        "    # step\n",
        "    optimizer.step()\n",
        "\n",
        "    # update loss \n",
        "    tr_loss += loss.item()\n",
        "\n",
        "    \n",
        "  print(tr_loss)\n",
        "\n",
        "  # test\n",
        "  test_loss = 0.0\n",
        "  rnn.eval()\n",
        "\n",
        "  for x, y in test_loader:\n",
        "    x = x.to(device)\n",
        "    x = nn.functional.pad(x, (1,191), \"constant\", 0)\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    h = rnn.init_hidden(x.shape[0])\n",
        "    h = h.data\n",
        "\n",
        "    test_out,h = rnn(x,h)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(test_out, y)\n",
        "    #loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    test_loss += loss.item()\n",
        "\n",
        "  print(test_loss)\n",
        "\n",
        "\n",
        "  new_row = {\"Epoch\":epoch, \"Training_Loss\":tr_loss, \"Test_Loss\":test_loss}\n",
        "  results5.append(new_row)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:65: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
            "  \"num_layers={}\".format(dropout, num_layers))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0\n",
            "570.5583353042603\n",
            "500.8762016296387\n",
            "Epoch:  1\n",
            "566.5199484825134\n",
            "496.7789912223816\n",
            "Epoch:  2\n",
            "561.6800026893616\n",
            "492.4031505584717\n",
            "Epoch:  3\n",
            "556.4382696151733\n",
            "487.95789766311646\n",
            "Epoch:  4\n",
            "551.009006023407\n",
            "483.6071186065674\n",
            "Epoch:  5\n",
            "545.4930462837219\n",
            "479.5981502532959\n",
            "Epoch:  6\n",
            "539.9418325424194\n",
            "476.5681686401367\n",
            "Epoch:  7\n",
            "534.3852300643921\n",
            "474.2381582260132\n",
            "Epoch:  8\n",
            "528.8379578590393\n",
            "471.9789834022522\n",
            "Epoch:  9\n",
            "523.3165564537048\n",
            "468.1721658706665\n",
            "Epoch:  10\n",
            "517.8265299797058\n",
            "462.96067333221436\n",
            "Epoch:  11\n",
            "512.3736081123352\n",
            "457.3577356338501\n",
            "Epoch:  12\n",
            "506.9615201950073\n",
            "451.3305320739746\n",
            "Epoch:  13\n",
            "501.5957441329956\n",
            "445.9388747215271\n",
            "Epoch:  14\n",
            "496.2691740989685\n",
            "440.7384009361267\n",
            "Epoch:  15\n",
            "491.01319551467896\n",
            "435.4718928337097\n",
            "Epoch:  16\n",
            "485.79095911979675\n",
            "430.3207712173462\n",
            "Epoch:  17\n",
            "480.6211154460907\n",
            "425.22025537490845\n",
            "Epoch:  18\n",
            "475.50491642951965\n",
            "420.1946189403534\n",
            "Epoch:  19\n",
            "470.43946528434753\n",
            "415.02545499801636\n",
            "Epoch:  20\n",
            "465.4261176586151\n",
            "409.8793640136719\n",
            "Epoch:  21\n",
            "460.46595788002014\n",
            "405.07526445388794\n",
            "Epoch:  22\n",
            "455.558735370636\n",
            "400.43478298187256\n",
            "Epoch:  23\n",
            "450.7038505077362\n",
            "395.88741755485535\n",
            "Epoch:  24\n",
            "445.90184330940247\n",
            "391.39427518844604\n",
            "Epoch:  25\n",
            "441.1523952484131\n",
            "386.9271023273468\n",
            "Epoch:  26\n",
            "436.45531034469604\n",
            "382.5305893421173\n",
            "Epoch:  27\n",
            "431.81048107147217\n",
            "378.20458221435547\n",
            "Epoch:  28\n",
            "427.21758341789246\n",
            "373.9514539241791\n",
            "Epoch:  29\n",
            "422.67640018463135\n",
            "369.7576060295105\n",
            "Epoch:  30\n",
            "418.1866121292114\n",
            "365.61638736724854\n",
            "Epoch:  31\n",
            "413.7480163574219\n",
            "361.5294373035431\n",
            "Epoch:  32\n",
            "409.36021518707275\n",
            "357.49841713905334\n",
            "Epoch:  33\n",
            "405.0228807926178\n",
            "353.52162647247314\n",
            "Epoch:  34\n",
            "400.7358441352844\n",
            "349.59287667274475\n",
            "Epoch:  35\n",
            "396.4986045360565\n",
            "345.7236499786377\n",
            "Epoch:  36\n",
            "392.3106532096863\n",
            "341.919709444046\n",
            "Epoch:  37\n",
            "388.17175137996674\n",
            "338.1605976819992\n",
            "Epoch:  38\n",
            "384.0815395116806\n",
            "334.42955124378204\n",
            "Epoch:  39\n",
            "380.03977286815643\n",
            "330.7729845046997\n",
            "Epoch:  40\n",
            "376.04366660118103\n",
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            "Epoch:  41\n",
            "372.0949817895889\n",
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            "Epoch:  42\n",
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            "Epoch:  43\n",
            "364.3532178401947\n",
            "316.9340249300003\n",
            "Epoch:  44\n",
            "360.54599237442017\n",
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            "Epoch:  45\n",
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            "Epoch:  46\n",
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            "Epoch:  49\n",
            "342.20338106155396\n",
            "298.53709185123444\n",
            "Epoch:  50\n",
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            "Epoch:  51\n",
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            "Epoch:  55\n",
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            "Epoch:  56\n",
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            "Epoch:  57\n",
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            "Epoch:  60\n",
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            "Epoch:  61\n",
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            "Epoch:  62\n",
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            "Epoch:  63\n",
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            "Epoch:  64\n",
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            "Epoch:  68\n",
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            "Epoch:  69\n",
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            "Epoch:  70\n",
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            "Epoch:  87\n",
            "235.9557590484619\n",
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            "Epoch:  88\n",
            "233.87566089630127\n",
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            "Epoch:  89\n",
            "231.8003146648407\n",
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            "Epoch:  90\n",
            "229.7731761932373\n",
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            "Epoch:  94\n",
            "221.9747142791748\n",
            "196.55297446250916\n",
            "Epoch:  95\n",
            "220.1037404537201\n",
            "194.84067678451538\n",
            "Epoch:  96\n",
            "218.26237845420837\n",
            "193.1840844154358\n",
            "Epoch:  97\n",
            "216.46503353118896\n",
            "192.0069649219513\n",
            "Epoch:  98\n",
            "214.67780876159668\n",
            "190.5757417678833\n",
            "Epoch:  99\n",
            "212.94036555290222\n",
            "189.26788997650146\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5a3xdiECS0F4",
        "outputId": "6522dc60-72cb-468c-f255-3b56796936ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df5 = pd.DataFrame(results5)\n",
        "df5.head()\n",
        "\n",
        "df5.sort_values(by = \"Training_Loss\").head()\n"
      ],
      "execution_count": 502,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
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              "      <th>99</th>\n",
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              "      <td>189.267890</td>\n",
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              "      <th>98</th>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Epoch  Training_Loss   Test_Loss\n",
              "99     99     212.940366  189.267890\n",
              "98     98     214.677809  190.575742\n",
              "97     97     216.465034  192.006965\n",
              "96     96     218.262378  193.184084\n",
              "95     95     220.103740  194.840677"
            ]
          },
          "metadata": {},
          "execution_count": 502
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jktRBbaTZ485",
        "outputId": "1f7f7ac4-65c5-4df8-9083-7774bca130db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df5.sort_values(by = \"Test_Loss\").head()"
      ],
      "execution_count": 503,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
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              "  </thead>\n",
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              "      <th>99</th>\n",
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              "      <td>189.267890</td>\n",
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              "      <th>98</th>\n",
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              "      <td>190.575742</td>\n",
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              "      <td>216.465034</td>\n",
              "      <td>192.006965</td>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
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            ],
            "text/plain": [
              "    Epoch  Training_Loss   Test_Loss\n",
              "99     99     212.940366  189.267890\n",
              "98     98     214.677809  190.575742\n",
              "97     97     216.465034  192.006965\n",
              "96     96     218.262378  193.184084\n",
              "95     95     220.103740  194.840677"
            ]
          },
          "metadata": {},
          "execution_count": 503
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUjxfDlKZ5aQ",
        "outputId": "189f251c-6275-4d02-bcf4-1eb5d54e1ea5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Training_Loss\"]/2048, color = \"dodgerblue\")\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Test_Loss\"]/2048, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 100 Epochs For GRU-2\")\n",
        "plt.legend([\"Train\", \"Test\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 504,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0Ik81RDfzXA"
      },
      "source": [
        "# CNN-LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxzAu-x8Z_7g"
      },
      "source": [
        "# LSTM 2\n",
        "class LSTM2(nn.ModuleList):\n",
        "  def __init__(self):\n",
        "    super(LSTM2, self).__init__()\n",
        "    # Number of samples per time step\n",
        "    self.batch_size = 2048\n",
        "    \n",
        "    # Dimension of weight vectors\n",
        "    self.hidden_dim = 128\n",
        "    \n",
        "    # Dimension of embedded tensor\n",
        "    self.embedding_dim = 1235\n",
        "    \n",
        "    # The vocabulary size\n",
        "    self.input_size = 2470\n",
        "    \n",
        "    # Number of time steps\n",
        "    self.sequence_len = 2\n",
        "    \n",
        "    # Initialize embedding layer\n",
        "    self.embedding = nn.Embedding(self.input_size, self.embedding_dim, padding_idx=0)\n",
        "    \n",
        "    # Initialize LSTM Cell\n",
        "    self.lstm_cell = nn.LSTMCell(self.embedding_dim, self.hidden_dim)\n",
        "\n",
        "\n",
        "    # Initialize LSTM Cell for the first layer\n",
        "    self.lstm_cell_1 = nn.LSTMCell(self.embedding_dim, self.hidden_dim)\n",
        "\n",
        "    # Initialize LSTM Cell for the second layer\n",
        "    self.lstm_cell_2 = nn.LSTMCell(self.hidden_dim, self.hidden_dim)\n",
        "\n",
        "    self.fully_connected = nn.Linear(128, 64)\n",
        "    self.lrelu = nn.LeakyReLU(negative_slope=2)\n",
        "    self.nrelu = nn.LeakyReLU(negative_slope=2)\n",
        "    self.fc2 = nn.Linear(64, 1)\n",
        "    self.fc3 = nn.Linear(12, 1)\n",
        "\n",
        "\n",
        "    self.layers = nn.Sequential( nn.Linear(128,64),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01), \n",
        "                                \n",
        "                                nn.Linear(64,12),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01),\n",
        "                                nn.Linear(12,1),\n",
        "                                nn.Dropout(p=0),\n",
        "                                nn.LeakyReLU(negative_slope=0.01))\n",
        "    \n",
        "    self.con_test = nn.Sequential(nn.Conv1d(in_channels=1, out_channels = 12, kernel_size= 240), \n",
        "                                  nn.Conv1d(in_channels = 12, out_channels = 2, kernel_size= 240),\n",
        "                                  nn.Flatten(), nn.Linear(3984, 2470), nn.LeakyReLU())\n",
        "\n",
        "\n",
        "  def forward(self, x):\n",
        "    #print(x.shape)\n",
        "    x = self.con_test(x)\n",
        "    #print(out.shape)\n",
        "    #kill = 3/0\n",
        "    # batch_size x hidden_size\n",
        "    hidden_state = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    cell_state = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    hidden_state_2 = torch.zeros(x.size(0), self.hidden_dim)\n",
        "    cell_state_2 = torch.zeros(x.size(0), self.hidden_dim)\n",
        "\n",
        "    hidden_state = hidden_state.to(device)\n",
        "    cell_state = cell_state.to(device)\n",
        "    hidden_state_2 = hidden_state_2.to(device)\n",
        "    cell_state_2 = cell_state_2.to(device)\n",
        "    \n",
        "    # weights initialization\n",
        "    torch.nn.init.xavier_normal_(hidden_state)\n",
        "    torch.nn.init.xavier_normal_(cell_state)\n",
        "    torch.nn.init.xavier_normal_(hidden_state_2)\n",
        "    torch.nn.init.xavier_normal_(cell_state_2)\n",
        "    \n",
        "    # From idx to embedding\n",
        "    #out = self.embedding(x)\n",
        "    out = x\n",
        "    \n",
        "    # Prepare the shape for LSTMCell\n",
        "    out = out.view(self.sequence_len, x.size(0), -1)\n",
        "    \n",
        "    # Unfolding LSTM\n",
        "    # Last hidden_state will be used to feed the fully connected neural net\n",
        "    for i in range(self.sequence_len):\n",
        "      hidden_state, cell_state = self.lstm_cell_1(out[i], (hidden_state, cell_state))\n",
        "      hidden_state_2, cell_state_2 = self.lstm_cell_2(hidden_state, (hidden_state_2, cell_state_2))\n",
        "      \n",
        "    # Last hidden state is passed through a fully connected neural net\n",
        "    #print(hidden_state_2.shape)\n",
        "    out = self.layers(hidden_state_2)\n",
        "    #out = self.fully_connected(hidden_state_2)\t\n",
        "    #out = self.lrelu(out)\n",
        "    #out = self.fc2(out)\n",
        "    #out = self.nrelu(out)\n",
        "    #out = self.fc3(out)    \n",
        "    return out"
      ],
      "execution_count": 622,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kupHSWRclggf",
        "outputId": "b870f697-d569-4e2f-f7fc-d665ecb24a66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#rnn = nn.RNN(input_size = 2470, hidden_size= 128, num_layers=1 , batch_first=True )\n",
        "from torch.autograd import Variable \n",
        "\n",
        "rnn = LSTM2()\n",
        "\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "torch.manual_seed(5)\n",
        "\n",
        "optimizer = torch.optim.Adam(rnn.parameters(), lr=1e-5, weight_decay=1e-5)\n",
        "\n",
        "results4 = []\n",
        "\n",
        "for epoch in range(0, 100):\n",
        "  print(\"Epoch: \", epoch)\n",
        "  # train\n",
        "  tr_loss = 0.0\n",
        "  rnn.train()\n",
        "\n",
        "  for x, y in train_loader:\n",
        "    rnn = rnn.to(device)\n",
        "    x = x.to(device)\n",
        "    #print(x.shape)\n",
        "    #print(x.shape[0])\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "    #x = x.long()\n",
        "\n",
        "    output = rnn(x)\n",
        "    #print(\"O: \", output.shape)\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(output, y)\n",
        "    loss.backward()\n",
        "\n",
        "    # step\n",
        "    optimizer.step()\n",
        "\n",
        "    # update loss \n",
        "    tr_loss += loss.item()\n",
        "\n",
        "    \n",
        "  print(tr_loss)\n",
        "\n",
        "  # test\n",
        "  test_loss = 0.0\n",
        "  rnn.eval()\n",
        "\n",
        "  yhat = []\n",
        "  yreal = []\n",
        "  for x, y in test_loader:\n",
        "    x = x.to(device)\n",
        "    x = nn.functional.pad(x, (1,191), \"constant\", 0)\n",
        "    x = torch.reshape(x,(x.shape[0],1, 2470))\n",
        "\n",
        "    test_out = rnn(x)\n",
        "\n",
        "    bsize = test_out.shape[0]\n",
        "    if(epoch == 99):\n",
        "      for i in range(bsize):\n",
        "        yhat.append(test_out[i].tolist()[0])\n",
        "        yreal.append(y[i].tolist()[0])\n",
        "\n",
        "\n",
        "    y = y.to(device)\n",
        "    loss = loss_fn(test_out, y)\n",
        "    #loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    test_loss += loss.item()\n",
        "\n",
        "  print(test_loss)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  new_row = {\"Epoch\":epoch, \"Training_Loss\":tr_loss, \"Test_Loss\":test_loss}\n",
        "  results4.append(new_row)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "for x, y in train_loader:\n",
        "  print(x[0])\n",
        "  print(y[0])\n",
        "  print(x.shape)\n",
        "  x = torch.reshape(x,(512,1, 2470))\n",
        "  print(x.shape)\n",
        "  out, h_n = rnn(x) \n",
        "  break\n",
        "\"\"\""
      ],
      "execution_count": 623,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0\n",
            "543.9446568489075\n",
            "477.84624004364014\n",
            "Epoch:  1\n",
            "541.5289816856384\n",
            "475.2434549331665\n",
            "Epoch:  2\n",
            "538.1611137390137\n",
            "471.92378520965576\n",
            "Epoch:  3\n",
            "533.9028820991516\n",
            "467.85770082473755\n",
            "Epoch:  4\n",
            "529.3620624542236\n",
            "463.7075777053833\n",
            "Epoch:  5\n",
            "524.7625350952148\n",
            "459.86525201797485\n",
            "Epoch:  6\n",
            "520.6015753746033\n",
            "456.15680170059204\n",
            "Epoch:  7\n",
            "516.3036518096924\n",
            "452.24257946014404\n",
            "Epoch:  8\n",
            "511.87488985061646\n",
            "448.3355860710144\n",
            "Epoch:  9\n",
            "507.4147071838379\n",
            "444.1357192993164\n",
            "Epoch:  10\n",
            "502.36700534820557\n",
            "439.33962297439575\n",
            "Epoch:  11\n",
            "496.7075529098511\n",
            "433.9550061225891\n",
            "Epoch:  12\n",
            "490.44233536720276\n",
            "428.5758662223816\n",
            "Epoch:  13\n",
            "484.1066720485687\n",
            "422.8274817466736\n",
            "Epoch:  14\n",
            "477.62223052978516\n",
            "416.95054173469543\n",
            "Epoch:  15\n",
            "470.5312476158142\n",
            "410.4728636741638\n",
            "Epoch:  16\n",
            "463.272828578949\n",
            "403.8752074241638\n",
            "Epoch:  17\n",
            "455.5630979537964\n",
            "396.93536138534546\n",
            "Epoch:  18\n",
            "447.5337302684784\n",
            "389.8290820121765\n",
            "Epoch:  19\n",
            "439.40736293792725\n",
            "382.51958990097046\n",
            "Epoch:  20\n",
            "430.630891084671\n",
            "374.0640377998352\n",
            "Epoch:  21\n",
            "420.83973002433777\n",
            "365.1166138648987\n",
            "Epoch:  22\n",
            "410.6031620502472\n",
            "356.56600046157837\n",
            "Epoch:  23\n",
            "400.8369162082672\n",
            "347.881023645401\n",
            "Epoch:  24\n",
            "390.8599305152893\n",
            "339.0407919883728\n",
            "Epoch:  25\n",
            "380.52110171318054\n",
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            "Epoch:  26\n",
            "370.2568030357361\n",
            "320.91275012493134\n",
            "Epoch:  27\n",
            "359.87891590595245\n",
            "311.5887314081192\n",
            "Epoch:  28\n",
            "349.1853860616684\n",
            "302.3632023334503\n",
            "Epoch:  29\n",
            "338.68465769290924\n",
            "293.2469148635864\n",
            "Epoch:  30\n",
            "328.2778160572052\n",
            "284.2284473180771\n",
            "Epoch:  31\n",
            "317.9903668165207\n",
            "275.30228662490845\n",
            "Epoch:  32\n",
            "307.77787458896637\n",
            "266.5264790058136\n",
            "Epoch:  33\n",
            "297.75245928764343\n",
            "257.96767139434814\n",
            "Epoch:  34\n",
            "287.9829695224762\n",
            "249.6288560628891\n",
            "Epoch:  35\n",
            "278.4585282802582\n",
            "241.53932285308838\n",
            "Epoch:  36\n",
            "269.20152974128723\n",
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            "Epoch:  37\n",
            "260.2368665933609\n",
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            "Epoch:  38\n",
            "251.58027184009552\n",
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            "Epoch:  39\n",
            "243.25724697113037\n",
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            "Epoch:  40\n",
            "235.27093505859375\n",
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            "Epoch:  41\n",
            "227.62908625602722\n",
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            "Epoch:  42\n",
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            "Epoch:  43\n",
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            "Epoch:  44\n",
            "206.9232461452484\n",
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            "Epoch:  45\n",
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            "Epoch:  46\n",
            "194.97782850265503\n",
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            "Epoch:  47\n",
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            "Epoch:  48\n",
            "184.4957332611084\n",
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            "Epoch:  49\n",
            "179.83971643447876\n",
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            "Epoch:  50\n",
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            "Epoch:  51\n",
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            "Epoch:  52\n",
            "168.13298845291138\n",
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            "Epoch:  53\n",
            "164.99926900863647\n",
            "150.5541377067566\n",
            "Epoch:  54\n",
            "162.24214458465576\n",
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            "Epoch:  55\n",
            "159.88490772247314\n",
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            "Epoch:  56\n",
            "157.90091514587402\n",
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            "Epoch:  57\n",
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            "Epoch:  58\n",
            "155.049729347229\n",
            "145.17288303375244\n",
            "Epoch:  59\n",
            "154.1698932647705\n",
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            "Epoch:  60\n",
            "153.6412591934204\n",
            "145.3428440093994\n",
            "Epoch:  61\n",
            "153.45524978637695\n",
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            "Epoch:  62\n",
            "153.60350131988525\n",
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            "Epoch:  63\n",
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            "Epoch:  64\n",
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            "Epoch:  65\n",
            "155.9409065246582\n",
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            "Epoch:  66\n",
            "157.3060474395752\n",
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            "Epoch:  67\n",
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            "155.02073192596436\n",
            "Epoch:  68\n",
            "160.8359932899475\n",
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            "Epoch:  69\n",
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            "Epoch:  70\n",
            "165.2175579071045\n",
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            "Epoch:  71\n",
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            "Epoch:  72\n",
            "170.35588932037354\n",
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            "Epoch:  73\n",
            "173.1221444606781\n",
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            "Epoch:  74\n",
            "176.01345562934875\n",
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            "Epoch:  75\n",
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            "Epoch:  78\n",
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            "Epoch:  79\n",
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            "Epoch:  80\n",
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            "Epoch:  81\n",
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            "Epoch:  82\n",
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            "Epoch:  83\n",
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            "Epoch:  84\n",
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            "Epoch:  85\n",
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            "Epoch:  86\n",
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            "Epoch:  87\n",
            "213.0962450504303\n",
            "212.2254717350006\n",
            "Epoch:  88\n",
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            "Epoch:  89\n",
            "216.62898778915405\n",
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            "Epoch:  90\n",
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            "Epoch:  91\n",
            "218.93875074386597\n",
            "217.46168875694275\n",
            "Epoch:  92\n",
            "219.5660743713379\n",
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            "Epoch:  93\n",
            "219.8519127368927\n",
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            "Epoch:  94\n",
            "219.8173167705536\n",
            "217.82809567451477\n",
            "Epoch:  95\n",
            "219.45777833461761\n",
            "217.28860664367676\n",
            "Epoch:  96\n",
            "218.7682648897171\n",
            "216.4209098815918\n",
            "Epoch:  97\n",
            "217.7609726190567\n",
            "215.24024963378906\n",
            "Epoch:  98\n",
            "216.45026552677155\n",
            "213.75859355926514\n",
            "Epoch:  99\n",
            "214.85822474956512\n",
            "212.00342106819153\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nfor x, y in train_loader:\\n  print(x[0])\\n  print(y[0])\\n  print(x.shape)\\n  x = torch.reshape(x,(512,1, 2470))\\n  print(x.shape)\\n  out, h_n = rnn(x) \\n  break\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 623
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1JZibiv4_Z4",
        "outputId": "3593a838-0aa1-4997-a375-7bae4ba70228",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "difference = []\n",
        "\n",
        "mean_diffs = [[],[],[],[],[],[],[],[],[],[]]\n",
        "print(len(mean_diffs))\n",
        "for i in range(len(yhat)):\n",
        "  d = yhat[i] - float(yreal[i])\n",
        "  difference.append(d)\n",
        "  #print(int(yreal[i]))\n",
        "  mean_diffs[(int(yreal[i])-1)].append(d)\n",
        "\n",
        "final_mean_diffs = []\n",
        "\n",
        "for i in range(len(mean_diffs)):\n",
        "  try:\n",
        "    print(sum(mean_diffs[i])/ len(mean_diffs[i]))\n",
        "    avgd = sum(mean_diffs[i])/ len(mean_diffs[i])\n",
        "    final_mean_diffs.append(avgd)\n",
        "  except:\n",
        "    print(\"\", end= \"\")\n",
        "    #final_mean_diffs.append(0)\n",
        "\n",
        "print(final_mean_diffs)\n",
        "\n",
        "xs = [1,2,3,4,7,8,9,10]\n",
        "\n",
        "plt.title(\"Model A: MLP\")\n",
        "plt.scatter(yreal, difference)\n",
        "plt.show()\n"
      ],
      "execution_count": 624,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "6.796450829325516\n",
            "5.7964483005497165\n",
            "4.796692894369258\n",
            "3.7967026200194964\n",
            "0.7882525914245182\n",
            "-0.2116172711054484\n",
            "-1.2113754837362616\n",
            "-2.2115915009259566\n",
            "[6.796450829325516, 5.7964483005497165, 4.796692894369258, 3.7967026200194964, 0.7882525914245182, -0.2116172711054484, -1.2113754837362616, -2.2115915009259566]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6u6re3fd8+lRVV1bVpqqaYPBE1peqas2uuqrqALA3yVnDTRcAu3qcUp++A5yb5MTh/5kLWKNP/M7yKWDz8PvNwCe7GNSQj+484FIGq8/bh18X9j0prRhvArYluQN4DvCXPc+nF8O/SrYDtwH/waA1a+ql+kk+AnwdOCvJviRvAK4CXpLk2wz+armqk335En1JapsrcklqnCGXpMYZcklqnCGXpMYZcklqnCGXpMYZcklq3P8DaCddtw7DP74AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwd1R5iF4_86",
        "outputId": "6ec6be45-0640-4531-ea60-c7387cad7ff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(xs, final_mean_diffs)\n",
        "plt.title(\"Average Error Commited Per True Rating Model C (LSTM-1)\")\n",
        "plt.xlabel(\"True Rating\")\n",
        "plt.ylabel(\"Average Error\")\n",
        "plt.show()\n",
        "meta_diffs_save.append(final_mean_diffs)"
      ],
      "execution_count": 625,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1r8_BzGlvLG",
        "outputId": "6418f742-62d0-4a17-b876-2f299cc59c0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df5 = pd.DataFrame(results4)\n",
        "df5.head()\n",
        "\n",
        "df5.sort_values(by = \"Test_Loss\").head()"
      ],
      "execution_count": 505,
      "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>Epoch</th>\n",
              "      <th>Training_Loss</th>\n",
              "      <th>Test_Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>57</td>\n",
              "      <td>154.136909</td>\n",
              "      <td>145.023659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>56</td>\n",
              "      <td>155.122021</td>\n",
              "      <td>145.130843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>58</td>\n",
              "      <td>153.556569</td>\n",
              "      <td>145.279519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>55</td>\n",
              "      <td>156.525118</td>\n",
              "      <td>145.613267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>59</td>\n",
              "      <td>153.366961</td>\n",
              "      <td>145.886431</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Epoch  Training_Loss   Test_Loss\n",
              "57     57     154.136909  145.023659\n",
              "56     56     155.122021  145.130843\n",
              "58     58     153.556569  145.279519\n",
              "55     55     156.525118  145.613267\n",
              "59     59     153.366961  145.886431"
            ]
          },
          "metadata": {},
          "execution_count": 505
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycyuwENnwJiB",
        "outputId": "a611c08f-e037-4b1d-d6c3-797e69d155b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Training_Loss\"]/2048, color = \"dodgerblue\")\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Test_Loss\"]/2048, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 100 Epochs For CNN-LSTM\")\n",
        "plt.legend([\"Train\", \"Validation\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 508,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnlS8Su6tBXk",
        "outputId": "84b70171-27c3-4a04-abf8-aeea3f3b964d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Training_Loss\"]/2048, color = \"dodgerblue\")\n",
        "plt.plot(df5[\"Epoch\"], df5[\"Test_Loss\"]/2048, color = \"darkorange\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"MSE Over 200 Epochs For CNN-LSTM\")\n",
        "plt.legend([\"Train\", \"Test\"], title = \"Set\" )\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 443,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "maRqVxvNAUd-"
      },
      "source": [
        "# Aggregate Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKN3mfqmAcyH",
        "outputId": "82870e73-8832-4089-85e9-6ac4b552d174",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(xs, meta_diffs_save[0], color= \"red\")\n",
        "plt.scatter(xs, meta_diffs_save[1], color= \"blue\")\n",
        "plt.scatter(xs, meta_diffs_save[2], color= \"green\")\n",
        "plt.scatter(xs, meta_diffs_save[3], color= \"orange\")\n",
        "plt.scatter(xs, meta_diffs_save[4], color= \"purple\")\n",
        "\n",
        "plt.legend([\"MLP\", \"RNN\", \"LSTM\", \"GRU\", \"CNN-LSTM\"], title = \"Architecture\" )\n",
        "plt.plot(xs, [0,0,0,0,0,0,0,0], linestyle = 'dashed', color = \"black\")\n",
        "plt.title(\"Average Error Commited For Each True Score\")\n",
        "plt.xlabel(\"True Score\")\n",
        "plt.ylabel(\"Average Error\")\n",
        "plt.show()"
      ],
      "execution_count": 651,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Axpjk8Pzzz3PppZdSWVlJRUUF69ato3v37rz55pt1pjvjjDN4/PHHazfwgU1ADTnkkEPYsWMHAN/73vfo3r07zz33HOBsqJcvXw44TUPTpk0DnE7cqqqqOvMC5OTksHr1anbv3s327dtZsCD0qecnnXQSb731Fp9++ikAu3bt4uOPP464zk1lAcAYk/DKysoYOXJknbKf/vSnB+w5//znPyc7O5vevXuTl5fHzJkzI17HmDFjuP/+++nTpw+fffYZPp+PJ554gry8PHr06MFLL70EwEMPPcSiRYvo1asX/fr1Y/Xq1XTs2JGTTz6Znj17csstt9CtWzdGjRpFz549GTVqFH369Am5zs6dOzNjxgwuvvhievfuzYABA6I+xbUpWtQ9gfv37692QxhjWrY1a9Zw/PHHe12NpBXq8xWRparaP3haT48ARKSDiDwvIh+KyBoRab7eD2OMSXFedwI/BLyiqheKSBsgw+P6GGNMyvAsAIhIJnAKMBZAVfcAe7yqjzHGpBovjwC6A1uAP4tIHrAUGK+quwInEpEioAggOzu72SsZK5aW2hiTaLzsA2gN9AWmqWofYBdwW/BEqlqqqv1VtX/nzp2bu44x4U9LXVVZBfpdWuqVvpVeV80Yk8K8DADrgfWq+q47/DxOQEg6lpbaGJOIPAsAqroJWCcix7pFBUDjUvElOEtLbUxiEREuueSS2uF9+/bRuXPn2gRu/iRxwXJzc+nVqxe9e/fmzDPPZNOmTc1W53jw+kKwGwCfiKwA8oG7Pa5PXFhaamMSS7t27Vi1ahXffPMNAK+//jpdu3aNaN5FixaxYsUK+vfvz913t+xNlqcBQFXL3fb93qp6vqpGl1GphbC01MY0gc8HubnQqpXz7PPFZLFDhw7lb3/7G+BcaXzxxRdHNf8pp5xSm8KhpfL6CCAl9CrsxfDS4WTmZIJAZk4mw0uH21lAxjTE54OiIqisBFXnuagoJkFgzJgxzJo1i2+//ZYVK1Zw4oknRjX/vHnz6NWrZf+Hvb4QLGVYWmpjGqG4GKqr65ZVVzvlhYVNWnTv3r2pqKigrKyMoUOHRjzfaaedRlpaGr179+auu+5qUh28ZgHAGJO41q6NrjxKI0aMYOLEiSxevJht27ZFNM+iRYvo1KlTTNbvtaRvAvKt9JE7NZdWd7Yid2ouvpWxaT80xjSDcBd/xuii0CuvvJLf/OY3Lb4pp7GSOgD4VvoomltEZVUlilJZVUnR3CILAsa0FCUlkBGUIiwjwymPgaysLG688caQ42bMmEFWVlbtY/369TFZZyJJ6nTQuVNzqayqPKA8JzOHigkVMayZMSZSUaeD9vmcNv+1a509/5KSJrf/J7No0kEndR/A2qrQ7YThyo0xCaiw0Db4cZLUTUDZmaHbCcOVG2NMKknqAFBSUEJGet32w4z0DEoKYtN+aIwxLVlSNwEV9nIOG4sXFLO2ai3ZmdmUFJTUlqciS0ttjPFL6gAAThBI5Q1+IH9aan9mUn9aasCCgDEpKKmbgExdlpbaGBPIAkAKsbTUxjjS0tLIz8+nZ8+eDB8+nO3btwNQUVGBiPDwww/XTjtu3DhmzJgBwNixY+natSu7d+8GYOvWreTm5jZ39WPGAkAKsbTUxjgOPvhgysvLWbVqFYcddhh//OMfa8d16dKFhx56iD17Qt+iPC0tjSeffLK5qhpXFgBSiKWlNi1RnLJB1xowYAAbNmyoHe7cuTMFBQU89dRTIaefMGECDz74IPv27YttRTxgASCFWFpq09LEMRs0ADU1NSxYsIARI0bUKZ80aRJTpkyhpqbmgHmys7MZNGgQzzzzTGwq4aGkPwvI1GVpqU1LEq9s0N988w35+fls2LCB448/njPOOKPO+COPPJITTzyRmTNnhpx/8uTJnHfeeZx77rmNr0QCsCMAY0zCilc2aH8fQGVlJapapw/A7/bbb+fee+8lVL60o48+mvz8fGbPnt20injMAkAzsbTUxkQvztmgycjI4H//93954IEHDmjTP+644zjhhBOYO3duyHmLi4uZMmVKbCriEQsAzcDSUhvTOHHOBg1Anz596N27N2VlZQeMKy4uDpsGukePHvTt2zd2FfGA5+mgRSQNeB/YoKrD6ps22nTQicLSUhvznWjTQVs26OhEkw46EY4AxgNr4rb0eJ9DFgFLS21M4xUWQkUF7N/vPNvGP3Y8DQAikgWcC0yPywrifQ5ZhCwttTEmEXl9BDAVuBXYH24CESkSkfdF5P0tW7ZEt/T6ziFrRpaW+kArfSuZmjuVO1vdydTcqaz0rfS6SsakHM8CgIgMA75Q1aX1TaeqparaX1X7d+7cObqVxOscsigV9iqkdHgpOZk5CEJOZg6lw0tTNkupPytpVWUV6HdZSS0IGNO8vDwCOBkYISIVwCxgiIj8JaZriPc5ZFEoXAEVU2H/nc5z4Ypmr0LCsKykxiQGzwKAqk5W1SxVzQXGAAtV9ZKYrqQ5ziGLRIL0RSQKy0pqTGLwug8gvgoLobQUcnJAxHkuLW3+0wgSpC8iUVhWUuO19u3bH1D20Ucfceqpp5Kfn8/xxx9PUVERr776Kvn5+eTn59O+fXuOPfZY8vPzueyyy1i8eDEiwvTp353DUl5ejoi0mAvEEiIAqOrihq4BaLREOIcsQfoiEoVlJTWJ6MYbb+Smm26ivLycNWvWcMMNN3DWWWdRXl5OeXk5/fv3x+fzUV5eztNPPw1Az54966SDKCsrIy8vz6u3ELWECABJL4H6IhKBZSU10WiuNCobN24kKyurdrhXr4Z/jzk5OXz77bds3rwZVeWVV17hnHPOiUv94sGygTaHkhKnzT+wGciLvogEYllJTST8aVSq9zr/HX8aFSDmZ9HddNNNDBkyhIEDB3LmmWdyxRVX0KFDhwbnu/DCC3nuuefo06cPffv25aCDDoppveLJjgCaQ6L0RUBCXBltTKSKFxTXbvz9qvdWU7wg9v1nV1xxBWvWrOGiiy5i8eLFnHTSSbW3fqzPqFGjeO655ygrK+Piiy+Oeb3iyQJAc0mEvgifD9+DV5A7spJWv1ZyR1bie/AKCwImYTV3GpUjjjiCK6+8kpdeeonWrVuzatWqBuc5/PDDSU9P5/XXX6egoGX1Y1kASCG+6eMpOmsvlR1ABSo7QNFZe/FNH+911YwJqTnTqLzyyivs3etcn7Jp0ya2bdtG165dI5r3t7/9Lffeey9paWkxr1c8WR9ACinO30Z1m7pl1W2c8tS8JtkkupKCkjp9ABCbNCrV1dV1Onx/8YtfsH79esaPH0/btm0BuP/++zn88MMjWt7AgQObVB+veJ4OOhotNR10omh1h6ByYLko7L+j5fwOTMsWdTrolT6KFxSztmot2ZnZlBSUpGwalUhEkw7ajgBSSHZ6Ryr3bQtZbkyiKuxVaBv8OLE+gBRSMuIhMqRuG1CGtKFkxEMe1cgY4yULACmksFchpSOfrJuVdOSTtndlTIqyJqAUY4fTxhg/OwIwxpgUZQHAGGNSVPIHgM998GIuzGzlPH9uV70ak+o2b97Mz372M4488kj69evHgAEDmDNnDosXLyYzM5P8/HyOO+44Jk6cWDvPHXfccUCa59zcXLZu3drc1Y+Z5A4An/vgvSKorgTUeX6vyIJAIrCcRMYjqsr555/PKaecwn/+8x+WLl3KrFmzWL9+PQCDBw+mvLycf//738ybN4+33nrL4xrHT3IHgOXFUBN0I5aaaqfceMfukGaiEeOj+IULF9KmTRuuueaa2rKcnBxuuOGGOtMdfPDB5Ofns2HDhiatL5EldwCoDpMwKly5aR52hzQTqTgcxX/wwQf07du3wem++uorPvnkE0455ZRGryvRJXUA2KmhE0aFKzfNxO6QZiLVDEfx119/PXl5efzoRz8C4M033yQvL4+uXbty1lln1eYDEgmRR6We8pYgqQPA7bNL2LW77k3hd+3O4PbZqXsjloSQnY2vF+ROgFa/cZ59vUjZO6SZesThKL5Hjx4sW7asdviPf/wjCxYsYMuWLYDTB7B8+XI++OADnnjiCcrLywHo2LEjX331VZ1l7dixI6KbxiSqegOAiLQSkVHNVZlYe2RuIVdNL6ViSw779wsVW3K4anopj8y1C6G85Js0lKIR1E1LPcIpN6aOjDA7BeHKIzBkyBC+/fZbpk2bVltWHdwkCXTv3p3bbruNe++9F4BTTjmFl19+mR07dgDwwgsvkJeX1+JSQAeq90pgVd0vIrcCs+ubLlFlZ0PZkkLKltTd4OfkeFQhA0Dx7r9TXfee8FSnO+UWmk0deSVOm39gM1BahlPeSCLCiy++yE033cR9991H586dadeuXe2GPtA111zDlClTqKiooHfv3owbN45BgwYhInTp0oXp06c3uh6JoMF00CJyD7AVeBbY5S9X1S+btGKRbsDTwPcBBUpVtd6sZNGmg/afbBJ8K16v7sZoHK3ubIVy4O9OEPb/Zr8HNTLNKdp00Hzuc9r8q9c6e/55JdDd/sDhxDod9Gj3+fqAMgWObHQNHfuAm1V1mYgcAiwVkddVdXUTl1vLv5EvLnb6F7Oznfuw28bfW9mZ2VRWVYYsN+YA3Qttgx8nDQYAVe0ejxWr6kZgo/t6h4isAboCMQsA4GzsbYOfWOJ1lydjTHQaPAtIRNJF5EYRed59jBOR9Ibmi4aI5AJ9gHdDjCsSkfdF5H1/L71p2Qp7FVI6vLRuWurhpZal1JhmFkkT0DQgHXjUHb7ULft5LCogIu2BvwITVPXr4PGqWgqUgtMHEIt1Gu9ZWmpjvBdJAPiRquYFDC8UkeWxWLl7JPFXwKeqL8RimcYYYyITyYVgNSJylH9ARI4Eapq6YnEun3sCWKOqf2jq8hKeZSU1xiSYSALARGCRiCwWkTeAhcDNMVj3yTjNSUNEpNx9JOeVQJaV1JiEsmnTJsaMGcNRRx1Fv379GDp0KB9//DEiwsMPP1w73bhx45gxYwYAY8eOpWvXruzevRuArVu3kpubG3L5oVJHA5SUlNCjRw969+5Nfn4+7777LiNHjiQ/P58f/vCHtamo8/PzWbJkCaeeeirZ2dkEnq5//vnn0759+5h8DvU2AYlIGpAHHA0c6xZ/pKq7m7piVf0X0HKTaESjvnwmdnqbMc1KVRk5ciSXX345s2bNAmD58uVs3ryZLl268NBDD3H11VfTpk2bA+ZNS0vjySef5Nprr416vW+//Tbz5s1j2bJlHHTQQWzdupU9e/YwZ84cABYvXsyUKVOYN29enfk6dOjAW2+9xaBBg9i+fTsbN25sxLsOrd4jAFWtAS5W1d2qusJ9NHnjn3IsK6kxjbbSt5KpuVO5s9WdTM2dykrfyiYtb9GiRaSnp9dJB52Xl0e3bt3o3LkzBQUFPPXUUyHnnTBhAg8++CD79u2Ler0bN26kU6dOHHTQQQB06tSJI444osH5xowZUxuoXnjhBS644IKo1x1OJE1Ab4nIIyIyWET6+h8xq0EKsKykxjTOSt9K5hbNpaqyChSqKquYWzS3SUFg1apV9OvXL+z4SZMmMWXKFGpqDuzqzM7OZtCgQTzzzDNRr/fMM89k3bp1HHPMMVx33XW88cYbEc1XUFDAP//5T2pqapg1axajR49ueKYIRRIA8oEewG+BB9zHgY1bJizLSmpM4ywoXsDe6r11yvZW72VB8YK4rfPII4/kxBNPZObMmSHHT548mfvvv5/9+6NLW9K+fXuWLl1KaWkpnTt3ZvTo0bX9C/VJS0tj0KBBzJo1i2+++SZsv0NjNJQNNA14WVVPC3oMiVkNUoBlJTWmcarWVkVVHokePXqwdOnSeqe5/fbbuffeewmVK+3oo48mPz+f2bO/y5FZXFxc23lbn7S0NE499VTuvPNOHnnkEf76179GVOcxY8Zw4403MmpUbJMzR9QHENM1piB/VtLuEypIu3Q/3SdUULak0NLfG9OAzOzMqMojMWTIEHbv3k1paWlt2YoVK1i3bl3t8HHHHccJJ5zA3LlzQy6juLi4zlk+JSUllJeX1947IJSPPvqITz75pHa4vLycnAhTEw8ePJjJkydz8cWx3RxbH0AzKClxspAGyshwyo0x4RWUFOMNRJAAABRsSURBVJCeUTfzTHpGOgUlBY1epogwZ84c5s+fz1FHHUWPHj2YPHly7Z2//IqLi2tvFB+sR48eDd5W8q677iIrK6v2sXPnTi6//HJOOOEEevfuzerVq7njjjsirvPEiRPp1KlTRNNHKpJ00ItCFKsXzUDRpoNOJD6fZSU1BqJPB73St5IFxQuoWltFZnYmBSUF9CrsFccatmwxTQetqqfFsG4py7KSGtM4vQp72QY/TsI2AYnI1IDX44PGzYhjnYwxxjSD+o4ATgl4fTkQeLeu3vGpjjHGK83Z1KKqOOnATCw11KQfrL5OYAnz2hiTZOJxwVU4bdu2Zdu2bVFvrEz9VJVt27bRtm3biOep7wiglYgcihMk/K/9gSCt8dU0xiSa+i64ivVRQFZWFuvXr8du8BR7bdu2JSsrK+Lp6wsAmcBSvtvoLwsYZ6G7pbIbbJsQ4nHBVTjp6el07x6XO82aKIUNAKqa24z1MM3Bn5ban5nUn5YaLAikuMzsTKf5J0S5SV6RXAhmkkV9aalNSovHBVcm8UVyS0iTLCwttQnD385vF1ylFgsAKWSnZtNeKkOXe1Afk1jsgqvUE1ETkIgMEpEr3NedRcR6cFogS0ttjAnUYAAQkd8Ak4DJblE68Jd4VsrEh6WlNsYEiqQJaCTQB/c0UFX9r4gcEtdambjwp6UuW1J3gx9hRlpjTJKJpAlojzqX7CmAiLSL1cpF5GwR+UhEPhWR22K1XBOapaU2xgSKJADMFpHHgQ4ichUwH/hTU1fs3m3sj8A5wAnAxSJyQlOXa8IrLITSUmePX8R5Li21LKXGpKpI0kFPEZEzgK+BY4Ffq+rrMVj3j4FPVfU/ACIyCzgPWB2DZR/g1FNPPaBs1KhRXHfddVRXVzN06NADxo8dO5axY8eydetWLrzwwgPGX3vttYwePZp169Zx6aWXHjD+5ptvZvjw4Xz00UdcffXVB4z/5S9/yemnn055eTkTJkw4YPzdd9/NwIEDWbJkCbfffvsB46dOnUp+fj7z58/nrrvuOmD8448/zrHHHsvcuXN54IEHastzc53HM888Q7du3Xj22WeZNm3aAfM///zzdOrUiRkzZoS8d+nf//53MjIyePTRR+vcHs9v8eLFAEyZMoV58+bVGXfwwQfzj3/8A4Df/e53LFhQ9x6vHTt2rL1d3uTJk3n77bfrjM/KyuIvf3G6oiZMmHDAnZiOOeaY2js+FRUV8fHHH9cZn5+fz9SpTsLbSy655IAbfwwYMIDf//73APz0pz9l27ZtdcYXFBTwq1/9CoBzzjmHb775ps74YcOGMXHiRMB+e4G/PT/77UX/2/O/p1iK6DRQd4Mfi41+oK7AuoDh9cCJwROJSBFQBJBt91A0Junt2ryLx/o8Rs2GGj467CN2dd5Fu+/HrOXZBIjkjmA7ODD3TxXwPnCzfw8+6hWLXAicrao/d4cvBU5U1XHh5mnJdwQzxjTMn5U0MDFdekY6w0uH2zUKTRDujmCR9AFMBW7B2WPPAiYCM4FZwJNNqNMGoFvAcJZbZoxJUfVlJTWxF0kAGKGqj6vqDlX9WlVLgbNU9Vng0Cas+/+Ao0Wku4i0AcYALzdhecaYFq45s5KayAJAtYiMEpFW7mMU8K07rtFpoVV1HzAOeBVYA8xW1Q8auzzTwnzugxdzYWYr5/lzn9c1MgkgXPZRy0oaH5EEgELgUuALYLP7+hIRORhnA95oqvp3VT1GVY9SVTsbPVX401JXVwL6XVpqCwIpz7KSNq9ITgP9DzA8zOh/xbY6JiXUl5ba7kuQ0iwrafNqMACISFvgf4AeQO3NJlX1yjjWyyQzS0tt6mFZSZtPJE1AzwCHA2cBb+CcrbMjnpUyyW2nhr6eI1y5aSY+n3OFYKtWzrPPmuSSXSQB4Ieq+itgl6o+BZxLiAu2jImUpaVOQD4fvgevIHdkJa1+reSOrMT34BUWBJJcJAHAf1LudhHpiXOz+C7xq5JJdpaWOvH4po+n6Ky9VHYAFajsAEVn7cU3fbzXVTNxFEkqiFIRORT4Jc55+u2BX8W1ViapWVrqxFOcv43qNnXLqts45RaWk1e9RwAi0gr4WlW/UtV/quqRqtpFVR9vpvqZJGRpqRPP2jCn2YcrN8mh3gCgqvuBW5upLiZFWFrqxJOd3jGqcpMcIukDmC8iE0Wkm4gc5n/EvWYmqRUWQkUF7N/vPNvG31slIx4iQ+q2AWVIG0pGPORRjUxziKQPYLT7fH1AmQJHxr46xhgvFPZyInDxgmLWVq0lOzObkoKS2vJUtNK3MukvSGswHXQisXTQxpjmkGxpqRudDlpEMkTklyJS6g4fLSLD4lFJY4xJBKmSljqSPoA/A3uAge7wBuDAe8AZY0ySSJW01JEEgKNU9T7cC8JUtRqQuNbKGGM8lCppqSMJAHvc1M8KICJHAbvjWitjjPFQqqSljuQsoDuAV4BuIuIDTgbGxrFOxhjjqVRJSx3RWUAi0hE4Cafp5x1V3RrvioViZwEZY0z0mnIW0FzgTGCxqs7zauNvjEkNvpU+cqfm0urOVuROzcW30jKSxkskfQBTgMHAahF5XkQudG8SY4wxMeVb6aNobhGVVZUoSmVVJUVziywIxEmDAUBV31DV63Cu/H0cGIVzf2BjjImp4gXFVO+te7vQ6r3VFC8o9qhGyS2SIwDcs4B+ClwD/Ah4qikrFZH7ReRDEVkhInNEpENTlmeMSQ5rqyqjKjdNE0kfwGxgDTAEeATnuoAbmrje14Geqtob+BiY3MTlGWOSQPbOtKjKTdNEcgTwBM5G/xpVXQQMFJE/NmWlqvqaqu5zB9/Buc+wMSbFlbxaQ8aeumUZe5xyE3uR9AG8CvQWkftEpAL4HfBhDOtwJfCPGC7PGNNCFX6dQ+lcyNkOos5z6Vyn3MRe2AvBROQY4GL3sRV4Fue6gdMiWbCIzAcODzGqWFVfcqcpBvYBYbv4RaQIKALIzs6OZNXGmJaqpITCoiIKVwZ0BGdkQGnq3i4unmmpw14IJiL7gTeB/1HVT92y/6hqTO4DICJjgauBAje/UIPsQjBjUoDPB8XFsHatcwPpkpKUvWNQrNJSN+ZCsAuAjcAiEfmTiBQQoyRwInI2zq0mR0S68TfGpAi7XVyteKelDhsAVPVFVR0DHAcsAiYAXURkmoic2cT1PgIcArwuIuUi8lgTl2eMMUkn3mmpG0wGp6q7gJnATBE5FLgImAS81tiVquoPGzuvMcakiszsTKoqD9zYxyotdUQXgvmp6leqWqqqyZUT1RhjElC801JHkg7aGGOMB+KdltoCgDHGJLBehb3idh+CqJqAjDEmVaRCWmo7AjDGmCD+tNT+zKT+tNQAhb2S57RUOwIwxpggqZKW2gKAMcYEWVu1NqrylsoCgDHGBMnODJ13LFx5S2UBwBhjgpQUlJAhbeqUZUgbSgqSKymdBQBjjAlSuAJKX9a6aalfVgpXeF2z2AqbDTQRWTZQY0yzyM2FyhC3oczJcRLUtTCNyQZqjDGpaW2Yzt5w5S2UBQBjjAkW7uZTSXZTKgsAxhjH5z54MRdmtnKeP0++K18jVlLi3IksUEaGU55ELAAYY5yN/XtFUF0JqPP8XlHqBoHCQigtddr8RZzn0tKkuzmNdQIbY5w9/uoQnZ4ZOXB+RXPXxsSYdQIbY8LSXaE7N8OVm+RgAcAYw4btoTs3w5Wb5GABwBjDpJkl7Npdt9Nz1+4MJs1Mrk7PliieaaktABhjeGtDIVdNL6ViSw779wsVW3K4anopb21Irk7PlsaflrqyqhJFa9NSxyoIWCewMQafD4qKoDogA3JGRlKe+NKi5E7NpbLqwM75nMwcKiZURLychOwEFpGbRURFpJOX9TAm1aXIWY8tTrzTUnt2RzAR6QacCdhpBsYkgMJC2+AnmuzM7JBHALFKS+3lEcCDwK1Ay2mDMsaYZlRSUEJGet3O+Yz0jJilpfYkAIjIecAGVV0ewbRFIvK+iLy/ZcuWZqidMcYkhsJehZQOLyUnMwdByMnMoXR4aczuSxy3TmARmQ8cHmJUMXA7cKaqVolIBdBfVbc2tEzrBDbGmOiF6wSOWx+Aqp4epiK9gO7AchEByAKWiciPVXVTvOpjjDGmrmbvBFbVlUAX/3A0RwDGGGNixy4EM8YkFktL3Ww8Ow3UT1Vzva6DMSZB+NNS17hXpPnTUgN0t3NUY82OAIwxiWN58Xcbf7+aaqfcxJwFAGNMwrC01M3LAoAxJmFYWurmZQHAGJMwLC11CD4f5OZCq1bOs8/SQRtjkpClpQ7iT9NaWQmqznNRUcyCgKWDNsYkDEtLHSQ319noB8vJgYqKiBeTkOmgjTEmkKWlDrI2TOd3uPIoeX4dgDHGBLK01AGys0MfAWS3/HTQxhhj6lNS4rSBBcrIcMpjwAKAMcYkqji3iVkTkDHGJLI4tonZEYAxxqQoCwDGGBNKCmQltSYgY4wJliJZSe0IwBhjgqVIVlILAMYYE6w6zIVW4cpbKAsAxhgTZKeGvtAqXHlLZQHAGGOC3D47dFbS22cnV1ZSCwDGGBPkkbmhs5I+Mjd5OoDBzgIyxpgDZGdD2ZJCypbU3eDn5HhUoTjx7AhARG4QkQ9F5AMRuc+rehhjTLA4p+BJGJ4cAYjIacB5QJ6q7haRLl7UwxhjQvFnXigudjIvZ2c7G/9ky1LqVRPQtcA9qrobQFW/8KgexhgTUiqkpfaqCegYYLCIvCsib4jIj8JNKCJFIvK+iLy/ZcuWZqyiMcYkt7gdAYjIfODwEKOK3fUeBpwE/AiYLSJHaoj7U6pqKVAKzi0h41VfY4xJNXELAKp6erhxInIt8IK7wX9PRPYDnQDbxTfGmGbiVRPQi8BpACJyDNAG2OpRXYwxJiV5FQCeBI4UkVXALODyUM0/xhiT8uKYltqTs4BUdQ9wiRfrNsaYFiPOaaktFYQxxiSqOKeltgBgjDGJKs5pqS0AGGNMgop3WmoLAMYYk6DinZbaAoAxxiSoeKeltnTQxhiToOKdltqOAIwxJkHFOy21BQBjjElQhYVQWurs8Ys4z6WlsctSak1AxhiTwOKZltqOAIwxJkVZADDGmBRlAcAYY1KUBQBjjElRFgCMMSZFSUtKwy8iW4BKr+vRRJ2wm98Ess/jO/ZZ1GWfR11N+TxyVLVzcGGLCgDJQETeV9X+XtcjUdjn8R37LOqyz6OueHwe1gRkjDEpygKAMcakKAsAza/U6wokGPs8vmOfRV32edQV88/D+gCMMSZF2RGAMcakKAsAxhiToiwANBMR6SYii0RktYh8ICLjva6T10QkTUT+LSLzvK6L10Skg4g8LyIfisgaERngdZ28IiI3uf+RVSJSJiJtva5TcxKRJ0XkCxFZFVB2mIi8LiKfuM+HxmJdFgCazz7gZlU9ATgJuF5ETvC4Tl4bD6zxuhIJ4iHgFVU9DsgjRT8XEekK3Aj0V9WeQBowxttaNbsZwNlBZbcBC1T1aGCBO9xkFgCaiapuVNVl7usdOH/wrt7WyjsikgWcC0z3ui5eE5FM4BTgCQBV3aOq272tladaAweLSGsgA/ivx/VpVqr6T+DLoOLzgKfc108B58diXRYAPCAiuUAf4F1va+KpqcCtwH6vK5IAugNbgD+7TWLTRaSd15XygqpuAKYAa4GNQJWqvuZtrRLC91V1o/t6E/D9WCzUAkAzE5H2wF+BCar6tdf18YKIDAO+UNWlXtclQbQG+gLTVLUPsIsYHeK3NG7b9nk4QfEIoJ2IXOJtrRKLOufux+T8fQsAzUhE0nE2/j5VfcHr+njoZGCEiFQAs4AhIvIXb6vkqfXAelX1HxE+jxMQUtHpwOequkVV9wIvAAM9rlMi2CwiPwBwn7+IxUItADQTERGcNt41qvoHr+vjJVWdrKpZqpqL08G3UFVTdi9PVTcB60TkWLeoAFjtYZW8tBY4SUQy3P9MASnaIR7kZeBy9/XlwEuxWKgFgOZzMnApzt5uufsY6nWlTMK4AfCJyAogH7jb4/p4wj0Keh5YBqzE2UalVEoIESkD3gaOFZH1IvI/wD3AGSLyCc5R0j0xWZelgjDGmNRkRwDGGJOiLAAYY0yKsgBgjDEpygKAMcakKAsAxhiToiwAmKQmIh0DTrvdJCIbAobbxGgdw9wUDsvdbK9Xx2K5xsSbnQZqUoaI3AHsVNUpAWWtVXVfE5aZDlQCP1bV9SJyEJCrqh81YZmC89+0PEkmruwIwKQcEZkhIo+JyLvAfSJyh4hMDBi/yk3Yh4hcIiLvuUcMj4tIWtDiDsHJ5bMNQFV3+zf+IvJ9EZnjHhksF5GBbvkv3HWsEpEJblmuiHwkIk8Dq4BuInKLiPyfiKwQkTvj+6mYVGQBwKSqLGCgqv4i3AQicjwwGjhZVfOBGqAwcBpV/RLnMv1K9+YlhSLi/1/9L/CGqubh5Pb5QET6AVcAJ+LcF+IqEenjTn808Kiq9gCOdYd/jHNlcD8ROSUWb9wYv9ZeV8AYjzynqjUNTFMA9AP+z2mV4WBCJOFS1Z+LSC+cS/QnAmcAY4EhwGXuNDVAlYgMAuao6i4AEXkBGIwbRFT1HXexZ7qPf7vD7XECwj8b82aNCcUCgElVuwJe76Pu0bD/FoQCPKWqkxtamKquBFaKyDPA5zgBoCl1EuD3qvp4I5ZjTESsCcgYqMBNvywifXFy0YNz670LRaSLO+4wEckJnFFE2ovIqQFF+Tidwv75r3WnS3Pv/PUmcL6b7bIdMNItC/YqcKV7/whEpKu/HsbEih0BGOPco+EyEfkA5y5tHwOo6moR+SXwmtuuvxe4nu828ODsqd8qIo8D3+DsxY91x40HSt1sjjXAtar6tojMAN5zp5muqv/2dzr7qeprbh/E227z007gEmKUB94YsNNAjTEmZVkTkDHGpCgLAMYYk6IsABhjTIqyAGCMMSnKAoAxxqQoCwDGGJOiLAAYY0yK+n8xuEPqBiVdfwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKUX8x5BAavg",
        "outputId": "17acfb17-3243-4d5f-f027-990bc14573af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "xs"
      ],
      "execution_count": 627,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 7, 8, 9, 10]"
            ]
          },
          "metadata": {},
          "execution_count": 627
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaRn5ab6BOnI",
        "outputId": "3e1b961e-1e38-4277-cb3e-3a54e2cacb6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(meta_diffs_save)"
      ],
      "execution_count": 635,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 635
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Nkt3NBTtKbP",
        "outputId": "7097c4a5-e7e5-4cdb-aa59-1eacec1aa079",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "meta_diffs_save\n"
      ],
      "execution_count": 626,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[3.9826509320969907,\n",
              "  3.653916366837294,\n",
              "  3.310600705026272,\n",
              "  2.4934723858620824,\n",
              "  -0.755933402099695,\n",
              "  -1.8862142715813583,\n",
              "  -3.1357301495327436,\n",
              "  -4.795108168139063],\n",
              " [2.3268760812087916,\n",
              "  1.3422035079329662,\n",
              "  0.3566266908649383,\n",
              "  -0.6377309439983042,\n",
              "  -3.6113215139618626,\n",
              "  -4.616145386556873,\n",
              "  -5.624743058015635,\n",
              "  -6.640417375862343],\n",
              " [5.15652894005037,\n",
              "  4.172389991095617,\n",
              "  3.189596166873114,\n",
              "  2.212981247612859,\n",
              "  -0.7778056215130116,\n",
              "  -1.7688372725759531,\n",
              "  -2.786206452916357,\n",
              "  -3.7930758493953873],\n",
              " [2.5502254335303935,\n",
              "  1.5507912597482252,\n",
              "  0.5512687110000124,\n",
              "  -0.4498274745253503,\n",
              "  -3.442553816239039,\n",
              "  -4.442064516836574,\n",
              "  -5.44343492243741,\n",
              "  -6.442504580965197],\n",
              " [6.796450829325516,\n",
              "  5.7964483005497165,\n",
              "  4.796692894369258,\n",
              "  3.7967026200194964,\n",
              "  0.7882525914245182,\n",
              "  -0.2116172711054484,\n",
              "  -1.2113754837362616,\n",
              "  -2.2115915009259566]]"
            ]
          },
          "metadata": {},
          "execution_count": 626
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXxtqlynAZpe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}