{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-layer perceptron name generator"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "\n",
    "First, let's import our dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ritsuko/.local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we import our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emily</td>\n",
       "      <td>F</td>\n",
       "      <td>26539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hannah</td>\n",
       "      <td>F</td>\n",
       "      <td>21677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alexis</td>\n",
       "      <td>F</td>\n",
       "      <td>19234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samantha</td>\n",
       "      <td>F</td>\n",
       "      <td>19040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name gender  count\n",
       "0     Emily      F  26539\n",
       "1    Hannah      F  21677\n",
       "2    Alexis      F  19234\n",
       "3     Sarah      F  19112\n",
       "4  Samantha      F  19040"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"datasets/names/yob1999.csv\", header=None)\n",
    "# Cool idea: maybe weight this by frequency somehow?\n",
    "df.columns = [\"name\", \"gender\", \"count\"]\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reprise our data cleaning steps from earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emily</td>\n",
       "      <td>F</td>\n",
       "      <td>26539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hannah</td>\n",
       "      <td>F</td>\n",
       "      <td>21677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alexis</td>\n",
       "      <td>F</td>\n",
       "      <td>19234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>samantha</td>\n",
       "      <td>F</td>\n",
       "      <td>19040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16939</th>\n",
       "      <td>zohal</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16940</th>\n",
       "      <td>zophia</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16941</th>\n",
       "      <td>zuha</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16942</th>\n",
       "      <td>zuhal</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16943</th>\n",
       "      <td>zuzu</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16944 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name gender  count\n",
       "0         emily      F  26539\n",
       "1        hannah      F  21677\n",
       "2        alexis      F  19234\n",
       "3         sarah      F  19112\n",
       "4      samantha      F  19040\n",
       "...         ...    ...    ...\n",
       "16939     zohal      F      5\n",
       "16940    zophia      F      5\n",
       "16941      zuha      F      5\n",
       "16942     zuhal      F      5\n",
       "16943      zuzu      F      5\n",
       "\n",
       "[16944 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df[\"gender\"] == \"F\"]\n",
    "df[\"name\"] = df[\"name\"].str.lower()\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we break up the names such that we get the preceding three chars (or \".\") in one col and the char itself in another:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('m', '...'),\n",
       " ('a', '..m'),\n",
       " ('d', '.ma'),\n",
       " ('i', 'mad'),\n",
       " ('s', 'adi'),\n",
       " ('o', 'dis'),\n",
       " ('n', 'iso'),\n",
       " ('.', 'son')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_name = \"madison\"\n",
    "def split_name(name):\n",
    "    pairs = []\n",
    "    for idx, char in enumerate(name):\n",
    "        triple = (name[max(0, idx-3):idx].rjust(3, '.'))\n",
    "        pairs.append((char, triple))\n",
    "\n",
    "    pairs.append(('.', name[max(0, len(name) - 3):].rjust(3, '.')))\n",
    "    return(pairs)\n",
    "\n",
    "split_name(test_name)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply this to all names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>count</th>\n",
       "      <th>pairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emily</td>\n",
       "      <td>F</td>\n",
       "      <td>26539</td>\n",
       "      <td>(e, ...)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emily</td>\n",
       "      <td>F</td>\n",
       "      <td>26539</td>\n",
       "      <td>(m, ..e)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emily</td>\n",
       "      <td>F</td>\n",
       "      <td>26539</td>\n",
       "      <td>(i, .em)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emily</td>\n",
       "      <td>F</td>\n",
       "      <td>26539</td>\n",
       "      <td>(l, emi)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emily</td>\n",
       "      <td>F</td>\n",
       "      <td>26539</td>\n",
       "      <td>(y, mil)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16943</th>\n",
       "      <td>zuzu</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>(z, ...)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16943</th>\n",
       "      <td>zuzu</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>(u, ..z)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16943</th>\n",
       "      <td>zuzu</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>(z, .zu)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16943</th>\n",
       "      <td>zuzu</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>(u, zuz)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16943</th>\n",
       "      <td>zuzu</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>(., uzu)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124361 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        name gender  count     pairs\n",
       "0      emily      F  26539  (e, ...)\n",
       "0      emily      F  26539  (m, ..e)\n",
       "0      emily      F  26539  (i, .em)\n",
       "0      emily      F  26539  (l, emi)\n",
       "0      emily      F  26539  (y, mil)\n",
       "...      ...    ...    ...       ...\n",
       "16943   zuzu      F      5  (z, ...)\n",
       "16943   zuzu      F      5  (u, ..z)\n",
       "16943   zuzu      F      5  (z, .zu)\n",
       "16943   zuzu      F      5  (u, zuz)\n",
       "16943   zuzu      F      5  (., uzu)\n",
       "\n",
       "[124361 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"pairs\"] = df[\"name\"].apply(split_name)\n",
    "df = df.explode(\"pairs\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>label</th>\n",
       "      <th>triple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m</td>\n",
       "      <td>..e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i</td>\n",
       "      <td>.em</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l</td>\n",
       "      <td>emi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>y</td>\n",
       "      <td>mil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124356</th>\n",
       "      <td>z</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124357</th>\n",
       "      <td>u</td>\n",
       "      <td>..z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124358</th>\n",
       "      <td>z</td>\n",
       "      <td>.zu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124359</th>\n",
       "      <td>u</td>\n",
       "      <td>zuz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124360</th>\n",
       "      <td>.</td>\n",
       "      <td>uzu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124361 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label triple\n",
       "0          e    ...\n",
       "1          m    ..e\n",
       "2          i    .em\n",
       "3          l    emi\n",
       "4          y    mil\n",
       "...      ...    ...\n",
       "124356     z    ...\n",
       "124357     u    ..z\n",
       "124358     z    .zu\n",
       "124359     u    zuz\n",
       "124360     .    uzu\n",
       "\n",
       "[124361 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo = pd.DataFrame(df['pairs'].apply(lambda p: list(p)), index=df.index)\n",
    "data = pd.DataFrame(foo['pairs'].tolist(), columns=[\"label\", \"triple\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_old = df\n",
    "df = data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we make this into tensors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 0, 0,  ..., 0, 0, 0],\n",
       "        [0, 0, 0,  ..., 0, 0, 0],\n",
       "        [0, 0, 0,  ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0,  ..., 0, 0, 1],\n",
       "        [0, 0, 0,  ..., 0, 0, 0],\n",
       "        [1, 0, 0,  ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def char2idx(c):\n",
    "    # Assumes valid input\n",
    "    if c == \".\":\n",
    "        return 0\n",
    "    else:\n",
    "        return ord(c) - ord(\"a\") + 1\n",
    "\n",
    "def idx2char(i):\n",
    "    if i == 0:\n",
    "        return \"\"\n",
    "    elif i < 28:\n",
    "        return(chr(i+ord(\"a\")-1))\n",
    "    else:\n",
    "        return(\"ERR\")\n",
    "\n",
    "df[\"label_int\"] = df['label'].apply(char2idx)\n",
    "labels = F.one_hot(torch.tensor(df[\"label_int\"]), 27)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         ...\n",
       "1         ..e\n",
       "2         .em\n",
       "3         emi\n",
       "4         mil\n",
       "         ... \n",
       "124356    ...\n",
       "124357    ..z\n",
       "124358    .zu\n",
       "124359    zuz\n",
       "124360    uzu\n",
       "Name: triple, Length: 124361, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['triple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0,  0,  0],\n",
       "        [ 0,  0,  5],\n",
       "        [ 0,  5, 13],\n",
       "        ...,\n",
       "        [ 0, 26, 21],\n",
       "        [26, 21, 26],\n",
       "        [21, 26, 21]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contexts = torch.tensor(df[\"triple\"].apply(lambda t: list(map(char2idx, t))))\n",
    "contexts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we put this into a *bona fide* PyTorch dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "dataset = TensorDataset(contexts, labels)\n",
    "\n",
    "train_size = int(0.8 * len(contexts))\n",
    "test_size = len(contexts) - train_size\n",
    "train_len = int(train_size / len(dataset) * len(dataset))\n",
    "test_len = len(dataset) - train_len\n",
    "train_dataset, test_dataset= torch.utils.data.random_split(dataset, [train_len,test_len])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model setup"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embed\n",
    "\n",
    "Now that we have `contexts` and `labels` properly constructed, let's construct the embedding layer.\n",
    "\n",
    "We embed the *output classes*: 27 chars, in 2 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name could be better\n",
    "EMBEDDING_NDIM = 2\n",
    "C = torch.randn(27, EMBEDDING_NDIM)\n",
    "C.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can index into a tensor in **multiple dimensions**, which can effectively \"index for all\" if given another tensor to index in. This way, we can convert *all* our input \"context\" of 3 chars into embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can index into a tensor **mu\n",
    "emb = C[contexts]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hidden layer\n",
    "\n",
    "Our \"hidden layer\" is an arbitrary set of neurons between the input embedding and the target extraction. Let's init:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "HIDDEN_NDIM = 100\n",
    "W1 = torch.randn(6, HIDDEN_NDIM, requires_grad=True)\n",
    "b1 = torch.randn(HIDDEN_NDIM, requires_grad=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can \"index into\" this tensor to get our stuff:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([124361, 100])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: SRS this\n",
    "foo = emb.view(-1, 6)\n",
    "(foo @ W1).shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A standard activation would be as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([124361, 100])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n",
    "h.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, as in any other classification problem, we translate from this hidden layer to our output layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "W2 = torch.randn(HIDDEN_NDIM, 27, requires_grad=True)\n",
    "B2 = torch.randn(27, requires_grad=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our loss function is the same as many other classification problems: plain old cross-entropy loss again."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training loop"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm just going to write out the entire MLP class at once. This is terrible, but my laziness and depression have forced my hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP:\n",
    "    def __init__(self, embedding_size, hidden_size):\n",
    "        self.embedding_size = embedding_size\n",
    "        self.C = torch.randn(27, embedding_size, requires_grad=True)\n",
    "        # Hidden layer\n",
    "        self.W1 = torch.randn(3 * embedding_size, hidden_size, requires_grad=True)\n",
    "        self.B1 = torch.randn(hidden_size, requires_grad=True)\n",
    "        # Output layer\n",
    "        self.W2 = torch.randn(hidden_size, 27, requires_grad=True)\n",
    "        self.B2 = torch.randn(27, requires_grad=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"Takes batch of contexts; returns logits\"\"\"\n",
    "        # Embed batch\n",
    "        emb = self.C[x]\n",
    "        # Run hidden layer; apply nonlinearity\n",
    "        hidden_layer_output = torch.tanh(emb.view(-1,3 * self.embedding_size) @ self.W1 + self.B1)\n",
    "        # Final layer: crunch down to output logits\n",
    "        logits = hidden_layer_output @ self.W2 + self.B2\n",
    "        return(logits)\n",
    "\n",
    "    def backward(self, x, y_true):\n",
    "        y_pred = self.forward(x)\n",
    "        loss = F.cross_entropy(y_pred, y_true)\n",
    "\n",
    "        # Zero out gradients\n",
    "        self.C.grad = None\n",
    "        self.W1.grad = None\n",
    "        self.B1.grad = None\n",
    "        self.W2.grad = None\n",
    "        self.B2.grad = None\n",
    "\n",
    "        loss.backward()\n",
    "        print(f\"Loss: {loss.item()}\")\n",
    "        return loss\n",
    "    \n",
    "    def update_parameters(self, lr):\n",
    "        # Subtract the gradients\n",
    "        with torch.no_grad():\n",
    "            # There HAS to be a better way to do this\n",
    "            self.C -= lr * self.C.grad\n",
    "            self.W1 -= lr * self.W1.grad\n",
    "            self.B1 -= lr * self.B1.grad\n",
    "            self.W2 -= lr * self.W2.grad\n",
    "            self.B2 -= lr * self.B2.grad\n",
    "\n",
    "    def fit_one_cycle(self, x, y_true, lr):\n",
    "        self.backward(x, y_true)\n",
    "        self.update_parameters(lr)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it merely falls to us to write the driver code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Loss: 1.9088902473449707\n",
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      "Loss: 2.451435089111328\n",
      "Step 9960:\n",
      "Loss: 2.25925612449646\n",
      "Step 9961:\n",
      "Loss: 2.118016004562378\n",
      "Step 9962:\n",
      "Loss: 2.499746322631836\n",
      "Step 9963:\n",
      "Loss: 2.8655967712402344\n",
      "Step 9964:\n",
      "Loss: 2.6778054237365723\n",
      "Step 9965:\n",
      "Loss: 2.205094575881958\n",
      "Step 9966:\n",
      "Loss: 2.3053317070007324\n",
      "Step 9967:\n",
      "Loss: 2.851883888244629\n",
      "Step 9968:\n",
      "Loss: 2.021969795227051\n",
      "Step 9969:\n",
      "Loss: 2.455393075942993\n",
      "Step 9970:\n",
      "Loss: 1.8965373039245605\n",
      "Step 9971:\n",
      "Loss: 2.022745132446289\n",
      "Step 9972:\n",
      "Loss: 2.336927652359009\n",
      "Step 9973:\n",
      "Loss: 2.1811270713806152\n",
      "Step 9974:\n",
      "Loss: 1.8288350105285645\n",
      "Step 9975:\n",
      "Loss: 2.125086545944214\n",
      "Step 9976:\n",
      "Loss: 2.1947741508483887\n",
      "Step 9977:\n",
      "Loss: 2.2787113189697266\n",
      "Step 9978:\n",
      "Loss: 2.004173755645752\n",
      "Step 9979:\n",
      "Loss: 2.109562397003174\n",
      "Step 9980:\n",
      "Loss: 2.5385079383850098\n",
      "Step 9981:\n",
      "Loss: 1.7757718563079834\n",
      "Step 9982:\n",
      "Loss: 1.9577202796936035\n",
      "Step 9983:\n",
      "Loss: 2.2701191902160645\n",
      "Step 9984:\n",
      "Loss: 2.1620612144470215\n",
      "Step 9985:\n",
      "Loss: 2.126265525817871\n",
      "Step 9986:\n",
      "Loss: 1.9994237422943115\n",
      "Step 9987:\n",
      "Loss: 2.0713918209075928\n",
      "Step 9988:\n",
      "Loss: 2.251432418823242\n",
      "Step 9989:\n",
      "Loss: 2.092933177947998\n",
      "Step 9990:\n",
      "Loss: 2.1644740104675293\n",
      "Step 9991:\n",
      "Loss: 2.2986063957214355\n",
      "Step 9992:\n",
      "Loss: 1.8067524433135986\n",
      "Step 9993:\n",
      "Loss: 2.086857557296753\n",
      "Step 9994:\n",
      "Loss: 1.9345773458480835\n",
      "Step 9995:\n",
      "Loss: 2.163364887237549\n",
      "Step 9996:\n",
      "Loss: 2.070192337036133\n",
      "Step 9997:\n",
      "Loss: 2.30253267288208\n",
      "Step 9998:\n",
      "Loss: 1.992342233657837\n",
      "Step 9999:\n",
      "Loss: 1.9567601680755615\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 16\n",
    "EMBEDDING_NDIM = 5\n",
    "HIDDEN_NDIM = 50\n",
    "STEPS=10000\n",
    "\n",
    "# Simple exponential decay\n",
    "lrs = 10 ** np.linspace(0, -3, STEPS + 1)\n",
    "\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE)\n",
    "model = MLP(EMBEDDING_NDIM, HIDDEN_NDIM)\n",
    "\n",
    "global_step = 0\n",
    "epoch = 0\n",
    "while global_step < STEPS:\n",
    "    for step, (data, labels) in enumerate(train_dataloader):\n",
    "        print(f\"Step {global_step}:\")\n",
    "        model.fit_one_cycle(data, labels.float(), lrs[step])\n",
    "\n",
    "        global_step += 1\n",
    "        if global_step >= STEPS:\n",
    "            break\n",
    "    epoch += 1\n",
    "    if global_step >= STEPS:\n",
    "        break\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After some tinkering, we find that small batch sizes (8), linear decay LR from 0.5 to 1e-2, and higher steps decrease rate (severe overfitting begins...)\n",
    "Finally, we get the loss on the training set (we technically need a validation, but I can't be bothered):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 2.196570873260498\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataloader = DataLoader(test_dataset, batch_size=len(test_dataset))\n",
    "test_loss = 0\n",
    "for data, labels in test_dataloader:\n",
    "    loss = model.backward(data, labels.float())\n",
    "\n",
    "test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_embeddings()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tringelyn\n",
      "tara\n",
      "ali\n",
      "wia\n",
      "jalon\n",
      "teliezmina\n",
      "lanellan\n",
      "mavkelyanna\n",
      "maietmarye\n",
      "cora\n",
      "lu\n",
      "zicfania\n",
      "kristona\n",
      "kvcena\n",
      "llon\n",
      "azauelmala\n",
      "maki\n",
      "farti\n",
      "tura\n",
      "andanea\n"
     ]
    }
   ],
   "source": [
    "g = torch.Generator()\n",
    "\n",
    "for _ in range(20):\n",
    "    out = []\n",
    "    context = [0,0,0]\n",
    "    while True:\n",
    "        logits = model.forward(torch.tensor([context]))\n",
    "        probs = F.softmax(logits, dim=1)\n",
    "        # randomly sample pred from distribution\n",
    "        ix = torch.multinomial(probs, num_samples=1, generator=g).item()\n",
    "        # shift context\n",
    "        context = context[1:] + [ix]\n",
    "        out.append(ix)\n",
    "        if ix == 0:\n",
    "            break\n",
    "\n",
    "    print(''.join(idx2char(i) for i in out))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing and validation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the learning rate\n",
    "\n",
    "This hyperparameter is kind of important (citation needed). To start, we want to implement something like the \"learning rate finder\", where we exponentially increase lr until we hit a wall and then find it somewhere around there. Let's give it a try! Let's go from $10^{-3}$ to $10^1$ in increments of 2x???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 7.430624008178711\n",
      "Loss: 5.907592296600342\n",
      "Loss: 6.736423969268799\n",
      "Loss: 6.276147842407227\n",
      "Loss: 8.191972732543945\n",
      "Loss: 6.006835460662842\n",
      "Loss: 7.029684066772461\n",
      "Loss: 5.80147647857666\n",
      "Loss: 6.876686096191406\n",
      "Loss: 6.443017482757568\n",
      "Loss: 6.2708916664123535\n",
      "Loss: 5.996418476104736\n",
      "Loss: 6.9694013595581055\n",
      "Loss: 7.181060314178467\n",
      "Loss: 7.285386562347412\n",
      "Loss: 6.76036262512207\n",
      "Loss: 4.900427341461182\n",
      "Loss: 6.592562198638916\n",
      "Loss: 7.163669586181641\n",
      "Loss: 6.860597133636475\n",
      "Loss: 6.350022315979004\n",
      "Loss: 6.802441120147705\n",
      "Loss: 5.725580215454102\n",
      "Loss: 6.045586109161377\n",
      "Loss: 7.162632465362549\n",
      "Loss: 6.112359046936035\n",
      "Loss: 6.745479106903076\n",
      "Loss: 8.428348541259766\n",
      "Loss: 7.566260814666748\n",
      "Loss: 7.213047027587891\n",
      "Loss: 6.1751508712768555\n",
      "Loss: 6.329876899719238\n",
      "Loss: 5.869903087615967\n",
      "Loss: 6.829679489135742\n",
      "Loss: 7.537769317626953\n",
      "Loss: 6.489545822143555\n",
      "Loss: 6.2317705154418945\n",
      "Loss: 6.0110626220703125\n",
      "Loss: 7.265320301055908\n",
      "Loss: 8.121577262878418\n",
      "Loss: 6.227502822875977\n",
      "Loss: 6.25087833404541\n",
      "Loss: 6.188232421875\n",
      "Loss: 6.488334655761719\n",
      "Loss: 6.584030628204346\n",
      "Loss: 7.6949005126953125\n",
      "Loss: 5.654526710510254\n",
      "Loss: 7.727835655212402\n",
      "Loss: 7.618391036987305\n",
      "Loss: 6.659497261047363\n",
      "Loss: 6.021166801452637\n",
      "Loss: 5.73284912109375\n",
      "Loss: 7.275735855102539\n",
      "Loss: 7.702166557312012\n",
      "Loss: 6.327794075012207\n",
      "Loss: 7.663393020629883\n",
      "Loss: 7.484796524047852\n",
      "Loss: 5.963756084442139\n",
      "Loss: 6.558468818664551\n",
      "Loss: 6.1140923500061035\n",
      "Loss: 6.24030876159668\n",
      "Loss: 7.007579803466797\n",
      "Loss: 6.0864386558532715\n",
      "Loss: 5.9844818115234375\n",
      "Loss: 6.91998291015625\n",
      "Loss: 6.734277725219727\n",
      "Loss: 6.336721420288086\n",
      "Loss: 7.672022819519043\n",
      "Loss: 5.989048957824707\n",
      "Loss: 6.733485698699951\n",
      "Loss: 5.5550537109375\n",
      "Loss: 5.841381072998047\n",
      "Loss: 5.7277727127075195\n",
      "Loss: 6.139680862426758\n",
      "Loss: 6.417255401611328\n",
      "Loss: 5.787105560302734\n",
      "Loss: 4.683682441711426\n",
      "Loss: 5.241340637207031\n",
      "Loss: 6.151970863342285\n",
      "Loss: 5.528073310852051\n",
      "Loss: 5.105097770690918\n",
      "Loss: 5.662375450134277\n",
      "Loss: 5.931535243988037\n",
      "Loss: 4.8722124099731445\n",
      "Loss: 5.265300750732422\n",
      "Loss: 4.733351707458496\n",
      "Loss: 4.9881744384765625\n",
      "Loss: 5.1370649337768555\n",
      "Loss: 3.952605724334717\n",
      "Loss: 4.298478126525879\n",
      "Loss: 4.196227073669434\n",
      "Loss: 5.217850208282471\n",
      "Loss: 4.802611351013184\n",
      "Loss: 5.492189884185791\n",
      "Loss: 4.618756294250488\n",
      "Loss: 4.8330888748168945\n",
      "Loss: 4.640590190887451\n",
      "Loss: 5.387992858886719\n",
      "Loss: 5.0618767738342285\n",
      "Loss: 3.449434757232666\n",
      "Loss: 4.841926574707031\n",
      "Loss: 4.253867149353027\n",
      "Loss: 4.5569047927856445\n",
      "Loss: 5.104475021362305\n",
      "Loss: 4.032567501068115\n",
      "Loss: 3.5253655910491943\n",
      "Loss: 3.4031808376312256\n",
      "Loss: 4.417699813842773\n",
      "Loss: 4.6852946281433105\n",
      "Loss: 3.9664392471313477\n",
      "Loss: 3.41298770904541\n",
      "Loss: 3.531296968460083\n",
      "Loss: 4.684194564819336\n",
      "Loss: 3.7923243045806885\n",
      "Loss: 3.382225751876831\n",
      "Loss: 4.090125560760498\n",
      "Loss: 3.427050828933716\n",
      "Loss: 2.887465476989746\n",
      "Loss: 3.5737202167510986\n",
      "Loss: 3.6871047019958496\n",
      "Loss: 3.307684898376465\n",
      "Loss: 3.452899932861328\n",
      "Loss: 3.6090240478515625\n",
      "Loss: 3.590327739715576\n",
      "Loss: 3.6102278232574463\n",
      "Loss: 3.439237117767334\n",
      "Loss: 2.721630573272705\n",
      "Loss: 3.2753853797912598\n",
      "Loss: 3.287172317504883\n",
      "Loss: 2.9930880069732666\n",
      "Loss: 3.0358457565307617\n",
      "Loss: 2.8183953762054443\n",
      "Loss: 3.1593668460845947\n",
      "Loss: 2.7558887004852295\n",
      "Loss: 3.4922285079956055\n",
      "Loss: 2.4574546813964844\n",
      "Loss: 2.955148220062256\n",
      "Loss: 2.599187135696411\n",
      "Loss: 2.853722333908081\n",
      "Loss: 2.9035115242004395\n",
      "Loss: 2.849900722503662\n",
      "Loss: 2.9229700565338135\n",
      "Loss: 2.854335308074951\n",
      "Loss: 3.0771515369415283\n",
      "Loss: 2.834442138671875\n",
      "Loss: 2.8727121353149414\n",
      "Loss: 2.5702261924743652\n",
      "Loss: 3.5756044387817383\n",
      "Loss: 3.2493510246276855\n",
      "Loss: 3.4860177040100098\n",
      "Loss: 3.0239930152893066\n",
      "Loss: 3.4398820400238037\n",
      "Loss: 3.3091492652893066\n",
      "Loss: 3.1710703372955322\n",
      "Loss: 2.9252288341522217\n",
      "Loss: 3.1941142082214355\n",
      "Loss: 3.1485743522644043\n",
      "Loss: 3.181817054748535\n",
      "Loss: 3.405029773712158\n",
      "Loss: 3.1574606895446777\n",
      "Loss: 4.207228660583496\n",
      "Loss: 4.8890156745910645\n",
      "Loss: 3.880110263824463\n",
      "Loss: 5.588428020477295\n",
      "Loss: 5.6451287269592285\n",
      "Loss: 4.581236839294434\n",
      "Loss: 4.6465253829956055\n",
      "Loss: 4.523091793060303\n",
      "Loss: 4.723302364349365\n",
      "Loss: 4.5816216468811035\n",
      "Loss: 4.333004474639893\n",
      "Loss: 4.123793601989746\n",
      "Loss: 5.062790870666504\n",
      "Loss: 4.318625450134277\n",
      "Loss: 6.228353023529053\n",
      "Loss: 4.212017059326172\n",
      "Loss: 7.172618865966797\n",
      "Loss: 6.527228355407715\n",
      "Loss: 6.876657009124756\n",
      "Loss: 9.410812377929688\n",
      "Loss: 6.641336441040039\n",
      "Loss: 7.134503364562988\n",
      "Loss: 5.322292804718018\n",
      "Loss: 6.1632280349731445\n",
      "Loss: 6.492981910705566\n",
      "Loss: 12.327178955078125\n",
      "Loss: 10.03088092803955\n",
      "Loss: 12.911327362060547\n",
      "Loss: 11.744782447814941\n"
     ]
    }
   ],
   "source": [
    "lr_model = MLP(EMBEDDING_NDIM, HIDDEN_NDIM)\n",
    "lr = 10e-4\n",
    "\n",
    "# WHY IS THIS LANGUAGE NOT TYPED AARGH\n",
    "losses = []\n",
    "i = 0\n",
    "\n",
    "while lr < 10:\n",
    "    start, end = i * BATCH_SIZE, (i+1) * BATCH_SIZE\n",
    "    loss = lr_model.backward(contexts[start:end], labels[start:end].float())\n",
    "    lr_model.update_parameters(lr)\n",
    "    losses.append({\"loss\" : loss.item(), \"lr\": lr})\n",
    "    lr *= 1.05\n",
    "    i+=1\n",
    "\n",
    "loss_df = pd.DataFrame.from_records(losses)\n",
    "loss_df[\"loss_smooth\"] = loss_df[\"loss\"].rolling(window=5).mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's graph this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_df['lr'], loss_df['loss_smooth'])\n",
    "plt.xscale('log')\n",
    "plt.xlabel('lr')\n",
    "plt.ylabel('loss')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like our initial learning rate wasn't aggressive enough! torch.linspace, -3, 0 10** that. instead of what we did here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9 (main, Dec 19 2022, 17:35:49) [GCC 12.2.0]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}