Week_8/Day_3/RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python import keras\n",
    "from tensorflow.python.keras import preprocessing\n",
    "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Dense, Dropout, LSTM, Reshape\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data"
   ]
  },
  {
   "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>red</th>\n",
       "      <th>green</th>\n",
       "      <th>blue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18th Century Green</td>\n",
       "      <td>165</td>\n",
       "      <td>147</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975 Earth Red</td>\n",
       "      <td>123</td>\n",
       "      <td>70</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989 Miami Hotline</td>\n",
       "      <td>221</td>\n",
       "      <td>51</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000 Leagues Under the Sea</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3AM in Shibuya</td>\n",
       "      <td>34</td>\n",
       "      <td>85</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  red  green  blue\n",
       "0           18th Century Green  165    147    68\n",
       "1               1975 Earth Red  123     70    59\n",
       "2           1989 Miami Hotline  221     51   102\n",
       "3  20000 Leagues Under the Sea   25     25   112\n",
       "4               3AM in Shibuya   34     85   119"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"colors.csv\")\n",
    "names = data[\"name\"]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        18\n",
       "1        14\n",
       "2        18\n",
       "3        27\n",
       "4        14\n",
       "         ..\n",
       "18601    14\n",
       "18602    15\n",
       "18603     7\n",
       "18604     4\n",
       "18605    11\n",
       "Name: name, Length: 18606, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(names.str.len())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = sorted(names.str.len().values)\n",
    "import scipy.stats as stats\n",
    "import pylab as plt\n",
    "\n",
    "fit = stats.norm.pdf(h, np.mean(h), np.std(h))  #this is a fitting indeed\n",
    "plt.plot(h,fit,'-o')\n",
    "plt.hist(h) \n",
    "#plt.hist(h,normal=True)      #use this to draw histogram of your data\n",
    "plt.xlabel('Chars')\n",
    "plt.ylabel('Probability density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18606, 25)\n"
     ]
    }
   ],
   "source": [
    "maxlen = 25\n",
    "t = Tokenizer(char_level=True)\n",
    "t.fit_on_texts(names)\n",
    "tokenized = t.texts_to_sequences(names)\n",
    "padded_names = preprocessing.sequence.pad_sequences(tokenized, maxlen=maxlen)\n",
    "print(padded_names.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0, ...,  1,  1,  7],\n",
       "       [ 0,  0,  0, ...,  4,  1, 13],\n",
       "       [ 0,  0,  0, ...,  5,  7,  1],\n",
       "       ...,\n",
       "       [ 0,  0,  0, ..., 16,  8,  4],\n",
       "       [ 0,  0,  0, ..., 12,  7,  5],\n",
       "       [ 0,  0,  0, ...,  6, 12,  1]], dtype=int32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "padded_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18606, 25)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "padded_names.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18th Century Green'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  0,  0,  0,  0,  0, 54, 38, 10, 16,  3, 11,  1,  7, 10, 12,\n",
       "        4, 19,  3, 14,  4,  1,  1,  7], dtype=int32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "padded_names[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One hot encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'e': 1, 'a': 2, ' ': 3, 'r': 4, 'i': 5, 'l': 6, 'n': 7, 'o': 8, 's': 9, 't': 10, 'c': 11, 'u': 12, 'd': 13, 'g': 14, 'p': 15, 'h': 16, 'b': 17, 'm': 18, 'y': 19, 'w': 20, 'k': 21, 'f': 22, 'v': 23, 'z': 24, 'j': 25, 'q': 26, 'x': 27, \"'\": 28, '-': 29, 'ō': 30, 'è': 31, 'é': 32, 'ā': 33, 'á': 34, 'ó': 35, 'ū': 36, '0': 37, '8': 38, '.': 39, 'ē': 40, 'ī': 41, 'ǎ': 42, '!': 43, 'í': 44, '&': 45, 'ǜ': 46, '9': 47, '2': 48, 'à': 49, 'ǐ': 50, '’': 51, '6': 52, 'ú': 53, '1': 54, '3': 55, 'â': 56, '4': 57, 'ǔ': 58, 'ì': 59, '7': 60, '5': 61, 'ê': 62, 'ö': 63, 'ł': 64, 'š': 65, 'ü': 66, '₂': 67, 'ò': 68, 'ñ': 69, 'ě': 70, 'ń': 71, 'ä': 72, 'œ': 73, 'ß': 74, '%': 75, 'ı': 76, 'ż': 77, '/': 78, 'î': 79, 'ë': 80, '(': 81, ')': 82, 'å': 83, '$': 84, 'я': 85, 'ő': 86, 'ğ': 87, 'ç': 88, 'ù': 89}\n"
     ]
    }
   ],
   "source": [
    "# len of 89\n",
    "print(t.word_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.utils import np_utils\n",
    "one_hot_names = np_utils.to_categorical(padded_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 90)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_names[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_names[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18606, 25, 90)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_names.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Always normalize pixels!\n",
    "# The RGB values are between 0 - 255\n",
    "# scale them to be between 0 - 1\n",
    "def norm(value):\n",
    "    return value / 255.0\n",
    "\n",
    "normalized_values = np.column_stack([norm(data[\"red\"]), norm(data[\"green\"]), norm(data[\"blue\"])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.64705882, 0.57647059, 0.26666667],\n",
       "       [0.48235294, 0.2745098 , 0.23137255],\n",
       "       [0.86666667, 0.2       , 0.4       ],\n",
       "       ...,\n",
       "       [0.80392157, 0.83529412, 0.83529412],\n",
       "       [0.        , 0.5372549 , 0.58823529],\n",
       "       [0.14117647, 0.54509804, 0.8       ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSTM very COMPLICATED!!\n",
    "model = Sequential()\n",
    "model.add(LSTM(256, return_sequences=True, input_shape=(maxlen, 90)))\n",
    "model.add(LSTM(128))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(3, activation='sigmoid'))\n",
    "model.compile(optimizer='adam', loss='mse', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "466/466 [==============================] - 39s 84ms/step - loss: 0.0616 - acc: 0.6326 - val_loss: 0.0607 - val_acc: 0.5991\n",
      "Epoch 2/10\n",
      "466/466 [==============================] - 36s 78ms/step - loss: 0.0595 - acc: 0.6352 - val_loss: 0.0586 - val_acc: 0.6169\n",
      "Epoch 3/10\n",
      "466/466 [==============================] - 36s 77ms/step - loss: 0.0571 - acc: 0.6344 - val_loss: 0.0570 - val_acc: 0.5825\n",
      "Epoch 4/10\n",
      "466/466 [==============================] - 36s 78ms/step - loss: 0.0551 - acc: 0.6411 - val_loss: 0.0574 - val_acc: 0.6166\n",
      "Epoch 5/10\n",
      "466/466 [==============================] - 38s 82ms/step - loss: 0.0530 - acc: 0.6399 - val_loss: 0.0564 - val_acc: 0.5919\n",
      "Epoch 6/10\n",
      "466/466 [==============================] - 39s 84ms/step - loss: 0.0509 - acc: 0.6450 - val_loss: 0.0558 - val_acc: 0.6263\n",
      "Epoch 7/10\n",
      "466/466 [==============================] - 36s 77ms/step - loss: 0.0487 - acc: 0.6462 - val_loss: 0.0558 - val_acc: 0.6290\n",
      "Epoch 8/10\n",
      "466/466 [==============================] - 36s 76ms/step - loss: 0.0464 - acc: 0.6547 - val_loss: 0.0552 - val_acc: 0.6206\n",
      "Epoch 9/10\n",
      "466/466 [==============================] - 38s 83ms/step - loss: 0.0439 - acc: 0.6569 - val_loss: 0.0563 - val_acc: 0.5927\n",
      "Epoch 10/10\n",
      "466/466 [==============================] - 35s 76ms/step - loss: 0.0410 - acc: 0.6658 - val_loss: 0.0557 - val_acc: 0.6171\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(one_hot_names, normalized_values,\n",
    "                    epochs=10,\n",
    "                    batch_size=32,\n",
    "                    validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot a color image\n",
    "def plot_rgb(rgb):\n",
    "    data = [[rgb]]\n",
    "    plt.figure(figsize=(2,2))\n",
    "    plt.imshow(data, interpolation='nearest')\n",
    "    plt.show()\n",
    "\n",
    "def scale(n):\n",
    "    return int(n * 255) \n",
    "\n",
    "def predict(name):\n",
    "    name = name.lower()\n",
    "    tokenized = t.texts_to_sequences([name])\n",
    "    padded = preprocessing.sequence.pad_sequences(tokenized, maxlen=maxlen)\n",
    "    one_hot = np_utils.to_categorical(padded, num_classes=90)\n",
    "    pred = model.predict(np.array(one_hot))[0]\n",
    "    print(pred)\n",
    "    r, g, b = scale(pred[0]), scale(pred[1]), scale(pred[2])\n",
    "    print(name + ',', 'R,G,B:', r,g,b)\n",
    "    plot_rgb(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4217394  0.46592587 0.3894645 ]\n",
      "forest, R,G,B: 107 118 99\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACMCAYAAAD/VHJdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAHs0lEQVR4nO3da4hcZx3H8e8vCbloNjWXmiwaGoNFiSKtLsULXjARqi/SQGutKO5CSoQgCOKLQMAX7QvbipcXLWhIpbEFjQ1II41ok7T0jYkueAmppJsUxcQ0NlGCpWml+vfFnA2T7czu7J6zZ/6e+X1gmXN5Zp7nsD/OzOHMfx5FBGb9tqDfAzADB9GScBAtBQfRUnAQLQUH0VJY1O8BdLPsTUtjaMVQv4dhs/DShYsXI+L6uTw3bRCHVgxx5+ht/R6GzcJDDzz8l7k+12/NloKDaCk4iJaCg2gpOIiWQqkgSlol6SlJE8XjymnarpB0VtKDZfq0Zip7RtwFHImIG4EjxXo39wLPluzPGqpsEG8D9hXL+4BtnRpJ+gCwFvhVyf6socoGcW1EnC+WX6QVtmtIWgB8G/h6yb6swWa8syLpMLCuw67d7SsREZI6fd17J3AoIs5KmqmvHcAOgOUr3jzT0KxBZgxiRGzptk/SBUnDEXFe0jDw9w7NPgR8VNJOYDmwWNLLEfGGz5MRsQfYA/DWdde7hmGAlL3XfBAYBe4rHp+Y2iAivjC5LGkMGOkUQhtsZT8j3gd8StIEsKVYR9KIpL1lB2eDo9QZMSIuAZs7bB8H7u6w/RHgkTJ9WjP5zoql4CBaCg6ipeAgWgoOoqXgIFoKDqKl4CBaCg6ipeAgWgoOoqXgIFoKDqKlMO9VfJJukvRrSScl/VHS58r0ac1URxXfK8CXIuI9wK3A9yS9pWS/1jDzXsUXEc9HxESx/Dda5QRz+ukya655r+JrJ+kWYDFwpmS/1jB1VPFNvs4w8CgwGhH/7dLGVXwDqo4qPiStAJ4EdkfEsWn6chXfgCr71jxZxQddqvgkLQZ+BvwoIg6U7M8aqo4qvjuBjwFjkn5f/N1Usl9rmHmv4ouIx4DHyvRjzec7K5aCg2gpOIiWgoNoKTiIloKDaCk4iJaCg2gpOIiWgoNoKTiIloKDaCk4iJZCJUGUdKukU5JOS3pDAZWkJZL2F/uPS9pQRb/WHKWDKGkh8BDwaWAT8HlJm6Y02w78MyLeCXwXuL9sv9YsVZwRbwFOR8QLEfFv4Ce0qvvatVf7HQA2a6ZpqGygVBHEtwF/bVs/W2zr2CYiXgcuA6unvpCkHZLGJY1fuXKlgqHZ/4tUFysRsSciRiJiZNmyZf0ejtWoiiCeA9a3rb+92NaxjaRFwHXApQr6toaoIoi/BW6U9I6iYu8uWtV97dqr/e4AjkaEy0XtqrKTQhIRr0v6CvBLYCHww4g4KekeYDwiDgIPA49KOg38g1ZYza4qHUSAiDgEHJqy7Rtty68Cn62iL2umVBcrNrgcREvBQbQUHERLwUG0FBxES8FBtBQcREvBQbQUHERLwUG0FBxES6Gu4qmvSXqumALtiKQbqujXmqOu4qnfASMR8T5aNSsPlO3XmqWW4qmIeDoiXilWj9H6FrfZVXUVT7XbDvyign6tQSr5YmyvJH0RGAE+3mW/p0AbUHUVTyFpC635+7ZGxGudXshVfIOrluIpSTcDP6AVwo7z9dlgKx3EomB+snjqT8BPJ4unJG0tmn0LWA48XkyBNrXKzwZcXcVTXWc4NQPfWbEkHERLwUG0FBxES8FBtBQcREvBQbQUHERLwUG0FBxES8FBtBQcREvBQbQUaqnia2t3u6SQNFJFv9YcdVXxIWkI+CpwvGyf1jx1TYEGcC+tOfheraBPa5haqvgkvR9YHxFPVtCfNdC8V/FJWgB8Bxjroa2r+AZUHVV8Q8B7gWck/Rn4IHCw0wWLq/gG17xX8UXE5YhYExEbImIDrV962BoR4xX0bQ1RVxWf2bRqqeKbsv0TVfRpzeI7K5aCg2gpOIiWgoNoKTiIloKDaCkoIvo9ho4k/Qs41e9xzJM1wMV+D2IevCsihubyxFp/MXaWTkVEI7+3KGm8iccmac53y/zWbCk4iJZC5iDu6fcA5lFTj23Ox5X2YsUGS+Yzog2QNEGUtErSU5ImiseVXdr9p/hB+PQ/Ct/DHIVLJO0v9h+XtKEPw5y1Ho5rTNJLbf+nu2d80YhI8Udrfr5dxfIu4P4u7V7u91h7PJ6FwBlgI7AY+AOwaUqbncD3i+W7gP39HndFxzUGPDib101zRqRV+bevWN4HbOvfUCrRS3Vj+zEfADZLUo1jnIteqzZnJVMQ10bE+WL5RWBtl3ZLJY1LOiZpWz1Dm5Ne5ii82iZa33S/DKyuZXRz1+vci7cX0yIfkLS+w/5r1D0X32FgXYddu9tXIiIkdbucvyEizknaCByVdCIizlQ9Vivl58CPI+I1SV+mddb/5HRPqDWIMc3EP5IuSBqOiPOShoGOU6VFxLni8QVJzwA30/rMkk0vcxROtjkraRFwHXCpnuHN2YzHFRHtx7CXHubnzvTWfBAYLZZHgSemNpC0UtKSYnkN8BHgudpGODszzlHItcd8B3A0ik/7ifUy9+Jw2+pWWkV10+v3VVjbldZq4AgwARwGVhXbR4C9xfKHgRO0rtROANv7Pe4ZjukzwPO0zti7i2330CqnBVgKPA6cBn4DbOz3mCs6rm8CJ4v/09PAu2d6Td9ZsRQyvTXbAHMQLQUH0VJwEC0FB9FScBAtBQfRUnAQLYX/AQ8zk53zV23vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.52163583 0.63600016 0.5454346 ]\n",
      "covid, R,G,B: 133 162 139\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict(\"forest\")\n",
    "predict(\"COVID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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