{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "05d3cb31-000b-47f1-bef8-63010a2fd096",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e5ca0b-4285-46f9-b097-b04e2e18582c",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eb77b3b-5ef6-45d9-adea-458569e1ec96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastcore.all import *\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import rich\n",
    "from rich.console import Console\n",
    "import nltk\n",
    "from nltk.corpus import twitter_samples\n",
    "import re                                  # library for regular expression operations\n",
    "import string                              # for string operations\n",
    "from nltk.corpus import stopwords          # module for stop words that come with NLTK\n",
    "from nltk.stem import PorterStemmer        # module for stemming\n",
    "from nltk.tokenize import TweetTokenizer   # module for tokenizing strings\n",
    "import string\n",
    "from matplotlib.patches import Ellipse\n",
    "import matplotlib.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf153bbf-4fae-4ce6-a357-59778f174b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()\n",
    "console = Console()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24bd3684-4516-4c0d-b03c-f2a488c1c4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">Hello Naive Bayes</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[31mHello Naive Bayes\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "console.print(\"Hello Naive Bayes\", style='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802a95f3-a373-4b11-9733-bcb3a2feb248",
   "metadata": {},
   "source": [
    "## Download Dataset and Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8faa4513-bb84-42f8-990e-170e2b096801",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package twitter_samples to\n",
      "[nltk_data]     /home/rahul.saraf/nltk_data...\n",
      "[nltk_data]   Package twitter_samples is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/rahul.saraf/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('twitter_samples')\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3f9a47c-c030-457b-a2ef-578405828fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ptweets = twitter_samples.strings('positive_tweets.json')\n",
    "ntweets = twitter_samples.strings('negative_tweets.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2625810-baaf-4e47-b6d2-44f494c0c644",
   "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>class</th>\n",
       "      <th>tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>positive</td>\n",
       "      <td>#FollowFriday @France_Inte @PKuchly57 @Milipol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>@Lamb2ja Hey James! How odd :/ Please call our...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>positive</td>\n",
       "      <td>@DespiteOfficial we had a listen last night :)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>positive</td>\n",
       "      <td>@97sides CONGRATS :)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>positive</td>\n",
       "      <td>yeaaaah yippppy!!!  my accnt verified rqst has...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      class                                             tweets\n",
       "0  positive  #FollowFriday @France_Inte @PKuchly57 @Milipol...\n",
       "1  positive  @Lamb2ja Hey James! How odd :/ Please call our...\n",
       "2  positive  @DespiteOfficial we had a listen last night :)...\n",
       "3  positive                               @97sides CONGRATS :)\n",
       "4  positive  yeaaaah yippppy!!!  my accnt verified rqst has..."
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'positive':ptweets, 'negative':ntweets}).unstack().reset_index().drop(columns=['level_1']).rename(columns={'level_0':'class', 0:'tweets'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68948a6c-bf3b-465f-b200-67338b041747",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   class   10000 non-null  object\n",
      " 1   tweets  10000 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 156.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086c88e0-f5ca-4f6e-824f-eba5cdc248a1",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecdb6cd0-72ee-4ad2-aa28-dca8967f2de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'My beautiful sunflowers on a sunny Friday morning off :) #sunflowers #favourites #happy #Friday off… https://t.co/3tfYom0N1i'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet = df.loc[2277, 'tweets']; tweet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8f54db-ed1a-49af-9adb-f9a3c4c19a36",
   "metadata": {},
   "source": [
    "### Clean & Stem Tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0910ade2-6482-4a2c-aaf7-5b88c57a6b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['beauti',\n",
       " 'sunflow',\n",
       " 'sunni',\n",
       " 'friday',\n",
       " 'morn',\n",
       " ':)',\n",
       " 'sunflow',\n",
       " 'favourit',\n",
       " 'happi',\n",
       " 'friday',\n",
       " '…']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_old_style(tweet): return re.sub(r'^RT[\\s]+', '', tweet)\n",
    "def remove_url(tweet): return re.sub(r'https?://[^\\s\\n\\r]+', '', tweet)\n",
    "def remove_hash(tweet): return re.sub(r'#', \"\", tweet)\n",
    "tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True, reduce_len=True)\n",
    "skip_words = stopwords.words('english')+list(string.punctuation)\n",
    "stemmer = PorterStemmer() \n",
    "def filter_stem_tokens(tweet_tokens, skip_words=skip_words, stemmer=stemmer): \n",
    "    return [ stemmer.stem(token) for token in tweet_tokens if token not in skip_words]\n",
    "\n",
    "process_tweet = compose(remove_old_style, remove_url, remove_hash, tokenizer.tokenize, filter_stem_tokens)\n",
    "process_tweet(tweet)\n",
    "# skip_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72e96801-9485-4e92-a29a-874e2ef3d458",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ptweets'] = df['tweets'].apply(process_tweet)\n",
    "# df['Ptweets_join'] = df['Ptweets'].apply(lambda row: u\" \".join(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6927bfff-5800-4bd1-b8f2-689e96ba81f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "':)'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u':)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b4aa2c5-47a1-4065-a539-7f3fdc6898c3",
   "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>class</th>\n",
       "      <th>tweets</th>\n",
       "      <th>Ptweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>positive</td>\n",
       "      <td>#FollowFriday @France_Inte @PKuchly57 @Milipol...</td>\n",
       "      <td>[followfriday, top, engag, member, commun, wee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>@Lamb2ja Hey James! How odd :/ Please call our...</td>\n",
       "      <td>[hey, jame, odd, :/, pleas, call, contact, cen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>positive</td>\n",
       "      <td>@DespiteOfficial we had a listen last night :)...</td>\n",
       "      <td>[listen, last, night, :), bleed, amaz, track, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>positive</td>\n",
       "      <td>@97sides CONGRATS :)</td>\n",
       "      <td>[congrat, :)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>positive</td>\n",
       "      <td>yeaaaah yippppy!!!  my accnt verified rqst has...</td>\n",
       "      <td>[yeaaah, yipppi, accnt, verifi, rqst, succeed,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>positive</td>\n",
       "      <td>@RachelLiskeard Thanks for the shout-out :) It...</td>\n",
       "      <td>[thank, shout-out, :), great, aboard]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>positive</td>\n",
       "      <td>@side556 Hey!  :)  Long time no talk...</td>\n",
       "      <td>[hey, :), long, time, talk, ...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>positive</td>\n",
       "      <td>@staybubbly69 as Matt would say. WELCOME TO AD...</td>\n",
       "      <td>[matt, would, say, welcom, adulthood, ..., :)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6736</th>\n",
       "      <td>negative</td>\n",
       "      <td>@Israelgirly They sure do, esp now when ppl ar...</td>\n",
       "      <td>[sure, esp, ppl, talk, crap, milli, &gt;:(, i'll,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7244</th>\n",
       "      <td>negative</td>\n",
       "      <td>@wtfxmbs AMBS please it's harry's jeans :)):):):(</td>\n",
       "      <td>[amb, pleas, harry', jean, :), ):, ):, ):]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3543 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         class                                             tweets  \\\n",
       "0     positive  #FollowFriday @France_Inte @PKuchly57 @Milipol...   \n",
       "1     positive  @Lamb2ja Hey James! How odd :/ Please call our...   \n",
       "2     positive  @DespiteOfficial we had a listen last night :)...   \n",
       "3     positive                               @97sides CONGRATS :)   \n",
       "4     positive  yeaaaah yippppy!!!  my accnt verified rqst has...   \n",
       "...        ...                                                ...   \n",
       "4996  positive  @RachelLiskeard Thanks for the shout-out :) It...   \n",
       "4997  positive            @side556 Hey!  :)  Long time no talk...   \n",
       "4998  positive  @staybubbly69 as Matt would say. WELCOME TO AD...   \n",
       "6736  negative  @Israelgirly They sure do, esp now when ppl ar...   \n",
       "7244  negative  @wtfxmbs AMBS please it's harry's jeans :)):):):(   \n",
       "\n",
       "                                                Ptweets  \n",
       "0     [followfriday, top, engag, member, commun, wee...  \n",
       "1     [hey, jame, odd, :/, pleas, call, contact, cen...  \n",
       "2     [listen, last, night, :), bleed, amaz, track, ...  \n",
       "3                                         [congrat, :)]  \n",
       "4     [yeaaah, yipppi, accnt, verifi, rqst, succeed,...  \n",
       "...                                                 ...  \n",
       "4996              [thank, shout-out, :), great, aboard]  \n",
       "4997                   [hey, :), long, time, talk, ...]  \n",
       "4998     [matt, would, say, welcom, adulthood, ..., :)]  \n",
       "6736  [sure, esp, ppl, talk, crap, milli, >:(, i'll,...  \n",
       "7244         [amb, pleas, harry', jean, :), ):, ):, ):]  \n",
       "\n",
       "[3543 rows x 3 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet = df.loc[2277, \"Ptweets\"]\n",
    "def check_token(tweet, token): \n",
    "    if token in tweet : return True\n",
    "    else: return False\n",
    "\n",
    "token = \":)\"\n",
    "df[df['Ptweets'].apply(lambda row: check_token(row, token))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0a4e206-509c-41f1-9391-a615da1aab42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['Ptweets_join'].str.contains"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ccac41-951f-4b74-8a70-07c7bffc9fcc",
   "metadata": {},
   "source": [
    "### Creating Freqeuncy Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4c0d73a-5f72-41bc-9857-2c9265c5ae16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68430"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['Ptweets'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cc9ef0e-3f90-48dc-8406-2c3bf7961a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10507"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(df['Ptweets'].sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a5323dd-f93a-4a1c-9080-f31ba9fab8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_word_count(token):\n",
    "    d = df[df['Ptweets'].apply(lambda row: check_token(row, token))]['class'].value_counts().to_dict()\n",
    "    return {'word': token, 'positive':d.get('positive',0), 'negative':d.get('negative', 0)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6162b562-2869-4f4b-b496-375126586ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_freqs(df):\n",
    "    tokens = list(set(df['Ptweets'].sum()))\n",
    "    df_freqs = pd.DataFrame([get_word_count(token) for token in tokens]).set_index('word'); \n",
    "    # Laplace smoothing formulae for probability\n",
    "    V = df_freqs.shape[0]\n",
    "    df_freqs['log_pos_prob'] = np.log((df_freqs['positive']+1)/(df_freqs['positive'].sum()+V))\n",
    "    df_freqs['log_neg_prob'] = np.log((df_freqs['negative']+1)/(df_freqs['negative'].sum()+V))\n",
    "    df_freqs['lambda'] = df_freqs['log_pos_prob'] - df_freqs['log_neg_prob']\n",
    "    return df_freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1fa3971-b801-4744-bb49-b2c8ebdc367a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freqs = build_freqs(df)\n",
    "# np.log(df_freqs['pos_prob'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64366c3c-eef2-4fef-b370-aa4b47ac939b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>log_pos_prob</th>\n",
       "      <th>log_neg_prob</th>\n",
       "      <th>lambda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sweden</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.688279</td>\n",
       "      <td>-9.972150</td>\n",
       "      <td>-0.716129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jackson</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-10.688279</td>\n",
       "      <td>-9.279003</td>\n",
       "      <td>-1.409276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gl</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.995132</td>\n",
       "      <td>-10.665298</td>\n",
       "      <td>0.670166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shake</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.589667</td>\n",
       "      <td>-9.972150</td>\n",
       "      <td>0.382484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hee</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.995132</td>\n",
       "      <td>-10.665298</td>\n",
       "      <td>0.670166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         positive  negative  log_pos_prob  log_neg_prob    lambda\n",
       "word                                                             \n",
       "sweden          0         1    -10.688279     -9.972150 -0.716129\n",
       "jackson         0         3    -10.688279     -9.279003 -1.409276\n",
       "gl              1         0     -9.995132    -10.665298  0.670166\n",
       "shake           2         1     -9.589667     -9.972150  0.382484\n",
       "hee             1         0     -9.995132    -10.665298  0.670166"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# good_keys = df.index.intersection()\n",
    "# df_freqs.loc[good_keys]\n",
    "l = df_freqs.head().index.tolist()\n",
    "l.append(\"lalala\")\n",
    "df_freqs.loc[df_freqs.index.intersection(l)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3addcc-8b8d-42aa-8515-185eea863cd5",
   "metadata": {},
   "source": [
    "### Extract Features from tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "934caba0-b44d-44b3-936c-5d550b0741a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4174.0, 119.0, -76.26418672683839, -96.03713892386281, 19.77295219702441, 1]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet = df.loc[2277, \"Ptweets\"]\n",
    "l = df_freqs.loc[tweet].sum().tolist()\n",
    "l.append(1)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c53a67cc-3732-43cd-8525-a24a43e23e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['beauti',\n",
       "  'sunflow',\n",
       "  'sunni',\n",
       "  'friday',\n",
       "  'morn',\n",
       "  ':)',\n",
       "  'sunflow',\n",
       "  'favourit',\n",
       "  'happi',\n",
       "  'friday',\n",
       "  '…'],\n",
       " [4063.0,\n",
       "  107.0,\n",
       "  -60.290305886425145,\n",
       "  -77.27149318108225,\n",
       "  16.981187294657104,\n",
       "  1])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def score_tweet(tweet, df_freqs):\n",
    "    l = df_freqs.loc[df_freqs.index.intersection(tweet)].sum().tolist() \n",
    "    # Do intersection to take keys that exist in frequency table and skip which don't \n",
    "    l.append(1)\n",
    "    return l\n",
    "tweet, score_tweet(tweet, df_freqs)\n",
    "# df_freqs.loc[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ebdc97a-bb88-43e2-bf1f-149e25dcb808",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a data leak . Build Frequency and scoring only on train_df\n",
    "df['positive'], df['negative'],df['log_pos_prob'], df['log_neg_prob'], df['lambda'] , df['bias']=zip(*df['Ptweets'].map(lambda row : score_tweet(row, df_freqs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01e0d3e8-31d8-4510-9c4a-13c5d3547853",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sentiment'] = 0\n",
    "df.loc[df['class']=='positive', 'sentiment'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93ca3a4b-55ab-4ff8-b9ed-68e73bdd9873",
   "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>class</th>\n",
       "      <th>tweets</th>\n",
       "      <th>Ptweets</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>log_pos_prob</th>\n",
       "      <th>log_neg_prob</th>\n",
       "      <th>lambda</th>\n",
       "      <th>bias</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>positive</td>\n",
       "      <td>#FollowFriday @France_Inte @PKuchly57 @Milipol...</td>\n",
       "      <td>[followfriday, top, engag, member, commun, wee...</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>-47.021071</td>\n",
       "      <td>-64.579054</td>\n",
       "      <td>17.557983</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>@Lamb2ja Hey James! How odd :/ Please call our...</td>\n",
       "      <td>[hey, jame, odd, :/, pleas, call, contact, cen...</td>\n",
       "      <td>4448.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>-107.276901</td>\n",
       "      <td>-116.195717</td>\n",
       "      <td>8.918815</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>positive</td>\n",
       "      <td>@DespiteOfficial we had a listen last night :)...</td>\n",
       "      <td>[listen, last, night, :), bleed, amaz, track, ...</td>\n",
       "      <td>3728.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>-58.478652</td>\n",
       "      <td>-67.157334</td>\n",
       "      <td>8.678683</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>positive</td>\n",
       "      <td>@97sides CONGRATS :)</td>\n",
       "      <td>[congrat, :)]</td>\n",
       "      <td>3562.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-10.113069</td>\n",
       "      <td>-19.133371</td>\n",
       "      <td>9.020302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>positive</td>\n",
       "      <td>yeaaaah yippppy!!!  my accnt verified rqst has...</td>\n",
       "      <td>[yeaaah, yipppi, accnt, verifi, rqst, succeed,...</td>\n",
       "      <td>3878.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>-129.201531</td>\n",
       "      <td>-141.211416</td>\n",
       "      <td>12.009885</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      class                                             tweets  \\\n",
       "0  positive  #FollowFriday @France_Inte @PKuchly57 @Milipol...   \n",
       "1  positive  @Lamb2ja Hey James! How odd :/ Please call our...   \n",
       "2  positive  @DespiteOfficial we had a listen last night :)...   \n",
       "3  positive                               @97sides CONGRATS :)   \n",
       "4  positive  yeaaaah yippppy!!!  my accnt verified rqst has...   \n",
       "\n",
       "                                             Ptweets  positive  negative  \\\n",
       "0  [followfriday, top, engag, member, commun, wee...    3737.0      69.0   \n",
       "1  [hey, jame, odd, :/, pleas, call, contact, cen...    4448.0     473.0   \n",
       "2  [listen, last, night, :), bleed, amaz, track, ...    3728.0     159.0   \n",
       "3                                      [congrat, :)]    3562.0       4.0   \n",
       "4  [yeaaah, yipppi, accnt, verifi, rqst, succeed,...    3878.0     273.0   \n",
       "\n",
       "   log_pos_prob  log_neg_prob     lambda  bias  sentiment  \n",
       "0    -47.021071    -64.579054  17.557983     1          1  \n",
       "1   -107.276901   -116.195717   8.918815     1          1  \n",
       "2    -58.478652    -67.157334   8.678683     1          1  \n",
       "3    -10.113069    -19.133371   9.020302     1          1  \n",
       "4   -129.201531   -141.211416  12.009885     1          1  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46580f6d-c733-4695-ba75-7ba7ce658729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive         33332.000000\n",
       "negative         32336.000000\n",
       "log_pos_prob   -104991.038240\n",
       "log_neg_prob   -104871.983649\n",
       "lambda            -119.054592\n",
       "dtype: float64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freqs.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d03656f-3d5f-429b-96a6-a5ef955d170f",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53888461-0299-4f20-aadb-eeb137f3c969",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'positive':ptweets, 'negative':ntweets}).unstack().reset_index().drop(columns=['level_1']).rename(columns={'level_0':'class', 0:'tweets'})\n",
    "df['Ptweets'] = df['tweets'].apply(process_tweet)\n",
    "train_df = pd.concat([df[:4000],df[5000:9000]])\n",
    "test_df =  pd.concat([df[4000:5000],df[9000:10000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2696d8b9-4167-40fd-8e36-1dc1c784aa3c",
   "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>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>log_pos_prob</th>\n",
       "      <th>log_neg_prob</th>\n",
       "      <th>lambda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sweden</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.638928</td>\n",
       "      <td>-9.923462</td>\n",
       "      <td>-0.715466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jackson</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-10.638928</td>\n",
       "      <td>-9.230315</td>\n",
       "      <td>-1.408613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gl</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.945780</td>\n",
       "      <td>-10.616609</td>\n",
       "      <td>0.670828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shake</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.540315</td>\n",
       "      <td>-9.923462</td>\n",
       "      <td>0.383146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hee</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.945780</td>\n",
       "      <td>-10.616609</td>\n",
       "      <td>0.670828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-9.945780</td>\n",
       "      <td>-9.517997</td>\n",
       "      <td>-0.427784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.638928</td>\n",
       "      <td>-9.923462</td>\n",
       "      <td>-0.715466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>who'</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>-8.336343</td>\n",
       "      <td>-8.537167</td>\n",
       "      <td>0.200825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school'</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.945780</td>\n",
       "      <td>-10.616609</td>\n",
       "      <td>0.670828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ladygaga</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.638928</td>\n",
       "      <td>-9.923462</td>\n",
       "      <td>-0.715466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9162 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          positive  negative  log_pos_prob  log_neg_prob    lambda\n",
       "word                                                              \n",
       "sweden           0         1    -10.638928     -9.923462 -0.715466\n",
       "jackson          0         3    -10.638928     -9.230315 -1.408613\n",
       "gl               1         0     -9.945780    -10.616609  0.670828\n",
       "shake            2         1     -9.540315     -9.923462  0.383146\n",
       "hee              1         0     -9.945780    -10.616609  0.670828\n",
       "...            ...       ...           ...           ...       ...\n",
       "control          1         2     -9.945780     -9.517997 -0.427784\n",
       "590              0         1    -10.638928     -9.923462 -0.715466\n",
       "who'             9         7     -8.336343     -8.537167  0.200825\n",
       "school'          1         0     -9.945780    -10.616609  0.670828\n",
       "ladygaga         0         1    -10.638928     -9.923462 -0.715466\n",
       "\n",
       "[9162 rows x 5 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freqs = build_freqs(train_df)\n",
    "df_freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dff9930-fcae-4957-a0b3-ab0b4270ab3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd = train_df['class'].value_counts().to_dict()\n",
    "bias = np.log(bd['positive']/bd['negative'])\n",
    "bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "474a1d23-2ff9-4a04-b843-2809c0fcb126",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['positive'], train_df['negative'], train_df['log_pos_prob'], train_df['log_neg_prob'], train_df['lambda'] , train_df['bias']=zip(*train_df['Ptweets'].map(lambda row : score_tweet(row, df_freqs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf96276a-c46e-46b4-877d-c30e32734925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['prediction'] = train_df['lambda']+bias > 0\n",
    "train_df['actual'] = train_df['class'] == 'positive'\n",
    "(train_df['actual'] == train_df['prediction']).mean() # accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0b6369b-732a-4b83-b028-d975a6cf9fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9985"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['positive'], test_df['negative'], test_df['log_pos_prob'], test_df['log_neg_prob'], test_df['lambda'] , test_df['bias']=zip(*test_df['Ptweets'].map(lambda row : score_tweet(row, df_freqs)))\n",
    "test_df['prediction'] = test_df['lambda']+bias > 0\n",
    "test_df['actual'] = test_df['class'] == 'positive'\n",
    "(test_df['actual'] == test_df['prediction']).mean() # accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68d29cc-4a0e-463c-86a9-2bd023f872f4",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e471195-522a-42fc-ab01-6369d06452d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='log_pos_prob', ylabel='log_neg_prob'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=train_df, x='log_pos_prob', y='log_neg_prob', hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2701ea-d380-4ec7-9061-2a86fdec27b8",
   "metadata": {},
   "source": [
    "### Confidence Elipse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d59e5350-3719-4530-8e9c-656f073dbe0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pos = train_df[train_df['class']=='positive']\n",
    "data_neg = train_df[train_df['class']=='negative']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548b98dc-8da8-45ff-9e89-c8512cf8f6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[832.64706392, 866.45359717],\n",
       "       [866.45359717, 911.72992453]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data_pos['log_pos_prob']\n",
    "y = data_pos['log_neg_prob']\n",
    "n_std=3.0\n",
    "cov_mat= np.cov(x,y)\n",
    "cov_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2ba1bb3-c204-4b0d-b08a-b1ef5ac28a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.994447194605905"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearson = cov_mat[0,1]/np.sqrt(cov_mat[0,0]*cov_mat[1,1])\n",
    "pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49c857ee-fd08-444d-82e1-f779f1e8b331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4122489846361743, 0.07451714832234908)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ell_radius_x = np.sqrt(1+pearson)\n",
    "ell_radius_y = np.sqrt(1-pearson)\n",
    "ell_radius_x, ell_radius_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ce97c71-da99-4577-9179-1518f694d985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86.56687342915261, -45.98846279441421)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale_x = np.sqrt(cov_mat[0,0])*n_std;  mean_x = np.mean(x)\n",
    "scale_x, mean_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e47686e1-a0d1-4fd6-8d31-051d4d03d080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90.58459759147846, -55.61467522963381)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale_y = np.sqrt(cov_mat[1,1])*n_std;  mean_y = np.mean(y)\n",
    "scale_y, mean_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1083a4a-8c9f-4dc4-b426-cff88962fcda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4122489846361743,\n",
       " 86.56687342915261,\n",
       " -45.98846279441421,\n",
       " 0.07451714832234908,\n",
       " 90.58459759147846,\n",
       " -55.61467522963381)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_ellipses_data(x,y, n_std=3.0):\n",
    "    cov_mat= np.cov(x,y)\n",
    "    pearson = cov_mat[0,1]/np.sqrt(cov_mat[0,0]*cov_mat[1,1])\n",
    "    ell_radius_x = np.sqrt(1+pearson)\n",
    "    ell_radius_y = np.sqrt(1-pearson)\n",
    "    scale_x = np.sqrt(cov_mat[0,0])*n_std\n",
    "    mean_x = np.mean(x)\n",
    "    scale_y = np.sqrt(cov_mat[1,1])*n_std\n",
    "    mean_y = np.mean(y)\n",
    "    return ell_radius_x, scale_x, mean_x, ell_radius_y, scale_y, mean_y\n",
    "calc_ellipses_data(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "193014b2-5a96-4b59-9730-979117d109bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_ellipse(data, ax, facecolor='None', **kwargs):\n",
    "    ell_radius_x, scale_x, mean_x, ell_radius_y, scale_y, mean_y = data\n",
    "    ellipse = Ellipse((0, 0),\n",
    "                  width=ell_radius_x * 2,\n",
    "                  height=ell_radius_y * 2,\n",
    "                  facecolor=facecolor,\n",
    "                  **kwargs)\n",
    "    transf = transforms.Affine2D() \\\n",
    "        .rotate_deg(45) \\\n",
    "        .scale(scale_x, scale_y) \\\n",
    "        .translate(mean_x, mean_y)\n",
    "    ellipse.set_transform(transf + ax.transData)\n",
    "    return ax.add_patch(ellipse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c45a349a-8bfa-4dba-abfe-99a80e0c6d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Test'}, xlabel='log_pos_prob', ylabel='log_neg_prob'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_naive_bayes(df, ax=None, title='data'):\n",
    "    data_pos = df[df['class']=='positive']\n",
    "    data_neg = df[df['class']=='negative']\n",
    "    if ax is None:fig, ax = plt.subplots(figsize=(11.7, 8.27))\n",
    "    sns.scatterplot(data=train_df, x='log_pos_prob', y='log_neg_prob', hue='class', ax=ax)\n",
    "    x = data_pos['log_pos_prob']\n",
    "    y = data_pos['log_neg_prob']\n",
    "    ellipse_data_2std=calc_ellipses_data(x,y, n_std=2)\n",
    "    draw_ellipse(ellipse_data_2std, ax, edgecolor='black', linestyle=':',label=r'$2\\sigma$')\n",
    "    ellipse_data_3std=calc_ellipses_data(x,y, n_std=3)\n",
    "    draw_ellipse(ellipse_data_3std, ax, edgecolor='black')\n",
    "    x = data_neg['log_pos_prob']\n",
    "    y = data_neg['log_neg_prob']\n",
    "    ellipse_data_2std=calc_ellipses_data(x,y, n_std=2)\n",
    "    draw_ellipse(ellipse_data_2std, ax, edgecolor='red', linestyle=':')\n",
    "    ellipse_data_3std=calc_ellipses_data(x,y, n_std=3)\n",
    "    draw_ellipse(ellipse_data_3std, ax, edgecolor='red',label=r'$3\\sigma$')\n",
    "    ax.legend()\n",
    "    ax.set_title(title)\n",
    "    return ax\n",
    "\n",
    "plot_naive_bayes(train_df, title='Train')\n",
    "plot_naive_bayes(test_df, title='Test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e46d31d5-fefe-4775-9896-3851e2d9fc99",
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