{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c3d8bbc-473d-46dd-8d89-cb3f23c1f2de",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Lectures 8: Class demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9babf668-3776-484d-a83f-9b1af3b43dd7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d812265e-c771-4be8-9c02-5eae4a42121b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(os.path.join(os.path.abspath(\"..\"), \"code\"))\n",
    "from utils import *\n",
    "import matplotlib.pyplot as plt\n",
    "import mglearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from plotting_functions import *\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.model_selection import cross_val_score, cross_validate, train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fceade0f-bdc9-4989-b140-a5a10d62ad47",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Demo: Model interpretation of linear classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9861131-b23b-4471-99a7-b4c54652da4b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- One of the primary advantage of linear classifiers is their ability to interpret models. \n",
    "- For example, with the sign and magnitude of learned coefficients we could answer questions such as which features are driving the prediction to which direction. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf88452-bd66-4a36-b1d3-4f04c56291c5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- We'll demonstrate this by training `LogisticRegression` on the famous [IMDB movie review](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) dataset. The dataset is a bit large for demonstration purposes. So I am going to put a big portion of it in the test split to speed things up. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84f30540-e09d-4d51-b9d7-8a36bf743162",
   "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>review</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Once again Mr. Costner has dragged out a movie for far longer than necessary. Aside from the terrific sea rescue sequences, of which there are very few I just did not care about any of the charact...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This is an example of why the majority of action films are the same. Generic and boring, there's really nothing worth watching here. A complete waste of the then barely-tapped talents of Ice-T and...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>First of all I hate those moronic rappers, who could'nt act if they had a gun pressed against their foreheads. All they do is curse and shoot each other and acting like cliché'e version of gangst...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not even the Beatles could write songs everyone liked, and although Walter Hill is no mop-top he's second to none when it comes to thought provoking action movies. The nineties came and social pla...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brass pictures (movies is not a fitting word for them) really are somewhat brassy. Their alluring visual qualities are reminiscent of expensive high class TV commercials. But unfortunately Brass p...</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                    review  \\\n",
       "0  Once again Mr. Costner has dragged out a movie for far longer than necessary. Aside from the terrific sea rescue sequences, of which there are very few I just did not care about any of the charact...   \n",
       "1  This is an example of why the majority of action films are the same. Generic and boring, there's really nothing worth watching here. A complete waste of the then barely-tapped talents of Ice-T and...   \n",
       "2  First of all I hate those moronic rappers, who could'nt act if they had a gun pressed against their foreheads. All they do is curse and shoot each other and acting like cliché'e version of gangst...   \n",
       "3  Not even the Beatles could write songs everyone liked, and although Walter Hill is no mop-top he's second to none when it comes to thought provoking action movies. The nineties came and social pla...   \n",
       "4  Brass pictures (movies is not a fitting word for them) really are somewhat brassy. Their alluring visual qualities are reminiscent of expensive high class TV commercials. But unfortunately Brass p...   \n",
       "\n",
       "  label  \n",
       "0   neg  \n",
       "1   neg  \n",
       "2   neg  \n",
       "3   neg  \n",
       "4   neg  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_df = pd.read_csv(\"../data/imdb_master.csv\", encoding=\"ISO-8859-1\")\n",
    "imdb_df = imdb_df[imdb_df[\"label\"].str.startswith((\"pos\", \"neg\"))]\n",
    "imdb_df.drop([\"Unnamed: 0\", \"type\", \"file\"], axis=1, inplace=True)\n",
    "imdb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf4d270-7c3f-489c-b88a-ae70957274a8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's clean up the data a bit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1e4b670-3087-4426-aa82-a7bbc78e67d4",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def replace_tags(doc):\n",
    "    doc = doc.replace(\"<br />\", \" \")\n",
    "    doc = re.sub(\"https://\\S*\", \"\", doc)\n",
    "    return doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b452783c-98a4-48f6-9f6d-a8c1a0b38766",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "imdb_df[\"review_pp\"] = imdb_df[\"review\"].apply(replace_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef93373-043d-4a96-a2d6-19d542cccee1",
   "metadata": {},
   "source": [
    "Are we breaking the Golden rule here? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d3353d-0b4a-4019-904e-ccf51ea538fa",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's split the data and create bag of words representation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "520ffaee-3d50-4ddd-8d3c-26818cc5d13a",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df, test_df = train_test_split(imdb_df, test_size=0.9, random_state=123)\n",
    "X_train, y_train = train_df[\"review_pp\"], train_df[\"label\"]\n",
    "X_test, y_test = test_df[\"review_pp\"], test_df[\"label\"]\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20d205ba-8331-4864-b8e4-2fd1a0f41f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5000x38153 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 433027 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec = CountVectorizer(stop_words=\"english\")\n",
    "bow = vec.fit_transform(X_train)\n",
    "bow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "244f58c2-06d9-4317-9c9f-53e05f63fefb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Examining the vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb41f6d8-94be-489a-93b0-1e886cbcb0e9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The vocabulary (mapping from feature indices to actual words) can be obtained using `get_feature_names_out()` on the `CountVectorizer` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dbb6fe4e-f01e-45b9-aaad-e40c0bf34431",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = vec.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f816df9-1ea0-4728-9f38-cf3f168c7466",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00', '000', '00015', '007', '0093638', '00pm', '00s', '01', '02',\n",
       "       '03'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[0:10]  # first few words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d20bba9-94a2-46ec-8046-87667322a32c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['arabs', 'arachnids', 'aragorn', 'aran', 'araãºjo', 'arbiter',\n",
       "       'arbitrarily', 'arbitrary', 'arbogast', 'arbor'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[2000:2010]  # some middle words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70c9290f-c4e2-4855-8261-f7c2633a865c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00', '_everything_', 'aesthetics', 'amelia', 'arabs', 'atwill',\n",
       "       'barbour', 'bereft', 'blunders', 'breathy', 'butterfly', 'cashed',\n",
       "       'chemical', 'clint', 'compiling', 'controlness', 'creeping',\n",
       "       'dandruff', 'delusional', 'differentiate', 'divya', 'ds',\n",
       "       'electra', 'envisage', 'expecially', 'faze', 'flesh', 'freely',\n",
       "       'gellar', 'gomez', 'guilted', 'hastened', 'hilly', 'hullabaloo',\n",
       "       'imprisons', 'insists', 'ivor', 'jungles', 'kolonel', 'lds',\n",
       "       'listens', 'ma', 'markings', 'melee', 'mishap', 'mourned',\n",
       "       'needless', 'nudge', 'org', 'panoply', 'perceptive', 'pizza',\n",
       "       'pour', 'promoters', 'quilty', 'reccoemnd', 'repaid', 'riders',\n",
       "       'ruining', 'scaring', 'senselessly', 'short', 'slasher', 'sonja',\n",
       "       'squishes', 'stratas', 'superlative', 'talley', 'theories',\n",
       "       'tormentors', 'trows', 'understatement', 'uprooted', 'vilifies',\n",
       "       'waxman', 'withdraw', 'yyz'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[::500]  # words with a step of 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "062ae6ee-90b5-40f2-a537-3e60c42863b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "neg    2525\n",
       "pos    2475\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb91777-ffce-4202-885a-82911f13ae77",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Model building on the dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17825ada-4c67-4025-85c8-4a9c41ed591e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "First let's try `DummyClassifier` on the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3faf6b7d-a526-4774-afc7-9d6b068126df",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.001120</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001259</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001237</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.001551    0.001493       0.505        0.505\n",
       "1  0.001266    0.001120       0.505        0.505\n",
       "2  0.001259    0.001061       0.505        0.505\n",
       "3  0.001214    0.001099       0.505        0.505\n",
       "4  0.001237    0.001087       0.505        0.505"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = DummyClassifier()\n",
    "scores = cross_validate(dummy, X_train, y_train, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b09701-e6a8-49b7-ba2c-be3418d187c4",
   "metadata": {},
   "source": [
    "We have a balanced dataset. So the `DummyClassifier` score is around 0.5. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3e889b-c90f-4483-9a57-024303200cac",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now let's try logistic regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4d4940a-ade4-4726-850e-776d03393e24",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.648565</td>\n",
       "      <td>0.089887</td>\n",
       "      <td>0.851</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.641437</td>\n",
       "      <td>0.087010</td>\n",
       "      <td>0.834</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.632287</td>\n",
       "      <td>0.088347</td>\n",
       "      <td>0.838</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.676811</td>\n",
       "      <td>0.083822</td>\n",
       "      <td>0.859</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.644781</td>\n",
       "      <td>0.086424</td>\n",
       "      <td>0.840</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.648565    0.089887       0.851          1.0\n",
       "1  0.641437    0.087010       0.834          1.0\n",
       "2  0.632287    0.088347       0.838          1.0\n",
       "3  0.676811    0.083822       0.859          1.0\n",
       "4  0.644781    0.086424       0.840          1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr = make_pipeline(\n",
    "    CountVectorizer(stop_words=\"english\"),\n",
    "    LogisticRegression(max_iter=1000),\n",
    ")\n",
    "scores = cross_validate(pipe_lr, X_train, y_train, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada0ee11-b543-4a95-8e20-93f27dc80fc9",
   "metadata": {},
   "source": [
    "Seems like we are overfitting. Let's optimize the hyperparameter `C` of LR and `max_features` of `CountVectorizer`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63811c83-a038-4ff2-b2f8-ec12ce50c53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;countvectorizer&#x27;,\n",
       "                                              CountVectorizer(stop_words=&#x27;english&#x27;)),\n",
       "                                             (&#x27;logisticregression&#x27;,\n",
       "                                              LogisticRegression(max_iter=1000))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;countvectorizer__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x16f8a7880&gt;,\n",
       "                                        &#x27;logisticregression__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x16f8a6a70&gt;},\n",
       "                   return_train_score=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;countvectorizer&#x27;,\n",
       "                                              CountVectorizer(stop_words=&#x27;english&#x27;)),\n",
       "                                             (&#x27;logisticregression&#x27;,\n",
       "                                              LogisticRegression(max_iter=1000))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;countvectorizer__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x16f8a7880&gt;,\n",
       "                                        &#x27;logisticregression__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x16f8a6a70&gt;},\n",
       "                   return_train_score=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;countvectorizer&#x27;, CountVectorizer(stop_words=&#x27;english&#x27;)),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(stop_words=&#x27;english&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(estimator=Pipeline(steps=[('countvectorizer',\n",
       "                                              CountVectorizer(stop_words='english')),\n",
       "                                             ('logisticregression',\n",
       "                                              LogisticRegression(max_iter=1000))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'countvectorizer__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x16f8a7880>,\n",
       "                                        'logisticregression__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x16f8a6a70>},\n",
       "                   return_train_score=True)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import loguniform, randint, uniform\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "param_dist = {\n",
    "    \"countvectorizer__max_features\": randint(10, len(vocab)),\n",
    "    \"logisticregression__C\": loguniform(1e-3, 1e3)\n",
    "}\n",
    "pipe_lr = make_pipeline(CountVectorizer(stop_words=\"english\"), LogisticRegression(max_iter=1000))\n",
    "random_search = RandomizedSearchCV(pipe_lr, param_dist, n_iter=10, n_jobs=-1, return_train_score=True)\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d82b087d-3898-496f-a96b-e56fa30218aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_countvectorizer__max_features</th>\n",
       "      <th>param_logisticregression__C</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.887960</td>\n",
       "      <td>0.031929</td>\n",
       "      <td>0.129097</td>\n",
       "      <td>0.006647</td>\n",
       "      <td>3690</td>\n",
       "      <td>447.077429</td>\n",
       "      <td>{'countvectorizer__max_features': 3690, 'logisticregression__C': 447.07742899169847}</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.808</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8104</td>\n",
       "      <td>0.009135</td>\n",
       "      <td>8</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.889278</td>\n",
       "      <td>0.033943</td>\n",
       "      <td>0.123936</td>\n",
       "      <td>0.004859</td>\n",
       "      <td>2541</td>\n",
       "      <td>912.930835</td>\n",
       "      <td>{'countvectorizer__max_features': 2541, 'logisticregression__C': 912.9308349152036}</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8010</td>\n",
       "      <td>0.011713</td>\n",
       "      <td>10</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.747122</td>\n",
       "      <td>0.071033</td>\n",
       "      <td>0.130589</td>\n",
       "      <td>0.005867</td>\n",
       "      <td>22317</td>\n",
       "      <td>0.056543</td>\n",
       "      <td>{'countvectorizer__max_features': 22317, 'logisticregression__C': 0.056542860051277476}</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8558</td>\n",
       "      <td>0.007139</td>\n",
       "      <td>1</td>\n",
       "      <td>0.97675</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.97975</td>\n",
       "      <td>0.98025</td>\n",
       "      <td>0.98200</td>\n",
       "      <td>0.97975</td>\n",
       "      <td>0.001696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.978819</td>\n",
       "      <td>0.047634</td>\n",
       "      <td>0.132974</td>\n",
       "      <td>0.006072</td>\n",
       "      <td>17016</td>\n",
       "      <td>910.358522</td>\n",
       "      <td>{'countvectorizer__max_features': 17016, 'logisticregression__C': 910.3585221562778}</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8336</td>\n",
       "      <td>0.008114</td>\n",
       "      <td>6</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.698466</td>\n",
       "      <td>0.057317</td>\n",
       "      <td>0.137204</td>\n",
       "      <td>0.009667</td>\n",
       "      <td>9733</td>\n",
       "      <td>0.030182</td>\n",
       "      <td>{'countvectorizer__max_features': 9733, 'logisticregression__C': 0.03018189029798178}</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8532</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95650</td>\n",
       "      <td>0.9590</td>\n",
       "      <td>0.95775</td>\n",
       "      <td>0.95800</td>\n",
       "      <td>0.95775</td>\n",
       "      <td>0.95780</td>\n",
       "      <td>0.000797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.119875</td>\n",
       "      <td>0.046236</td>\n",
       "      <td>0.137406</td>\n",
       "      <td>0.008026</td>\n",
       "      <td>25540</td>\n",
       "      <td>32.463393</td>\n",
       "      <td>{'countvectorizer__max_features': 25540, 'logisticregression__C': 32.46339324613553}</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8378</td>\n",
       "      <td>0.010186</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.645274</td>\n",
       "      <td>0.029789</td>\n",
       "      <td>0.118591</td>\n",
       "      <td>0.008782</td>\n",
       "      <td>570</td>\n",
       "      <td>0.307999</td>\n",
       "      <td>{'countvectorizer__max_features': 570, 'logisticregression__C': 0.3079991489892481}</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8102</td>\n",
       "      <td>0.011338</td>\n",
       "      <td>9</td>\n",
       "      <td>0.88300</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.88925</td>\n",
       "      <td>0.88100</td>\n",
       "      <td>0.88925</td>\n",
       "      <td>0.88600</td>\n",
       "      <td>0.003387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.587483</td>\n",
       "      <td>0.013845</td>\n",
       "      <td>0.129797</td>\n",
       "      <td>0.019168</td>\n",
       "      <td>2215</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>{'countvectorizer__max_features': 2215, 'logisticregression__C': 0.008402692418779856}</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8376</td>\n",
       "      <td>0.009790</td>\n",
       "      <td>5</td>\n",
       "      <td>0.89350</td>\n",
       "      <td>0.8965</td>\n",
       "      <td>0.89175</td>\n",
       "      <td>0.88875</td>\n",
       "      <td>0.89175</td>\n",
       "      <td>0.89245</td>\n",
       "      <td>0.002537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.743434</td>\n",
       "      <td>0.053850</td>\n",
       "      <td>0.127350</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>3764</td>\n",
       "      <td>8.112047</td>\n",
       "      <td>{'countvectorizer__max_features': 3764, 'logisticregression__C': 8.112047408258451}</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.824</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8198</td>\n",
       "      <td>0.010833</td>\n",
       "      <td>7</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.906519</td>\n",
       "      <td>0.097151</td>\n",
       "      <td>0.115202</td>\n",
       "      <td>0.022380</td>\n",
       "      <td>21987</td>\n",
       "      <td>6.429147</td>\n",
       "      <td>{'countvectorizer__max_features': 21987, 'logisticregression__C': 6.429147249317815}</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.835</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8392</td>\n",
       "      <td>0.009347</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.887960      0.031929         0.129097        0.006647   \n",
       "1       0.889278      0.033943         0.123936        0.004859   \n",
       "2       0.747122      0.071033         0.130589        0.005867   \n",
       "3       0.978819      0.047634         0.132974        0.006072   \n",
       "4       0.698466      0.057317         0.137204        0.009667   \n",
       "5       1.119875      0.046236         0.137406        0.008026   \n",
       "6       0.645274      0.029789         0.118591        0.008782   \n",
       "7       0.587483      0.013845         0.129797        0.019168   \n",
       "8       0.743434      0.053850         0.127350        0.005814   \n",
       "9       0.906519      0.097151         0.115202        0.022380   \n",
       "\n",
       "  param_countvectorizer__max_features param_logisticregression__C  \\\n",
       "0                                3690                  447.077429   \n",
       "1                                2541                  912.930835   \n",
       "2                               22317                    0.056543   \n",
       "3                               17016                  910.358522   \n",
       "4                                9733                    0.030182   \n",
       "5                               25540                   32.463393   \n",
       "6                                 570                    0.307999   \n",
       "7                                2215                    0.008403   \n",
       "8                                3764                    8.112047   \n",
       "9                               21987                    6.429147   \n",
       "\n",
       "                                                                                    params  \\\n",
       "0     {'countvectorizer__max_features': 3690, 'logisticregression__C': 447.07742899169847}   \n",
       "1      {'countvectorizer__max_features': 2541, 'logisticregression__C': 912.9308349152036}   \n",
       "2  {'countvectorizer__max_features': 22317, 'logisticregression__C': 0.056542860051277476}   \n",
       "3     {'countvectorizer__max_features': 17016, 'logisticregression__C': 910.3585221562778}   \n",
       "4    {'countvectorizer__max_features': 9733, 'logisticregression__C': 0.03018189029798178}   \n",
       "5     {'countvectorizer__max_features': 25540, 'logisticregression__C': 32.46339324613553}   \n",
       "6      {'countvectorizer__max_features': 570, 'logisticregression__C': 0.3079991489892481}   \n",
       "7   {'countvectorizer__max_features': 2215, 'logisticregression__C': 0.008402692418779856}   \n",
       "8      {'countvectorizer__max_features': 3764, 'logisticregression__C': 8.112047408258451}   \n",
       "9     {'countvectorizer__max_features': 21987, 'logisticregression__C': 6.429147249317815}   \n",
       "\n",
       "   split0_test_score  split1_test_score  split2_test_score  ...  \\\n",
       "0              0.823              0.800              0.808  ...   \n",
       "1              0.821              0.795              0.805  ...   \n",
       "2              0.861              0.853              0.850  ...   \n",
       "3              0.843              0.826              0.827  ...   \n",
       "4              0.852              0.852              0.852  ...   \n",
       "5              0.846              0.825              0.832  ...   \n",
       "6              0.824              0.797              0.807  ...   \n",
       "7              0.827              0.832              0.833  ...   \n",
       "8              0.827              0.803              0.824  ...   \n",
       "9              0.845              0.827              0.835  ...   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  split0_train_score  \\\n",
       "0           0.8104        0.009135                8             1.00000   \n",
       "1           0.8010        0.011713               10             1.00000   \n",
       "2           0.8558        0.007139                1             0.97675   \n",
       "3           0.8336        0.008114                6             1.00000   \n",
       "4           0.8532        0.005269                2             0.95650   \n",
       "5           0.8378        0.010186                4             1.00000   \n",
       "6           0.8102        0.011338                9             0.88300   \n",
       "7           0.8376        0.009790                5             0.89350   \n",
       "8           0.8198        0.010833                7             1.00000   \n",
       "9           0.8392        0.009347                3             1.00000   \n",
       "\n",
       "   split1_train_score  split2_train_score  split3_train_score  \\\n",
       "0              1.0000             1.00000             1.00000   \n",
       "1              1.0000             1.00000             1.00000   \n",
       "2              0.9800             0.97975             0.98025   \n",
       "3              1.0000             1.00000             1.00000   \n",
       "4              0.9590             0.95775             0.95800   \n",
       "5              1.0000             1.00000             1.00000   \n",
       "6              0.8875             0.88925             0.88100   \n",
       "7              0.8965             0.89175             0.88875   \n",
       "8              1.0000             1.00000             1.00000   \n",
       "9              1.0000             1.00000             1.00000   \n",
       "\n",
       "   split4_train_score  mean_train_score  std_train_score  \n",
       "0             1.00000           1.00000         0.000000  \n",
       "1             1.00000           1.00000         0.000000  \n",
       "2             0.98200           0.97975         0.001696  \n",
       "3             1.00000           1.00000         0.000000  \n",
       "4             0.95775           0.95780         0.000797  \n",
       "5             1.00000           1.00000         0.000000  \n",
       "6             0.88925           0.88600         0.003387  \n",
       "7             0.89175           0.89245         0.002537  \n",
       "8             1.00000           1.00000         0.000000  \n",
       "9             1.00000           1.00000         0.000000  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(random_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d445755e-68fb-48de-b287-f71ce4815099",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_scores = random_search.cv_results_['mean_test_score']\n",
    "train_scores = random_search.cv_results_['mean_train_score']\n",
    "countvec_max_features = random_search.cv_results_['param_countvectorizer__max_features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91cad6ea-6908-49ce-8140-8afda41aec21",
   "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>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_logisticregression__C</th>\n",
       "      <th>param_countvectorizer__max_features</th>\n",
       "      <th>mean_fit_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</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>1</th>\n",
       "      <td>0.8558</td>\n",
       "      <td>0.97975</td>\n",
       "      <td>0.056543</td>\n",
       "      <td>22317</td>\n",
       "      <td>0.747122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.8532</td>\n",
       "      <td>0.95780</td>\n",
       "      <td>0.030182</td>\n",
       "      <td>9733</td>\n",
       "      <td>0.698466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.8392</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.429147</td>\n",
       "      <td>21987</td>\n",
       "      <td>0.906519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8378</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>32.463393</td>\n",
       "      <td>25540</td>\n",
       "      <td>1.119875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8376</td>\n",
       "      <td>0.89245</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>2215</td>\n",
       "      <td>0.587483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.8336</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>910.358522</td>\n",
       "      <td>17016</td>\n",
       "      <td>0.978819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8198</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>8.112047</td>\n",
       "      <td>3764</td>\n",
       "      <td>0.743434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8104</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>447.077429</td>\n",
       "      <td>3690</td>\n",
       "      <td>0.887960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.8102</td>\n",
       "      <td>0.88600</td>\n",
       "      <td>0.307999</td>\n",
       "      <td>570</td>\n",
       "      <td>0.645274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.8010</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>912.930835</td>\n",
       "      <td>2541</td>\n",
       "      <td>0.889278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean_test_score  mean_train_score  \\\n",
       "rank_test_score                                      \n",
       "1                         0.8558           0.97975   \n",
       "2                         0.8532           0.95780   \n",
       "3                         0.8392           1.00000   \n",
       "4                         0.8378           1.00000   \n",
       "5                         0.8376           0.89245   \n",
       "6                         0.8336           1.00000   \n",
       "7                         0.8198           1.00000   \n",
       "8                         0.8104           1.00000   \n",
       "9                         0.8102           0.88600   \n",
       "10                        0.8010           1.00000   \n",
       "\n",
       "                param_logisticregression__C  \\\n",
       "rank_test_score                               \n",
       "1                                  0.056543   \n",
       "2                                  0.030182   \n",
       "3                                  6.429147   \n",
       "4                                 32.463393   \n",
       "5                                  0.008403   \n",
       "6                                910.358522   \n",
       "7                                  8.112047   \n",
       "8                                447.077429   \n",
       "9                                  0.307999   \n",
       "10                               912.930835   \n",
       "\n",
       "                param_countvectorizer__max_features  mean_fit_time  \n",
       "rank_test_score                                                     \n",
       "1                                             22317       0.747122  \n",
       "2                                              9733       0.698466  \n",
       "3                                             21987       0.906519  \n",
       "4                                             25540       1.119875  \n",
       "5                                              2215       0.587483  \n",
       "6                                             17016       0.978819  \n",
       "7                                              3764       0.743434  \n",
       "8                                              3690       0.887960  \n",
       "9                                               570       0.645274  \n",
       "10                                             2541       0.889278  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(random_search.cv_results_)[\n",
    "    [\n",
    "        \"mean_test_score\",\n",
    "        \"mean_train_score\",        \n",
    "        \"param_logisticregression__C\",\n",
    "        \"param_countvectorizer__max_features\",\n",
    "        \"mean_fit_time\",\n",
    "        \"rank_test_score\",\n",
    "    ]\n",
    "].set_index(\"rank_test_score\").sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497ad30d-deb5-4083-a434-65d34d801ef4",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's train a model on the full training set with the optimized hyperparameter values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ffe237a-944a-4251-9632-4b805ff98ef6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "best_model = random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7a8641-7b35-4fe3-b39e-381ea072942a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Examining learned coefficients "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f51909-32d4-4f64-946c-2182b006da5b",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- The learned coefficients are exposed by the `coef_` attribute of [LogisticRegression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a00cbdb7-7da2-4472-b5f1-3b7b60ce170f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get feature names\n",
    "feature_names = best_model.named_steps['countvectorizer'].get_feature_names_out().tolist()\n",
    "\n",
    "# Get coefficients \n",
    "coeffs = best_model.named_steps[\"logisticregression\"].coef_.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6cf51300-765c-49d2-a87c-8fca9410e00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00</th>\n",
       "      <td>-0.054476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000</th>\n",
       "      <td>-0.058816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>007</th>\n",
       "      <td>0.004202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>-0.019589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>-0.025555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zulu</th>\n",
       "      <td>-0.033189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zuniga</th>\n",
       "      <td>-0.026531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>â½</th>\n",
       "      <td>0.004713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ãªtre</th>\n",
       "      <td>-0.023960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ã¼ber</th>\n",
       "      <td>0.005421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22317 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Coefficient\n",
       "00        -0.054476\n",
       "000       -0.058816\n",
       "007        0.004202\n",
       "01        -0.019589\n",
       "02        -0.025555\n",
       "...             ...\n",
       "zulu      -0.033189\n",
       "zuniga    -0.026531\n",
       "â½         0.004713\n",
       "ãªtre     -0.023960\n",
       "ã¼ber      0.005421\n",
       "\n",
       "[22317 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_coeff_df = pd.DataFrame(coeffs, index=feature_names, columns=[\"Coefficient\"])\n",
    "word_coeff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc85fc24-317d-4e67-bbc4-f4bf794cad5a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's sort the coefficients in descending order. \n",
    "- Interpretation\n",
    "    - if $w_j > 0$ then increasing $x_{ij}$ moves us toward predicting $+1$. \n",
    "    - if $w_j < 0$ then increasing $x_{ij}$ moves us toward predicting $-1$. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ff5bcbb-da25-4e14-8ba3-c879a33a07c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>0.754531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>0.598293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wonderful</th>\n",
       "      <td>0.539038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amazing</th>\n",
       "      <td>0.535003</td>\n",
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       "      <th>favorite</th>\n",
       "      <td>0.487617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terrible</th>\n",
       "      <td>-0.528488</td>\n",
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       "    <tr>\n",
       "      <th>boring</th>\n",
       "      <td>-0.582887</td>\n",
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       "    <tr>\n",
       "      <th>waste</th>\n",
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       "    <tr>\n",
       "      <th>worst</th>\n",
       "      <td>-0.862533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22317 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Coefficient\n",
       "excellent     0.754531\n",
       "great         0.598293\n",
       "wonderful     0.539038\n",
       "amazing       0.535003\n",
       "favorite      0.487617\n",
       "...                ...\n",
       "terrible     -0.528488\n",
       "boring       -0.582887\n",
       "waste        -0.645884\n",
       "bad          -0.674181\n",
       "worst        -0.862533\n",
       "\n",
       "[22317 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_coeff_df.sort_values(by=\"Coefficient\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0daa02b-da50-429f-a4e2-9256b9ed25bd",
   "metadata": {},
   "source": [
    "- The coefficients make sense!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696edbf8-6ae8-425c-a5da-d0e627cefb6c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's visualize the top 20 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "287c277a-acfb-41b9-aa35-5f1bbe1a96ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.tools.visualize_coefficients(coeffs, feature_names, n_top_features=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab454ea-de44-4c17-912a-771fb2359fd1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's explore prediction of the following new review. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "89b90ff4-d3f9-4bb9-9ce0-3479581d5d6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_reviews = [\"It got a bit boring at times but the direction was excellent and the acting was flawless. Overall I enjoyed the movie and I highly recommend it!\",\n",
    " \"The plot was shallower than a kiddie pool in a drought, but hey, at least we now know emojis should stick to texting and avoid the big screen.\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3348e587-8cb2-4e4d-a321-d10fde70070c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's get prediction probability scores of the fake review. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2087419c-8602-4303-983a-1f7415d0495a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['pos', 'neg'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.predict(fake_reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c985b9a-52be-4153-a61d-596f4ffb956c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.20880123, 0.79119877],\n",
       "       [0.64462303, 0.35537697]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get prediction probabilities for fake reviews \n",
    "best_model.predict_proba(fake_reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8392db22-4c70-41d0-accb-a33e7aebbdbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['neg', 'pos'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "915ec98e-e607-43fd-837b-34cee994d756",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can find which of the vocabulary words are present in this review:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "731965cb-66a5-4827-b74e-ec4ff19e817e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_coeff_example(model, review, coeffs, feature_names, n_top_feats=6):\n",
    "    print(review)\n",
    "    feat_vec = model.named_steps[\"countvectorizer\"].transform([review])\n",
    "    words_in_ex = feat_vec.toarray().ravel().astype(bool)\n",
    "\n",
    "    ex_df = pd.DataFrame(\n",
    "        data=coeffs[words_in_ex],\n",
    "        index=np.array(feature_names)[words_in_ex],\n",
    "        columns=[\"Coefficient\"],\n",
    "    )\n",
    "    mglearn.tools.visualize_coefficients(\n",
    "    coeffs[words_in_ex], np.array(feature_names)[words_in_ex], n_top_features=n_top_feats\n",
    "    )\n",
    "    return ex_df.sort_values(by=[\"Coefficient\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e62c022f-7adf-4c62-907a-aef04551d954",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It got a bit boring at times but the direction was excellent and the acting was flawless. Overall I enjoyed the movie and I highly recommend it!\n"
     ]
    },
    {
     "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>0.754531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enjoyed</th>\n",
       "      <td>0.475706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highly</th>\n",
       "      <td>0.450138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bit</th>\n",
       "      <td>0.332370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overall</th>\n",
       "      <td>0.119454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <td>0.110395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flawless</th>\n",
       "      <td>0.078456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recommend</th>\n",
       "      <td>0.066069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie</th>\n",
       "      <td>-0.035531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>got</th>\n",
       "      <td>-0.104489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acting</th>\n",
       "      <td>-0.128763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direction</th>\n",
       "      <td>-0.207460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boring</th>\n",
       "      <td>-0.582887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Coefficient\n",
       "excellent     0.754531\n",
       "enjoyed       0.475706\n",
       "highly        0.450138\n",
       "bit           0.332370\n",
       "overall       0.119454\n",
       "times         0.110395\n",
       "flawless      0.078456\n",
       "recommend     0.066069\n",
       "movie        -0.035531\n",
       "got          -0.104489\n",
       "acting       -0.128763\n",
       "direction    -0.207460\n",
       "boring       -0.582887"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_coeff_example(best_model, fake_reviews[0], coeffs, feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "049fa489-046b-424c-b2b1-22c4c255c409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The plot was shallower than a kiddie pool in a drought, but hey, at least we now know emojis should stick to texting and avoid the big screen.\n"
     ]
    },
    {
     "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>know</th>\n",
       "      <td>0.100463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pool</th>\n",
       "      <td>0.000071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>big</th>\n",
       "      <td>-0.021950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>screen</th>\n",
       "      <td>-0.024977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hey</th>\n",
       "      <td>-0.025391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stick</th>\n",
       "      <td>-0.047701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plot</th>\n",
       "      <td>-0.192990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avoid</th>\n",
       "      <td>-0.387191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Coefficient\n",
       "know       0.100463\n",
       "pool       0.000071\n",
       "big       -0.021950\n",
       "screen    -0.024977\n",
       "hey       -0.025391\n",
       "stick     -0.047701\n",
       "plot      -0.192990\n",
       "avoid     -0.387191"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_coeff_example(best_model, fake_reviews[1], coeffs, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f50d3ed-b26a-4fb4-ad47-a3058b4f8cb8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf58fa9d-81c7-4288-9f1b-339b8f8d0e33",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Most positive review "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a507cd-f97c-4d2e-9178-21b75855d82e",
   "metadata": {},
   "source": [
    "- Remember that you can look at the probabilities (confidence) of the classifier's prediction using the `model.predict_proba` method.\n",
    "- Can we find the reviews where our classifier is most certain or least certain?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc71504d-6ef7-4d4d-bf8e-aed651f7d4fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93149138, 0.82269379, 0.8832539 , ..., 0.87023258, 0.08331949,\n",
       "       0.7576988 ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only get probabilities associated with pos class\n",
    "pos_probs = best_model.predict_proba(X_train)[\n",
    "    :, 1\n",
    "]  # only get probabilities associated with pos class\n",
    "pos_probs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bc44c2-5387-4346-96de-42d9e98620dd",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What's the index of the example where the classifier is most certain (highest `predict_proba` score for positive)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e1c44e8-aeae-44db-82c8-ff3db10fb328",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_positive_id = np.argmax(pos_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e4d065d-5ab3-4b6f-8fe6-c0f19c654202",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True target: pos\n",
      "\n",
      "Predicted target: pos\n",
      "\n",
      "Prediction probability: 1.0000\n"
     ]
    }
   ],
   "source": [
    "print(\"True target: %s\\n\" % (y_train.iloc[most_positive_id]))\n",
    "print(\"Predicted target: %s\\n\" % (best_model.predict(X_train.iloc[[most_positive_id]])[0]))\n",
    "print(\"Prediction probability: %0.4f\" % (pos_probs[most_positive_id]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3587da-acb1-490a-8d09-cabc6ff2c9ce",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's examine the features associated with the review. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5d136b23-c082-4b76-9f00-4890e534248a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving beyond words is this heart breaking story of a divorce which results in a tragic custody battle over a seven year old boy.  One of \"Kramer v. Kramer's\" great strengths is its screenwriter director Robert Benton, who has marvellously adapted Avery Corman's novel to the big screen. He keeps things beautifully simple and most realistic, while delivering all the drama straight from the heart. His talent for telling emotional tales like this was to prove itself again with \"Places in the Heart\", where he showed, as in \"Kramer v. Kramer\", that he has a natural ability for working with children.  The picture's other strong point is the splendid acting which deservedly received four of the film's nine Academy Award nominations, two of them walking away winners. One of those was Dustin Hoffman (Best Actor), who is superb as frustrated business man Ted Kramer, a man who has forgotten that his wife is a person. As said wife Joanne, Meryl Streep claimed the supporting actress Oscar for a strong, sensitive portrayal of a woman who had lost herself in eight years of marriage. Also nominated was Jane Alexander for her fantastic turn as the Kramer's good friend Margaret. Final word in the acting stakes must go to young Justin Henry, whose incredibly moving performance will find you choking back tears again and again, and a thoroughly deserved Oscar nomination came his way.  Brilliant also is Nestor Almendros' cinematography and Jerry Greenberg's timely editing, while musically Henry Purcell's classical piece is used to effect.  Truly this is a touching story of how a father and son come to depend on each other when their wife and mother leaves. They grow together, come to know each other and form an entirely new and wonderful relationship. Ted finds himself with new responsibilities and a new outlook on life, and slowly comes to realise why Joanne had to go.  Certainly if nothing else, \"Kramer v. Kramer\" demonstrates that nobody wins when it comes to a custody battle over a young child, especially not the child himself.  Saturday, June 10, 1995 - T.V.  Strong drama from Avery Corman's novel about the heartache of a custody battle between estranged parents who both feel they have the child's best interests at heart. Aside from a superb screenplay and amazingly controlled direction, both from Robert Benton, it's the superlative cast that make this picture such a winner.  Hoffman is brilliant as Ted Kramer, the man torn between his toppling career and the son whom he desperately wants to keep. Excellent too is Streep as the woman lost in eight years of marriage who had to get out before she faded to nothing as a person. In support of these two is a very strong Jane Alexander as mutual friend Margaret, an outstanding Justin Henry as the boy caught in the middle, and a top cast of extras.  This highly emotional, heart rending drama more than deserved it's 1979 Academy Awards for best film, best actor (Hoffman) and best supporting actress (Streep).  Wednesday, February 28, 1996 - T.V.\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>0.754531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>0.598293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wonderful</th>\n",
       "      <td>0.539038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highly</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>best</th>\n",
       "      <td>0.440914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>-0.189219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>piece</th>\n",
       "      <td>-0.192853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child</th>\n",
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       "    </tr>\n",
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       "      <th>direction</th>\n",
       "      <td>-0.207460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>-0.268883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</div>"
      ],
      "text/plain": [
       "           Coefficient\n",
       "excellent     0.754531\n",
       "great         0.598293\n",
       "wonderful     0.539038\n",
       "highly        0.450138\n",
       "best          0.440914\n",
       "...                ...\n",
       "director     -0.189219\n",
       "piece        -0.192853\n",
       "child        -0.203941\n",
       "direction    -0.207460\n",
       "point        -0.268883\n",
       "\n",
       "[187 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_coeff_example(best_model, X_train.iloc[most_positive_id], coeffs, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767203c4-8837-4b87-9e5c-432e76a4634b",
   "metadata": {},
   "source": [
    "The review has both positive and negative words but the words with **positive** coefficients win in this case! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007f00b9-1466-4052-854e-a59c70b414d6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Most negative review "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a49743b5-cae4-4530-aeb1-ff4e8db1e6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06850862, 0.17730621, 0.1167461 , ..., 0.12976742, 0.91668051,\n",
       "       0.2423012 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_probs = best_model.predict_proba(X_train)[\n",
    "    :, 0\n",
    "]  # only get probabilities associated with neg class\n",
    "neg_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f1ffe697-3b4f-4e53-afc0-95439faa31d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_negative_id = np.argmax(neg_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "00df177e-ac45-4085-a750-6b60a2ff2d15",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review: 36555    I made the big mistake of actually watching this whole movie a few nights ago. God I'm still trying to recover. This movie does not even deserve a 1.4 average. IMDb needs to have 0 vote ratings po...\n",
      "Name: review_pp, dtype: object\n",
      "\n",
      "True target: neg\n",
      "\n",
      "Predicted target: neg\n",
      "\n",
      "Prediction probability: 1.0000\n"
     ]
    }
   ],
   "source": [
    "print(\"Review: %s\\n\" % (X_train.iloc[[most_negative_id]]))\n",
    "print(\"True target: %s\\n\" % (y_train.iloc[most_negative_id]))\n",
    "print(\"Predicted target: %s\\n\" % (best_model.predict(X_train.iloc[[most_negative_id]])[0]))\n",
    "print(\"Prediction probability: %0.4f\" % (neg_probs[most_negative_id]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4df862bd-343c-4f02-9cda-02c70122a8b7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I made the big mistake of actually watching this whole movie a few nights ago. God I'm still trying to recover. This movie does not even deserve a 1.4 average. IMDb needs to have 0 vote ratings possible for movies that really deserve it like this one. A 1.4 is TOO HIGH.  I had heard how awful this movie was, but I really did not think a movie could actually be that bad, especially in this day and era. I figured all of the cheesy god awful movies were only from the 1950s and 1960s. My god was I wrong. Trust me folks, this movie REALLY IS THAT BAD. It is beyond horrible; it is beyond pathetic; it is beyond any type of word that I can think of for it. BATTLEFIELD EARTH looks like Best Picture of the Year compared to this movie. SNAKE ISLAND (which up until now was the worst movie I'd ever seen) looks like it deserves a few Oscars compared to this pathetic effort.  I seriously can not believe that the makers of this movie thought this was a legitimate serious effort of producing a Hollywood movie. This has no business being called a movie. In the first 25 seconds of the film, I seriously thought I was watching some high school theater class attempting to make a short movie. Or better yet, I thought it was some Saturday night Live ripoff skit of the real thing. I mean, it looks exactly like that. The acting is horrible; the whole movie almost looks like it was shot with a 20 year old VHS video camera. the special effects.......well good lord Bewitched from back in the day had better special effects than this movie. The scene where he gets shot at the door is beyond laughable and beyond cheesy. I mean seriously, my Intro to Acting class from 4 years ago in college, all of us could have put together a better movie than this. And the worst part of the entire movie, where Arthur is naked in the bathroom. Oh my god I almost thew up right there. I have a strong stomach, but wow that was horrible. Some people should never be naked, and he's one of them. The plot of this movie just seems to go absolutely nowhere. They talk about legal issues that we never hear about again; Ben talks about getting into music that we never hear about again; arthur says he is looking for a job and money for college and the next thing we see is he's running a porn shop. Everything about the movie is just horrible.  This really doesn't have much to do with my critique, but just so everyone knows, I am not a gay man. I DO however support gay rights and believe we should all be treated as equals. And I would support any gay person in my church, unlike the cruel priest in this movie, who by the way seems to cuss every other word. (WHERE IS THE F*(#*ing white out?) hahaha But I didn't want anyone to think I hated this movie just because of it being about two gay guys. It has nothing to do with that: This would have been just as horrible of a movie if it was Ben & Jill instead of Ben & Arthur.  I just watched this movie to see if it really was as bad as they say. And yes it was even WORSE than I had read. Let this be a warning to everyone: ONLY watch this movie if you want to just sit back and laugh at how pathetic some movies in the 21st century can still be. If you watch this movie and are actually expecting a good movie or some entertainment, I have no sympathy for you whatsoever.  On a final thought: How in the world are there 7 movies ranked BELOW this on IMDb? There is no way there are 7 movies out there that are worse than this!\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>best</th>\n",
       "      <td>0.440914</td>\n",
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       "      <td>0.307902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>music</th>\n",
       "      <td>0.302964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>especially</th>\n",
       "      <td>0.289070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strong</th>\n",
       "      <td>0.262629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>worse</th>\n",
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       "      <td>-0.674181</td>\n",
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      ],
      "text/plain": [
       "            Coefficient\n",
       "best           0.440914\n",
       "job            0.307902\n",
       "music          0.302964\n",
       "especially     0.289070\n",
       "strong         0.262629\n",
       "...                 ...\n",
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       "worse         -0.474938\n",
       "horrible      -0.481359\n",
       "bad           -0.674181\n",
       "worst         -0.862533\n",
       "\n",
       "[173 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_coeff_example(best_model, X_train.iloc[most_negative_id], coeffs, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d0bb45-61ba-4c50-b20e-7e37f06b9e07",
   "metadata": {},
   "source": [
    "The review has both positive and negative words but the words with negative coefficients win in this case! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385256d5-a866-42d5-bf52-07a5556cb598",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14e1d17-1783-410a-a4ed-3cc1c4d54623",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Question for you to ponder on \n",
    "\n",
    "- Is it possible to identify most important features using $k$-NNs? What about decision trees?  \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "571",
   "language": "python",
   "name": "571"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}