{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d5ff525a-e72f-4e00-a381-8040519bd55f",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Lectures 7: Class demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7fca5e2b-94aa-49d8-9810-1b03861840e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../code/.\")\n",
    "\n",
    "import IPython\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\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",
    "from utils import *\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfda548e-7def-4519-b10e-8458f972a98f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Demo: Model interpretation of linear classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364a3bbe-696f-49a8-bc59-cfdf60fd3a5c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- One of the primary advantages of linear classifiers is their ability to interpret models.\n",
    "- For instance, by analyzing the sign and magnitude of the learned coefficients, we can address questions regarding which features are influencing the prediction and in which direction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943b5df2-0ca5-4c7d-9e85-c349245f8c13",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- We'll demonstrate this by training `LogisticRegression` on the famous [IMDB movie review](https://www.kaggle.com/datasets/uttam94/imdb-mastercsv) 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": "a0d7fba1-5649-4bf5-acd9-ecca9a9c6ba6",
   "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": "code",
   "execution_count": null,
   "id": "14bc7a34-2509-4420-8525-e8eccb5b82c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5d958f04-51d1-40a2-80ce-8f257800c62e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's clean up the data a bit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ede5a105-6f0f-4167-84ae-32286782a1b8",
   "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": "2184db51-7841-4510-9399-9d4c66a24efd",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "imdb_df[\"review_pp\"] = imdb_df[\"review\"].apply(replace_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a3bd860-2f05-4a4b-8377-9bae3b53d1d4",
   "metadata": {},
   "source": [
    "Are we breaking the Golden rule here? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbba06e-68af-4344-9b9f-9ad5ab9885cb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's split the data and create bag of words representation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc2323ae-49a4-492c-886d-e57c0a4c37ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imdb_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb6a4ed9-6ebd-46c3-ba94-4ae27c58f460",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 3)"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "id": "b1542e74-abac-4241-877e-1c07cd73af29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47278    Best animated movie ever made. This film explores not only the vast world of modern animation with absolutely boggling effects, but the branches of the human mind, soul, and philosophy. The story ...\n",
       "19664    This film does for Infantry what Das Boot did for Submariners. If you appreciated Das Boot then that is all you really need to know.  This is a well done piece of cinema. On a par with Das Boot. B...\n",
       "22648    New Yorkers contemporaneous with this film will recall how reflective of its time it is and how well cast and crew captured America, New York City of that era.  Norman Wexler's script delineates t...\n",
       "33662    Well, you might not actually SEE any women in love in this movie, but you'll certainly hear women TALKING about love, and men talking about love, and women talking about men, and men talking about...\n",
       "31230    Well I just gave away 95 minutes and 47 seconds that I'll never get back on this piece of trash. I heard someone online describe this movie's villains as \"subhuman cannibals\", and I thought it was...\n",
       "                                                                                                          ...                                                                                                   \n",
       "7763     I actually went into this film with some expectations, not because I thought the film sounded particularly good, but because I'm a fan of Italian exploitation flicks and with a cast that sees Fran...\n",
       "15377    SPOILER ALERT! Don't read on unless you're prepared for some spoilers.  I think this film had a lot beneath its shell. Besides the apparent connections with \"Oldboy\" (and Park-wook's other films),...\n",
       "17730    A stunning film which brought into the open so much about disability that generally makes people afraid. It showed how minds can be captured by less than willing bodies and how difficult it must b...\n",
       "28030    this film has its good points: hot chicks people die  the problem... the hot Chicks barley get nude and you don't get to see many of the people dieing, mostly just lots of fast movements and screa...\n",
       "15725    Run, don't walk, to rent this movie. It is re-released on an excellent DVD version. It is primo acting/directing/cinematography in the world of suspense/film noir. Tribute this to the blacklisted ...\n",
       "Name: review_pp, Length: 5000, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11aa1f28-9f04-4e45-9468-98257404b4cd",
   "metadata": {},
   "source": [
    "Is there any missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9b08daf-4b68-491f-8117-c59745aaeccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review       0\n",
       "label        0\n",
       "review_pp    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cf70e7-cbec-4511-b38b-ba20e4607257",
   "metadata": {},
   "source": [
    "There is no missing data. We don't need imputation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83302a83-2fe7-4885-9181-c6b5b1dda33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5000x10000 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 383702 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's try CountVectorizer\n",
    "vec = CountVectorizer(max_features=10_000, stop_words=\"english\")\n",
    "bow = vec.fit_transform(X_train)\n",
    "bow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b31c51a-b6df-46c6-aaea-c66b1ea88c39",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Examining the vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cc4cac6-d789-4da2-81e7-33ef7978a838",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The vocabulary (mapping from feature indices to actual words) can be obtained using `get_feature_names()` on the `CountVectorizer` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ed2e485-b06f-4b53-9626-075d644db027",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = vec.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "193df8b7-5185-4bfd-88bc-b29814b59e0e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00', '000', '01', '10', '100', '1000', '101', '11', '12', '13'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[0:10]  # first few words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d7f9cf8-6f84-408e-9822-83e805d43700",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['conrad', 'cons', 'conscience', 'conscious', 'consciously',\n",
       "       'consciousness', 'consequence', 'consequences', 'conservative',\n",
       "       'conservatory'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[2000:2010]  # some middle words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd5ea432-355d-4f4f-94f3-0097c390a73c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00', 'announcement', 'bird', 'cell', 'conrad', 'depth', 'elite',\n",
       "       'finnish', 'grimy', 'illusions', 'kerr', 'maltin', 'narrates',\n",
       "       'patients', 'publicity', 'reynolds', 'sfx', 'starting', 'thats',\n",
       "       'vance'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[::500]  # words with a step of 500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f05edd7-3b6f-4431-bd5b-daa0037a4dce",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Model building on the dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab803512-7f24-4fa2-bd2d-e85c292a82f0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "First let's try `DummyClassifier` on the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d08438e8-f3cf-44f2-8021-dcdfae0a4c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.505"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = DummyClassifier()\n",
    "cross_val_score(dummy, X_train, y_train).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461d858b-48ba-40c3-8563-85994e59c1eb",
   "metadata": {},
   "source": [
    "We have a balanced dataset. So the `DummyClassifier` score is around 0.5. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828bcfaf-d656-4eca-b693-11c3e7308b03",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now let's try logistic regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc691c78-f1b8-4b08-b398-071c267c94ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>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>0.514270</td>\n",
       "      <td>0.095453</td>\n",
       "      <td>0.847</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.507434</td>\n",
       "      <td>0.091247</td>\n",
       "      <td>0.832</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.495814</td>\n",
       "      <td>0.095641</td>\n",
       "      <td>0.842</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.512299</td>\n",
       "      <td>0.087657</td>\n",
       "      <td>0.853</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.540719</td>\n",
       "      <td>0.090419</td>\n",
       "      <td>0.839</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  0.514270    0.095453       0.847          1.0\n",
       "1  0.507434    0.091247       0.832          1.0\n",
       "2  0.495814    0.095641       0.842          1.0\n",
       "3  0.512299    0.087657       0.853          1.0\n",
       "4  0.540719    0.090419       0.839          1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pipeline with CountVectorizer and LogisticRegression \n",
    "pipe_lr = make_pipeline(CountVectorizer(max_features=10_000, 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": "e72469c0-d6b1-4e70-84e8-a1d382c363f6",
   "metadata": {},
   "source": [
    "Seems like we are overfitting. Let's optimize the hyperparameter `C`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f108ec94-255f-4d39-b4ab-ccc5750f9e96",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>C</th>\n",
       "      <th>mean_train_scores</th>\n",
       "      <th>mean_cv_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.83470</td>\n",
       "      <td>0.7964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.92265</td>\n",
       "      <td>0.8456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.100</td>\n",
       "      <td>0.98585</td>\n",
       "      <td>0.8520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.8426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.8376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.8350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C  mean_train_scores  mean_cv_scores\n",
       "0    0.001            0.83470          0.7964\n",
       "1    0.010            0.92265          0.8456\n",
       "2    0.100            0.98585          0.8520\n",
       "3    1.000            1.00000          0.8426\n",
       "4   10.000            1.00000          0.8376\n",
       "5  100.000            1.00000          0.8350"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_dict = {\n",
    "    \"C\": 10.0 ** np.arange(-3, 3, 1),\n",
    "    \"mean_train_scores\": list(),\n",
    "    \"mean_cv_scores\": list(),\n",
    "}\n",
    "for C in scores_dict[\"C\"]:\n",
    "    pipe_lr = make_pipeline(CountVectorizer(max_features=10_000, stop_words=\"english\"),\n",
    "                        LogisticRegression(max_iter=1000, C=C)\n",
    "                       )\n",
    "    scores = cross_validate(pipe_lr, X_train, y_train, return_train_score=True)\n",
    "    scores_dict[\"mean_train_scores\"].append(scores[\"train_score\"].mean())\n",
    "    scores_dict[\"mean_cv_scores\"].append(scores[\"test_score\"].mean())\n",
    "\n",
    "results_df = pd.DataFrame(scores_dict)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "48fcf649-d025-4570-9278-98d0cd87e54a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum validation score is 0.852 at C = 0.10 \n"
     ]
    }
   ],
   "source": [
    "optimized_C = results_df[\"C\"].iloc[np.argmax(results_df[\"mean_cv_scores\"])]\n",
    "print(\n",
    "    \"The maximum validation score is %0.3f at C = %0.2f \"\n",
    "    % (\n",
    "        np.max(results_df[\"mean_cv_scores\"]),\n",
    "        optimized_C,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba72239-0329-456c-837e-ccd655bdd8ca",
   "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": "b8913c96-7707-417c-8d98-c5c7c75f7390",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Pipeline(steps=[(&#x27;countvectorizer&#x27;,\n",
       "                 CountVectorizer(max_features=10000, stop_words=&#x27;english&#x27;)),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=0.1, max_iter=1000))])</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\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;countvectorizer&#x27;,\n",
       "                 CountVectorizer(max_features=10000, stop_words=&#x27;english&#x27;)),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=0.1, max_iter=1000))])</pre></div></div></div><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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(max_features=10000, 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.1, max_iter=1000)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('countvectorizer',\n",
       "                 CountVectorizer(max_features=10000, stop_words='english')),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(C=0.1, max_iter=1000))])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr = make_pipeline(CountVectorizer(max_features=10000, stop_words=\"english\"),\n",
    "                        LogisticRegression(max_iter=1000, C = 0.10)\n",
    "                       )\n",
    "pipe_lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e573adc-11b8-481f-91ad-93213180aff0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Examining learned coefficients "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96a2eb9-8b8c-4283-b7ca-8af5fc58649a",
   "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": "eb2be41c-d208-4284-bc56-17f23b5b0174",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = np.array(pipe_lr.named_steps[\"countvectorizer\"].get_feature_names_out())\n",
    "coeffs = pipe_lr.named_steps[\"logisticregression\"].coef_.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa7203a8-4c25-4e4e-a75a-90b7ab6eb343",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fec2fc7e-8bb0-499b-b329-97ec1124e701",
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00</th>\n",
       "      <td>-0.074949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000</th>\n",
       "      <td>-0.083893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>-0.034402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.056493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.041633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zoom</th>\n",
       "      <td>-0.013299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zooms</th>\n",
       "      <td>-0.022139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zorak</th>\n",
       "      <td>0.021878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zorro</th>\n",
       "      <td>0.130075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>â½</th>\n",
       "      <td>0.012649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Coefficient\n",
       "00       -0.074949\n",
       "000      -0.083893\n",
       "01       -0.034402\n",
       "10        0.056493\n",
       "100       0.041633\n",
       "...            ...\n",
       "zoom     -0.013299\n",
       "zooms    -0.022139\n",
       "zorak     0.021878\n",
       "zorro     0.130075\n",
       "â½        0.012649\n",
       "\n",
       "[10000 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "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": "946d4237-22a5-43de-b849-e25245e426b0",
   "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": 22,
   "id": "d7f0fef3-deec-4ad2-8d12-2c26f1e49553",
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>0.903484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>0.659922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amazing</th>\n",
       "      <td>0.653301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wonderful</th>\n",
       "      <td>0.651763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite</th>\n",
       "      <td>0.607887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terrible</th>\n",
       "      <td>-0.621695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boring</th>\n",
       "      <td>-0.701030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad</th>\n",
       "      <td>-0.736608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waste</th>\n",
       "      <td>-0.799353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst</th>\n",
       "      <td>-0.986970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Coefficient\n",
       "excellent     0.903484\n",
       "great         0.659922\n",
       "amazing       0.653301\n",
       "wonderful     0.651763\n",
       "favorite      0.607887\n",
       "...                ...\n",
       "terrible     -0.621695\n",
       "boring       -0.701030\n",
       "bad          -0.736608\n",
       "waste        -0.799353\n",
       "worst        -0.986970\n",
       "\n",
       "[10000 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_coeff_df.sort_values(by=\"Coefficient\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7923950f-a77f-46a3-aaa2-2201208f835c",
   "metadata": {},
   "source": [
    "- The coefficients make sense!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2769e763-2d74-416f-b0a7-258d8ab95599",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's visualize the top 20 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8c630f6-ddbf-4c47-a1c4-4fecd2473a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": "7d20bea3-925a-43a5-9433-bb769c0b8c03",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's explore prediction of the following new review. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8e77299-2aae-43a7-b0af-30e06eb8b6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_review = \"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!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "614b1954-119e-41de-8683-b2796b07166f",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_vec = pipe_lr.named_steps[\"countvectorizer\"].transform([fake_review])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9882de7f-d046-41db-a9be-6dd2f02043b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x10000 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 13 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_vec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca844028-a170-44ee-977c-d890e6021eb1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's get prediction probability scores of the fake review. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "359de100-ac47-4c6e-88f9-05222717fdd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16423497, 0.83576503]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr.predict_proba([fake_review])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0b2360f6-3895-4b54-a6af-8f580f1d2b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['neg', 'pos'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68b2863-0d1d-4440-b553-62eba0c78db4",
   "metadata": {},
   "source": [
    "The model is 83.5% confident that it's a positive review. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0fc59d2c-3921-4798-be46-90f6abe90507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pos'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr.predict([fake_review])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4fde22-01b8-4a14-a55e-0f35abc92c2e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can find which of the vocabulary words are present in this review:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "515c6d99-506a-4dd8-9188-f5fbd2ca2d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_vec.toarray().ravel().astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "96fb012e-05f1-4e00-85da-5d0c3ca1b7a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_in_ex = feat_vec.toarray().ravel().astype(bool)\n",
    "words_in_ex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01120ebb-68ef-4eda-925e-43b349352db5",
   "metadata": {},
   "source": [
    "How many of the words are in this review?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f0012438-3471-40c5-bf3b-23ae05e7c557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(words_in_ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8463a1a6-c660-4f6b-afe4-7ddebb14c34c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['acting', 'bit', 'boring', 'direction', 'enjoyed', 'excellent',\n",
       "       'flawless', 'got', 'highly', 'movie', 'overall', 'recommend',\n",
       "       'times'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(feature_names)[words_in_ex]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c384edca-331d-4e83-968f-a62053a1c1f1",
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acting</th>\n",
       "      <td>-0.126498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bit</th>\n",
       "      <td>0.390053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boring</th>\n",
       "      <td>-0.701030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direction</th>\n",
       "      <td>-0.268316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enjoyed</th>\n",
       "      <td>0.578879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>0.903484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flawless</th>\n",
       "      <td>0.113743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>got</th>\n",
       "      <td>-0.122759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highly</th>\n",
       "      <td>0.582012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie</th>\n",
       "      <td>-0.037942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overall</th>\n",
       "      <td>0.136288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recommend</th>\n",
       "      <td>0.054205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <td>0.133895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Coefficient\n",
       "acting       -0.126498\n",
       "bit           0.390053\n",
       "boring       -0.701030\n",
       "direction    -0.268316\n",
       "enjoyed       0.578879\n",
       "excellent     0.903484\n",
       "flawless      0.113743\n",
       "got          -0.122759\n",
       "highly        0.582012\n",
       "movie        -0.037942\n",
       "overall       0.136288\n",
       "recommend     0.054205\n",
       "times         0.133895"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex_df = pd.DataFrame(\n",
    "    data=coeffs[words_in_ex],\n",
    "    index=np.array(feature_names)[words_in_ex],\n",
    "    columns=[\"Coefficient\"],\n",
    ")\n",
    "ex_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1948622-5475-401b-bceb-a4e897f5877e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's visualize how the words with positive and negative coefficients are driving the prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "880af16d-fa78-468b-8ea0-5dc94a9bdca0",
   "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(\n",
    "    coeffs[words_in_ex], np.array(feature_names)[words_in_ex], n_top_features=6\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "98580e75-2fe3-4176-a811-91e118648c29",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def plot_coeff_example(feat_vect, coeffs, feature_names):\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",
    "    return ex_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed424aae-0a13-45f5-8002-aca241e707db",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Most positive review "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ac2f69-137f-4154-b4cc-8fbb9bf47d05",
   "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 confident or least confident?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "03a61595-a835-40fc-9ea6-d462d0b5b72b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95205899, 0.83301769, 0.9093526 , ..., 0.89247531, 0.05736279,\n",
       "       0.79360853])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_probs = pipe_lr.predict_proba(X_train)[\n",
    "    :, 1\n",
    "]  # only get probabilities associated with pos class\n",
    "pos_probs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d655755-b6f3-4671-b352-21d1e93a8f1d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's get the index of the example where the classifier is most confident (highest `predict_proba` score for positive). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fd679117-7da8-49c8-aa67-772de50b53a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_positive = np.argmax(pos_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5a788d1c-85be-40ce-9e8b-d664047dc09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'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.'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.iloc[most_positive]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4ee5516c-4b35-418f-96cc-b157f5584d7d",
   "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]))\n",
    "print(\"Predicted target: %s\\n\" % (pipe_lr.predict(X_train.iloc[[most_positive]])[0]))\n",
    "print(\"Prediction probability: %0.4f\" % (pos_probs[most_positive]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d92fd7-06b0-4d22-b81d-d9c5a4a1cbcd",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's examine the features associated with the review. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5e9f9a00-3714-4406-97ce-bcf0637d3367",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_vec = pipe_lr.named_steps[\"countvectorizer\"].transform(\n",
    "    X_train.iloc[[most_positive]]\n",
    ")\n",
    "words_in_ex = feat_vec.toarray().ravel().astype(bool)\n",
    "mglearn.tools.visualize_coefficients(\n",
    "    coeffs[words_in_ex], np.array(feature_names)[words_in_ex], n_top_features=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c74401c-9890-48e9-a0da-411ee905ce81",
   "metadata": {},
   "source": [
    "The review has both positive and negative words but the words with **positive** coefficients win in this case! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9f0d0e-c685-4c03-9bc6-44d3aefd2142",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Most negative review "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "efabb5bc-f636-4bb7-b11c-0981f02fada1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04794101, 0.16698231, 0.0906474 , ..., 0.10752469, 0.94263721,\n",
       "       0.20639147])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_probs = pipe_lr.predict_proba(X_train)[\n",
    "    :, 0\n",
    "]  # only get probabilities associated with pos class\n",
    "neg_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "52985c78-a5ee-427b-8560-9b59f7e0418a",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_negative = np.argmax(neg_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dfd0f5ca-a9ba-440b-8c30-86436a0576a9",
   "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: 0.0000\n"
     ]
    }
   ],
   "source": [
    "print(\"Review: %s\\n\" % (X_train.iloc[[most_negative]]))\n",
    "print(\"True target: %s\\n\" % (y_train.iloc[most_negative]))\n",
    "print(\"Predicted target: %s\\n\" % (pipe_lr.predict(X_train.iloc[[most_negative]])[0]))\n",
    "print(\"Prediction probability: %0.4f\" % (pos_probs[most_negative]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1ea8a612-bb9d-4004-b5be-d3c5b167437f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_vec = pipe_lr.named_steps[\"countvectorizer\"].transform(\n",
    "    X_train.iloc[[most_negative]]\n",
    ")\n",
    "words_in_ex = feat_vec.toarray().ravel().astype(bool)\n",
    "mglearn.tools.visualize_coefficients(\n",
    "    coeffs[words_in_ex], np.array(feature_names)[words_in_ex], n_top_features=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47bc03c8-c87f-4f27-a74d-46397c98fa15",
   "metadata": {},
   "source": [
    "The review has both positive and negative words but the words with negative coefficients win in this case! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9b09e7-f5c8-459f-9785-8b9a94d169ed",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "069d9784-d5aa-4751-8642-739c50711083",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "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": "cpsc330",
   "language": "python",
   "name": "cpsc330"
  },
  "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
}