07_19_text_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning in Python\n",
    "\n",
    "- Tools: scikit-learn (`sklearn`)\n",
    "    - Data Partitioning\n",
    "    - Feature selection\n",
    "    - Modeling: SVM\n",
    "    - Model Assessment\n",
    "    \n",
    " [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/COGS108/Lectures-Fa23/blob/main/Week_07/07_19_ml.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more reading on scikit-learn (`sklearn`) and machine learning in Python: https://scikit-learn.org/stable/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning: General Steps\n",
    "\n",
    "1. Data Partitioning\n",
    "2. Feature Selection\n",
    "3. Model\n",
    "4. Model Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import ds/plotting packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Import nltk package \n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "# import random for randomizing\n",
    "import random\n",
    "\n",
    "# ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# scikit-learn imports\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import classification_report, precision_recall_fscore_support, confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/jasonfleischer/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/jasonfleischer/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uncomment if you need to download the NLTK English tokenizer and the stopwords of all languages\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Class Responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Student responses on COGS 108 Mid-course survey to the following two questions: \n",
    "\n",
    "- What have you enjoyed MOST about COGS 108 so far? Please explain.\n",
    "- What have you enjoyed LEAST about COGS 108 so far? Please explain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>most_least</th>\n",
       "      <th>quarter</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4597</th>\n",
       "      <td>Jupyter Notebook because it's very confusing t...</td>\n",
       "      <td>0</td>\n",
       "      <td>wi24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4598</th>\n",
       "      <td>i just dont love how the assignments take so l...</td>\n",
       "      <td>0</td>\n",
       "      <td>wi24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>the assignments are long</td>\n",
       "      <td>0</td>\n",
       "      <td>wi24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4600</th>\n",
       "      <td>The project proposal</td>\n",
       "      <td>0</td>\n",
       "      <td>wi24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4601</th>\n",
       "      <td>Weekly quizzes</td>\n",
       "      <td>0</td>\n",
       "      <td>wi24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                               response  most_least quarter\n",
       "4597  Jupyter Notebook because it's very confusing t...           0    wi24\n",
       "4598  i just dont love how the assignments take so l...           0    wi24\n",
       "4599                           the assignments are long           0    wi24\n",
       "4600                               The project proposal           0    wi24\n",
       "4601                                     Weekly quizzes           0    wi24"
      ]
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   ],
   "source": [
    "# read data in\n",
    "# 1 = most; 0 = least\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/shanellis/datasets/master/COGS108_ml.csv', encoding=\"ISO-8859-1\")\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>most_least</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The labs and assignment are interesting to com...</td>\n",
       "      <td>1</td>\n",
       "      <td>wi24</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Understanding data science</td>\n",
       "      <td>1</td>\n",
       "      <td>sp20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I would prefer the due date for the Friday qui...</td>\n",
       "      <td>0</td>\n",
       "      <td>fa20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Whenever I'm able to figure stuff out on my ow...</td>\n",
       "      <td>1</td>\n",
       "      <td>wi20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The lectures, easy to follow</td>\n",
       "      <td>1</td>\n",
       "      <td>wi21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                            response  most_least quarter\n",
       "0  The labs and assignment are interesting to com...           1    wi24\n",
       "1                         Understanding data science           1    sp20\n",
       "2  I would prefer the due date for the Friday qui...           0    fa20\n",
       "3  Whenever I'm able to figure stuff out on my ow...           1    wi20\n",
       "4                       The lectures, easy to follow           1    wi21"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# randomly sort data frame\n",
    "df = df.sample(frac=1, random_state=200).reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomly sorted data frame:\n",
    "- for selection of training and test set\n",
    "- will be approximately balanced between outcomes in each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4602, 3)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see how much data we're working with\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quarter\n",
       "fa20     551\n",
       "sp19     631\n",
       "sp20     705\n",
       "sp21     466\n",
       "wi20     491\n",
       "wi21     615\n",
       "wi24    1143\n",
       "Name: response, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('quarter')['response'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train,  Validate, Test\n",
    "\n",
    "- Training set is what we use for creating the model\n",
    "- Validation set is what we use for fine tuning the model -- checking which hyper-parameters are best\n",
    "- Test set is what we use to figure out how good the model will do next year with new data arriving\n",
    "\n",
    "#### Method (1) - Manual split, split by quarter\n",
    "\n",
    "- We'll test the model on one quarter's responses\n",
    "- We'll validate the model on another quarter's responses\n",
    "- We'll train the model on everything else\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "## Validation\n",
    "df_validation = df[df['quarter']=='sp20']\n",
    "              \n",
    "## Test\n",
    "df_test = df[df['quarter']=='sp19']\n",
    "\n",
    "## Train\n",
    "df_train = df[~df['quarter'].isin(['sp19','sp20'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3266, 3) (705, 3) (631, 3)\n"
     ]
    }
   ],
   "source": [
    "print(df_train.shape, df_validation.shape, df_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clicker Question #1\n",
    "\n",
    "How well do you think we will be able to predict whether a student comment is a response to what they liked most vs what they liked least in our ***training*** dataset?\n",
    "\n",
    "- A) Accuracy ~0%\n",
    "- B) Accuracy ~25%\n",
    "- C) Accuracy ~50%\n",
    "- D) Accuracy ~75%\n",
    "- E) Accuracy ~100%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clicker Question #2\n",
    "\n",
    "How well do you think we will be able to predict whether a student comment is a response to what they liked most vs what they liked least in our ***validation*** dataset?\n",
    "\n",
    "- A) Accuracy ~0%\n",
    "- B) Accuracy ~25%\n",
    "- C) Accuracy ~50%\n",
    "- D) Accuracy ~75%\n",
    "- E) Accuracy ~100%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Thinking this through\n",
    "\n",
    "Is this a good idea to split up by quarter?  What could happen here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method (2) - sklearn split, randomize the data\n",
    "- Training set 60% of the data randomly mixed across quarters\n",
    "- Validation set 20% of the data randomly mixed across quarters\n",
    "- Test set 20% of the data randomly mixed across quarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2760,) (921,) (921,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_trainvalid, X_test, Y_trainvalid, Y_test = train_test_split(\n",
    "    df['response'], df['most_least'], test_size=0.2, random_state=42)\n",
    "\n",
    "X_train, X_valid, Y_train, Y_valid = train_test_split(\n",
    "    X_trainvalid, Y_trainvalid, test_size=0.25\n",
    "\n",
    ")\n",
    "\n",
    "print(X_train.shape, X_valid.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Task: \n",
    "\n",
    "**Classify text from students as 'most liked' or 'least liked'**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11 Steps to Prediction:\n",
    "\n",
    "1. Specify parameters for TF-IDF calculation\n",
    "2. Calculate TF-IDF from text input (predictors)\n",
    "3. Extract most or least (outcome)\n",
    "4. Specify how data will be partitioned\n",
    "5. Partition the data\n",
    "6. Train model\n",
    "7. Predict in training\n",
    "8. Predict in testing\n",
    "9. Assess accuracy in training\n",
    "10. Assess accuracy in test set\n",
    "11. IF NEEDED: Model selection + test process that replaces steps 6-10 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Determine how you'll convert a collection of raw documents to a matrix of TF-IDF features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create vectorizer & specify parameters\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, #apply sublinear TF scaling\n",
    "                        analyzer='word',   #specify tokenizer\n",
    "                        max_features=500, # specify max # of features to include\n",
    "                        tokenizer=word_tokenize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* sublinear TF scaling - replaces term frequency (TF) with $1 + log(TF)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: Generate matrix of TF-IDF features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2760, 500)\n",
      "min:  0.0 \n",
      " mean:  0.0071667535567917304 \n",
      " max:  1.0\n"
     ]
    }
   ],
   "source": [
    "# Learn vocabulary and idf, return term-document matrix.\n",
    "# return an array;our predictor\n",
    "tfidf_X = tfidf.fit_transform(X_train).toarray()\n",
    "\n",
    "# take a look at the output\n",
    "print(tfidf_X.shape)\n",
    "\n",
    "print(\"min: \" , np.min(tfidf_X), '\\n',\n",
    "      \"mean: \", np.mean(tfidf_X), '\\n',\n",
    "      \"max: \",  np.max(tfidf_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token</th>\n",
       "      <th>weight</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>tests</td>\n",
       "      <td>6.438442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>step</td>\n",
       "      <td>6.438442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>correct</td>\n",
       "      <td>6.438442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>subject</td>\n",
       "      <td>6.358399</td>\n",
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       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>6.284291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>and</td>\n",
       "      <td>1.888960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>to</td>\n",
       "      <td>1.819204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>i</td>\n",
       "      <td>1.625580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>.</td>\n",
       "      <td>1.470366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>the</td>\n",
       "      <td>1.416756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
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       "       token    weight\n",
       "421    tests  6.438442\n",
       "394     step  6.438442\n",
       "102  correct  6.438442\n",
       "404  subject  6.358399\n",
       "14         3  6.284291\n",
       "..       ...       ...\n",
       "37       and  1.888960\n",
       "441       to  1.819204\n",
       "223        i  1.625580\n",
       "12         .  1.470366\n",
       "424      the  1.416756\n",
       "\n",
       "[500 rows x 2 columns]"
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     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## get IDF to visualize\n",
    "idf = tfidf.idf_\n",
    "rr = dict(zip(tfidf.fit(X_train).get_feature_names(), idf))\n",
    "\n",
    "token_weight = pd.DataFrame.from_dict(rr, orient='index').reset_index()\n",
    "token_weight.columns=('token','weight')\n",
    "token_weight = token_weight.sort_values(by='weight', ascending=False)\n",
    "token_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='token', \n",
    "            y='weight', \n",
    "            data=token_weight[0:10], \n",
    "            color=\"gray\")            \n",
    "plt.title(\"Inverse Document Frequency(IDF) per token\")\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: Extract outcome variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify outcome variable\n",
    "tfidf_Y = np.array(Y_train)\n",
    "tfidf_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2760, 500), (2760,))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_X.shape, tfidf_Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clicker Question #3\n",
    "\n",
    "Looking at the code above and thinking about what we've done so far in this analysis, what is stored in `tfidf_Y`?\n",
    "\n",
    "- A) predictor variable - training data\n",
    "- B) outcome variable - training data\n",
    "- C) predictor variable - test data\n",
    "- D) outcome variable - test data\n",
    "- E) validation DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avoiding data leakage with feature selection\n",
    "\n",
    "NEVER do feature selection or feature transformation fitting on ALL of the data.\n",
    "\n",
    "You don't want information about your validation and test set inside your training set.\n",
    "\n",
    "If you fit your features on the whole dataset you have implictly told the training set information about the vocuabulary and frequency of things that aren't in the training set! BAD!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_train = tfidf.fit_transform(X_train)\n",
    "tfidf_valid = tfidf.transform(X_valid)\n",
    "tfidf_test = tfidf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2760, 500), (921, 500), (921, 500))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_train.shape, tfidf_valid.shape, tfidf_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM: Support Vector Machines\n",
    "\n",
    "- simple & interpretable machine learning model\n",
    "- based in linear regression\n",
    "- classification task\n",
    "- supervized\n",
    "    - input: labeled training data\n",
    "    - model determines hyperplane that best discriminates between categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM: Tuning Parameters\n",
    "- **regularization** parameter\n",
    "    - can determine how this line is drawn\n",
    "    - can increase accuracy of prediction\n",
    "    - can lead to overfitting of the data\n",
    "- **kernel** parameter\n",
    "    - specifies how to model & transform data\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more reading on SVMs using `sklearn`: https://scikit-learn.org/stable/modules/svm.html\n",
    "\n",
    "OR just scroll to the very end of this notebook where we get a bit crazy with them "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Generation\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 6: Generate and train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to read documentation for model\n",
    "SVC?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.svm._classes.SVC"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function we'll use to run the model\n",
    "\n",
    "svm_model = SVC(kernel='linear')\n",
    "type(svm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train model\n",
    "svm_model = svm_model.fit(tfidf_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "          1.00215097e+00,  8.32071341e-01,  3.59111907e-01,\n",
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       "         -1.10491857e-01,  3.41540769e-01,  9.23486143e-01,\n",
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       "         -7.66108238e-01, -7.25026780e-02,  3.05792548e-02,\n",
       "         -1.20355355e+00,  4.13900714e-01, -1.48623777e-01,\n",
       "         -1.25710601e-01,  9.67374386e-02,  2.71352030e-01,\n",
       "          4.96695886e-01, -7.23431679e-01, -3.26570604e-01,\n",
       "         -2.00664982e+00,  8.03243676e-01, -8.74771465e-01,\n",
       "         -3.48558865e-01,  1.84300634e+00,  6.78586002e-01,\n",
       "         -2.21460772e+00, -1.43443686e-01, -6.04331823e-01,\n",
       "          1.25977608e+00,  6.61272961e-01]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model.coef_.todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 7: Predict in the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 1]\n",
      "1743    0\n",
      "3253    0\n",
      "4514    0\n",
      "3626    0\n",
      "2252    1\n",
      "Name: most_least, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# predict on training\n",
    "df_predicted_train_Y = svm_model.predict(tfidf_train)\n",
    "\n",
    "print(df_predicted_train_Y[0:5])\n",
    "print(Y_train[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1450\n",
       "0    1310\n",
       "dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see how many were predicted most vs. least\n",
    "pd.Series(df_predicted_train_Y).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 8: Predict in the testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0]\n",
      "3683    0\n",
      "4468    1\n",
      "2584    1\n",
      "69      0\n",
      "1844    0\n",
      "Name: most_least, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# predict on training\n",
    "df_predicted_test_Y = svm_model.predict(tfidf_test)\n",
    "print(df_predicted_test_Y[0:5])\n",
    "print(Y_test[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    494\n",
       "0    427\n",
       "dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see how many were predicted most vs. least\n",
    "pd.Series(df_predicted_test_Y).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy Assessment\n",
    "\n",
    "- RMSE (continuous)\n",
    "- Accuracy, Sensitivity, Specificity, AUC\n",
    "    - TP, TN, FP, FN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![confusion matrix](https://miro.medium.com/max/924/1*7EYylA6XlXSGBCF77j_rOA.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![sensitivity recell](https://miro.medium.com/max/878/1*Ub0nZTXYT8MxLzrz0P7jPA.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accuracy** - What % were predicted correctly?  \n",
    "**Sensitivity (Recall)** - Of those that were positives, what % were predicted to be positive?  ; $\\frac {TP}{(TP + FN)}$  \n",
    "**Specificity** - Of those that were actually negatives, what % were predicted to be negative?  $\\frac {TN}{(TN + FP)}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Precision (Positive Predictive Value, PPV)** = $\\frac {TP}{(TP + FP)}$\n",
    "\n",
    "- probability that predicted positive truly is positive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 9: Assess accuracy in training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.92      0.91      1298\n",
      "           1       0.92      0.92      0.92      1462\n",
      "\n",
      "    accuracy                           0.92      2760\n",
      "   macro avg       0.92      0.92      0.92      2760\n",
      "weighted avg       0.92      0.92      0.92      2760\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_train, df_predicted_train_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1462\n",
       "0    1298\n",
       "Name: most_least, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# where 'support' comes from\n",
    "pd.Series(Y_train).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HAy8XrUZmVt6yMtsLHJD3uZ7cGODvilMdM+sUKr3ll9zc3DcivrSf6mNmZU5U+ISHpK4RUd/acvZmllEVEPxaW9ig6Q1tz0iaI+njkj7atO2PyplZGerAVV0kHSjpt5L+IWmJpJMkDZT0gKRlyc8BefmnSVouaamkCftyGYWs6jIQ2ETunR2nA/83+WlmWdVY4Na2HwL3RsTRwHHAEmAqMDciRgJzk30kjQYmk7vHeCJwQzI01y6tjfkNTmZ6F/LmTc5NKqDRa2bt1RFjfpL6AScD/wYQEbVAraRJwLgk283Aw8BXgEnA7RGxC1ghaTkwFnisPeW3Fvy6AH1pHvSaOPiZZVnhEWCQpHl5+zMiYkby+Uhyt839XNJxwHzgCmBIRKwFiIi1kgYn+YcCj+edqyZJa5fWgt/aiPhGe09sZhUq3dvbNkbEmBaOdSV33/BnI+IJST8k6eK2oEMbYq2N+ZX3MqxmVjIdNOFRA9RExBPJ/m/JBcP1kqoBkp8b8vIPz/v+MGBNe6+hteB3WntPamYVLgrcWjtFxDpglaRRSdJpwGJgDjAlSZsC3JV8ngNMltRD0ghgJG/elZJaay8t39zek5pZZevAx9s+C9yWvBrjBeAT5BplsyVdAKwEzgKIiEWSZpMLkPXApRHR0N6C/epKM0sn3Zhf66eKeAbY25jgXnueETEdmN4RZTv4mVkqojImBBz8zCy9CrjZzcHPzFKr6IUNzMxa5OBnZpmTocVMzcyac8vPzLLIY35mlk0OfmaWRW75mVn2BIUuVFrWHPzMLJWKf4GRmVmLHPzMLIsUnT/6OfiZWToduKpLKTn4mVlqHvMzs0zy421mlk1u+ZlZ5hT2cqKy5+BnZuk5+JlZ1vgmZzPLLDV2/ujn4Gdm6fg+PwP4wrUvccL4rbyysSsXjR8NwKf+vYYTx2+lrk6sfakH133hcF7b1pXj37+NT05bTdfuQX2t+Mm3hvH3vx1Q4ivIhus+P5wn/tyPAwfVM+OhpQDc/N1DeOy+/khw4KA6vviDlRx0SP0b39lQ041Pjzua865cx1mXvAzAsmd7ce3nDmPXzirGnrqNS765GlXCq8xSqoRbXaqKdWJJMyVtkLSwWGWUg/t/M5CrzjuqWdqCR/px4WmjueT/jGb1Cz2ZfNl6ALZu7srXPvEWLh4/mu99/gi+fP2LJahxNv3LxzYz/bYXmqWdeckGbpq7lBv/vJQTxm/j1v88pNnxm74+lPecur1Z2vVTh3HFd1fx878uYfWKHsx7KKO/vKLArYwVLfgBvwAmFvH8ZWHhEwew/ZUuzdIWPNKPxoZcc2DJgj4Mqq4F4PlFvdm8vjsALy3tSfcejXTrXgG/QjuBt5/4GgcMaGiW1ueAN//sd75e1awF97c/9af6sFoOf+vON9I2re/Kju1dGD1mBxKMP3Mzf7u3f9HrXo4UhW3lrGjBLyIeATYX6/ydxYSPbeSph/rtkf5PH3qF5xf2pq62mL9/rC0/v+YQzn33aB68YwDnf2ktADt3VDH7hsGcd+W6Znk3revGoOq6N/YHHVrHxnXd9mt9y0IAEYVtZazk//MkXShpnqR5dewqdXU61NmfXUtDg3jwjoHN0g9/6+tcMG01P5x6WIlqZk0+MXUdt81fzKkf3cKcmQcD8MvvHcIZn36ZXn2at8r39n85g8N9QG7Mr5CtoHNJXSQ9LekPyf5ASQ9IWpb8HJCXd5qk5ZKWSpqwL9dQ8uAXETMiYkxEjOlGj1JXp8OMP3MTY8dv4zuXjSD/v8ig6lq+9tMX+N7njmDtS5VzvZ3dKWds4dF7cl3Yfzzdm59961DOHzua3//0YG7/ryHcNXMQg6rr2Lj2zZbexjXdOOiQupZOWbGa7vPrwG7vFcCSvP2pwNyIGAnMTfaRNBqYDBxDbkjtBkldaCfP9hbBmHFb+X+fWc+XzhzJrp1v/n7p06+eb978PD+/5lAWz+tbwhoawOoXujP0yNx47OP39Wf4Ubmex/fvXP5GnluuPYSefRqY9MmNAPTu28iS+b05+vgd/Pm3A5n0yZf3f8VLrQO7tJKGAR8CpgNfSJInAeOSzzcDDwNfSdJvj4hdwApJy4GxwGPtKdvBbx9N/dEK3nHSdvoPrOfWp57jluuqmXzZerp1b+Tbs3L/if6xoA/XTzuMD//byxx6xC7OuWId51yRG0+ads5RbN2UwXGj/ezblxzOs4/1Zevmrpz77tF8/Mp1PPlgP2qe70FVFQweWsvl36lp8zyfvWYV137uMGp3VjHmlG17zAZnRQdOZvwA+DKQP20+JCLWAkTEWkmDk/ShwON5+WqStHYpWvCTNItc9B4kqQa4OiJ+VqzySuWay0bskXbf7YP2mnfW9dXMur662FWyvZh240t7pE08p+35uI9/sfmkx1uPe/2N+wQzrfDgN0jSvLz9GRExA0DS6cCGiJgvaVwB59rbEGu7w3DRgl9EnF2sc5tZaaVo+W2MiDEtHHsf8GFJHwR6Av0k3Qqsl1SdtPqqgQ1J/hpgeN73hwFrUlc+UfIJDzPrZAJoiMK21k4TMS0ihkXEEeQmMh6MiPOAOcCUJNsU4K7k8xxgsqQekkYAI4En23sZHvMzs9SKfAPzNcBsSRcAK4GzACJikaTZwGKgHrg0IhpaPk3rHPzMLL0OvoE5Ih4mN6tLRGwCTmsh33RyM8P7zMHPzFIr90fXCuHgZ2bpdIJFCwrh4GdmqQhQG5MZnYGDn5mlpjJftKAQDn5mlo67vWaWTeW/XFUhHPzMLDXP9ppZNrnlZ2aZE57tNbOs6vyxz8HPzNLzrS5mlk0OfmaWOQFUwBtXHfzMLBUR7vaaWUY1dv6mn4OfmaXjbq+ZZZW7vWaWTQ5+ZpY9XtjAzLKo6e1tnZyDn5ml5jE/M8smBz8zy5wAGh38zCxzPOFhZlnl4GdmmRNAQ+d/xMPBz8xSCojOH/yqSl0BM+uEIgrbWiFpuKSHJC2RtEjSFUn6QEkPSFqW/ByQ951pkpZLWippwr5cgoOfmaXTNNtbyNa6euDKiHgbcCJwqaTRwFRgbkSMBOYm+yTHJgPHABOBGyR1ae9lOPiZWXod0PKLiLURsSD5vB1YAgwFJgE3J9luBj6SfJ4E3B4RuyJiBbAcGNveS3DwM7P0Cg9+gyTNy9su3NvpJB0BvAt4AhgSEWtzxcRaYHCSbSiwKu9rNUlau3jCw8zSiYCGhkJzb4yIMa1lkNQX+B3wuYjYJqnFrHurTaEV2Z1bfmaWXgd0ewEkdSMX+G6LiDuS5PWSqpPj1cCGJL0GGJ739WHAmvZegoOfmaXXMbO9An4GLImI7+cdmgNMST5PAe7KS58sqYekEcBI4Mn2XoK7vWaWUkEzuYV4H/Bx4DlJzyRpXwWuAWZLugBYCZwFEBGLJM0GFpObKb40Igruf+/Owc/M0gmIDrjJOSIeZe/jeACntfCd6cD0fS4cBz8zaw8/3mZmmRPhV1eaWUZ5VRczy6Jwy8/MsseLmZpZFnkZezPLogCi8MfbypaDn5mlE5WxmKmDn5mlFu72mlkmVUDLT1FGszaSXgZeKnU9imAQsLHUlbBUKvXv7PCIOHhfTiDpXnJ/PoXYGBET96W8Yimr4FepJM1ra00zKy/+O6t8XtLKzDLJwc/MMsnBb/+YUeoKWGr+O6twHvMzs0xyy8/MMsnBz8wyycGviCRNlLRU0nJJU0tdH2ubpJmSNkhaWOq6WHE5+BWJpC7Aj4EPAKOBsyWNLm2trAC/AMryplzrWA5+xTMWWB4RL0RELXA7MKnEdbI2RMQjwOZS18OKz8GveIYCq/L2a5I0MysDDn7Fs7dX8vm+IrMy4eBXPDXA8Lz9YcCaEtXFzHbj4Fc8TwEjJY2Q1B2YDMwpcZ3MLOHgVyQRUQ9cBtwHLAFmR8Si0tbK2iJpFvAYMEpSjaQLSl0nKw4/3mZmmeSWn5llkoOfmWWSg5+ZZZKDn5llkoOfmWWSg18nIqlB0jOSFkr6jaTe+3CuX0g6M/n809YWXZA0TtJ721HGi5L2eMtXS+m75Xk1ZVlfl/TFtHW07HLw61xej4h3RsSxQC1wcf7BZCWZ1CLiUxGxuJUs44DUwc+snDn4dV5/AY5KWmUPSfoV8JykLpK+J+kpSc9KughAOT+StFjSH4HBTSeS9LCkMcnniZIWSPq7pLmSjiAXZD+ftDrfL+lgSb9LynhK0vuS7x4k6X5JT0v6b/b+fHMzku6UNF/SIkkX7nbsuqQucyUdnKS9RdK9yXf+IunoDvnTtMzpWuoKWHqSupJbJ/DeJGkscGxErEgCyNaIeI+kHsBfJd0PvAsYBbwdGAIsBmbudt6DgZ8AJyfnGhgRmyXdBLwaEdcm+X4F/GdEPCrpMHJPsbwNuBp4NCK+IelDQLNg1oJPJmX0Ap6S9LuI2AT0ARZExJWSvpac+zJyLxa6OCKWSToBuAE4tR1/jJZxDn6dSy9JzySf/wL8jFx39MmIWJGk/wvwjqbxPKA/MBI4GZgVEQ3AGkkP7uX8JwKPNJ0rIlpa1248MFp6o2HXT9IBSRkfTb77R0lbCrimyyWdkXwentR1E9AI/DpJvxW4Q1Lf5Hp/k1d2jwLKMNuDg1/n8npEvDM/IQkCr+UnAZ+NiPt2y/dB2l5SSwXkgdxwyUkR8fpe6lLw85KSxpELpCdFxA5JDwM9W8geSbmv7P5nYNYeHvOrPPcBl0jqBiDprZL6AI8Ak5MxwWrglL189zHgnyWNSL47MEnfDhyQl+9+cl1QknzvTD4+ApybpH0AGNBGXfsDW5LAdzS5lmeTKqCp9XoOue70NmCFpLOSMiTpuDbKMNsrB7/K81Ny43kLkpfw/De5Fv7vgWXAc8CNwP/s/sWIeJncON0dkv7Om93Ou4EzmiY8gMuBMcmEymLenHX+D+BkSQvIdb9XtlHXe4Gukp4Fvgk8nnfsNeAYSfPJjel9I0k/F7ggqd8i/GoAayev6mJmmeSWn5llkoOfmWWSg5+ZZZKDn5llkoOfmWWSg5+ZZZKDn5ll0v8HLy3Sra5K+TcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(Y_train, df_predicted_train_Y)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**support** - the number of occurrences of each class  \n",
    "**precision (PPV)** - ability of the classifier not to label a positive sample as negative  \n",
    "**recall (sensitivity)** - ability of the classifer to find all the positive samples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**f1-score** - weighted harmonic mean of the precision and recall; score reaches its best value at 1 and worst score at 0  \n",
    "**macro average** - averaging the unweighted mean per label  \n",
    "**weighted average** - averaging the support-weighted mean per label  \n",
    "**micro average** - averaging the total true positives, false negatives and false positives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 10 or 11: Assess accuracy in testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.88      0.90       446\n",
      "           1       0.89      0.92      0.91       475\n",
      "\n",
      "    accuracy                           0.90       921\n",
      "   macro avg       0.90      0.90      0.90       921\n",
      "weighted avg       0.90      0.90      0.90       921\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test, df_predicted_test_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(Y_test, df_predicted_test_Y)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clicker Question #4\n",
    "\n",
    "Given this output, would you use this model to predict whether or not text was something someone liked or disliked about COGS 108?\n",
    "\n",
    "- A) Yes\n",
    "- B) No\n",
    "- C) Unsure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doing model selection with the validation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, usually BEFORE you test, you'll want to play with hyperparameters in validation to get the best result.  Fine tuning your model.\n",
    "\n",
    "\n",
    "So consider this an optional step, that usually comes before test if your model has a set of knobs and dials to twiddle.  \n",
    "\n",
    "We only did test first above to build some intuition for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.83      0.85       469\n",
      "           1       0.83      0.88      0.86       452\n",
      "\n",
      "    accuracy                           0.86       921\n",
      "   macro avg       0.86      0.86      0.86       921\n",
      "weighted avg       0.86      0.86      0.86       921\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# play with the parameter C until it looks good\n",
    "svm_model = SVC(kernel='linear', C=10)\n",
    "svm_model.fit(tfidf_train, Y_train)\n",
    "df_predicted_validation_Y = svm_model.predict(tfidf_valid)\n",
    "\n",
    "# assess accuracy\n",
    "cm = confusion_matrix(Y_valid, df_predicted_validation_Y)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();\n",
    "\n",
    "print(classification_report(Y_valid, df_predicted_validation_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.87       446\n",
      "           1       0.87      0.90      0.88       475\n",
      "\n",
      "    accuracy                           0.88       921\n",
      "   macro avg       0.88      0.88      0.88       921\n",
      "weighted avg       0.88      0.88      0.88       921\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#assuming we left the cell above with our BEST model setting\n",
    "# lets refit it with all of train+valid sets\n",
    "svm_model.fit(np.concatenate([tfidf_train.todense(), tfidf_valid.todense()]), \n",
    "              np.concatenate([Y_train, Y_valid]))\n",
    "\n",
    "# get test set predictions\n",
    "df_predicted_test_Y = svm_model.predict(tfidf_test.todense())\n",
    "\n",
    "print(classification_report(Y_test, df_predicted_test_Y))\n",
    "\n",
    "cm = confusion_matrix(Y_test, df_predicted_test_Y)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 80:20 Partition\n",
    "2. Specified TF-IDF as predictor and most/least (0,1) as outcome\n",
    "3. Trained SVM linear classifier\n",
    "4. Built model on Training data\n",
    "5. Predicted in training data and on testing data\n",
    "6. Assessed overall accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approaches For Improvement?\n",
    "\n",
    "- Data Cleaning/Stemming\n",
    "- Different Tuning Parameters?\n",
    "- Cross-Validation?\n",
    "- Train/Test on all data OR Train only Sp20/Fa20/Wi21 (remote quarters)\n",
    "- Different Model?\n",
    "\n",
    "To start with use sklearn the EASY way... don't try to hand code things like Train-Test split or K-Fold cross-validation... read the docs and learn how to use these wonderful functions https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.855201758008377"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "results = cross_val_score(svm_model, \n",
    "                         X=np.concatenate([tfidf_train.todense(), tfidf_valid.todense()]), \n",
    "                         y=np.concatenate([Y_train, Y_valid]),\n",
    "                         cv=5) \n",
    "import numpy as np\n",
    "np.mean(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "One thing we didn't talk about is how you need to engineer features for NON-text kinds of inputs\n",
    "\n",
    "In general this is a huge topic that I will try condense for you into two bullet points.\n",
    "\n",
    "- If your data has different kinds of continuous variables, and especially if some of those variables tend to be big numbers (distance to the moon in meters) and some tend to be small numbers (the earths mass as a fraction of the earth's mass), then you need to STANDARDIZE those two variables to have roughly the same magnitude. If you don't then the ML system may think that the large magnitude variable is more predictive than the small magnitude variable.  See this link https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-scaler\n",
    "- If your data has categorical variables that do not have a natural order to them (red is not greater than blue), then you need to ONE-HOT encode those categorical variables.  Otherwise your ML system will think that red is greater than blue and that can make for some bad predictions.  See this link https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-categorical-features\n",
    "\n",
    "OK lets talk about how to do things with the non text input..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd \n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# lets get some 🐧 data to work with\n",
    "df = sns.load_dataset('penguins').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 804.125x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue='species');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm... lets take a close look at body mass vs bill length.... that seems really promising!!\n",
    "\n",
    "Let's imagine what it would be like to use k-Nearest Neighbors on only those two inputs... it might be enough to make things predict well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>bill_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.1</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.5</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.3</td>\n",
       "      <td>3250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.7</td>\n",
       "      <td>3450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.3</td>\n",
       "      <td>3650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>47.2</td>\n",
       "      <td>4925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>46.8</td>\n",
       "      <td>4850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>50.4</td>\n",
       "      <td>5750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>45.2</td>\n",
       "      <td>5200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>49.9</td>\n",
       "      <td>5400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>333 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bill_length_mm  body_mass_g\n",
       "0              39.1       3750.0\n",
       "1              39.5       3800.0\n",
       "2              40.3       3250.0\n",
       "4              36.7       3450.0\n",
       "5              39.3       3650.0\n",
       "..              ...          ...\n",
       "338            47.2       4925.0\n",
       "340            46.8       4850.0\n",
       "341            50.4       5750.0\n",
       "342            45.2       5200.0\n",
       "343            49.9       5400.0\n",
       "\n",
       "[333 rows x 2 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what we want to predict\n",
    "y = df['species']\n",
    "# the data we will predict from\n",
    "X = df[['bill_length_mm','body_mass_g']]\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UGH!!! the vector difference $\\mathbf{x}_{new} -  \\mathbf{x}_{i}$ is going to be absolutely dominated by body mass... it's two orders of magnitude bigger than bill length!\n",
    "\n",
    "We may not want that!  From the scatterplot it looks like both variables are equally contibuting to understanding species.\n",
    "\n",
    "We need to normalize the data so each variable is a z-scored version of itself... that means both variables will now be on roughly the same order of magnitued (about -3 to 3).  There are in fact many diffrerent kinds of normalization that could work here!  Take a look at this list: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>bill_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.896042</td>\n",
       "      <td>-0.568475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.822788</td>\n",
       "      <td>-0.506286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.676280</td>\n",
       "      <td>-1.190361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.335566</td>\n",
       "      <td>-0.941606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.859415</td>\n",
       "      <td>-0.692852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>0.587352</td>\n",
       "      <td>0.892957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>0.514098</td>\n",
       "      <td>0.799674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>1.173384</td>\n",
       "      <td>1.919069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>0.221082</td>\n",
       "      <td>1.234995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>1.081817</td>\n",
       "      <td>1.483749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>333 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bill_length_mm  body_mass_g\n",
       "0         -0.896042    -0.568475\n",
       "1         -0.822788    -0.506286\n",
       "2         -0.676280    -1.190361\n",
       "4         -1.335566    -0.941606\n",
       "5         -0.859415    -0.692852\n",
       "..              ...          ...\n",
       "338        0.587352     0.892957\n",
       "340        0.514098     0.799674\n",
       "341        1.173384     1.919069\n",
       "342        0.221082     1.234995\n",
       "343        1.081817     1.483749\n",
       "\n",
       "[333 rows x 2 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "preproc = StandardScaler()\n",
    "\n",
    "preproc.fit(X)\n",
    "scaled_data = preproc.transform(X)\n",
    "X['bill_length_mm'] = scaled_data[:,0]\n",
    "X['body_mass_g'] = scaled_data[:,1]\n",
    "\n",
    "X\n",
    "\n",
    "# some things to note here... we could just use scaled_data instead of stuffing\n",
    "# the results back into the dataframe X... I'm just doing this so we can keep\n",
    "# the column labels intact... scaled_data is a numpy ndarray without those labels\n",
    "# sklearn will accept numpy arrays or pandas dataframes/series or any other iterable \n",
    "# as input, so use whatever you want"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The thing we decided to learn above is strictly numerical.  And preprocessing the way we just described is only good for numerical data.\n",
    "\n",
    "What if we had been interested in categorical data? For categorical data inputs we usually wish to use a one-hot encoding.\n",
    "\n",
    "One-hotting a single categorical variable that has N possible categories makes a vector representation that is N long. Because one-hot assumes each category is mutually exclusive with every other category, only one of those N category holders is allowed to be non-zero.\n",
    "\n",
    "Why use one-hot encoding?  This eliminates unwanted \"fake relationships\" in categorical data that a different encoding might cause an ML system to learn.\n",
    "\n",
    "Imagine we have categories like (red, blue, green). There is no ordinal relationship here... red is not > blue, nor is blue > green.  But if we turned (red, blue, green) into a representation like (3, 2, 1) that is exactly what our ML system would learn. Even worse, it would be more likely to confuse blue and green than red and green... because blue-green is smaller than red-green.\n",
    "\n",
    "One hot eliminates this because the 3 long vectors representing each color [1, 0, 0], [0, 1,0], and [0, 0, 0] are orthogonal (unrelated)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        island     sex\n",
      "0    Torgersen    Male\n",
      "1    Torgersen  Female\n",
      "2    Torgersen  Female\n",
      "4    Torgersen  Female\n",
      "5    Torgersen    Male\n",
      "..         ...     ...\n",
      "338     Biscoe  Female\n",
      "340     Biscoe  Female\n",
      "341     Biscoe    Male\n",
      "342     Biscoe  Female\n",
      "343     Biscoe    Male\n",
      "\n",
      "[333 rows x 2 columns]\n",
      "[[0. 0. 1. 0. 1.]\n",
      " [0. 0. 1. 1. 0.]\n",
      " [0. 0. 1. 1. 0.]\n",
      " ...\n",
      " [1. 0. 0. 0. 1.]\n",
      " [1. 0. 0. 1. 0.]\n",
      " [1. 0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "preproc = OneHotEncoder()\n",
    "\n",
    "onehot = preproc.fit_transform(df[['island','sex']])\n",
    "print(df[['island','sex']])\n",
    "print(onehot.todense())\n",
    "\n",
    "# there are 3 different islands, so 3 columns get used to encode which island\n",
    "# only 1 of those 3 columns can be \"hot\" (non-zero) for each penguin\n",
    "# likewise there are 2 different sexes recorded here, so 2 columns get used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "anyway we're not going to use categorical data right now, so lets stick to the numerical data we did above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Adelie       0.95      0.95      0.95       146\n",
      "   Chinstrap       1.00      0.90      0.95        68\n",
      "      Gentoo       0.92      0.97      0.95       119\n",
      "\n",
      "    accuracy                           0.95       333\n",
      "   macro avg       0.96      0.94      0.95       333\n",
      "weighted avg       0.95      0.95      0.95       333\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay, confusion_matrix\n",
    "\n",
    "pclassifier = KNeighborsClassifier(n_neighbors=15)\n",
    "\n",
    "pclassifier.fit(X,y)\n",
    "predicted = pclassifier.predict(X)\n",
    "\n",
    "print(classification_report(y,predicted))\n",
    "\n",
    "cm = confusion_matrix(y,predicted)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result shows us that with just 2 variables we can get to 95% accuracy!\n",
    "\n",
    "BUT note this is TRAINING SET accuracy... something of limited use.\n",
    "\n",
    "Let's figure out how to make a training set/test set\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training/test set sizes\n",
      "(233, 2) (100, 2) (233,) (100,)\n",
      "\n",
      "training set performance\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Adelie       0.95      0.94      0.94        97\n",
      "   Chinstrap       0.98      0.88      0.92        48\n",
      "      Gentoo       0.91      0.98      0.95        88\n",
      "\n",
      "    accuracy                           0.94       233\n",
      "   macro avg       0.95      0.93      0.94       233\n",
      "weighted avg       0.94      0.94      0.94       233\n",
      "\n",
      "\n",
      "test set performance\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Adelie       0.96      0.96      0.96        49\n",
      "   Chinstrap       1.00      0.95      0.97        20\n",
      "      Gentoo       0.94      0.97      0.95        31\n",
      "\n",
      "    accuracy                           0.96       100\n",
      "   macro avg       0.97      0.96      0.96       100\n",
      "weighted avg       0.96      0.96      0.96       100\n",
      "\n",
      "\n",
      "test set confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=777)\n",
    "\n",
    "print('training/test set sizes')\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "print()\n",
    "\n",
    "pclassifier = KNeighborsClassifier(n_neighbors=15)\n",
    "\n",
    "pclassifier.fit(X_train, y_train)\n",
    "predicted_train = pclassifier.predict(X_train)\n",
    "predicted_test = pclassifier.predict(X_test)\n",
    "\n",
    "print('training set performance')\n",
    "print(classification_report(y_train,predicted_train))\n",
    "print()\n",
    "print('test set performance')\n",
    "print(classification_report(y_test,predicted_test))\n",
    "print()\n",
    "print('test set confusion matrix')\n",
    "cm = confusion_matrix(y_test,predicted_test)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well what if instead of train/test, we do cross validation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets do 5 fold cross validation...\n",
      "xval/test set sizes\n",
      "(233, 2) (100, 2) (233,) (100,)\n",
      "\n",
      "xval performance\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Adelie       0.94      0.92      0.93        97\n",
      "   Chinstrap       0.98      0.85      0.91        48\n",
      "      Gentoo       0.90      0.98      0.93        88\n",
      "\n",
      "    accuracy                           0.93       233\n",
      "   macro avg       0.94      0.92      0.92       233\n",
      "weighted avg       0.93      0.93      0.93       233\n",
      "\n",
      "\n",
      "test set performance\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Adelie       0.96      0.96      0.96        49\n",
      "   Chinstrap       1.00      0.95      0.97        20\n",
      "      Gentoo       0.94      0.97      0.95        31\n",
      "\n",
      "    accuracy                           0.96       100\n",
      "   macro avg       0.97      0.96      0.96       100\n",
      "weighted avg       0.96      0.96      0.96       100\n",
      "\n",
      "\n",
      "test set confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "print('lets do 5 fold cross validation...')\n",
    "X_xval, X_test, y_xval, y_test = train_test_split( X, y, test_size=0.3, random_state=777)\n",
    "\n",
    "print('xval/test set sizes')\n",
    "print(X_xval.shape, X_test.shape, y_xval.shape, y_test.shape)\n",
    "print()\n",
    "\n",
    "pclassifier = KNeighborsClassifier(n_neighbors=15)\n",
    "\n",
    "predicted_xval = cross_val_predict(pclassifier, X_xval, y_xval, cv=5)\n",
    "predicted_test = pclassifier.fit(X_xval,y_xval).predict(X_test)\n",
    "\n",
    "print('xval performance')\n",
    "print(classification_report(y_xval,predicted_xval))\n",
    "print()\n",
    "print('test set performance')\n",
    "print(classification_report(y_test,predicted_test))\n",
    "print()\n",
    "print('test set confusion matrix')\n",
    "cm = confusion_matrix(y_test,predicted_test)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow... but cross-validation is really useful for looking at the BEST set of hyperparameters possible... is there a way to do that?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets do grid search using 5 fold cross validation... and this time lets use all the measurements not just the best two\n",
      "xval/test set sizes\n",
      "(233, 4) (100, 4) (233,) (100,)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#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>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [1, 3, 5, 7, 9, 11, 13, 15]})</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\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [1, 3, 5, 7, 9, 11, 13, 15]})</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: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [1, 3, 5, 7, 9, 11, 13, 15]})"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "print('lets do grid search using 5 fold cross validation... and this time lets use all the measurements not just the best two')\n",
    "X = df[['bill_length_mm','bill_depth_mm','flipper_length_mm', 'body_mass_g']]\n",
    "y = df['species']\n",
    "\n",
    "preproc = StandardScaler()\n",
    "\n",
    "preproc.fit(X)\n",
    "scaled_data = preproc.transform(X)\n",
    "X['bill_length_mm'] = scaled_data[:,0]\n",
    "X['bill_depth_mm'] = scaled_data[:,1]\n",
    "X['flipper_length_mm'] = scaled_data[:,2]\n",
    "X['body_mass_g'] = scaled_data[:,3]\n",
    "\n",
    "X_xval, X_test, y_xval, y_test = train_test_split( X, y, test_size=0.3, random_state=777)\n",
    "\n",
    "print('xval/test set sizes')\n",
    "print(X_xval.shape, X_test.shape, y_xval.shape, y_test.shape)\n",
    "print()\n",
    "\n",
    "# note we don't need to put n_neightbors here now :)\n",
    "pclassifier = KNeighborsClassifier()\n",
    "params = { 'n_neighbors':[1,3,5,7,9,11,13,15]}\n",
    "\n",
    "searcher = GridSearchCV(pclassifier, param_grid=params, cv=5)\n",
    "\n",
    "# at this point we run a bunch of cross validations\n",
    "searcher.fit(X_xval, y_xval)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00164022, 0.00148902, 0.00140734, 0.00128641, 0.00112829,\n",
       "        0.00111313, 0.00111642, 0.00111265]),\n",
       " 'std_fit_time': array([1.10869674e-04, 1.36888197e-04, 1.29515503e-05, 1.01601523e-04,\n",
       "        2.01203811e-05, 3.88088035e-06, 1.40323057e-06, 4.53221114e-06]),\n",
       " 'mean_score_time': array([0.00338545, 0.00267363, 0.00263739, 0.00231676, 0.00207858,\n",
       "        0.0020577 , 0.00208979, 0.00208173]),\n",
       " 'std_score_time': array([1.20514835e-04, 2.73222574e-04, 1.40614661e-04, 2.06453387e-04,\n",
       "        3.18973052e-05, 1.58253913e-05, 3.54111872e-05, 1.01249047e-05]),\n",
       " 'param_n_neighbors': masked_array(data=[1, 3, 5, 7, 9, 11, 13, 15],\n",
       "              mask=[False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'n_neighbors': 1},\n",
       "  {'n_neighbors': 3},\n",
       "  {'n_neighbors': 5},\n",
       "  {'n_neighbors': 7},\n",
       "  {'n_neighbors': 9},\n",
       "  {'n_neighbors': 11},\n",
       "  {'n_neighbors': 13},\n",
       "  {'n_neighbors': 15}],\n",
       " 'split0_test_score': array([0.9787234 , 0.95744681, 0.95744681, 0.95744681, 0.95744681,\n",
       "        0.9787234 , 0.9787234 , 0.95744681]),\n",
       " 'split1_test_score': array([0.9787234, 0.9787234, 1.       , 1.       , 1.       , 1.       ,\n",
       "        0.9787234, 0.9787234]),\n",
       " 'split2_test_score': array([0.95744681, 0.9787234 , 1.        , 0.9787234 , 1.        ,\n",
       "        1.        , 1.        , 1.        ]),\n",
       " 'split3_test_score': array([0.97826087, 0.97826087, 1.        , 1.        , 0.97826087,\n",
       "        0.97826087, 0.97826087, 0.97826087]),\n",
       " 'split4_test_score': array([0.97826087, 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        ]),\n",
       " 'mean_test_score': array([0.97428307, 0.9786309 , 0.99148936, 0.98723404, 0.98714154,\n",
       "        0.99139685, 0.98714154, 0.98288622]),\n",
       " 'std_test_score': array([0.00842067, 0.01345777, 0.01702128, 0.01702128, 0.01706847,\n",
       "        0.01053767, 0.01050025, 0.01594772]),\n",
       " 'rank_test_score': array([8, 7, 1, 3, 4, 2, 4, 6], dtype=int32)}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a fitted set of cvs, one for each param set in the grid\n",
    "searcher.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 5}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can match this up with mean_test_score above\n",
    "searcher.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test set performance\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Adelie       1.00      1.00      1.00        49\n",
      "   Chinstrap       1.00      1.00      1.00        20\n",
      "      Gentoo       1.00      1.00      1.00        31\n",
      "\n",
      "    accuracy                           1.00       100\n",
      "   macro avg       1.00      1.00      1.00       100\n",
      "weighted avg       1.00      1.00      1.00       100\n",
      "\n",
      "\n",
      "test set confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# searcher, when used as a classifier, uses the best_params_ by default\n",
    "predicted_test = searcher.fit(X_xval,y_xval).predict(X_test)\n",
    "\n",
    "\n",
    "print('test set performance')\n",
    "print(classification_report(y_test,predicted_test))\n",
    "print()\n",
    "print('test set confusion matrix')\n",
    "cm = confusion_matrix(y_test,predicted_test)\n",
    "disp = ConfusionMatrixDisplay(cm)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines: Maximizing the *Margin*\n",
    "\n",
    "Support vector machines are another ML technique\n",
    "\n",
    "The intuition is this: rather than simply drawing a zero-width line between the classes, we can draw around each line a *margin* of some width, up to the nearest point.\n",
    "Here is an example of how this might look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X, y = make_blobs(n_samples=50, centers=2,\n",
    "                  random_state=0, cluster_std=0.60)\n",
    "\n",
    "xfit = np.linspace(-1, 3.5)\n",
    "sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, s=75);\n",
    "\n",
    "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n",
    "    yfit = m * xfit + b\n",
    "    plt.plot(xfit, yfit, '-k')\n",
    "    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\n",
    "                     color='#AAAAAA', alpha=0.4)\n",
    "\n",
    "plt.xlim(-1, 3.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In support vector machines, the line that maximizes this margin is the one we will choose as the optimal model.\n",
    "Support vector machines are an example of such a *maximum margin* estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a support vector machine\n",
    "\n",
    "Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data.\n",
    "For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=10000000000.0, kernel='linear')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC # \"Support vector classifier\"\n",
    "model = SVC(kernel='linear', C=1E10)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C',\n",
       " '__abstractmethods__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_abc_impl',\n",
       " '_check_feature_names',\n",
       " '_check_n_features',\n",
       " '_check_proba',\n",
       " '_compute_kernel',\n",
       " '_decision_function',\n",
       " '_dense_decision_function',\n",
       " '_dense_fit',\n",
       " '_dense_predict',\n",
       " '_dense_predict_proba',\n",
       " '_dual_coef_',\n",
       " '_estimator_type',\n",
       " '_gamma',\n",
       " '_get_coef',\n",
       " '_get_param_names',\n",
       " '_get_tags',\n",
       " '_impl',\n",
       " '_intercept_',\n",
       " '_more_tags',\n",
       " '_n_support',\n",
       " '_num_iter',\n",
       " '_probA',\n",
       " '_probB',\n",
       " '_repr_html_',\n",
       " '_repr_html_inner',\n",
       " '_repr_mimebundle_',\n",
       " '_sparse',\n",
       " '_sparse_decision_function',\n",
       " '_sparse_fit',\n",
       " '_sparse_kernels',\n",
       " '_sparse_predict',\n",
       " '_sparse_predict_proba',\n",
       " '_validate_data',\n",
       " '_validate_for_predict',\n",
       " '_validate_targets',\n",
       " '_warn_from_fit_status',\n",
       " 'break_ties',\n",
       " 'cache_size',\n",
       " 'class_weight',\n",
       " 'class_weight_',\n",
       " 'classes_',\n",
       " 'coef0',\n",
       " 'coef_',\n",
       " 'decision_function',\n",
       " 'decision_function_shape',\n",
       " 'degree',\n",
       " 'dual_coef_',\n",
       " 'epsilon',\n",
       " 'fit',\n",
       " 'fit_status_',\n",
       " 'gamma',\n",
       " 'get_params',\n",
       " 'intercept_',\n",
       " 'kernel',\n",
       " 'max_iter',\n",
       " 'n_features_in_',\n",
       " 'n_iter_',\n",
       " 'n_support_',\n",
       " 'nu',\n",
       " 'predict',\n",
       " 'predict_log_proba',\n",
       " 'predict_proba',\n",
       " 'probA_',\n",
       " 'probB_',\n",
       " 'probability',\n",
       " 'random_state',\n",
       " 'score',\n",
       " 'set_params',\n",
       " 'shape_fit_',\n",
       " 'shrinking',\n",
       " 'support_',\n",
       " 'support_vectors_',\n",
       " 'tol',\n",
       " 'verbose']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svc_decision_function(model, ax=None, plot_support=True):\n",
    "    \"\"\"Plot the decision function for a 2D SVC\"\"\"\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    xlim = ax.get_xlim()\n",
    "    ylim = ax.get_ylim()\n",
    "    \n",
    "    # create grid to evaluate model\n",
    "    x = np.linspace(xlim[0], xlim[1], 30)\n",
    "    y = np.linspace(ylim[0], ylim[1], 30)\n",
    "    Y, X = np.meshgrid(y, x)\n",
    "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
    "    P = model.decision_function(xy).reshape(X.shape)\n",
    "    \n",
    "    # plot decision boundary and margins\n",
    "    ax.contour(X, Y, P, colors='k',\n",
    "               levels=[-1, 0, 1], alpha=0.5,\n",
    "               linestyles=['--', '-', '--'])\n",
    "    \n",
    "    # plot support vectors\n",
    "    if plot_support:\n",
    "        ax.scatter(model.support_vectors_[:, 0],\n",
    "                   model.support_vectors_[:, 1],\n",
    "                   s=300, linewidth=3, facecolors='none', edgecolor='k');\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, s=75);\n",
    "\n",
    "plot_svc_decision_function(model);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the dividing line that maximizes the margin between the two sets of points.\n",
    "Notice that a few of the training points just touch the margin: they are indicated by the black circles in this figure.\n",
    "These points are the pivotal elements of this fit, and are known as the *support vectors*, and give the algorithm its name.\n",
    "In Scikit-Learn, the identity of these points are stored in the ``support_vectors_`` attribute of the classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.44359863, 3.11530945],\n",
       "       [2.33812285, 3.43116792],\n",
       "       [2.06156753, 1.96918596]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.support_vectors_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A key to this classifier's success is that for the fit, only the position of the support vectors matter; any points further from the margin which are on the correct side do not modify the fit!\n",
    "Technically, this is because these points do not contribute to the loss function used to fit the model, so their position and number do not matter so long as they do not cross the margin.\n",
    "\n",
    "We can see this, for example, if we plot the model learned from the first 60 points and first 120 points of this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def plot_svm(N=10, ax=None):\n",
    "    X, y = make_blobs(n_samples=200, centers=2,\n",
    "                      random_state=0, cluster_std=0.60)\n",
    "    X = X[:N]\n",
    "    y = y[:N]\n",
    "    model = SVC(kernel='linear', C=1E10)\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    ax = ax or plt.gca()\n",
    "    sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, s=75, ax=ax)\n",
    "    ax.set_xlim(-1, 4)\n",
    "    ax.set_ylim(-1, 6)\n",
    "    plot_svc_decision_function(model, ax)\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(16, 6))\n",
    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
    "for axi, N in zip(ax, [30, 60, 120]):\n",
    "    plot_svm(N, axi)\n",
    "    axi.set_title('N = {0}'.format(N))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the left panel, we see the model and the support vectors for just 10 training points, in the middle we see  60 training points and on the right panel 120 points.  While going from 30 to 60 caused changes, going from 60 to 120 did not becuase the support vectors (the points on the margin line) had not changed.\n",
    "\n",
    "This insensitivity to the exact behavior of distant points is one of the strengths of the SVM model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beyond linear boundaries: Kernel SVM\n",
    "\n",
    "Where SVM becomes extremely powerful is when it is combined with *kernels*.\n",
    "We have seen a version of kernels before: Remember polynomial features from the regression lectures?  \n",
    "\n",
    "There we projected our data into higher-dimensional space defined by polynomials functions, and thereby were able to fit for nonlinear relationships with a linear regression.\n",
    "\n",
    "In SVM models, we can use a version of the same idea.\n",
    "To motivate the need for kernels, let's look at some data that is not linearly separable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ChRBRopSSfb1Fv5C40y+EEEIkBAkUQgghIpJ2sYiaQNCgyuWmodWP2QRpSdaEycckhNh/EihEVDS6fXyxoYrfvb5mzz7JQ7KT+du5k5hUnN639g0QYoCTricRFd/taOD6Z7/dEyRAbzx00cML+b4XNsERQsSOBArR62qaPPzp7TUhy/xBg/s+2USLNzHzHQkhupJAIXqd2xdg7S5X2PJvttbS5JZAIURfIWMU/VSTx09dsxe3L0CyzUJ2ii1mi77MJkWyzUyLNxCyPDPZ1uNd0oQQsSOBoh/a1dDKn95ayzurdhMIGtgtJi4+ooQfzx4ekw1+spx2LppWwiMLtoYsv/qooZLKQsRMi9dPtcvDx+srKa9zM2N4NmMHpVGQLjPw9pcEin6myuXh6ieXdNrS0uMP8tiX26hp9vL7M8aTnmSNah1sFhNXHzWMBZuqWbe7cxfUsaPyOGpkTlRfX4h2LR4/H62r5Obnv6V9F9qHv9hCYbqD566dzpBsZ+QTCEACRb+zo7Yl7L7Hr6/YyS3HHxL1QAFQkO7gyR9OY2VZA/9ZWobNrLhk+hCG5aaQmyqtCREbFS43Nz3/bZf9QXY2uPnp88t59IopZDnl89gdCRT9zMItobexBL2ZzraaZkpzYnMXlZ/mIH+sg2MOyUWZwGKSuRMituYv3xl2E6lvd9RT3+KTQLEfJFD0M1nd7Gmc6oj9r9xqkQAhYsMXCFDT5MMwDGwWE83dbDvbGmbChehMAkU/M3NEDibFnv7YvWUkWynKSI59pYSIgYpGN//+ehtPfb2dRrefcYVp/OyEQwgEDR77cluX55tNitQYdMP2B3Kr189kO23ccc7ELsdtZhP3XzyZnJTILQ4h+qLKRjfXPLmYez/ZTGPbGp3VOxu56sklDM1xcuTw7C4/c8ahhd22wIUmLYp+Jtlu4eTxBUwanMFTX21ja00zhw7OYN6UwRSmO7CY5d5A9D/rdrv4rjz0JI5/fLiRO86dyFeb9fidUvCDCYO4de7ohN6/PJHIu9QPpTisHOKw8rvTx+HxB3FYTJglQIh+Khg0eGlZWdjymmYvg9IcvHPzUTR5/OSl2sl02khzSLfT/pJA0Y9ZzCZpQYgBwdrNSn+r2cQhBakxqk33Wjx+/EGDFLsFUx/IUiCBQgjRp5lMigumlvDysvKQ5YXpDtKTE6P1UN3kYc3ORh5bsJUmj58TxxXwg4mDKEx3oFTiBgwJFEKIPm9orpM5Y/L4aG1lp+Nmk+Kv504iLwEWeVY3efjVqyt5b3XFnmNLttfxwGebefnHRzI0RuubDkZc+yWUUnOVUuuVUpuUUreGKJ+tlGpQSi1v+7otHvUUQiS2nBQ7t589kbvmTWJUfiq5KXbmjs/nrRtncfiQjIS4W19d3tgpSLSrbfZy22sraWj1xqFW+yduLQqllBm4DzgBKAMWK6VeNwxj340MvjAM49SYV1AI0afkpto55/BiZo/KxR80cNotCTOrqdnj57EvQyfJBFiwuYbGVj/pSYk5XTee7+I0YJNhGFsAlFLPA2cAoXe8EUKI/ZCImYn9wSCuCHuwGAb4AsEY1ujAxLPrqQjYsdfjsrZj+5qhlFqhlHpHKTUu3MmUUtcqpZYopZZUVVX1dl0HlIZWH9trmllV3sC26mbqWxK3SSxEX5Bit3LC2Lyw5UUZSQnT+gklnjUL1Wm4b+KJZcAQwzCalFKnAK8BI0OdzDCMh4CHAKZMmRImDZjozu4GN79+bSUfravck0xt+rAs7pw3ieJMSf8hxL6CQYNA0IiY08xsUpw+qYgHP99CfYuvS/kvTxmd0FmV49miKAMG7/W4GNi59xMMw2g0DKOp7fu3AatSSjYziJLaZg8/feFbPlxb2Snj5sIttVzx+GIqXe74VU6IBNPY6mP9bhe/f3MN1z+7jKe+2kZ5XSvBUInWgMIMB6/8+MhO6UQK0x3cd9FhzBqZmxAD7uHEs0WxGBiplBoKlAMXABft/QSlVAFQYRiGoZSahg5s4fNoix6pdnlZuKU2ZNmmyiZ21LaQF4Md8oRIdK5WH/9ZWsYf3uwYUn1/TQV3frCe//zoSEaFWNynlGJYbgr3XzyZBrcPX0AvuMtLtSd0kIA4tigMw/ADNwDvAWuBFw3DWK2Uuk4pdV3b084FVimlVgB3AxcYRrjs8qKntlQ3RSz/rqwhRjURIrFVujydgkS7xlY/1z+7jOomT9ifTU+2UZLlZHhuCvlpib3Qrl1cR0/aupPe3ufYA3t9fy9wb6zrNVBld7OBS36atCaEAHg5Qm6pTZVN1Ld4yUnA2VcHSxIBiT0GZyWFTbtst5iYNDgjthUSIkHtbow8XufxJ+5U14MhgaIfcbl9fF/TzIdrKvh8QxXl9a24ffu/g1duqoPHLp9CktXc6bjVrHj4sinkyl4WQgAwZ3T4qa42sykm+9LHUuJO3I0hrz/A7kYP763azcqdDUwsSuekcQXkp9uxmc3dnyAB1DR5uO+TTTzx1bY9u9vZLSb+dOZ4ThpfQOp+pFQ2mxTji9L54GdH8+n6KpZur2PsoDROGl9Afqodm6VvvBdCRNvhQzLJT7NT0dh1LOKS6SXdduP2Nao/jg1PmTLFWLJkyX491xcIsnhbLVc8thjvXisj7RYTT/1wGocPyUz4VN3BoMEz33zPb+avClk+//qZ0m0kRC/bXtPMz/+zgiXb6gB9zbjiyFKuOXpYnxyfUEotNQxjSsiygR4odta3csLfP6M5xCbrqXYL799yNIMyknq7ir2qotHNafcsoNIVeqbFnDF5/PP8w0hxSANSiN5U1+ylvtWHxx8gxW4hJ8WOw9o3W96RAsWAv3Ks3+0KGSQAXB4/myqbEj5QeP3BsEECYGNFE62+gAQKIXpZptNG5gDYdzux+1RioLvVxlUR5kMnCqvZREaEjVkGZyVhtw74X7UQ4iAN+KvH2ML0iOWjE2j7xHByUmxcPWto2PIbjhsp+wP3N24X1G6FpU/AV/fAru+guTretRL91IDvixiU5mBCURoryxu7lB1WktEnFplZzCbOn1rCyvKGThujKAU/O+EQxvSBYCcOQGs9LPs3fPgbOiXlGnYsnPUgpObHrWqifxrwg9kAu+pbufWV7/hsg74jUwqOHZXHn84az6D0xB6f2Ftds5dKl4evt9TgsJiYMTybLKdtv6bGij6kbDE8cnzoshk3wpzbwNL/+81F75LB7G4Mykji7gsOo67VR7PbT4rDQmayjbQ+tmimfWAtVEIy0U94m+GL/wtfvvRxmH4dpBfHrk6i35NA0SY92UZ6styFiQTna4X67eHLvU0QkI2mRO8a8IPZQvQptmTIHx++PDkLLH1vsZfoEAwaJNqQgLQohOhLrMkw8yZY+ULngex2R94MzoLY10v0WGWjm7W7Xby6rAybxcQFU0sYkp2cEHuAS6AQoq/JGALznoLXfqy7mgCUCQ7/IRx2MfSR/GSiQ0WDmx8+uZjVOztmX764pIyTxxfwhzPGkxPnbVIlUAjR19hT4JC5cP03ULdND3DnHALJ2eBIi3ftxAHy+oM8+uXWTkGi3TurdnPKhEGcNqkwDjXrIIEiRqqbPPgDQSwmU9zvDvoFv1svMAt4wWyHlHwwD6CPs8WmZzbJ7KY+r7bZwzMLw09QePDzzcwckU1WHDPSDqC/rPiobfaycEsN//hwA1urmynJcvLT40cyc0RO2E2CRDdcu2HBP2HZE+BrgaRMmPlT3e3izI137YQ4IIGgETbfHEBdsw9/IL6D2zLrKYpcbh+PfrGFnzyzjA0VTfgCBpurmrjxuW+5/9NNNLb64l3FvqepCl6+Cr75lw4SAK118OFv4dM7wN21+d5rPE3QVBHd14inBJtpM1DYrWZG5qWELZ88JJMUu4VA0GB3o5uyuhZ2N7oJBmP3+5JAEUV1zV7u/2xzyLJHFmylrkXmux+wxnLYtiB02ZJHobW291/T3Qjl38Kr18LjJ8N/roDtX+kA1dd5mqB6E3zyZ3j5ap0apKFMgkYM5aTY+eUpY0KWmU2KG48dQasvwBNfbeO0uxcw645POP2eBTy1cBs1MUpaKl1PUbSyvIFwQd8wYOn2OoZkO2Nbqb5uy6fhy4wg1GyCzNLeez1fK6x7C167ruNYzWbY/BHM+S1MvQYcbSvhA379b18ZK/E0wbo39f+tPTCsekl35V3xNuSPjW/9BpDDh2Ry17xJ/OGtNdS36J6GwnQHd86bRE6qjT+/tZaXlpXveX6ly8PvXl/Dht1N3Hry6Khnkegjn+j+Sal416APsoVvogN6nUFvaq6GN38auuzj38O4s8HfCpVrdfoMIwiHXgKDJkLqXusZWmp0Mr+AB+zpkJIH5jiniGmu6hwk2rXWwYuXwBXvSILBGElLsnLGoYXMHJFDfYsXk0mRnmQlL9XOlurmTkFib88u+p5rjx4mgaIvm1CUgUkRslWhFEwuyYx9pfq6EcfpNy9U14g9DTJKevf1qjfoGVahGIZex/DGTbD1s47ja+ZD4WS48Dk9G6t6A8z/CZS1Jap0ZMAx/w0Tzwdndu/W90CsfSN8F1PNZt2NJ4EiZixmEwXpDgrSO2esXrY9chfnd+UNlOZEt2dCxiiiKMtp5cbjRoYsu+7o4TLr6WAk58Lxv+96XJngrAf0nXpv8kfoAy6ZDls/7xwk2u1cBkseh6ZKeOykjiAB4K6H9/4HVv6no7tqb+5GaNypfzaa6r+PXO5tju7ri/1it0S+THdX3hukRRFFKQ4rV8wsZXxROv/4cANbqpopzUnm5jkjmTY0S9J/HwxHKky+DIbMgAX/0Any8ifotBYZJWDu5eCbNyZ8C2bcWbDoofA/u/ghGHlC+EHvz26HMadBepF+7G2Gxl1QuwkCPqjbDs4cGH6sbpn0tmGzYfHDocuUSb+2iLvDSjLD9kxYTIpxhdFfZCmBAmho8VHb4mFjRROpDgtDsp3kpNqw9UIqhMxkGyeMzWdySQa+gIHVrBIid0uflpQBxVPh7If0YLPNCdYo7RuSnK0HrEMFhMxh0BJhllVrHXhbQpdZk2HoMRD0dwShxnJY/iyseE7/7ODpMOMnsOxpmHxp77eWiiZDWqFuvexr4vmQlNW7rycOSpbTxq0nj+bPb6/rUvbrU8fEpGdiwAeKKpeb/31jDW9+t2vPsRS7hXsuPIwZw7NxWHsnb44EhyiwOfVXb/N7wGQBk1mnxDjmvyFrOCy4S3cHJWXC9J9AwQTd/bT+ndDnKZoCrTVdjx9xHYyYo8cI3vkvKD0axpwKr1wLO7/teN7Wz2DbF3rXOtfu3g8UaYVwxVt6XUr5Mn3MZIFJF8GcX0s6kAThtFs4f8pgJg3O4O6PNrK9poVhOU5umjOSkfmpJNuifxkf0DvcefwB7nxvPQ9/sbVLmdmkeP+Woxme280sG9F/NJTrMYe1r+tgMPVqPdU2OQuCQb3YLuDVF9P2lCE7V8DDs/Vsp31d8RZYHPDInI5jU6/WF/xP/txxLGsYzPoZvH5D6HplDIFT/6G7ikxR6I9urtED175WcKRDcg7YZdp2Imp0+3B7AyRZzaT28kynSDvcDejB7GqXl6cXhh7QCwQNnlm4HV8gxAVA9D912+DR4/V00fVvw/Jn4OFj4dO/6AupyQRpgyBziB5TMFugagMsexLOfrjzbKvUAjjjX7DqVd2tM/UafdxshdE/6BwkAEqP0q8ZTv12sERxPMuZDTkj9ZTezCESJBJYmsNKXpqj14NEdwZ011Orz0+rL3yOlVU7G2n1BrAmDeh42v+11sNbvwjdV7/oIRh/TtdprI274D+XQ/V6KF8Ks/9Hj2dg6IFoaxIseQSWPgqn/hOueBN2LNItllC6W1RjS+ud1kRLLQQDuuUg+2qL/TSgA4XDasZmNuEN02oYluPstTEKkcDc9bD5w/DlX/8LBk3qGDBvqgDXTjjyRkgrguZKWHi/Dhjtzn8Gig7XYw5v3AQXPAuOTKgL0YLd+jkc/XO9AjyUzKG6K6xLvRv1Go/9GatxVcD2BbDwX+BxwfDj4YhrIa2476wkF3EzoD8h2U47Zx5WyItLykKWXzmzFFsM5iiLOPN7I+c2aqnuaCVUb4DnLtSpQtoNngan36MHuj/5o14z0VQBh18Jc27Tg9ZbP9MX9hHH66y3e6vbqlOlFx3eOdiAnqZ66t8hc6+urZZaqFgJn9+lA9agQ/UYR+aQ0AGjqQJe/RFs+aTjWNV63W121fuQP25/3ykxQA3oq2CSzczPTxzFtNLO0wAtJsWd8yZSlBGlKZcisdicbd1GYQyfo5/TuBOePK1zkADdpfTGzfoCfvLfYPw8PQD+/q/h32fqADPiRD0Okj8u9Orxt38BJ/8VTvi9bqXYnPqu/9pP9TTZdq318OU/4cnTdfCp3qgX7j0wU7dMQi3g27m8c5Bo523qqLcQEQzoFgVAfpqDf10ymd0NbhZtrSUj2cq0oVlkOW1dpp3tbmhl7W4Xa8obGJqbwqGDM8hLtWMxD+h4m9gCfn3XvfED+H6hXkA37iw94NzelZRSALN/CW//vOvP29Ng4jw9VbZitZ6mGkrZYjjpzzrf07Cj9Y5zmaWwazl8dQ8MmQU+j+5+uvxNHRhSC2DkiXqQOxjUzy88TK9hMII6WDjSO79OUwV8+Y+ur28E9dao133ZsYAPwNMMix4M//6ULdZdb8myZkKEN+ADBeg0vzkpdsYXpYd9zsYKFxc9/A1Ve6X1ddrMPH31EUwoSpdgkYiCQdi9Ep78Qed0FJ/8ES54Ti94szr0HtPjz9JTXz+7HdwN+nkFE/QahrS2C+/O5ZFfr2q9HmdY9pTO53Tuo/DeL/XxBX+Hcx7SU2NNJt16+OJOvYYhpQCGH6cDhTLri77J0jVIgM72Gk5rnQ4kewcKDPCFyVW1530KP6FDxJ7L7aO+xUeVy4PTbiYz2UZemqP7H4yiiIFCKZUG5BqGsXmf4xMNw/guqjVLIJWNbq58YnGnIAHQ7A1w6aOLeP+WoymUbqrE07Qbnju/a86iYABeuARuXNrRDZScDdOugbFn6Dtss02vAN97x7zsYZFfz5mtu3NAn+ONn8Jxv4JXr9PjEDanDhINZfD4XL2I7/yndZeWazfUbNSzn965FVzlMONGGDW3c/qOcCu925ntULlOr+5uqoBRp8Dc2+GFi0PndsoYAvbUyOcUMVPl8vC399bx0tKyPSk7Bmcl8cAlhzO6IA2zKT4pp8PeBiulzgPWAS8rpVYrpabuVfxEtCuWSCoa3ZTVtYYsa/L4+a6sPrYVEvuncae+WIYS8ML2rzsfM1v13Xj+OL2uYN9tVYunhU9jnjlU39HvnUSwYYduWZhtkD1Sd3UFArDiBcgdDUf8CP5zpR4n+PQv8OJl+uukP+pzvXETvPYTvasf6B39Rp4Y/v979H9B5Rr9VTJdr9lYO1+f88wHQncvnfLXzunQRdy0egPc8/FGXlxS1imv047aVs5/cCG7GkJfg2IhUn/JL4HDDcM4FLgS+LdS6uy2sl4Ja0qpuUqp9UqpTUqpW0OUK6XU3W3l3ymlJvfG6x6oClfkXaS2VXdzlyfio7tBWleIdRORpBbAJS93DRbOXDjtH/D5nV1/prVOtyRm36qnuLpr9eZAs27RKTvaWyDtqjfAm7fAkTfpx5s+1C2Nmi3w1n/pAfHB07q+jiMDJp4H1ev0WMtzF+iZTvY0OPl2+Po+mHuH7tICHagufQ1KZh7YeyCipqbZw/OLdoQsa/L4+WBNmJueGIjU9WQ2DGMXgGEYi5RSxwJvKqWKgR7n/VBKmYH7gBOAMmCxUup1wzDW7PW0k4GRbV9HAPe3/RtTxZmRu5Vikb1RHISs0sjlxSEuuJGYrToZ4fWLYPuXevZT/jjdYnjn/0Htlq4/kz0CjvovPd4B4Pfp78uWhN/nomwxHPurjsfLngIjAN+9CGteg3mPw4b3YMXzOtCkFcIFL8BX93WeeutthiWPQdU6vcFS8RS4abk+lzW593NHiR5pcvvDrukCWLilhkumD8Eah/HQSK/oUkoNb3/QFjRmA2cAvTHxehqwyTCMLYZheIHn2869tzOApwxtIZChlBrUC699QPRAd+hgkJ9mZ2S+5INKSMk5et1CKNnD9deBMlshYzBMugDyxukd9975bz1gva/hx+tWxOGXdyyYUwoOmatbBpF4m/RMK4CgryOXlLcJnr9It5bOvF/Pcrr6Y3CkwLdPhT7X9q90MAn4dN0zSyVIJKDuFveWZCVjSbQxCuDHgEkptWfjXMMwXMBc4OpeeO0iYO92VlnbsQN9DgBKqWuVUkuUUkuqqqp6oXodclLsPHjJ4Uws7jwLpTQ7meeumU5BugxkJ6TkLDj9XhhzeucUGSVH6m6XnvbNly2Gt34OZ/5LL3prpxQcchLM/RMsflzPoqrdAps+1llaB02CkSdFPrctpWM20sgTO4+nBHyw+hV48VL46H/17KiaLaETE+6p65LQq7tFwshItjJ9WPhpyudPHYyK0/7JYbueDMNYAaCUWqWU+jfwV8DR9u8U4N89fO1Q/+N9u7T25zn6oGE8BDwEOntsz6rWVVFmMo9fMZWaZi/lda3kpdrJTbOTlxrfaWuiG2mD4Iz74MQ/6JXRNqe+YPbGuoFhs2HhfXqs4cgb4fjf6u4eW4q+i2+qhskX6/UXa17V01SHzdZTdgNenap84b+6nnfo0VDelv24eJoOGI2h90wm0NbasHWzV3hytl7DIRJWRrKNv54ziYseWdhp8oxS8JezJ1AQxymy+7OO4gjgDuArIBV4BuiNEbAyYPBej4uBfUcX9+c5MZOdYic7xc4h+TKdsE9xpEVnb4WC8Xp6bf33er0E6JQbRlCPCYz+AXxxF6x6ueNnVr8CuaP0OoqJ8/S6i/rtHeVFh8Mpd0HVWj2tddgx8NJV4esw6QKwp+h62FN1Hqd9tXd3RTMDregVJdnJvHTdDNbscrFgYxX5aQ5OGldATqqdFHv8lr3tzyv7gFYgCd2i2GoYkdq4+20xMFIpNRQoBy4ALtrnOa8DNyilnkcHrIb2AXYh4i6tEC57Xa+I/r6ta8hkhgkXwPhz9fTYvYNEu6r1ep1D8VS4bL7ulnJVQEquXm/x2In6oj/3dvj2Wd1aee3HXc+TNUy3PgCceXoK7IuXdu2CmvO76GylKqKiID2JgvQkjhudOONI3W5cpJRaAcwH/gBkAw8CPsMwzu3xiyt1CvAPwAw8ZhjGn5RS1wEYhvGA0h1y96LHRVqAKw3D6HZHov3duEiIXtFQrlsV7jo9A2r9O7qrZ9sCPTsqFIsdLnxBX+x3fgsf3KbXfOw9E8pkgUtfhTWvQ+ksnbpj57d6Pcaki+GoWyC9uOP53mbdOvnybt29lVGikwXmjNSLB4WIINLGRfsTKKbse3FWSl1qGEZPxyiiRgKFiLmmSv21bYHe56F4qk7PEWo2VLsr3wUUvHGjXj8RyoTz4NCLoHI9TDhLZ7pVJh2IrGH6rH2tOmhYkmQTIrHfIgWKbrueQt3BJ3KQECIu6nfAU6frwXO/B4bM1APR4QJFZqm+2NdtCx8kAHZ8A6NPgbGn7l/3kccFLTU6y6w1GZIzu64wF+IASVJAIXqquRreukWvcaje2HasCi56Eb57Qc9w2tfs/2lL+dG28dC++ajaObN1IDGMzt1MoTRVwIf/C9893zG1Nn+8XqCXc8jB///EgCcpT4XoKXcj7FrR+ZivVe8bce5jOg9UO0eG3rOidJZOWrh2vu5eCufQS2HVK/D1Pbql4Nqtk/7tXqXHRgI+/TyPCz74nd7re+9ssBWr4PFT9HOFOEjSohCix8KM8236EFy74MQ/6u4fT6OekbTkMb1ivHAyzL8eznsKyhbp9RZ7G3smWO06BUfmUD0z6vmL9Mwo0Avt5tym9/R2N+iWRCjNVboLK/3s0OVCdEMCRQzUNnvx+oOYFOSm2uO2ulLswzA6r9g+WI40nb9p98quZRWr9YX9izv1jKV2c2/XwePM++Hla3QWV78bNn2kZ0RNulB3ab16rX5+6Sz45sGOIAE6OLz1c0jKgqyhkVdmb/kUxp+tU380lOld8fwevYlT9nBJ6SEikkARRQ0tXlaU1XPHu+tZv9tFQbqDHx09jJPHDyIn1R7v6g1MLTX6QrnsKd1lNP4cKJwEqT1IIebMhVP/Dx6bC8F9tiLNOURfiPcOEoWTdXCxOqBgIsx7Qif787boDYwGHapXbC9/Rj/fbIPDr9DbsIby4e/gohci1zFziB7gfv83sPzpjuOLHtRbrZ73pKQbT0Autw9vIEiKzYK9m1xQ0SSBIko8vgBvfLeTX7/W0Z1QVtfKb+av5qvNNfzprAlkOW1xrOEA1FwNH/8Jlj7WcWzli3qdwaXz99kZ7gDlT4AffQ4f/V7fvdtTYML5MPJ4ne67ndmmg0pyNjTugidO0dNqDzlJr+a2JcMz8zpSoGeP0ClIlj2p96MIpX47WByQN1bvRbEvpWDECborbO8g0W7HQlj0sE6FbpbV24mgrtnL2t2NPPDZZiobPUwuyeCqWcMozkyKS8CQQBEl1c1e/vjW2pBl76zazQ3HjZBAEWu7V3YOEu2qN8LHf4Af/L37nEnhWB065fjZD7ftMaH0Ku1vn9bBwZEBw+fA7P/uGNyu2ahbNwBr39BfpUfBSX/Si+pSB+mAkpQBNZvDvDC6q8rigHlPwhMn64DYTik46S9Quzl0Xql2ix+GqVfp1eYirupbvNz7yUYeXbBtz7F1u128uKSMp344jenDsjHFOIusBIoo2d3QitsXvs/4/dUVjCsMv0e36GVuF3x1d/jyVS/rbUsPNlC02zev1JE36wVzRlBvImTfKyX9rhC7CW/7Qn8B/OAuvXAvYzDMvDn8Ku+JF+pgkjYIrv1MpxPZ/Kn+ucFH6Cm6JrNuuYTjbujabSbioqLR0ylItPMHDX76wnJev2EWBemxTRAo02PjJE5p5QeugEfP/glb7u2YatqbzGbd959W2DlIgE7fEUlKPmz9XH9fNBkm7ZsKDZ1g8Jj/p1sgoNdaTJind7VrroJnztF5pWo2waCJ4V8re7jeb1vE3esrwk9lrnR5qGsJsS4nyqRFESWD0pNItplp8QZClp84TgYOY8qeprf9DDUzCfSF3BLjfUUGTQyf8TW9WK/FsLUFF2euTpU+7RpY+oT+mYnn670t0kIMxHubdWba9jUVK16Acx7ReahCzY467jZIlcSBiaDJHbll54uwC160SIsiSrKdNn53euiNAM88tJCCNLl7iymLDY64Vvfnh3Lsr7vO+vG4dIqNtW/C+rd14r9wK6gPRkoBXPxyRzBol5wNp9+jU5QPP7bjuDNHtyxO+yec8yiMmhs6SIBO35E3puOxu14P3J9xnz5PO5sT5v61IwutiLuTxoe/ibRbTGSnxP7aIS2KKLFbzcwdV8CQrGT+9t561u12kZ/m4CezhzN7VC6ZTgkUMZc+GC5/E165pmMrUnuqTqcx6uTOayqaa+Dre3XG1vY7cJMFTvi9HnPojd3izBYoPBSu/gi2fa4DUfZIfSF/71e69ZCc0/XnlOp+/YcjDWb/EjZ/3HHsuxf14Pkpd+kWlNmqWyrJuXphn0gII/NSGFuYxpqdjV3KbjhuBNlxmATTbfbYvijRssfWt3hx+wJYTCZZP5EIXLt1nqWAT+9058zTLY69ffcfeCXMjr+Xva43FOotvladp2nDe3rA2pqsd7/LHKJXXx8sd6NuDb39846ptWYrzPo5TLtW55ESCWl3g5t7Pt7IS0vL8PiD5KfZ+enxh3DSuHyyonST2aM0431RogUK0cc0VejFc7VbQpcPPgIufL53tlPdm9+rL+jWpPBdZAfK54GWKt2CCvj0oHVStqQf7wPcvgC1zV58gSAOq5ncFHtUp8X2KM24EANOMBA+SIBe/7D3BkO9xWLr2rLpKatdD4x3l3lWJByH1UxhRownWIQhgUKIfSlzx17YoWSU9t4dvxA95A8EqXJ5qHB5cPsCFGcmkZFkJcXRe6vsJVDEQKXLTZXLQ6XLQ2G6g5wUe1xmLoj9lJIHR/0c3rg5dPkx/61nJgkRZ62+AN9sqeGnLyynvkWvA7KYFFfOLOW6Y4b32nVGAkWUbalq4qonl7C1umNa5fiiNB68ZApFmYnRrBT7UApGnwrlS3XywL2Pz7wFBk+NbX3cDR1ZXx1pevA9taB3Mt+KPq28roWrnlxCINgx1uwPGjz8xVby0xxceWQpZnPPV0HIYHYU7W5wc9a/vmRXQ9f+7HGFaTxx5TRyZRZU4mqt03mTti0AkxVKj9QtiZ7MRDpQrgp475ew+mWdFh301Nbzn9GZZ81yrzdQeXwBfvv6ap5fvCNkeZbTxls3zWJQ+v7dkMpgdpxsrmoKGSQAVu9spMrlkUCRyJIy9VfOyPi8vrcZPvkzrHqp83F3I7xwCfzwXT2WIgYUnz+I2aRo9gZYWd4Q9nm1zd6I+eYOhASKKFq7q+uCmb3tqGthbGFaxOeIAaylGlY80/F4xBy9/sHbomddtdbrTYv2zSEl+h3DMNjV4OazDVV8ur6S/FQHF0wbzKUzhnDry6HT0jisJmy90O0EEiiiqjQ7cibS/LTYZoAUfYzb1ZGo8LBLoGgKvHRVWxpzdEbY6dfrzLLOECu4Rb+xuaqZ8x78mtrmjoSATy3czi0njORHRw/jwc+7Tuc+b8pgslN6Z7q15HqKorGF6aTaQ8fioowkBsU4VbDoY9pTnjsyYOwZ8NYtHUEC9HqPr+6G1a9BMPaJ4kRs1DZ7uOWF5Z2CRLv/+2Ajx4/NJ2mfzYwOK8ng+mNH4OilTY6kRRFFeal2/n31NC55ZBFNno6MkDkpNp64cqq0KERkSZkw9Bg9RrLsqY7B7H19/lcY/YPwCQJFn1bf4os4FvHGip188otjeH3FLprcfmaPymVwVnKvjn9KoIgii9nE+MJ03r/laL4rq2dTZRPjCtMZPSh1v2ciiAEsKRPOuBc2fQRbPwv/vKYK8HtiVy8RUx5/5NZipctDToqda4/uZn+THpBAEWUWs4nCjKSEWYov+piMEr2f9pr5esvWUOxpstd1P5bmsOCwmsLOYDp2VB6WXhq0DkfGKIRIdGmFcPQvwpdPu0avJhf9Uk6KnatmDQ1Zlu20cdTI6E9kkEAhRF+QN06nDtnXsGPhiB9Ji6Ifs1vN/HDmUG48bkSnQetDB2fwn+tmxKS3QlZm90O1zV4CQYP0JCs2S+R7gcpGN+X1rWypbmZwZhIlWU7y0+woSQ+ReNwNeqX4pg/0WooRcyCtSKbGDhAef4Bql5dGtw+H1URGko3MXtzESFZmDxCVLjdfbqrhsQVbafL4mX1ILlfMLKUoIylkH+b2mmYuf2wR22pa9hzLTbXz9FXTOCQ/VYJFonGk66/s4fGuiYgDu8VMUWYSRcR+vFNaFP1ElcvNTc99y9dbajsdT7aZeeXHRzJ6UNo+z/dwwUML2VzVxL6ynTbePIAcMUKIvi9Si0LGKPqJFTsaugQJgBZvgF++upK6fRbrVDS6QwYJgJpmL+t3u6JSTyFE3yOBoh9o9vh58uttYcuXfV9Po9vX6djO+taI59xQIYFCCKFJoOgHAkGj20U5e+erB7qdKTEyL7XH9RJC9A8SKPqBVIeF0ycVhi0fkp1Myj45p/LS7AzLcYZ8fmayldEFEiiEEFpcAoVSKksp9YFSamPbv5lhnrdNKbVSKbVcKTWwRqcPgFKKOaPzKAyTZPB3p48jb5+8UnmpDh67YiqDszq3LHJSbDx7zXQKJGGhEKJNXGY9KaX+CtQahnG7UupWINMwjC6riZRS24AphmFUH8j5B+KsJ4Cyuhb+/sEG3lixE1/AYMygVH5z6lgmFqWH3Wi9stHNjroWNlU2UZKVzNAcJ/lpDpkaK8QAE2nWU7wCxXpgtmEYu5RSg4BPDcMYFeJ525BAcUBavH7qW3wEggbJNnOvba4uhOjfEnHBXb5hGLsA2oJFuEQ1BvC+UsoAHjQM46FwJ1RKXQtcC1BS0je3h6x2efAH9TaHuakH1/WTbLOQbJN1lEKI3hO1K4pS6kOgIETRrw7gNDMNw9jZFkg+UEqtMwzj81BPbAsiD4FuURxwheOottnDV5tq+L8PN7K1uokh2U5uPG4Esw/JI6uXdqgSQvSuQNCgusmDYRjYLCaynP239R61QGEYxvHhypRSFUqpQXt1PVWGOcfOtn8rlVKvAtOAkIGir3K5fTzyxVb+9enmPce2VjfzsxdXcMWRpfzshENIS5KEb0IkkiqXm1eXlfPIgq1UN3kYX5TOrXNHM74ovV/+vcZreuzrwOVt318OzN/3CUopp1Iqtf174ERgVcxqGCN1LT4e+GxzyLInvtoWcvtDIUT8VDd5+OkLy/nzO+uodHkIGvBdWQMXPfINH66twBfof9vSxitQ3A6coJTaCJzQ9hilVKFS6u225+QDC5RSK4BFwFuGYbwbl9pG0aryBoIROsoWb+ualkMIET/bqpv5clNNyLLfvbGa6qb+t9tgXEY9DcOoAeaEOL4TOKXt+y3ApBhXLea6m4VqkmmqQiSU15aXhy1rbPVT2+ztdwk1ZWV2nE0oSsdsCh0MlIIppSHXIgoh4sTczc2bov/d3EmgiLPMZBu3HD8yZNmPjxlOVi9uTCKE6LmzJheHLcty2shy9r/BbJlwH2dOu4WLpw9hfFE6f/9gA1urminJTuanx49kypAsUsOsqBZCxMfgzCROGJvPB2squpT9+azx5IZZ5Or1B6lq8tDi8WO3mslMtvaZv2/ZuCiB1DV78PgNrGYlK6qFSGBVLg8fr6vkoc83U9XkYWJROr84aRQjclNCpsupdnl4/KutPP7lNlq8AUwK5ozO53enj6MoMzHGMxIuhUe09dVAIYToW6pcHgLBIEk2C+lh1k80e3z89d31PPn19i5lw3KcPHftdPLT4p+EMxFTeAghRJ+Xm9p9y7+22cfT33wfsmxLdTMbdrsSIlBEIoPZCcrtC1Be38rqnQ1srHBR5ep/c7OFGAh0qyN8z80n60Mmpkgo0qJIQDVNHp74ahsPfb5lz851w3Od3H3hYYwuSAs7nVYIkXgcVnPE8pw+MB4pLYoE4wsEeH7xDu75eFOn7U03VzVz/oML2dUQea9rIURiyU6xURCha+nkCaFypyYWCRQJpsrl5YFPQ+d+avL4eX35ToKRcn4IIRJKXqqd+y+ZjMPa9XL7m1PH9okWhXQ9JZhmjx+Xxx+2/KvNNVx+ZClOu/zqhOgLlFKML0rn/VuO4dVlZSzaVktxZhKXzxhKcWZSn1hLIVebBGO3mlAKws1azku1YzVLQ1CIvsRqNlGSlcwNx43E7Qtgs5j61N9x36npAJGRZOOoETlhy6+cWYrNIr82Ifois0nhtFv6VJAACRQJJy3Jyh/PmkBRRtfVmj89fiSDs5LjUCsx0Hn9AXbWt7J+dyPbqpupk31SBhTpekpAJVnJvPzjGSz7vp73V+8mJ8XOvCmDKUizk54sSQJFbNU2e3hxcRn3frKJprbxs8klGdw5bxLDclPiXLvo8PoDVDd52VLdTLPHz6j8VDKdVtKTBubfn6TwSHCGYaBkTwoRJ15/gEcXbOWOd9d3Kct22njjxlkUhmj99mUtXj9fbKzmlheW0+IN7Dl+9uQifnnyGHL2YzV2XyQpPPqwvh4kapo8fF/bwktLy/AFgpx1WDEj8pzkpiZ2ygKhVTd5ue+T0NO1a5q9fLKukounD4lxraKrvK6VHz+9tMvOk68sK6coI4kbjxuBzRJ5EV1/I4FCRE21y8OvXlvJe6s70jG/uKSMKUMy+dfFk8lL8Pw2Alxu357uplA+WlfJOYcXd7v6uK/w+AM88sXWsNsTP/HlNi6cVtLvWlHdkcFsETWfb6zqFCTaLdlex3OLvsffDzeh72/s3dw556TYsJj7dqt3b63eAOt2N4Ytd3n8eHyBsOX9lQQKERXVTR4e/GxL2PInvtpGtcycSXjpSRYOG5wRtvzS6UOwmPrPZcRhNVGa7YxYPtC6nUAChYiSQMCguil8xtu6Fh/9cSJFf5PptHPneZNCbsl73THDEn66drPHT1ltC2t2NrK1uom6lsg3Jw6rhWuPHha2/IKpJeSkDLyZTzJGIaIi2W7m0MEZfLQudArlcYVpmJWivK6VbTXNBA2DoTlOspJtJEt6koQyLMfJmzfO4uN1lXy8rpIcp41LZwxhcFYyGQk8XbvK5eau9zfw0tIy/G2DDlNLM7nrvEMpiRDgSrKT+dNZ47lt/upO6cFnjsjmJ7OHY+8n4zEHQqbHiqhZvbOBU+9ZEDIdyVM/nEZds4f/9/JKPP4gYwelUZTp4PgxeZw4bhCZCXwBGsjcvgAWs0r47iaX28dvXlvFa8t3dikrzkzipeuOpCA9/GSKZo+f2mYvi7bW0uTxM7U0i/w0e7/eolimx4q4GJrt5NHLp/LfL31HVVs3VHqSldtOHUtOio3LHlvElCGZ3DRnJOt2u9hW3Ux9i5+qRg+pdguWPpbmYCDoK7Obapu9zF/RNUgAlNW1snZ3Y8RA4bRbcNotCd+1FisSKETUJNstzD4klzdunEV9qxfDgIxkK0lWM1c9uYRDB2fwo2OGc93TSzstbPrnRxt5+qojmFic3uvBoqHVR4vHj1KKjGQLDqv8CfRHlS5P2MSaAF9tquHYUXmxq1AfJ38lIqpMJkVBuqPT3dvuRjebKpu4c95EfvGfFZ2CBECLN8CVTyzm3Z8exaD03pmv7vYF2FzVxB/fXMPXW2qxW0ycdVgRNxw3guLM+Nw1Nrb6qG32smZnAzarmdEFqWQ5bSTb5M+yp1K6GecaFKE1IbqST6SIOatZMb4wjUa3n4ZWX8jnNLT62FzZ1GuBYt3uRs69/+s9g5oef5DnF+/g0/VVvPKTI2O+gKq6ycM/PtzAM998v+fO12Y28bvTx3LqxELSkhJ/j4JElu20UZyZRFld1x0hTQrmjJHWxIGQTmARc9lOO9ceM5wqV/jpswC7Gty98nq1zV5um796T5DY2+5GN2+siO2ugcGgwZvf7eTphd936h7xBoL88tVVbK5qilld+qu8NAcPXno4qfu0LJSCu86bRG4/HpSOBmlRiLiYUJhGdwt6RxWk9sprNXn8fFfWELZ8/vKdzJtSTJYzNhePSpcnbP4kgHs+3sQ/Lzi0T+x8lshGF6Tx7k+P5uN1lSzcUsPQnGTOnlxMfppDpmAfIHm3RFxkpdgZlpvCqPxU1le4upSPyk/ttW6n7hJMWMwqpskXfYFgxNbUxkoXbl+QRMubWNvspaHFizcQJM1hJSfVhtXceRZU+7TSmmYvSVYzmcnWuOX0MpsURZlJXDpjCBcfUYLJ1H9SjcSaBAoRN4UZSTx2xVRufG4Zy76v33N8ckkG91w4mdxeSuec5rAwc3g2X26uCVl+4bSSmK7bsJoVWU4btWFSmAzNduJIoF0MDcNgU2UTP3txBSvLdcss1W7hxjkjOffwoj0tsWqXHnd5bvGOPQvVBmclcf/FhzO6IDWu050lSPSMLLgTcVfb7KW+xUtts5csp42MZFvIlBE9saHCxZn3fdllhtUh+Sn8+6ojyI/hXW8gEOThL7Zy+7vrQpa/+KPpTBuaHbP6dKe8roVT7l4QcuLBb04dw2UzSgkEDe56fz0Pf7G1y3OcNjPv3XJ03GaXif0TacFd4ty2iAEry2ljWG4KU0qzGJab0utBAnQainduPoqLp5WQn2anNDuZX50yJuZBAsBsNnHulGJOn1TY6bhJwf+cPJpR+b0zNtMbDMPgnVW7w85O++dHG6lyeahp8vDU19tDPqfZG+DdVbujWU0RZdL1JAYEi9nEkGwnvzltLDcdPxKTUmQ7bXHrkshJsfO/Z4zjpjkjWbxNr+uYWppFZrKVlAQaxG7xBvh0fVXY8sZWP80eP0HDwOMPnzb+6y01XDp9yIDMk9QfSKAQA4rDak6YNBSZyTYyk22MyEvcfadtZhPZ3WRLte3HeMrgjCRJydKHyW9OCBGW1WLiyiNLw5ZPH5ZFRpKNjCQrM4eHH1e58IghmGVAuc+KS6BQSs1TSq1WSgWVUiEHT9qeN1cptV4ptUkpdWss6yjEgQgGDSoa3GzY7WJDhYuKRne/2W9jSI6Tq2aVdjmem2Ln9rMnkp5sJT3Zxu3nTKQ4s+uU5t+fMY7CjASb6ysOSFxmPSmlxgBB4EHgF4ZhdJmipJQyAxuAE4AyYDFwoWEYa7o7v8x6ErHU5PGzcEsNv3p1JRWNen1EYbqDO86ZyOGlmf0id1Nds5cddS08/fV2alu8nDC2gKNH5jBon9QnuxvcrNnVyKfrK8lLtfODiYPITbEn1LiLCC3h0owbhrEW6G6R0zRgk2EYW9qe+zxwBtBtoBAiljbsdnH1k51vTHY2uLn88UW8ceMsxhWmx6lmvSfTaSPTaePP56QRCBph99JuTwB53GjJpdSfJPIYRRGwY6/HZW3HhEgYdS1e/vLO2pBlQQPufG89rjBTS3uTLxBgV0MrZXUtVLp6J0dWKBaTKWyQEP1X1FoUSqkPgYIQRb8yDGP+/pwixLGw/WRKqWuBawFKSkr2q45C9JTHF+y0qnxfS7fX0ewNkBrFbLCVjW6e+Gob//56Oy6Pn5KsZP7rpFHMGpkjOwWKXhG1QGEYxvE9PEUZMHivx8VA6C2r9Os9BDwEeoyih68tBiivP0htswd/W/dKd2lElILMZCvVTaHTcWQk24jmrqFVLg8/eWYZS7bX7Tn2fW0LNz73Lb/5wRgumTFEWgCixxK562kxMFIpNVQpZQMuAF6Pc51EP1bR6Oau99cz567PmHXHJ1zw0EI+WltBQ2voIACQ47Rx+YzSsOVXzRoa1ZTW22qaOwWJvd35/gZqwgQwIQ5EvKbHnqWUKgNmAG8ppd5rO16olHobwDAMP3AD8B6wFnjRMIzV8aiv6P8qG91c/eRiHvx8C81t+aA2VzVx1ZNLeG91Bb5A6FXHZrOJ86YO5tDBXQesZwzL4pQJBVHNTPvOyl1hy1p9gW73/BBif8Rr1tOrwKshju8ETtnr8dvA2zGsmhig1u12sbK8MWTZH99aw1Ejuk4FbZef5uChS6ewdpeL5xd/j9mkuHBaCYfkp5Ab5Vzh3a0yt3S36YcQ+6HvT/AWoocMw+C15eVhyxtb/dS1+MIGCtA7quWlOZg5IhuUnh0UC6dNKuRfn4beBCnbaSM7CgkWxcCTyGMUQsSEUqrbAd/9TVNkMZtiFiQACtIcXDhtcJfjJgV/PXciObLlp+gFEiiEAM6f0vVi225QuoOMBJ1mmum08YsTR/HI5VOYXJJJcWYSP5gwiLduOooZw7MlEZ/oFdL1JAR6J7ZTJw7ize86Dw6bTYo7500ir5d224uG7BQ7x4/JZ8qQTLyBIE6bBafsCS16kXyahEBfbH93+jhOHl/Ag59vobbZy2Elmdx47AgGZyfHdE/tg5WorR7R90mgEKJNToqdH0wsZMbwHPyBICl2C8lyZy6EBAoh9hWNrViF6MtkpEsIIUREEiiEEEJEJIFCCCFERBIohBBCRBSXrVCjTSlVBWyPdz1CyAGq412JBCDvgybvQwd5L7R4vg9DDMPIDVXQLwNFolJKLQm3J+1AIu+DJu9DB3kvtER9H6TrSQghREQSKIQQQkQkgSK2Hop3BRKEvA+avA8d5L3QEvJ9kDEKIYQQEUmLQgghREQSKIQQQkQkgSKKlFLzlFKrlVJBpVTYKW9KqblKqfVKqU1KqVtjWcdYUEplKaU+UEptbPs3M8zztimlViqlliullsS6ntHS3e9XaXe3lX+nlJocj3pG2368D7OVUg1tv//lSqnb4lHPaFNKPaaUqlRKrQpTnnCfBwkU0bUKOBv4PNwTlFJm4D7gZGAscKFSamxsqhcztwIfGYYxEvio7XE4xxqGcWgiziU/GPv5+z0ZGNn2dS1wf0wrGQMH8Dn/ou33f6hhGL+PaSVj5wlgboTyhPs8SKCIIsMw1hqGsb6bp00DNhmGscUwDC/wPHBG9GsXU2cAT7Z9/yRwZvyqEnP78/s9A3jK0BYCGUqpQbGuaJQNhM/5fjEM43OgNsJTEu7zIIEi/oqAHXs9Lms71p/kG4axC6Dt37wwzzOA95VSS5VS18asdtG1P7/fgfAZ2N//4wyl1Aql1DtKqXGxqVrCSbjPg2xc1ENKqQ+BghBFvzIMY/7+nCLEsT43ZznS+3AAp5lpGMZOpVQe8IFSal3b3Vdftj+/337xGejG/vwfl6HzDTUppU4BXkN3vww0Cfd5kEDRQ4ZhHN/DU5QBg/d6XAzs7OE5Yy7S+6CUqlBKDTIMY1dbE7oyzDl2tv1bqZR6Fd1d0dcDxf78fvvFZ6Ab3f4fDcNo3Ov7t5VS/1JK5RiGMdCSBSbc50G6nuJvMTBSKTVUKWUDLgBej3OdetvrwOVt318OdGlpKaWcSqnU9u+BE9GTAfq6/fn9vg5c1jbbZTrQ0N5V1490+z4opQqUUqrt+2no61NNzGsafwn3eZAWRRQppc4C7gFygbeUUssNwzhJKVUIPGIYximGYfiVUjcA7wFm4DHDMFbHsdrRcDvwolLqKuB7YB7A3u8DkA+82nadsADPGobxbpzq22vC/X6VUte1lT8AvA2cAmwCWoAr41XfaNnP9+Fc4MdKKT/QClxg9MPUEUqp54DZQI5Sqgz4LWCFxP08SAoPIYQQEUnXkxBCiIgkUAghhIhIAoUQQoiIJFAIIYSISAKFEEKIiCRQCBFDSql3lVL1Sqk3410XIfaXBAohYutvwKXxroQQB0IChRBRoJSa2raXgKNt1flqpdR4wzA+Alzxrp8QB0JWZgsRBYZhLFZKvQ78EUgCnjYMoz+kJBEDkAQKIaLn9+gcR27gpjjXRYiDJl1PQkRPFpACpAKOONdFiIMmgUKI6HkI+A3wDHBHnOsixEGTrichokApdRngNwzj2bb9or9SSh0H/C8wGkhpyxx6lWEY78WzrkJ0R7LHCiGEiEi6noQQQkQkgUIIIUREEiiEEEJEJIFCCCFERBIohBBCRCSBQgghREQSKIQQQkT0/wG+YO1BOcBFIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "X, y = make_circles(100, factor=.1, noise=.1)\n",
    "\n",
    "clf = SVC(kernel='linear').fit(X, y)\n",
    "\n",
    "sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, s=75);\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2');\n",
    "#plot_svc_decision_function(clf, plot_support=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that no linear discrimination will *ever* be able to separate this data.\n",
    "\n",
    "One simple projection we could use would be to compute a *radial basis function* centered on the middle clump:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phi = np.exp(-(X ** 2).sum(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding phi as a 3rd dimension gives us this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* global mpl */\n",
       "window.mpl = {};\n",
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       "        return MozWebSocket;\n",
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       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
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       "\n",
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       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
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       "\n",
       "    this.context = undefined;\n",
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       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
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       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
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       "            'position: relative;' +\n",
       "            'resize: both;'\n",
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       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
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       "        'keydown',\n",
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       "\n",
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       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
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       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
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       "        'canvas'\n",
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       "                    height = entry.contentBoxSize.blockSize;\n",
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       "                // Chrome <84 implements even older version of spec.\n",
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       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
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       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
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      ]
     },
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     "output_type": "display_data"
    },
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AwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMHAwMTYUAAX4orIMAAAAASUVORK5CYII=\" width=\"431.5\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "from mpl_toolkits import mplot3d\n",
    "from matplotlib.colors import ListedColormap\n",
    "cmap = ListedColormap(sns.color_palette()[:2])\n",
    "\n",
    "ax = plt.subplot(projection='3d')\n",
    "ax.scatter3D(X[:, 0], X[:, 1], phi, c=y, s=7, cmap=cmap)\n",
    "ax.set_xlabel('x1')\n",
    "ax.set_ylabel('x2')\n",
    "ax.set_zlabel('$\\phi(\\mathbf{x})_3$');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that with this additional dimension, the data becomes trivially linearly separable, by drawing a separating plane at, say, *phi*=0.7.\n",
    "\n",
    "Here we had to choose and carefully tune our projection: if we had not centered our radial basis function in the right location, we would not have seen such clean, linearly separable results.\n",
    "In general, the need to make such a choice is a problem: we would like to somehow automatically find the best basis functions to use.\n",
    "\n",
    "One strategy to this end is to compute a basis function centered at *every* point in the dataset, and let the SVM algorithm sift through the results.\n",
    "This type of basis function transformation is known as a *kernel transformation*, as it is based on a similarity relationship (or kernel) between each pair of points.\n",
    "\n",
    "A potential problem with this strategy—projecting $N$ points into $N$ dimensions—is that it might become very computationally intensive as $N$ grows large.\n",
    "However, because of a neat little procedure known as the [*kernel trick*](https://en.wikipedia.org/wiki/Kernel_trick), a fit on kernel-transformed data can be done implicitly—that is, without ever building the full $N$-dimensional representation of the kernel projection!\n",
    "This kernel trick is built into the SVM, and is one of the reasons the method is so powerful.\n",
    "\n",
    "In Scikit-Learn, we can apply kernelized SVM simply by changing our linear kernel to an RBF (radial basis function) kernel, using the ``kernel`` model hyperparameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC(kernel='poly', C=1)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, s=75)\n",
    "plot_svc_decision_function(clf)\n",
    "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
    "            s=300, lw=1, facecolors='none', edgecolor='k');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this kernelized support vector machine, we learn a suitable nonlinear decision boundary.\n",
    "This kernel transformation strategy is used often in machine learning to turn fast linear methods into fast nonlinear methods, especially for models in which the kernel trick can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning the SVM: Softening Margins\n",
    "\n",
    "Our discussion thus far has centered around very clean datasets, in which a perfect decision boundary exists.\n",
    "But what if your data has some amount of overlap?\n",
    "For example, you may have data like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=100, centers=2,\n",
    "                  random_state=0, cluster_std=1.2)\n",
    "sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, s=75);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To handle this case, the SVM implementation has a bit of a fudge-factor which \"softens\" the margin: that is, it allows some of the points to creep into the margin if that allows a better fit.\n",
    "The hardness of the margin is controlled by a tuning parameter, most often known as $C$.\n",
    "For very large $C$, the margin is hard, and points cannot lie in it.\n",
    "For smaller $C$, the margin is softer, and can grow to encompass some points.\n",
    "\n",
    "The plot shown below gives a visual picture of how a changing $C$ parameter affects the final fit, via the softening of the margin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=100, centers=2,\n",
    "                  random_state=0, cluster_std=0.8)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
    "\n",
    "for axi, C in zip(ax, [10.0, 0.1]):\n",
    "    model = SVC(kernel='linear', C=C).fit(X, y)\n",
    "    sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, s=75, ax=axi)\n",
    "    plot_svc_decision_function(model, axi)\n",
    "    axi.scatter(model.support_vectors_[:, 0],\n",
    "                model.support_vectors_[:, 1],\n",
    "                s=300, lw=1, facecolors='none', edgecolor='k');\n",
    "    axi.set_title('C = {0:.1f}'.format(C), size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimal value of the $C$ parameter will depend on your dataset, and should be tuned using cross-validation or a similar model selection procedure.\n",
    "\n",
    "\n",
    "In fact in general we would want to do model selection on all the hyperparameters of the SVM... $C$, what kernel to use, and whatever hyperparameters are unique to each kernel like polynomial order in $\\phi$.\n",
    "\n",
    "In fact sklearn gives us a powerful tool to do exactly this. Which I will demonstrate below.\n",
    "\n",
    "If you're actually following us all the way to this point you are getting into the territory of where I want you to be during this point in the quarter in COGS118A.  So if you are NOT following don't worry too much about it... this is advanced stuff "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: this will take around 2-3 minutes to run\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.compose import make_column_selector as selector\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "\n",
    "# what we want to predict\n",
    "y = df['species']\n",
    "# the data we will predict from... note that we will include SEX\n",
    "# because penguin sex is actually pretty important in their size measurements\n",
    "X = df.drop(columns=['island','species'])\n",
    "\n",
    "# sex is a categorical variable... it needs to be one hot encoded!\n",
    "# we know from before that we need to z-score the numeric variables\n",
    "\n",
    "# here we setup a way to transform the categorical variables into one hot, \n",
    "# and to z-score the numeric variables.... completely automatically!!\n",
    "# remember how we had to do this with a bunch of different\n",
    "# steps manually the last time around??\n",
    "# if you'd like to understand how to do this more generally\n",
    "# search the sklearn docs for ColumnTransformer!\n",
    "numerical_columns_selector = selector(dtype_exclude=object)\n",
    "categorical_columns_selector = selector(dtype_include=object)\n",
    "\n",
    "numerical_columns = numerical_columns_selector(X)\n",
    "categorical_columns = categorical_columns_selector(X)\n",
    "\n",
    "onehot = OneHotEncoder()\n",
    "scaler = StandardScaler()\n",
    "\n",
    "preprocessor = ColumnTransformer([\n",
    "    ('onehot', onehot, categorical_columns),\n",
    "    ('zscore', scaler, numerical_columns)])\n",
    "\n",
    "\n",
    "# Create a pipeline \n",
    "# a pipeline puts together two (or 3 or however many) steps into one sklearn\n",
    "# estimator. That means we can use pipe instead of svc in any xval or grid search\n",
    "# and get the preprocessing done for us in the same step\n",
    "pipe = Pipeline([('make_features', preprocessor),\n",
    "                 ('classifier', SVC())])\n",
    "\n",
    "# SVM have very different hyperparameters depending on which kernel\n",
    "# potential hyperparams are gamma, coef0, degree and kernel.\n",
    "# here <x,x'> and ||x-x'|| are similarities between two vectors x,x'\n",
    "# linear kernel:     <x,x'>  [no hyperparams]\n",
    "# poly kernel:       ( gamma * <x,x'> + coef0 )^degree \n",
    "# rbf kernel:        exp( - gamma * ||x-x'||^2 )\n",
    "# sigmoid kernel:    tanh( gamma * <x,x'> + coef0 )\n",
    "\n",
    "# so how can we search using only using hypers appropriate for each kernel?\n",
    "# like this...make a list of different search spaces you want to execute\n",
    "search_space = [{'classifier__kernel': ['linear'],\n",
    "                 'classifier__C': np.logspace(-3, 2, 11) #11 steps between 10^-3 to 10^2\n",
    "                },\n",
    "                {'classifier__kernel': ['poly'],\n",
    "                 'classifier__gamma': np.logspace(-3, 2, 11),\n",
    "                 'classifier__degree': range(2,14),\n",
    "                 # NOPE... this isnt often important 'classifier__coef0': np.logspace(-3, 2, 11),\n",
    "                 'classifier__C': np.logspace(-3, 2, 11)\n",
    "                },\n",
    "                {'classifier__kernel': ['rbf'],\n",
    "                 'classifier__gamma': np.logspace(-3, 2, 11),\n",
    "                 'classifier__C': np.logspace(-3, 2, 11)\n",
    "                },\n",
    "                ]\n",
    "\n",
    "\n",
    "\n",
    "# Create a grid search object to find the best model\n",
    "best_model = GridSearchCV(pipe, search_space, cv=5, verbose=1)\n",
    "# play with different verbose=??\n",
    "\n",
    "# now lets split off some of our data for testing set... the rest will be used from cross validation\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.25, random_state=101)\n",
    "\n",
    "\n",
    "# Fit grid search\n",
    "%time best_model.fit(X_train, y_train)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets find out how well we do on the test set...\n",
    "print(best_model.best_params_)\n",
    "yhat = best_model.predict(X_test)\n",
    "print(classification_report(y_test, yhat))\n",
    "ConfusionMatrixDisplay.from_predictions(y_test, yhat);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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