05-cache/random-caching.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cc3b8bf-3d6f-4152-b4a8-c0cd7fdb47c3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ! wget -P data/full_dataset/ https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac39d9ad-5731-4931-b35d-91e05aa18b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "! rm -rf cache_demo_1 cache_demo_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b97e1c62-61ed-4d11-8fc0-4035dd1a62a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8345bca2-cab2-4390-8e3b-a804a30dfb5d",
   "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>text</th>\n",
       "      <th>surprise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28248</th>\n",
       "      <td>Dammmn girl them make up skills! Your eyes are...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51080</th>\n",
       "      <td>00:09 when the glue gun shows up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63928</th>\n",
       "      <td>I had to mute cause the sound was making me cry.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  surprise\n",
       "28248  Dammmn girl them make up skills! Your eyes are...         0\n",
       "51080                   00:09 when the glue gun shows up         0\n",
       "63928   I had to mute cause the sound was making me cry.         0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/full_dataset/goemotions_1.csv\")\n",
    "df[['text', 'surprise']].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "371d93c8-02cc-49e1-a092-b227a978aded",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['text']\n",
    "y = df['surprise']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93291c09-6734-4740-b7b0-481d19c6e168",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score, make_scorer\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d9d0171-78f9-4e00-a058-08bc4fafb29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(memory=&#x27;cache_demo_1&#x27;,\n",
       "         steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;truncatedsvd&#x27;, TruncatedSVD(n_components=10)),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression(C=0.1))])</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 \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(memory=&#x27;cache_demo_1&#x27;,\n",
       "         steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;truncatedsvd&#x27;, TruncatedSVD(n_components=10)),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression(C=0.1))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content \"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;TruncatedSVD<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.decomposition.TruncatedSVD.html\">?<span>Documentation for TruncatedSVD</span></a></label><div class=\"sk-toggleable__content \"><pre>TruncatedSVD(n_components=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(C=0.1)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(memory='cache_demo_1',\n",
       "         steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
       "                ('truncatedsvd', TruncatedSVD(n_components=10)),\n",
       "                ('logisticregression', LogisticRegression(C=0.1))])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1 = make_pipeline(\n",
    "    TfidfVectorizer(),\n",
    "    TruncatedSVD(n_components=10),\n",
    "    LogisticRegression(C=0.1),\n",
    "    memory=\"cache_demo_1\"\n",
    ")\n",
    "\n",
    "pipe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6fc40fe1-9f5f-4bf6-93e3-7e02023b26ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid1 = GridSearchCV(\n",
    "    pipe1, \n",
    "    param_grid={\n",
    "        \"logisticregression__C\": np.logspace(0.01, 2, 5), \n",
    "        \"truncatedsvd__n_components\": [10, 20, 50, 100]\n",
    "    },\n",
    "    scoring={\"f1_score\": make_scorer(f1_score)},\n",
    "    cv=5,\n",
    "    refit=False,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea27b0ff-56cc-4dfb-a922-23bfce6e87ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.52 s, sys: 709 ms, total: 2.23 s\n",
      "Wall time: 21.1 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(memory=&#x27;cache_demo_1&#x27;,\n",
       "                                steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                                       (&#x27;truncatedsvd&#x27;,\n",
       "                                        TruncatedSVD(n_components=10)),\n",
       "                                       (&#x27;logisticregression&#x27;,\n",
       "                                        LogisticRegression(C=0.1))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;logisticregression__C&#x27;: array([  1.02329299,   3.21736253,  10.11579454,  31.80533688,\n",
       "       100.        ]),\n",
       "                         &#x27;truncatedsvd__n_components&#x27;: [10, 20, 50, 100]},\n",
       "             refit=False,\n",
       "             scoring={&#x27;f1_score&#x27;: make_scorer(f1_score, response_method=&#x27;predict&#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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(memory=&#x27;cache_demo_1&#x27;,\n",
       "                                steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                                       (&#x27;truncatedsvd&#x27;,\n",
       "                                        TruncatedSVD(n_components=10)),\n",
       "                                       (&#x27;logisticregression&#x27;,\n",
       "                                        LogisticRegression(C=0.1))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;logisticregression__C&#x27;: array([  1.02329299,   3.21736253,  10.11579454,  31.80533688,\n",
       "       100.        ]),\n",
       "                         &#x27;truncatedsvd__n_components&#x27;: [10, 20, 50, 100]},\n",
       "             refit=False,\n",
       "             scoring={&#x27;f1_score&#x27;: make_scorer(f1_score, response_method=&#x27;predict&#x27;)})</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(memory=&#x27;cache_demo_1&#x27;,\n",
       "         steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;truncatedsvd&#x27;, TruncatedSVD(n_components=10)),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression(C=0.1))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TruncatedSVD<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.decomposition.TruncatedSVD.html\">?<span>Documentation for TruncatedSVD</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TruncatedSVD(n_components=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(memory='cache_demo_1',\n",
       "                                steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
       "                                       ('truncatedsvd',\n",
       "                                        TruncatedSVD(n_components=10)),\n",
       "                                       ('logisticregression',\n",
       "                                        LogisticRegression(C=0.1))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'logisticregression__C': array([  1.02329299,   3.21736253,  10.11579454,  31.80533688,\n",
       "       100.        ]),\n",
       "                         'truncatedsvd__n_components': [10, 20, 50, 100]},\n",
       "             refit=False,\n",
       "             scoring={'f1_score': make_scorer(f1_score, response_method='predict')})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "grid1.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5689ba5b-b4af-4165-9484-12d78397d05c",
   "metadata": {},
   "source": [
    "But just for comparison... let's make a new pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4fadf46-1319-4221-a5ba-98e581197ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe2 = make_pipeline(\n",
    "    TfidfVectorizer(),\n",
    "    TruncatedSVD(n_components=10),\n",
    "    LogisticRegression(C=0.1),\n",
    "    memory=\"cache_demo_2\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3ac2006-2a9e-4a82-a065-e2b599131f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "grid2 = RandomizedSearchCV(\n",
    "    pipe2, \n",
    "    n_iter=20,\n",
    "    param_distributions={\n",
    "        \"logisticregression__C\": np.logspace(0.001, 2, 100), \n",
    "        \"truncatedsvd__n_components\": [10, 20, 50, 100]\n",
    "    },\n",
    "    scoring={\"f1_score\": make_scorer(f1_score)},\n",
    "    cv=5,\n",
    "    refit=False,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "badaae70-dd4e-43c8-8378-570834a519c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.28 s, sys: 677 ms, total: 1.96 s\n",
      "Wall time: 15.5 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(memory=&#x27;cache_demo_2&#x27;,\n",
       "                                      steps=[(&#x27;tfidfvectorizer&#x27;,\n",
       "                                              TfidfVectorizer()),\n",
       "                                             (&#x27;truncatedsvd&#x27;,\n",
       "                                              TruncatedSVD(n_components=10)),\n",
       "                                             (&#x27;logisticregression&#x27;,\n",
       "                                              LogisticRegression(C=0.1))]),\n",
       "                   n_iter=20, n_jobs=-1,\n",
       "                   param_distributions={&#x27;logisticregression__C&#x27;: array([  1.00230524,   1.05000633,   1.09997759,   1.15232705,\n",
       "         1.2071679 ,   1.26461869,   1.324...\n",
       "        41.33838782,  43.30573904,  45.36671924,  47.52578434,\n",
       "        49.78760234,  52.1570634 ,  54.6392904 ,  57.23965002,\n",
       "        59.96376437,  62.81752309,  65.80709615,  68.93894715,\n",
       "        72.21984727,  75.65688998,  79.25750633,  83.029481  ,\n",
       "        86.98096917,  91.12051415,  95.45706582, 100.        ]),\n",
       "                                        &#x27;truncatedsvd__n_components&#x27;: [10, 20,\n",
       "                                                                       50,\n",
       "                                                                       100]},\n",
       "                   refit=False,\n",
       "                   scoring={&#x27;f1_score&#x27;: make_scorer(f1_score, response_method=&#x27;predict&#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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(memory=&#x27;cache_demo_2&#x27;,\n",
       "                                      steps=[(&#x27;tfidfvectorizer&#x27;,\n",
       "                                              TfidfVectorizer()),\n",
       "                                             (&#x27;truncatedsvd&#x27;,\n",
       "                                              TruncatedSVD(n_components=10)),\n",
       "                                             (&#x27;logisticregression&#x27;,\n",
       "                                              LogisticRegression(C=0.1))]),\n",
       "                   n_iter=20, n_jobs=-1,\n",
       "                   param_distributions={&#x27;logisticregression__C&#x27;: array([  1.00230524,   1.05000633,   1.09997759,   1.15232705,\n",
       "         1.2071679 ,   1.26461869,   1.324...\n",
       "        41.33838782,  43.30573904,  45.36671924,  47.52578434,\n",
       "        49.78760234,  52.1570634 ,  54.6392904 ,  57.23965002,\n",
       "        59.96376437,  62.81752309,  65.80709615,  68.93894715,\n",
       "        72.21984727,  75.65688998,  79.25750633,  83.029481  ,\n",
       "        86.98096917,  91.12051415,  95.45706582, 100.        ]),\n",
       "                                        &#x27;truncatedsvd__n_components&#x27;: [10, 20,\n",
       "                                                                       50,\n",
       "                                                                       100]},\n",
       "                   refit=False,\n",
       "                   scoring={&#x27;f1_score&#x27;: make_scorer(f1_score, response_method=&#x27;predict&#x27;)})</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(memory=&#x27;cache_demo_2&#x27;,\n",
       "         steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;truncatedsvd&#x27;, TruncatedSVD(n_components=10)),\n",
       "                (&#x27;logisticregression&#x27;, LogisticRegression(C=0.1))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TruncatedSVD<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.decomposition.TruncatedSVD.html\">?<span>Documentation for TruncatedSVD</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TruncatedSVD(n_components=10)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(memory='cache_demo_2',\n",
       "                                      steps=[('tfidfvectorizer',\n",
       "                                              TfidfVectorizer()),\n",
       "                                             ('truncatedsvd',\n",
       "                                              TruncatedSVD(n_components=10)),\n",
       "                                             ('logisticregression',\n",
       "                                              LogisticRegression(C=0.1))]),\n",
       "                   n_iter=20, n_jobs=-1,\n",
       "                   param_distributions={'logisticregression__C': array([  1.00230524,   1.05000633,   1.09997759,   1.15232705,\n",
       "         1.2071679 ,   1.26461869,   1.324...\n",
       "        41.33838782,  43.30573904,  45.36671924,  47.52578434,\n",
       "        49.78760234,  52.1570634 ,  54.6392904 ,  57.23965002,\n",
       "        59.96376437,  62.81752309,  65.80709615,  68.93894715,\n",
       "        72.21984727,  75.65688998,  79.25750633,  83.029481  ,\n",
       "        86.98096917,  91.12051415,  95.45706582, 100.        ]),\n",
       "                                        'truncatedsvd__n_components': [10, 20,\n",
       "                                                                       50,\n",
       "                                                                       100]},\n",
       "                   refit=False,\n",
       "                   scoring={'f1_score': make_scorer(f1_score, response_method='predict')})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "grid2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a14ec831-99bc-46bd-972b-271f978dd197",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "befc8b18-23d0-4d88-a2c6-e13e8c5fd232",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "\n",
    "X, y = load_breast_cancer(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8e3546cb-5648-4b45-84cf-b3a91e5912bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_full = GridSearchCV(\n",
    "    HistGradientBoostingClassifier(), \n",
    "    param_grid={\n",
    "        \"max_iter\": np.linspace(10, 200, 10).astype(int), \n",
    "        \"l2_regularization\": np.linspace(0, 4, 10)\n",
    "    },\n",
    "    cv=5,\n",
    "    refit=False,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2951f3cc-12dc-4279-ba84-595a48560a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_rand = RandomizedSearchCV(\n",
    "    HistGradientBoostingClassifier(), \n",
    "    n_iter=100,\n",
    "    param_distributions={\n",
    "        \"max_iter\": np.linspace(10, 200, 100).astype(int), \n",
    "        \"l2_regularization\": np.linspace(0, 4, 50)\n",
    "    },\n",
    "    cv=5,\n",
    "    refit=False,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8d33eda0-f166-4e62-9cfc-c079b7be57fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 392 ms, sys: 134 ms, total: 525 ms\n",
      "Wall time: 7.71 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "grid_full.fit(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c4451c03-bba6-49ef-a10b-d1bc755d15e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 296 ms, sys: 98.2 ms, total: 394 ms\n",
      "Wall time: 7.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "grid_rand.fit(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "1e6555f6-a5b0-4d4b-aead-7f9d54551de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "p = grid_full.cv_results_\n",
    "plt.scatter(p['param_l2_regularization'], p['param_max_iter'], c=p['mean_test_score'])\n",
    "plt.colorbar();\n",
    "plt.xlabel(\"l2 regularization\")\n",
    "plt.ylabel(\"max iter\")\n",
    "plt.title(\"GridSearchCV\")\n",
    "\n",
    "plt.subplot(122)\n",
    "p = grid_rand.cv_results_\n",
    "plt.scatter(p['param_l2_regularization'], p['param_max_iter'], c=p['mean_test_score'])\n",
    "plt.colorbar();\n",
    "plt.xlabel(\"l2 regularization\")\n",
    "plt.ylabel(\"max iter\")\n",
    "plt.title(\"RandomizedCV\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}