lectures/08_hyperparameter-optimization.ipynb

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    "![](img/330-banner.png)"
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   "source": [
    "# Lecture 8: Hyperparameter Optimization and Optimization Bias\n",
    "\n",
    "UBC 2022-23\n",
    "\n",
    "Instructor: Varada Kolhatkar"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Imports, Announcements, and LO"
   ]
  },
  {
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     "slide_type": "skip"
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   "source": [
    "### Imports"
   ]
  },
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   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"code/.\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mglearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from plotting_functions import *\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import cross_val_score, cross_validate, train_test_split\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from utils import *\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "<br><br>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Announcements\n",
    "\n",
    "- HW3 was due last night. \n",
    "- HW4 will be released by tonight. (It will be due next Tuesday.)\n",
    "- HW2 grades are released. \n",
    "- Apologies that no one showed up to the Zoom OH yesterday. I'll make sure this doesn't happen again.  "
   ]
  },
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   "source": [
    "### Learning outcomes \n",
    "\n",
    "From this lecture, you will be able to \n",
    "\n",
    "- explain the need for hyperparameter optimization  \n",
    "- carry out hyperparameter optimization using `sklearn`'s `GridSearchCV` and `RandomizedSearchCV` \n",
    "- explain different hyperparameters of `GridSearchCV`\n",
    "- explain the importance of selecting a good range for the values. \n",
    "- explain optimization bias\n",
    "- identify and reason when to trust and not trust reported accuracies "
   ]
  },
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   "source": [
    "<br><br><br><br>"
   ]
  },
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   "source": [
    "## Hyperparameter optimization motivation "
   ]
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   "source": [
    "### Motivation\n",
    "\n",
    "- Remember that the fundamental goal of supervised machine learning is to generalize beyond what we see in the training examples. \n",
    "- We have been using data splitting and cross-validation to provide a framework to approximate generalization error.  \n",
    "- With this framework, we can improve the model's generalization performance by tuning model hyperparameters using cross-validation on the training set. "
   ]
  },
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   "source": [
    "### Hyperparameters: the problem\n",
    "\n",
    "- In order to improve the generalization performance, finding the best values for the important hyperparameters of a model is necessary for almost all models and datasets. \n",
    "- Picking good hyperparameters is important because if we don't do it, we might end up with an underfit or overfit model. "
   ]
  },
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   "source": [
    "### Some ways to pick hyperparameters:\n",
    "- Manual or expert knowledge or heuristics based optimization \n",
    "- Data-driven or automated optimization"
   ]
  },
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   "source": [
    "#### Manual hyperparameter optimization\n",
    "\n",
    "- Advantage: we may have some intuition about what might work.\n",
    "  - E.g. if I'm massively overfitting, try decreasing `max_depth` or `C`.\n",
    "- Disadvantages\n",
    "    - it takes a lot of work\n",
    "    - not reproducible\n",
    "    - in very complicated cases, our intuition might be worse than a data-driven approach"
   ]
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   "source": [
    "### Automated hyperparameter optimization \n",
    "\n",
    "- Formulate the hyperparamter optimization as a one big search problem. \n",
    "- Often we have many hyperparameters of different types: Categorical, integer, and continuous.\n",
    "- Often, the search space is quite big and systematic search for optimal values is infeasible. "
   ]
  },
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   "source": [
    "In homework assignments, we have been carrying out hyperparameter search by exhaustively trying different possible combinations of the hyperparameters of interest. "
   ]
  },
  {
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\n",
      "text/plain": [
       "<Figure size 700x210 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_grid_search_overview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's look at an example of tuning `max_depth` of the `DecisionTreeClassifier` on the Spotify dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.833</td>\n",
       "      <td>204600</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.743</td>\n",
       "      <td>326933</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.838</td>\n",
       "      <td>185707</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>-7.148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>75.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.494</td>\n",
       "      <td>199413</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>-15.236</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>86.468</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.678</td>\n",
       "      <td>392893</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>-11.648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>174.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  duration_ms  energy  instrumentalness  key  \\\n",
       "0        0.0102         0.833       204600   0.434          0.021900    2   \n",
       "1        0.1990         0.743       326933   0.359          0.006110    1   \n",
       "2        0.0344         0.838       185707   0.412          0.000234    2   \n",
       "3        0.6040         0.494       199413   0.338          0.510000    5   \n",
       "4        0.1800         0.678       392893   0.561          0.512000    5   \n",
       "\n",
       "   liveness  loudness  mode  speechiness    tempo  time_signature  valence  \n",
       "0    0.1650    -8.795     1       0.4310  150.062             4.0    0.286  \n",
       "1    0.1370   -10.401     1       0.0794  160.083             4.0    0.588  \n",
       "2    0.1590    -7.148     1       0.2890   75.044             4.0    0.173  \n",
       "3    0.0922   -15.236     1       0.0261   86.468             4.0    0.230  \n",
       "4    0.4390   -11.648     0       0.0694  174.004             4.0    0.904  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spotify_df = pd.read_csv(\"data/spotify.csv\", index_col=0)\n",
    "X_spotify = spotify_df.drop(columns=[\"target\", \"song_title\", \"artist\"])\n",
    "y_spotify = spotify_df[\"target\"]\n",
    "X_spotify.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_spotify, y_spotify, test_size=0.2, random_state=123\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "best_score = 0\n",
    "\n",
    "param_grid = {\"max_depth\": np.arange(1, 20, 2)}\n",
    "\n",
    "results_dict = {\"max_depth\": [], \"mean_cv_score\": []}\n",
    "\n",
    "for depth in param_grid[\n",
    "    \"max_depth\"\n",
    "]:  # for each combination of parameters, train an SVC\n",
    "    dt = DecisionTreeClassifier(max_depth=depth)\n",
    "    scores = cross_val_score(dt, X_train, y_train)  # perform cross-validation\n",
    "    mean_score = np.mean(scores)  # compute mean cross-validation accuracy\n",
    "    if (\n",
    "        mean_score > best_score\n",
    "    ):  # if we got a better score, store the score and parameters\n",
    "        best_score = mean_score\n",
    "        best_params = {\"max_depth\": depth}\n",
    "    results_dict[\"max_depth\"].append(depth)\n",
    "    results_dict[\"mean_cv_score\"].append(mean_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7173009249466377"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's try SVM RBF and tuning `C` and `gamma` on the same dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "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>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;svc&#x27;, SVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;svc&#x27;, SVC())])</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\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()), ('svc', SVC())])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_svm = make_pipeline(StandardScaler(), SVC())  # We need scaling for SVM RBF\n",
    "pipe_svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's try cross-validation with default hyperparameters of SVC. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fit_time       0.029846\n",
       "score_time     0.010030\n",
       "test_score     0.738998\n",
       "train_score    0.814011\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_validate(pipe_svm, X_train, y_train, return_train_score=True)\n",
    "pd.DataFrame(scores).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Now let's try exhaustive hyperparameter search using for loops. \n",
    "\n",
    "This is what we have been doing for this:\n",
    "\n",
    "```\n",
    "for gamma in [0.01, 1, 10, 100]: # for some values of gamma\n",
    "    for C in [0.01, 1, 10, 100]: # for some values of C\n",
    "        for fold in folds:\n",
    "            fit in training portion with the given C\n",
    "            score on validation portion\n",
    "        compute average score\n",
    "        \n",
    "pick hyperparameter values which yield with best average score\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "best_score = 0\n",
    "\n",
    "param_grid = {\n",
    "    \"C\": [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "    \"gamma\": [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "}\n",
    "\n",
    "results_dict = {\"C\": [], \"gamma\": [], \"mean_cv_score\": []}\n",
    "\n",
    "for gamma in param_grid[\"gamma\"]:\n",
    "    for C in param_grid[\"C\"]:  # for each combination of parameters, train an SVC\n",
    "        pipe_svm = make_pipeline(StandardScaler(), SVC(gamma=gamma, C=C))\n",
    "        scores = cross_val_score(pipe_svm, X_train, y_train)  # perform cross-validation\n",
    "        mean_score = np.mean(scores)  # compute mean cross-validation accuracy\n",
    "        if (\n",
    "            mean_score > best_score\n",
    "        ):  # if we got a better score, store the score and parameters\n",
    "            best_score = mean_score\n",
    "            best_parameters = {\"C\": C, \"gamma\": gamma}\n",
    "        results_dict[\"C\"].append(C)\n",
    "        results_dict[\"gamma\"].append(gamma)\n",
    "        results_dict[\"mean_cv_score\"].append(mean_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 0.1}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7439609253312309"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>gamma</th>\n",
       "      <th>mean_cv_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.743961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.732792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.729091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.720391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.711715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.704284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.703034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.697473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.678851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.678244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        C  gamma  mean_cv_score\n",
       "15    1.0  0.100       0.743961\n",
       "11  100.0  0.010       0.732792\n",
       "16   10.0  0.100       0.729091\n",
       "10   10.0  0.010       0.720391\n",
       "17  100.0  0.100       0.711715\n",
       "5   100.0  0.001       0.704284\n",
       "14    0.1  0.100       0.703034\n",
       "9     1.0  0.010       0.697473\n",
       "8     0.1  0.010       0.678851\n",
       "4    10.0  0.001       0.678244"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"mean_cv_score\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1485759c0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.array(df.mean_cv_score).reshape(6, 6)\n",
    "\n",
    "mglearn.tools.heatmap(\n",
    "    scores,\n",
    "    xlabel=\"C\",\n",
    "    xticklabels=param_grid[\"C\"],\n",
    "    ylabel=\"gamma\",\n",
    "    yticklabels=param_grid[\"gamma\"],\n",
    "    cmap=\"viridis\",\n",
    ")\n",
    "# plot the mean cross-validation scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- We have 6 possible values for `C` and 6 possible values for `gamma`. \n",
    "- In 5-fold cross-validation, for each combination of parameter values, five accuracies are computed.\n",
    "- So to evaluate the accuracy of the SVM using 6 values of `C` and 6 values of `gamma` using five-fold cross-validation, we need to train 36 * 5 = 180 models! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.prod(list(map(len, param_grid.values())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Once we have optimized hyperparameters, we retrain a model on the full training set with these optimized hyperparameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svc&#x27;, SVC(C=1, gamma=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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svc&#x27;, SVC(C=1, gamma=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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=0.1)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('svc', SVC(C=1, gamma=0.1))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_svm = make_pipeline(StandardScaler(), SVC(**best_parameters))\n",
    "pipe_svm.fit(\n",
    "    X_train, y_train\n",
    ")  # Retrain a model with optimized hyperparameters on the combined training and validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally evaluate the performance of this model on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7376237623762376"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_svm.score(X_test, y_test)  # Final evaluation on the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This process is so common that there are some standard methods in `scikit-learn` where we can carry out all of this in a more compact way. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x210 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_grid_search_overview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In this lecture we are going to talk about two such most commonly used automated optimizations methods from `scikit-learn`. \n",
    "\n",
    "- Exhaustive grid search: [`sklearn.model_selection.GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)\n",
    "- Randomized search: [`sklearn.model_selection.RandomizedSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)\n",
    "\n",
    "The \"CV\" stands for cross-validation; these methods have built-in cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exhaustive grid search: [`sklearn.model_selection.GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- For `GridSearchCV` we need\n",
    "    - an instantiated model or a pipeline\n",
    "    - a parameter grid: A user specifies a set of values for each hyperparameter. \n",
    "    - other optional arguments \n",
    "\n",
    "The method considers product of the sets and evaluates each combination one by one.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "pipe_svm = make_pipeline(StandardScaler(), SVC())\n",
    "\n",
    "param_grid = {\n",
    "    \"svc__gamma\": [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
    "    \"svc__C\": [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    pipe_svm, param_grid, cv=5, n_jobs=-1, return_train_score=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "\n",
    "set_config(display=\"diagram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The `GridSearchCV` object above behaves like a classifier. We can call `fit`, `predict` or `score` on it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;svc&#x27;, SVC())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;svc__C&#x27;: [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
       "                         &#x27;svc__gamma&#x27;: [0.001, 0.01, 0.1, 1.0, 10, 100]},\n",
       "             return_train_score=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;svc&#x27;, SVC())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;svc__C&#x27;: [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
       "                         &#x27;svc__gamma&#x27;: [0.001, 0.01, 0.1, 1.0, 10, 100]},\n",
       "             return_train_score=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;svc&#x27;, SVC())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('svc', SVC())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'svc__C': [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
       "                         'svc__gamma': [0.001, 0.01, 0.1, 1.0, 10, 100]},\n",
       "             return_train_score=True)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train) # all the work is done here\n",
    "grid_search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Fitting the `GridSearchCV` object \n",
    "- Searches for the best hyperparameter values\n",
    "- You can access the best score and the best hyperparameters using `best_score_` and `best_params_` attributes, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7439609253312309"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svc__C': 1.0, 'svc__gamma': 0.1}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- It is often helpful to visualize results of all cross-validation experiments. \n",
    "- You can access this information using `cv_results_` attribute of a fitted `GridSearchCV` object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.066296</td>\n",
       "      <td>0.064128</td>\n",
       "      <td>0.059034</td>\n",
       "      <td>0.057335</td>\n",
       "      <td>0.054872</td>\n",
       "      <td>0.05148</td>\n",
       "      <td>0.058355</td>\n",
       "      <td>0.056237</td>\n",
       "      <td>0.05534</td>\n",
       "      <td>0.058058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062906</td>\n",
       "      <td>0.069162</td>\n",
       "      <td>0.070338</td>\n",
       "      <td>0.071929</td>\n",
       "      <td>0.05157</td>\n",
       "      <td>0.069807</td>\n",
       "      <td>0.110082</td>\n",
       "      <td>0.068692</td>\n",
       "      <td>0.076717</td>\n",
       "      <td>0.061162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.006657</td>\n",
       "      <td>0.006794</td>\n",
       "      <td>0.005885</td>\n",
       "      <td>0.005523</td>\n",
       "      <td>0.006118</td>\n",
       "      <td>0.007412</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>0.004874</td>\n",
       "      <td>0.003858</td>\n",
       "      <td>0.007704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006532</td>\n",
       "      <td>0.005437</td>\n",
       "      <td>0.001641</td>\n",
       "      <td>0.008631</td>\n",
       "      <td>0.005295</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.003563</td>\n",
       "      <td>0.008102</td>\n",
       "      <td>0.015702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.018925</td>\n",
       "      <td>0.022579</td>\n",
       "      <td>0.020238</td>\n",
       "      <td>0.020394</td>\n",
       "      <td>0.022572</td>\n",
       "      <td>0.019949</td>\n",
       "      <td>0.02293</td>\n",
       "      <td>0.023417</td>\n",
       "      <td>0.024093</td>\n",
       "      <td>0.021138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013304</td>\n",
       "      <td>0.020209</td>\n",
       "      <td>0.019604</td>\n",
       "      <td>0.022234</td>\n",
       "      <td>0.01363</td>\n",
       "      <td>0.012289</td>\n",
       "      <td>0.01075</td>\n",
       "      <td>0.022028</td>\n",
       "      <td>0.025075</td>\n",
       "      <td>0.016178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>0.002668</td>\n",
       "      <td>0.003075</td>\n",
       "      <td>0.000956</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.003729</td>\n",
       "      <td>0.004199</td>\n",
       "      <td>0.003785</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>0.005603</td>\n",
       "      <td>0.002371</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.001518</td>\n",
       "      <td>0.002944</td>\n",
       "      <td>0.002532</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.008888</td>\n",
       "      <td>0.006929</td>\n",
       "      <td>0.00487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__C</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__gamma</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 0.001}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 1.0}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 10}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 100}</td>\n",
       "      <td>{'svc__C': 0.01, 'svc__gamma': 0.001}</td>\n",
       "      <td>{'svc__C': 0.01, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 0.01, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 0.01, 'svc__gamma': 1.0}</td>\n",
       "      <td>...</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 1.0}</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 10}</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 100}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 0.001}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 1.0}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 10}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.702786</td>\n",
       "      <td>0.671827</td>\n",
       "      <td>0.517028</td>\n",
       "      <td>0.504644</td>\n",
       "      <td>0.73065</td>\n",
       "      <td>0.73065</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.671827</td>\n",
       "      <td>0.517028</td>\n",
       "      <td>0.504644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767802</td>\n",
       "      <td>0.671827</td>\n",
       "      <td>0.510836</td>\n",
       "      <td>0.510836</td>\n",
       "      <td>0.708978</td>\n",
       "      <td>0.758514</td>\n",
       "      <td>0.76161</td>\n",
       "      <td>0.671827</td>\n",
       "      <td>0.510836</td>\n",
       "      <td>0.510836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693498</td>\n",
       "      <td>0.662539</td>\n",
       "      <td>0.517028</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.662539</td>\n",
       "      <td>0.71517</td>\n",
       "      <td>0.671827</td>\n",
       "      <td>0.662539</td>\n",
       "      <td>0.517028</td>\n",
       "      <td>0.50774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>...</td>\n",
       "      <td>0.736025</td>\n",
       "      <td>0.667702</td>\n",
       "      <td>0.515528</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.723602</td>\n",
       "      <td>0.720497</td>\n",
       "      <td>0.708075</td>\n",
       "      <td>0.667702</td>\n",
       "      <td>0.515528</td>\n",
       "      <td>0.509317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.745342</td>\n",
       "      <td>0.68323</td>\n",
       "      <td>0.515528</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.73913</td>\n",
       "      <td>0.71118</td>\n",
       "      <td>0.68323</td>\n",
       "      <td>0.515528</td>\n",
       "      <td>0.509317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729091</td>\n",
       "      <td>0.671425</td>\n",
       "      <td>0.51519</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.704284</td>\n",
       "      <td>0.732792</td>\n",
       "      <td>0.711715</td>\n",
       "      <td>0.671425</td>\n",
       "      <td>0.51519</td>\n",
       "      <td>0.508371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027457</td>\n",
       "      <td>0.006819</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>0.002105</td>\n",
       "      <td>0.024115</td>\n",
       "      <td>0.015284</td>\n",
       "      <td>0.028734</td>\n",
       "      <td>0.006819</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>0.002105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.703876</td>\n",
       "      <td>0.80155</td>\n",
       "      <td>0.990698</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91938</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.712403</td>\n",
       "      <td>0.796899</td>\n",
       "      <td>0.987597</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.925581</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.726357</td>\n",
       "      <td>0.813953</td>\n",
       "      <td>0.989147</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923315</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.710302</td>\n",
       "      <td>0.797831</td>\n",
       "      <td>0.984508</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>...</td>\n",
       "      <td>0.915569</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.71495</td>\n",
       "      <td>0.793958</td>\n",
       "      <td>0.986057</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "      <td>0.999225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.92142</td>\n",
       "      <td>0.999535</td>\n",
       "      <td>0.999535</td>\n",
       "      <td>0.999535</td>\n",
       "      <td>0.713577</td>\n",
       "      <td>0.800838</td>\n",
       "      <td>0.987601</td>\n",
       "      <td>0.999535</td>\n",
       "      <td>0.999535</td>\n",
       "      <td>0.999535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00354</td>\n",
       "      <td>0.00038</td>\n",
       "      <td>0.00038</td>\n",
       "      <td>0.00038</td>\n",
       "      <td>0.007368</td>\n",
       "      <td>0.006992</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>0.00038</td>\n",
       "      <td>0.00038</td>\n",
       "      <td>0.00038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        0   \\\n",
       "mean_fit_time                                     0.066296   \n",
       "std_fit_time                                      0.006657   \n",
       "mean_score_time                                   0.018925   \n",
       "std_score_time                                    0.002668   \n",
       "param_svc__C                                         0.001   \n",
       "param_svc__gamma                                     0.001   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 0.001}   \n",
       "split0_test_score                                  0.50774   \n",
       "split1_test_score                                  0.50774   \n",
       "split2_test_score                                  0.50774   \n",
       "split3_test_score                                 0.506211   \n",
       "split4_test_score                                 0.509317   \n",
       "mean_test_score                                    0.50775   \n",
       "std_test_score                                    0.000982   \n",
       "rank_test_score                                         21   \n",
       "split0_train_score                                0.507752   \n",
       "split1_train_score                                0.507752   \n",
       "split2_train_score                                0.507752   \n",
       "split3_train_score                                0.508133   \n",
       "split4_train_score                                0.507359   \n",
       "mean_train_score                                   0.50775   \n",
       "std_train_score                                   0.000245   \n",
       "\n",
       "                                                       1   \\\n",
       "mean_fit_time                                    0.064128   \n",
       "std_fit_time                                     0.006794   \n",
       "mean_score_time                                  0.022579   \n",
       "std_score_time                                   0.003075   \n",
       "param_svc__C                                        0.001   \n",
       "param_svc__gamma                                     0.01   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 0.01}   \n",
       "split0_test_score                                 0.50774   \n",
       "split1_test_score                                 0.50774   \n",
       "split2_test_score                                 0.50774   \n",
       "split3_test_score                                0.506211   \n",
       "split4_test_score                                0.509317   \n",
       "mean_test_score                                   0.50775   \n",
       "std_test_score                                   0.000982   \n",
       "rank_test_score                                        21   \n",
       "split0_train_score                               0.507752   \n",
       "split1_train_score                               0.507752   \n",
       "split2_train_score                               0.507752   \n",
       "split3_train_score                               0.508133   \n",
       "split4_train_score                               0.507359   \n",
       "mean_train_score                                  0.50775   \n",
       "std_train_score                                  0.000245   \n",
       "\n",
       "                                                      2   \\\n",
       "mean_fit_time                                   0.059034   \n",
       "std_fit_time                                    0.005885   \n",
       "mean_score_time                                 0.020238   \n",
       "std_score_time                                  0.000956   \n",
       "param_svc__C                                       0.001   \n",
       "param_svc__gamma                                     0.1   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 0.1}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "rank_test_score                                       21   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "                                                      3   \\\n",
       "mean_fit_time                                   0.057335   \n",
       "std_fit_time                                    0.005523   \n",
       "mean_score_time                                 0.020394   \n",
       "std_score_time                                  0.000246   \n",
       "param_svc__C                                       0.001   \n",
       "param_svc__gamma                                     1.0   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 1.0}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "rank_test_score                                       21   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "                                                     4   \\\n",
       "mean_fit_time                                  0.054872   \n",
       "std_fit_time                                   0.006118   \n",
       "mean_score_time                                0.022572   \n",
       "std_score_time                                 0.003729   \n",
       "param_svc__C                                      0.001   \n",
       "param_svc__gamma                                     10   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 10}   \n",
       "split0_test_score                               0.50774   \n",
       "split1_test_score                               0.50774   \n",
       "split2_test_score                               0.50774   \n",
       "split3_test_score                              0.506211   \n",
       "split4_test_score                              0.509317   \n",
       "mean_test_score                                 0.50775   \n",
       "std_test_score                                 0.000982   \n",
       "rank_test_score                                      21   \n",
       "split0_train_score                             0.507752   \n",
       "split1_train_score                             0.507752   \n",
       "split2_train_score                             0.507752   \n",
       "split3_train_score                             0.508133   \n",
       "split4_train_score                             0.507359   \n",
       "mean_train_score                                0.50775   \n",
       "std_train_score                                0.000245   \n",
       "\n",
       "                                                      5   \\\n",
       "mean_fit_time                                    0.05148   \n",
       "std_fit_time                                    0.007412   \n",
       "mean_score_time                                 0.019949   \n",
       "std_score_time                                  0.004199   \n",
       "param_svc__C                                       0.001   \n",
       "param_svc__gamma                                     100   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 100}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "rank_test_score                                       21   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "                                                       6   \\\n",
       "mean_fit_time                                    0.058355   \n",
       "std_fit_time                                     0.006662   \n",
       "mean_score_time                                   0.02293   \n",
       "std_score_time                                   0.003785   \n",
       "param_svc__C                                         0.01   \n",
       "param_svc__gamma                                    0.001   \n",
       "params              {'svc__C': 0.01, 'svc__gamma': 0.001}   \n",
       "split0_test_score                                 0.50774   \n",
       "split1_test_score                                 0.50774   \n",
       "split2_test_score                                 0.50774   \n",
       "split3_test_score                                0.506211   \n",
       "split4_test_score                                0.509317   \n",
       "mean_test_score                                   0.50775   \n",
       "std_test_score                                   0.000982   \n",
       "rank_test_score                                        21   \n",
       "split0_train_score                               0.507752   \n",
       "split1_train_score                               0.507752   \n",
       "split2_train_score                               0.507752   \n",
       "split3_train_score                               0.508133   \n",
       "split4_train_score                               0.507359   \n",
       "mean_train_score                                  0.50775   \n",
       "std_train_score                                  0.000245   \n",
       "\n",
       "                                                      7   \\\n",
       "mean_fit_time                                   0.056237   \n",
       "std_fit_time                                    0.004874   \n",
       "mean_score_time                                 0.023417   \n",
       "std_score_time                                  0.003897   \n",
       "param_svc__C                                        0.01   \n",
       "param_svc__gamma                                    0.01   \n",
       "params              {'svc__C': 0.01, 'svc__gamma': 0.01}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "rank_test_score                                       21   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "                                                     8   \\\n",
       "mean_fit_time                                   0.05534   \n",
       "std_fit_time                                   0.003858   \n",
       "mean_score_time                                0.024093   \n",
       "std_score_time                                 0.005603   \n",
       "param_svc__C                                       0.01   \n",
       "param_svc__gamma                                    0.1   \n",
       "params              {'svc__C': 0.01, 'svc__gamma': 0.1}   \n",
       "split0_test_score                               0.50774   \n",
       "split1_test_score                               0.50774   \n",
       "split2_test_score                               0.50774   \n",
       "split3_test_score                              0.506211   \n",
       "split4_test_score                              0.509317   \n",
       "mean_test_score                                 0.50775   \n",
       "std_test_score                                 0.000982   \n",
       "rank_test_score                                      21   \n",
       "split0_train_score                             0.507752   \n",
       "split1_train_score                             0.507752   \n",
       "split2_train_score                             0.507752   \n",
       "split3_train_score                             0.508133   \n",
       "split4_train_score                             0.507359   \n",
       "mean_train_score                                0.50775   \n",
       "std_train_score                                0.000245   \n",
       "\n",
       "                                                     9   ...  \\\n",
       "mean_fit_time                                  0.058058  ...   \n",
       "std_fit_time                                   0.007704  ...   \n",
       "mean_score_time                                0.021138  ...   \n",
       "std_score_time                                 0.002371  ...   \n",
       "param_svc__C                                       0.01  ...   \n",
       "param_svc__gamma                                    1.0  ...   \n",
       "params              {'svc__C': 0.01, 'svc__gamma': 1.0}  ...   \n",
       "split0_test_score                               0.50774  ...   \n",
       "split1_test_score                               0.50774  ...   \n",
       "split2_test_score                               0.50774  ...   \n",
       "split3_test_score                              0.506211  ...   \n",
       "split4_test_score                              0.509317  ...   \n",
       "mean_test_score                                 0.50775  ...   \n",
       "std_test_score                                 0.000982  ...   \n",
       "rank_test_score                                      21  ...   \n",
       "split0_train_score                             0.507752  ...   \n",
       "split1_train_score                             0.507752  ...   \n",
       "split2_train_score                             0.507752  ...   \n",
       "split3_train_score                             0.508133  ...   \n",
       "split4_train_score                             0.507359  ...   \n",
       "mean_train_score                                0.50775  ...   \n",
       "std_train_score                                0.000245  ...   \n",
       "\n",
       "                                                   26  \\\n",
       "mean_fit_time                                0.062906   \n",
       "std_fit_time                                 0.006532   \n",
       "mean_score_time                              0.013304   \n",
       "std_score_time                               0.002246   \n",
       "param_svc__C                                       10   \n",
       "param_svc__gamma                                  0.1   \n",
       "params              {'svc__C': 10, 'svc__gamma': 0.1}   \n",
       "split0_test_score                            0.702786   \n",
       "split1_test_score                            0.767802   \n",
       "split2_test_score                            0.693498   \n",
       "split3_test_score                            0.736025   \n",
       "split4_test_score                            0.745342   \n",
       "mean_test_score                              0.729091   \n",
       "std_test_score                               0.027457   \n",
       "rank_test_score                                     3   \n",
       "split0_train_score                           0.923256   \n",
       "split1_train_score                            0.91938   \n",
       "split2_train_score                           0.925581   \n",
       "split3_train_score                           0.923315   \n",
       "split4_train_score                           0.915569   \n",
       "mean_train_score                              0.92142   \n",
       "std_train_score                               0.00354   \n",
       "\n",
       "                                                   27  \\\n",
       "mean_fit_time                                0.069162   \n",
       "std_fit_time                                 0.005437   \n",
       "mean_score_time                              0.020209   \n",
       "std_score_time                               0.000794   \n",
       "param_svc__C                                       10   \n",
       "param_svc__gamma                                  1.0   \n",
       "params              {'svc__C': 10, 'svc__gamma': 1.0}   \n",
       "split0_test_score                            0.671827   \n",
       "split1_test_score                            0.671827   \n",
       "split2_test_score                            0.662539   \n",
       "split3_test_score                            0.667702   \n",
       "split4_test_score                             0.68323   \n",
       "mean_test_score                              0.671425   \n",
       "std_test_score                               0.006819   \n",
       "rank_test_score                                    11   \n",
       "split0_train_score                                1.0   \n",
       "split1_train_score                                1.0   \n",
       "split2_train_score                           0.999225   \n",
       "split3_train_score                           0.999225   \n",
       "split4_train_score                           0.999225   \n",
       "mean_train_score                             0.999535   \n",
       "std_train_score                               0.00038   \n",
       "\n",
       "                                                  28  \\\n",
       "mean_fit_time                               0.070338   \n",
       "std_fit_time                                0.001641   \n",
       "mean_score_time                             0.019604   \n",
       "std_score_time                              0.001518   \n",
       "param_svc__C                                      10   \n",
       "param_svc__gamma                                  10   \n",
       "params              {'svc__C': 10, 'svc__gamma': 10}   \n",
       "split0_test_score                           0.517028   \n",
       "split1_test_score                           0.510836   \n",
       "split2_test_score                           0.517028   \n",
       "split3_test_score                           0.515528   \n",
       "split4_test_score                           0.515528   \n",
       "mean_test_score                              0.51519   \n",
       "std_test_score                              0.002278   \n",
       "rank_test_score                                   15   \n",
       "split0_train_score                               1.0   \n",
       "split1_train_score                               1.0   \n",
       "split2_train_score                          0.999225   \n",
       "split3_train_score                          0.999225   \n",
       "split4_train_score                          0.999225   \n",
       "mean_train_score                            0.999535   \n",
       "std_train_score                              0.00038   \n",
       "\n",
       "                                                   29  \\\n",
       "mean_fit_time                                0.071929   \n",
       "std_fit_time                                 0.008631   \n",
       "mean_score_time                              0.022234   \n",
       "std_score_time                               0.002944   \n",
       "param_svc__C                                       10   \n",
       "param_svc__gamma                                  100   \n",
       "params              {'svc__C': 10, 'svc__gamma': 100}   \n",
       "split0_test_score                            0.504644   \n",
       "split1_test_score                            0.510836   \n",
       "split2_test_score                             0.50774   \n",
       "split3_test_score                            0.509317   \n",
       "split4_test_score                            0.509317   \n",
       "mean_test_score                              0.508371   \n",
       "std_test_score                               0.002105   \n",
       "rank_test_score                                    18   \n",
       "split0_train_score                                1.0   \n",
       "split1_train_score                                1.0   \n",
       "split2_train_score                           0.999225   \n",
       "split3_train_score                           0.999225   \n",
       "split4_train_score                           0.999225   \n",
       "mean_train_score                             0.999535   \n",
       "std_train_score                               0.00038   \n",
       "\n",
       "                                                      30  \\\n",
       "mean_fit_time                                    0.05157   \n",
       "std_fit_time                                    0.005295   \n",
       "mean_score_time                                  0.01363   \n",
       "std_score_time                                  0.002532   \n",
       "param_svc__C                                         100   \n",
       "param_svc__gamma                                   0.001   \n",
       "params              {'svc__C': 100, 'svc__gamma': 0.001}   \n",
       "split0_test_score                                0.73065   \n",
       "split1_test_score                               0.708978   \n",
       "split2_test_score                               0.662539   \n",
       "split3_test_score                               0.723602   \n",
       "split4_test_score                               0.695652   \n",
       "mean_test_score                                 0.704284   \n",
       "std_test_score                                  0.024115   \n",
       "rank_test_score                                        6   \n",
       "split0_train_score                              0.703876   \n",
       "split1_train_score                              0.712403   \n",
       "split2_train_score                              0.726357   \n",
       "split3_train_score                              0.710302   \n",
       "split4_train_score                               0.71495   \n",
       "mean_train_score                                0.713577   \n",
       "std_train_score                                 0.007368   \n",
       "\n",
       "                                                     31  \\\n",
       "mean_fit_time                                  0.069807   \n",
       "std_fit_time                                   0.001971   \n",
       "mean_score_time                                0.012289   \n",
       "std_score_time                                 0.002715   \n",
       "param_svc__C                                        100   \n",
       "param_svc__gamma                                   0.01   \n",
       "params              {'svc__C': 100, 'svc__gamma': 0.01}   \n",
       "split0_test_score                               0.73065   \n",
       "split1_test_score                              0.758514   \n",
       "split2_test_score                               0.71517   \n",
       "split3_test_score                              0.720497   \n",
       "split4_test_score                               0.73913   \n",
       "mean_test_score                                0.732792   \n",
       "std_test_score                                 0.015284   \n",
       "rank_test_score                                       2   \n",
       "split0_train_score                              0.80155   \n",
       "split1_train_score                             0.796899   \n",
       "split2_train_score                             0.813953   \n",
       "split3_train_score                             0.797831   \n",
       "split4_train_score                             0.793958   \n",
       "mean_train_score                               0.800838   \n",
       "std_train_score                                0.006992   \n",
       "\n",
       "                                                    32  \\\n",
       "mean_fit_time                                 0.110082   \n",
       "std_fit_time                                  0.009852   \n",
       "mean_score_time                                0.01075   \n",
       "std_score_time                                0.002119   \n",
       "param_svc__C                                       100   \n",
       "param_svc__gamma                                   0.1   \n",
       "params              {'svc__C': 100, 'svc__gamma': 0.1}   \n",
       "split0_test_score                             0.705882   \n",
       "split1_test_score                              0.76161   \n",
       "split2_test_score                             0.671827   \n",
       "split3_test_score                             0.708075   \n",
       "split4_test_score                              0.71118   \n",
       "mean_test_score                               0.711715   \n",
       "std_test_score                                0.028734   \n",
       "rank_test_score                                      5   \n",
       "split0_train_score                            0.990698   \n",
       "split1_train_score                            0.987597   \n",
       "split2_train_score                            0.989147   \n",
       "split3_train_score                            0.984508   \n",
       "split4_train_score                            0.986057   \n",
       "mean_train_score                              0.987601   \n",
       "std_train_score                               0.002188   \n",
       "\n",
       "                                                    33  \\\n",
       "mean_fit_time                                 0.068692   \n",
       "std_fit_time                                  0.003563   \n",
       "mean_score_time                               0.022028   \n",
       "std_score_time                                0.008888   \n",
       "param_svc__C                                       100   \n",
       "param_svc__gamma                                   1.0   \n",
       "params              {'svc__C': 100, 'svc__gamma': 1.0}   \n",
       "split0_test_score                             0.671827   \n",
       "split1_test_score                             0.671827   \n",
       "split2_test_score                             0.662539   \n",
       "split3_test_score                             0.667702   \n",
       "split4_test_score                              0.68323   \n",
       "mean_test_score                               0.671425   \n",
       "std_test_score                                0.006819   \n",
       "rank_test_score                                     11   \n",
       "split0_train_score                                 1.0   \n",
       "split1_train_score                                 1.0   \n",
       "split2_train_score                            0.999225   \n",
       "split3_train_score                            0.999225   \n",
       "split4_train_score                            0.999225   \n",
       "mean_train_score                              0.999535   \n",
       "std_train_score                                0.00038   \n",
       "\n",
       "                                                   34  \\\n",
       "mean_fit_time                                0.076717   \n",
       "std_fit_time                                 0.008102   \n",
       "mean_score_time                              0.025075   \n",
       "std_score_time                               0.006929   \n",
       "param_svc__C                                      100   \n",
       "param_svc__gamma                                   10   \n",
       "params              {'svc__C': 100, 'svc__gamma': 10}   \n",
       "split0_test_score                            0.517028   \n",
       "split1_test_score                            0.510836   \n",
       "split2_test_score                            0.517028   \n",
       "split3_test_score                            0.515528   \n",
       "split4_test_score                            0.515528   \n",
       "mean_test_score                               0.51519   \n",
       "std_test_score                               0.002278   \n",
       "rank_test_score                                    15   \n",
       "split0_train_score                                1.0   \n",
       "split1_train_score                                1.0   \n",
       "split2_train_score                           0.999225   \n",
       "split3_train_score                           0.999225   \n",
       "split4_train_score                           0.999225   \n",
       "mean_train_score                             0.999535   \n",
       "std_train_score                               0.00038   \n",
       "\n",
       "                                                    35  \n",
       "mean_fit_time                                 0.061162  \n",
       "std_fit_time                                  0.015702  \n",
       "mean_score_time                               0.016178  \n",
       "std_score_time                                 0.00487  \n",
       "param_svc__C                                       100  \n",
       "param_svc__gamma                                   100  \n",
       "params              {'svc__C': 100, 'svc__gamma': 100}  \n",
       "split0_test_score                             0.504644  \n",
       "split1_test_score                             0.510836  \n",
       "split2_test_score                              0.50774  \n",
       "split3_test_score                             0.509317  \n",
       "split4_test_score                             0.509317  \n",
       "mean_test_score                               0.508371  \n",
       "std_test_score                                0.002105  \n",
       "rank_test_score                                     18  \n",
       "split0_train_score                                 1.0  \n",
       "split1_train_score                                 1.0  \n",
       "split2_train_score                            0.999225  \n",
       "split3_train_score                            0.999225  \n",
       "split4_train_score                            0.999225  \n",
       "mean_train_score                              0.999535  \n",
       "std_train_score                                0.00038  \n",
       "\n",
       "[22 rows x 36 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "results.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>rank_test_score</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
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       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.051213</td>\n",
       "      <td>0.069807</td>\n",
       "      <td>0.062906</td>\n",
       "      <td>0.04916</td>\n",
       "      <td>0.110082</td>\n",
       "      <td>0.05157</td>\n",
       "      <td>0.060647</td>\n",
       "      <td>0.058823</td>\n",
       "      <td>0.056439</td>\n",
       "      <td>0.051582</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05534</td>\n",
       "      <td>0.056237</td>\n",
       "      <td>0.058355</td>\n",
       "      <td>0.05148</td>\n",
       "      <td>0.054872</td>\n",
       "      <td>0.057335</td>\n",
       "      <td>0.059034</td>\n",
       "      <td>0.064128</td>\n",
       "      <td>0.063345</td>\n",
       "      <td>0.07472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.011911</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0.006532</td>\n",
       "      <td>0.006423</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.005295</td>\n",
       "      <td>0.003819</td>\n",
       "      <td>0.009956</td>\n",
       "      <td>0.005779</td>\n",
       "      <td>0.006732</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003858</td>\n",
       "      <td>0.004874</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>0.007412</td>\n",
       "      <td>0.006118</td>\n",
       "      <td>0.005523</td>\n",
       "      <td>0.005885</td>\n",
       "      <td>0.006794</td>\n",
       "      <td>0.002926</td>\n",
       "      <td>0.015091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.018556</td>\n",
       "      <td>0.012289</td>\n",
       "      <td>0.013304</td>\n",
       "      <td>0.011951</td>\n",
       "      <td>0.01075</td>\n",
       "      <td>0.01363</td>\n",
       "      <td>0.016769</td>\n",
       "      <td>0.017151</td>\n",
       "      <td>0.018264</td>\n",
       "      <td>0.014593</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024093</td>\n",
       "      <td>0.023417</td>\n",
       "      <td>0.02293</td>\n",
       "      <td>0.019949</td>\n",
       "      <td>0.022572</td>\n",
       "      <td>0.020394</td>\n",
       "      <td>0.020238</td>\n",
       "      <td>0.022579</td>\n",
       "      <td>0.01736</td>\n",
       "      <td>0.021388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>0.001704</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.002532</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>0.006553</td>\n",
       "      <td>0.002416</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005603</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>0.003785</td>\n",
       "      <td>0.004199</td>\n",
       "      <td>0.003729</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000956</td>\n",
       "      <td>0.003075</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.007943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__C</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__gamma</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'svc__C': 1.0, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 0.001}</td>\n",
       "      <td>{'svc__C': 0.1, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 1.0, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 0.1, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 0.001}</td>\n",
       "      <td>...</td>\n",
       "      <td>{'svc__C': 0.01, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 0.01, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 0.01, 'svc__gamma': 0.001}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 100}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 10}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 1.0}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 0.1}</td>\n",
       "      <td>{'svc__C': 0.001, 'svc__gamma': 0.01}</td>\n",
       "      <td>{'svc__C': 0.1, 'svc__gamma': 10}</td>\n",
       "      <td>{'svc__C': 0.1, 'svc__gamma': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.755418</td>\n",
       "      <td>0.73065</td>\n",
       "      <td>0.702786</td>\n",
       "      <td>0.739938</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.73065</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.702786</td>\n",
       "      <td>0.693498</td>\n",
       "      <td>0.69969</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.755418</td>\n",
       "      <td>0.758514</td>\n",
       "      <td>0.767802</td>\n",
       "      <td>0.733746</td>\n",
       "      <td>0.76161</td>\n",
       "      <td>0.708978</td>\n",
       "      <td>0.718266</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.702786</td>\n",
       "      <td>0.674923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.712074</td>\n",
       "      <td>0.71517</td>\n",
       "      <td>0.693498</td>\n",
       "      <td>0.696594</td>\n",
       "      <td>0.671827</td>\n",
       "      <td>0.662539</td>\n",
       "      <td>0.690402</td>\n",
       "      <td>0.659443</td>\n",
       "      <td>0.653251</td>\n",
       "      <td>0.653251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "      <td>0.50774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.754658</td>\n",
       "      <td>0.720497</td>\n",
       "      <td>0.736025</td>\n",
       "      <td>0.720497</td>\n",
       "      <td>0.708075</td>\n",
       "      <td>0.723602</td>\n",
       "      <td>0.692547</td>\n",
       "      <td>0.726708</td>\n",
       "      <td>0.664596</td>\n",
       "      <td>0.680124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.506211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.742236</td>\n",
       "      <td>0.73913</td>\n",
       "      <td>0.745342</td>\n",
       "      <td>0.71118</td>\n",
       "      <td>0.71118</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.708075</td>\n",
       "      <td>0.692547</td>\n",
       "      <td>0.680124</td>\n",
       "      <td>0.68323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.509317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.743961</td>\n",
       "      <td>0.732792</td>\n",
       "      <td>0.729091</td>\n",
       "      <td>0.720391</td>\n",
       "      <td>0.711715</td>\n",
       "      <td>0.704284</td>\n",
       "      <td>0.703034</td>\n",
       "      <td>0.697473</td>\n",
       "      <td>0.678851</td>\n",
       "      <td>0.678244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.016713</td>\n",
       "      <td>0.015284</td>\n",
       "      <td>0.027457</td>\n",
       "      <td>0.015566</td>\n",
       "      <td>0.028734</td>\n",
       "      <td>0.024115</td>\n",
       "      <td>0.010345</td>\n",
       "      <td>0.022019</td>\n",
       "      <td>0.018153</td>\n",
       "      <td>0.014994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.000982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.827132</td>\n",
       "      <td>0.80155</td>\n",
       "      <td>0.923256</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.990698</td>\n",
       "      <td>0.703876</td>\n",
       "      <td>0.730233</td>\n",
       "      <td>0.708527</td>\n",
       "      <td>0.675194</td>\n",
       "      <td>0.67907</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.829457</td>\n",
       "      <td>0.796899</td>\n",
       "      <td>0.91938</td>\n",
       "      <td>0.757364</td>\n",
       "      <td>0.987597</td>\n",
       "      <td>0.712403</td>\n",
       "      <td>0.724806</td>\n",
       "      <td>0.700775</td>\n",
       "      <td>0.676744</td>\n",
       "      <td>0.682946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.83876</td>\n",
       "      <td>0.813953</td>\n",
       "      <td>0.925581</td>\n",
       "      <td>0.760465</td>\n",
       "      <td>0.989147</td>\n",
       "      <td>0.726357</td>\n",
       "      <td>0.737984</td>\n",
       "      <td>0.723256</td>\n",
       "      <td>0.683721</td>\n",
       "      <td>0.694574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "      <td>0.507752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.831913</td>\n",
       "      <td>0.797831</td>\n",
       "      <td>0.923315</td>\n",
       "      <td>0.759876</td>\n",
       "      <td>0.984508</td>\n",
       "      <td>0.710302</td>\n",
       "      <td>0.729667</td>\n",
       "      <td>0.701007</td>\n",
       "      <td>0.681642</td>\n",
       "      <td>0.688613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "      <td>0.508133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.832688</td>\n",
       "      <td>0.793958</td>\n",
       "      <td>0.915569</td>\n",
       "      <td>0.75213</td>\n",
       "      <td>0.986057</td>\n",
       "      <td>0.71495</td>\n",
       "      <td>0.725019</td>\n",
       "      <td>0.718048</td>\n",
       "      <td>0.680868</td>\n",
       "      <td>0.687064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "      <td>0.507359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.83199</td>\n",
       "      <td>0.800838</td>\n",
       "      <td>0.92142</td>\n",
       "      <td>0.756355</td>\n",
       "      <td>0.987601</td>\n",
       "      <td>0.713577</td>\n",
       "      <td>0.729542</td>\n",
       "      <td>0.710323</td>\n",
       "      <td>0.679634</td>\n",
       "      <td>0.686453</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.003907</td>\n",
       "      <td>0.006992</td>\n",
       "      <td>0.00354</td>\n",
       "      <td>0.003679</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>0.007368</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.009034</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.00525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rank_test_score                                     1   \\\n",
       "mean_fit_time                                 0.051213   \n",
       "std_fit_time                                  0.011911   \n",
       "mean_score_time                               0.018556   \n",
       "std_score_time                                0.001704   \n",
       "param_svc__C                                       1.0   \n",
       "param_svc__gamma                                   0.1   \n",
       "params              {'svc__C': 1.0, 'svc__gamma': 0.1}   \n",
       "split0_test_score                             0.755418   \n",
       "split1_test_score                             0.755418   \n",
       "split2_test_score                             0.712074   \n",
       "split3_test_score                             0.754658   \n",
       "split4_test_score                             0.742236   \n",
       "mean_test_score                               0.743961   \n",
       "std_test_score                                0.016713   \n",
       "split0_train_score                            0.827132   \n",
       "split1_train_score                            0.829457   \n",
       "split2_train_score                             0.83876   \n",
       "split3_train_score                            0.831913   \n",
       "split4_train_score                            0.832688   \n",
       "mean_train_score                               0.83199   \n",
       "std_train_score                               0.003907   \n",
       "\n",
       "rank_test_score                                      2   \\\n",
       "mean_fit_time                                  0.069807   \n",
       "std_fit_time                                   0.001971   \n",
       "mean_score_time                                0.012289   \n",
       "std_score_time                                 0.002715   \n",
       "param_svc__C                                        100   \n",
       "param_svc__gamma                                   0.01   \n",
       "params              {'svc__C': 100, 'svc__gamma': 0.01}   \n",
       "split0_test_score                               0.73065   \n",
       "split1_test_score                              0.758514   \n",
       "split2_test_score                               0.71517   \n",
       "split3_test_score                              0.720497   \n",
       "split4_test_score                               0.73913   \n",
       "mean_test_score                                0.732792   \n",
       "std_test_score                                 0.015284   \n",
       "split0_train_score                              0.80155   \n",
       "split1_train_score                             0.796899   \n",
       "split2_train_score                             0.813953   \n",
       "split3_train_score                             0.797831   \n",
       "split4_train_score                             0.793958   \n",
       "mean_train_score                               0.800838   \n",
       "std_train_score                                0.006992   \n",
       "\n",
       "rank_test_score                                    3   \\\n",
       "mean_fit_time                                0.062906   \n",
       "std_fit_time                                 0.006532   \n",
       "mean_score_time                              0.013304   \n",
       "std_score_time                               0.002246   \n",
       "param_svc__C                                       10   \n",
       "param_svc__gamma                                  0.1   \n",
       "params              {'svc__C': 10, 'svc__gamma': 0.1}   \n",
       "split0_test_score                            0.702786   \n",
       "split1_test_score                            0.767802   \n",
       "split2_test_score                            0.693498   \n",
       "split3_test_score                            0.736025   \n",
       "split4_test_score                            0.745342   \n",
       "mean_test_score                              0.729091   \n",
       "std_test_score                               0.027457   \n",
       "split0_train_score                           0.923256   \n",
       "split1_train_score                            0.91938   \n",
       "split2_train_score                           0.925581   \n",
       "split3_train_score                           0.923315   \n",
       "split4_train_score                           0.915569   \n",
       "mean_train_score                              0.92142   \n",
       "std_train_score                               0.00354   \n",
       "\n",
       "rank_test_score                                     4   \\\n",
       "mean_fit_time                                  0.04916   \n",
       "std_fit_time                                  0.006423   \n",
       "mean_score_time                               0.011951   \n",
       "std_score_time                                0.002497   \n",
       "param_svc__C                                        10   \n",
       "param_svc__gamma                                  0.01   \n",
       "params              {'svc__C': 10, 'svc__gamma': 0.01}   \n",
       "split0_test_score                             0.739938   \n",
       "split1_test_score                             0.733746   \n",
       "split2_test_score                             0.696594   \n",
       "split3_test_score                             0.720497   \n",
       "split4_test_score                              0.71118   \n",
       "mean_test_score                               0.720391   \n",
       "std_test_score                                0.015566   \n",
       "split0_train_score                            0.751938   \n",
       "split1_train_score                            0.757364   \n",
       "split2_train_score                            0.760465   \n",
       "split3_train_score                            0.759876   \n",
       "split4_train_score                             0.75213   \n",
       "mean_train_score                              0.756355   \n",
       "std_train_score                               0.003679   \n",
       "\n",
       "rank_test_score                                     5   \\\n",
       "mean_fit_time                                 0.110082   \n",
       "std_fit_time                                  0.009852   \n",
       "mean_score_time                                0.01075   \n",
       "std_score_time                                0.002119   \n",
       "param_svc__C                                       100   \n",
       "param_svc__gamma                                   0.1   \n",
       "params              {'svc__C': 100, 'svc__gamma': 0.1}   \n",
       "split0_test_score                             0.705882   \n",
       "split1_test_score                              0.76161   \n",
       "split2_test_score                             0.671827   \n",
       "split3_test_score                             0.708075   \n",
       "split4_test_score                              0.71118   \n",
       "mean_test_score                               0.711715   \n",
       "std_test_score                                0.028734   \n",
       "split0_train_score                            0.990698   \n",
       "split1_train_score                            0.987597   \n",
       "split2_train_score                            0.989147   \n",
       "split3_train_score                            0.984508   \n",
       "split4_train_score                            0.986057   \n",
       "mean_train_score                              0.987601   \n",
       "std_train_score                               0.002188   \n",
       "\n",
       "rank_test_score                                       6   \\\n",
       "mean_fit_time                                    0.05157   \n",
       "std_fit_time                                    0.005295   \n",
       "mean_score_time                                  0.01363   \n",
       "std_score_time                                  0.002532   \n",
       "param_svc__C                                         100   \n",
       "param_svc__gamma                                   0.001   \n",
       "params              {'svc__C': 100, 'svc__gamma': 0.001}   \n",
       "split0_test_score                                0.73065   \n",
       "split1_test_score                               0.708978   \n",
       "split2_test_score                               0.662539   \n",
       "split3_test_score                               0.723602   \n",
       "split4_test_score                               0.695652   \n",
       "mean_test_score                                 0.704284   \n",
       "std_test_score                                  0.024115   \n",
       "split0_train_score                              0.703876   \n",
       "split1_train_score                              0.712403   \n",
       "split2_train_score                              0.726357   \n",
       "split3_train_score                              0.710302   \n",
       "split4_train_score                               0.71495   \n",
       "mean_train_score                                0.713577   \n",
       "std_train_score                                 0.007368   \n",
       "\n",
       "rank_test_score                                     7   \\\n",
       "mean_fit_time                                 0.060647   \n",
       "std_fit_time                                  0.003819   \n",
       "mean_score_time                               0.016769   \n",
       "std_score_time                                0.003261   \n",
       "param_svc__C                                       0.1   \n",
       "param_svc__gamma                                   0.1   \n",
       "params              {'svc__C': 0.1, 'svc__gamma': 0.1}   \n",
       "split0_test_score                             0.705882   \n",
       "split1_test_score                             0.718266   \n",
       "split2_test_score                             0.690402   \n",
       "split3_test_score                             0.692547   \n",
       "split4_test_score                             0.708075   \n",
       "mean_test_score                               0.703034   \n",
       "std_test_score                                0.010345   \n",
       "split0_train_score                            0.730233   \n",
       "split1_train_score                            0.724806   \n",
       "split2_train_score                            0.737984   \n",
       "split3_train_score                            0.729667   \n",
       "split4_train_score                            0.725019   \n",
       "mean_train_score                              0.729542   \n",
       "std_train_score                               0.004789   \n",
       "\n",
       "rank_test_score                                      8   \\\n",
       "mean_fit_time                                  0.058823   \n",
       "std_fit_time                                   0.009956   \n",
       "mean_score_time                                0.017151   \n",
       "std_score_time                                 0.006553   \n",
       "param_svc__C                                        1.0   \n",
       "param_svc__gamma                                   0.01   \n",
       "params              {'svc__C': 1.0, 'svc__gamma': 0.01}   \n",
       "split0_test_score                              0.702786   \n",
       "split1_test_score                              0.705882   \n",
       "split2_test_score                              0.659443   \n",
       "split3_test_score                              0.726708   \n",
       "split4_test_score                              0.692547   \n",
       "mean_test_score                                0.697473   \n",
       "std_test_score                                 0.022019   \n",
       "split0_train_score                             0.708527   \n",
       "split1_train_score                             0.700775   \n",
       "split2_train_score                             0.723256   \n",
       "split3_train_score                             0.701007   \n",
       "split4_train_score                             0.718048   \n",
       "mean_train_score                               0.710323   \n",
       "std_train_score                                0.009034   \n",
       "\n",
       "rank_test_score                                      9   \\\n",
       "mean_fit_time                                  0.056439   \n",
       "std_fit_time                                   0.005779   \n",
       "mean_score_time                                0.018264   \n",
       "std_score_time                                 0.002416   \n",
       "param_svc__C                                        0.1   \n",
       "param_svc__gamma                                   0.01   \n",
       "params              {'svc__C': 0.1, 'svc__gamma': 0.01}   \n",
       "split0_test_score                              0.693498   \n",
       "split1_test_score                              0.702786   \n",
       "split2_test_score                              0.653251   \n",
       "split3_test_score                              0.664596   \n",
       "split4_test_score                              0.680124   \n",
       "mean_test_score                                0.678851   \n",
       "std_test_score                                 0.018153   \n",
       "split0_train_score                             0.675194   \n",
       "split1_train_score                             0.676744   \n",
       "split2_train_score                             0.683721   \n",
       "split3_train_score                             0.681642   \n",
       "split4_train_score                             0.680868   \n",
       "mean_train_score                               0.679634   \n",
       "std_train_score                                0.003172   \n",
       "\n",
       "rank_test_score                                      10  ...  \\\n",
       "mean_fit_time                                  0.051582  ...   \n",
       "std_fit_time                                   0.006732  ...   \n",
       "mean_score_time                                0.014593  ...   \n",
       "std_score_time                                 0.002415  ...   \n",
       "param_svc__C                                         10  ...   \n",
       "param_svc__gamma                                  0.001  ...   \n",
       "params              {'svc__C': 10, 'svc__gamma': 0.001}  ...   \n",
       "split0_test_score                               0.69969  ...   \n",
       "split1_test_score                              0.674923  ...   \n",
       "split2_test_score                              0.653251  ...   \n",
       "split3_test_score                              0.680124  ...   \n",
       "split4_test_score                               0.68323  ...   \n",
       "mean_test_score                                0.678244  ...   \n",
       "std_test_score                                 0.014994  ...   \n",
       "split0_train_score                              0.67907  ...   \n",
       "split1_train_score                             0.682946  ...   \n",
       "split2_train_score                             0.694574  ...   \n",
       "split3_train_score                             0.688613  ...   \n",
       "split4_train_score                             0.687064  ...   \n",
       "mean_train_score                               0.686453  ...   \n",
       "std_train_score                                 0.00525  ...   \n",
       "\n",
       "rank_test_score                                      21  \\\n",
       "mean_fit_time                                   0.05534   \n",
       "std_fit_time                                   0.003858   \n",
       "mean_score_time                                0.024093   \n",
       "std_score_time                                 0.005603   \n",
       "param_svc__C                                       0.01   \n",
       "param_svc__gamma                                    0.1   \n",
       "params              {'svc__C': 0.01, 'svc__gamma': 0.1}   \n",
       "split0_test_score                               0.50774   \n",
       "split1_test_score                               0.50774   \n",
       "split2_test_score                               0.50774   \n",
       "split3_test_score                              0.506211   \n",
       "split4_test_score                              0.509317   \n",
       "mean_test_score                                 0.50775   \n",
       "std_test_score                                 0.000982   \n",
       "split0_train_score                             0.507752   \n",
       "split1_train_score                             0.507752   \n",
       "split2_train_score                             0.507752   \n",
       "split3_train_score                             0.508133   \n",
       "split4_train_score                             0.507359   \n",
       "mean_train_score                                0.50775   \n",
       "std_train_score                                0.000245   \n",
       "\n",
       "rank_test_score                                       21  \\\n",
       "mean_fit_time                                   0.056237   \n",
       "std_fit_time                                    0.004874   \n",
       "mean_score_time                                 0.023417   \n",
       "std_score_time                                  0.003897   \n",
       "param_svc__C                                        0.01   \n",
       "param_svc__gamma                                    0.01   \n",
       "params              {'svc__C': 0.01, 'svc__gamma': 0.01}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "rank_test_score                                        21  \\\n",
       "mean_fit_time                                    0.058355   \n",
       "std_fit_time                                     0.006662   \n",
       "mean_score_time                                   0.02293   \n",
       "std_score_time                                   0.003785   \n",
       "param_svc__C                                         0.01   \n",
       "param_svc__gamma                                    0.001   \n",
       "params              {'svc__C': 0.01, 'svc__gamma': 0.001}   \n",
       "split0_test_score                                 0.50774   \n",
       "split1_test_score                                 0.50774   \n",
       "split2_test_score                                 0.50774   \n",
       "split3_test_score                                0.506211   \n",
       "split4_test_score                                0.509317   \n",
       "mean_test_score                                   0.50775   \n",
       "std_test_score                                   0.000982   \n",
       "split0_train_score                               0.507752   \n",
       "split1_train_score                               0.507752   \n",
       "split2_train_score                               0.507752   \n",
       "split3_train_score                               0.508133   \n",
       "split4_train_score                               0.507359   \n",
       "mean_train_score                                  0.50775   \n",
       "std_train_score                                  0.000245   \n",
       "\n",
       "rank_test_score                                       21  \\\n",
       "mean_fit_time                                    0.05148   \n",
       "std_fit_time                                    0.007412   \n",
       "mean_score_time                                 0.019949   \n",
       "std_score_time                                  0.004199   \n",
       "param_svc__C                                       0.001   \n",
       "param_svc__gamma                                     100   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 100}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "rank_test_score                                      21  \\\n",
       "mean_fit_time                                  0.054872   \n",
       "std_fit_time                                   0.006118   \n",
       "mean_score_time                                0.022572   \n",
       "std_score_time                                 0.003729   \n",
       "param_svc__C                                      0.001   \n",
       "param_svc__gamma                                     10   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 10}   \n",
       "split0_test_score                               0.50774   \n",
       "split1_test_score                               0.50774   \n",
       "split2_test_score                               0.50774   \n",
       "split3_test_score                              0.506211   \n",
       "split4_test_score                              0.509317   \n",
       "mean_test_score                                 0.50775   \n",
       "std_test_score                                 0.000982   \n",
       "split0_train_score                             0.507752   \n",
       "split1_train_score                             0.507752   \n",
       "split2_train_score                             0.507752   \n",
       "split3_train_score                             0.508133   \n",
       "split4_train_score                             0.507359   \n",
       "mean_train_score                                0.50775   \n",
       "std_train_score                                0.000245   \n",
       "\n",
       "rank_test_score                                       21  \\\n",
       "mean_fit_time                                   0.057335   \n",
       "std_fit_time                                    0.005523   \n",
       "mean_score_time                                 0.020394   \n",
       "std_score_time                                  0.000246   \n",
       "param_svc__C                                       0.001   \n",
       "param_svc__gamma                                     1.0   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 1.0}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "rank_test_score                                       21  \\\n",
       "mean_fit_time                                   0.059034   \n",
       "std_fit_time                                    0.005885   \n",
       "mean_score_time                                 0.020238   \n",
       "std_score_time                                  0.000956   \n",
       "param_svc__C                                       0.001   \n",
       "param_svc__gamma                                     0.1   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 0.1}   \n",
       "split0_test_score                                0.50774   \n",
       "split1_test_score                                0.50774   \n",
       "split2_test_score                                0.50774   \n",
       "split3_test_score                               0.506211   \n",
       "split4_test_score                               0.509317   \n",
       "mean_test_score                                  0.50775   \n",
       "std_test_score                                  0.000982   \n",
       "split0_train_score                              0.507752   \n",
       "split1_train_score                              0.507752   \n",
       "split2_train_score                              0.507752   \n",
       "split3_train_score                              0.508133   \n",
       "split4_train_score                              0.507359   \n",
       "mean_train_score                                 0.50775   \n",
       "std_train_score                                 0.000245   \n",
       "\n",
       "rank_test_score                                        21  \\\n",
       "mean_fit_time                                    0.064128   \n",
       "std_fit_time                                     0.006794   \n",
       "mean_score_time                                  0.022579   \n",
       "std_score_time                                   0.003075   \n",
       "param_svc__C                                        0.001   \n",
       "param_svc__gamma                                     0.01   \n",
       "params              {'svc__C': 0.001, 'svc__gamma': 0.01}   \n",
       "split0_test_score                                 0.50774   \n",
       "split1_test_score                                 0.50774   \n",
       "split2_test_score                                 0.50774   \n",
       "split3_test_score                                0.506211   \n",
       "split4_test_score                                0.509317   \n",
       "mean_test_score                                   0.50775   \n",
       "std_test_score                                   0.000982   \n",
       "split0_train_score                               0.507752   \n",
       "split1_train_score                               0.507752   \n",
       "split2_train_score                               0.507752   \n",
       "split3_train_score                               0.508133   \n",
       "split4_train_score                               0.507359   \n",
       "mean_train_score                                  0.50775   \n",
       "std_train_score                                  0.000245   \n",
       "\n",
       "rank_test_score                                    21  \\\n",
       "mean_fit_time                                0.063345   \n",
       "std_fit_time                                 0.002926   \n",
       "mean_score_time                               0.01736   \n",
       "std_score_time                               0.003515   \n",
       "param_svc__C                                      0.1   \n",
       "param_svc__gamma                                   10   \n",
       "params              {'svc__C': 0.1, 'svc__gamma': 10}   \n",
       "split0_test_score                             0.50774   \n",
       "split1_test_score                             0.50774   \n",
       "split2_test_score                             0.50774   \n",
       "split3_test_score                            0.506211   \n",
       "split4_test_score                            0.509317   \n",
       "mean_test_score                               0.50775   \n",
       "std_test_score                               0.000982   \n",
       "split0_train_score                           0.507752   \n",
       "split1_train_score                           0.507752   \n",
       "split2_train_score                           0.507752   \n",
       "split3_train_score                           0.508133   \n",
       "split4_train_score                           0.507359   \n",
       "mean_train_score                              0.50775   \n",
       "std_train_score                              0.000245   \n",
       "\n",
       "rank_test_score                                     21  \n",
       "mean_fit_time                                  0.07472  \n",
       "std_fit_time                                  0.015091  \n",
       "mean_score_time                               0.021388  \n",
       "std_score_time                                0.007943  \n",
       "param_svc__C                                       0.1  \n",
       "param_svc__gamma                                   100  \n",
       "params              {'svc__C': 0.1, 'svc__gamma': 100}  \n",
       "split0_test_score                              0.50774  \n",
       "split1_test_score                              0.50774  \n",
       "split2_test_score                              0.50774  \n",
       "split3_test_score                             0.506211  \n",
       "split4_test_score                             0.509317  \n",
       "mean_test_score                                0.50775  \n",
       "std_test_score                                0.000982  \n",
       "split0_train_score                            0.507752  \n",
       "split1_train_score                            0.507752  \n",
       "split2_train_score                            0.507752  \n",
       "split3_train_score                            0.508133  \n",
       "split4_train_score                            0.507359  \n",
       "mean_train_score                               0.50775  \n",
       "std_train_score                               0.000245  \n",
       "\n",
       "[21 rows x 36 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = (\n",
    "    pd.DataFrame(grid_search.cv_results_).set_index(\"rank_test_score\").sort_index()\n",
    ")\n",
    "display(results.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's only look at the most relevant rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>rank_test_score</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.743961</td>\n",
       "      <td>0.732792</td>\n",
       "      <td>0.729091</td>\n",
       "      <td>0.720391</td>\n",
       "      <td>0.711715</td>\n",
       "      <td>0.704284</td>\n",
       "      <td>0.703034</td>\n",
       "      <td>0.697473</td>\n",
       "      <td>0.678851</td>\n",
       "      <td>0.678244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>0.50775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__gamma</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__C</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.051213</td>\n",
       "      <td>0.069807</td>\n",
       "      <td>0.062906</td>\n",
       "      <td>0.04916</td>\n",
       "      <td>0.110082</td>\n",
       "      <td>0.05157</td>\n",
       "      <td>0.060647</td>\n",
       "      <td>0.058823</td>\n",
       "      <td>0.056439</td>\n",
       "      <td>0.051582</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05534</td>\n",
       "      <td>0.056237</td>\n",
       "      <td>0.058355</td>\n",
       "      <td>0.05148</td>\n",
       "      <td>0.054872</td>\n",
       "      <td>0.057335</td>\n",
       "      <td>0.059034</td>\n",
       "      <td>0.064128</td>\n",
       "      <td>0.063345</td>\n",
       "      <td>0.07472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rank_test_score         1         2         3         4         5         6   \\\n",
       "mean_test_score   0.743961  0.732792  0.729091  0.720391  0.711715  0.704284   \n",
       "param_svc__gamma       0.1      0.01       0.1      0.01       0.1     0.001   \n",
       "param_svc__C           1.0       100        10        10       100       100   \n",
       "mean_fit_time     0.051213  0.069807  0.062906   0.04916  0.110082   0.05157   \n",
       "\n",
       "rank_test_score         7         8         9         10  ...       21  \\\n",
       "mean_test_score   0.703034  0.697473  0.678851  0.678244  ...  0.50775   \n",
       "param_svc__gamma       0.1      0.01      0.01     0.001  ...      0.1   \n",
       "param_svc__C           0.1       1.0       0.1        10  ...     0.01   \n",
       "mean_fit_time     0.060647  0.058823  0.056439  0.051582  ...  0.05534   \n",
       "\n",
       "rank_test_score         21        21       21        21        21        21  \\\n",
       "mean_test_score    0.50775   0.50775  0.50775   0.50775   0.50775   0.50775   \n",
       "param_svc__gamma      0.01     0.001      100        10       1.0       0.1   \n",
       "param_svc__C          0.01      0.01    0.001     0.001     0.001     0.001   \n",
       "mean_fit_time     0.056237  0.058355  0.05148  0.054872  0.057335  0.059034   \n",
       "\n",
       "rank_test_score         21        21       21  \n",
       "mean_test_score    0.50775   0.50775  0.50775  \n",
       "param_svc__gamma      0.01        10      100  \n",
       "param_svc__C         0.001       0.1      0.1  \n",
       "mean_fit_time     0.064128  0.063345  0.07472  \n",
       "\n",
       "[4 rows x 36 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_search.cv_results_)[\n",
    "    [\n",
    "        \"mean_test_score\",\n",
    "        \"param_svc__gamma\",\n",
    "        \"param_svc__C\",\n",
    "        \"mean_fit_time\",\n",
    "        \"rank_test_score\",\n",
    "    ]\n",
    "].set_index(\"rank_test_score\").sort_index().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Other than searching for best hyperparameter values, `GridSearchCV` also fits a new model on the whole training set with the parameters that yielded the best results. \n",
    "- So we can conveniently call `score` on the test set with a fitted `GridSearchCV` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7376237623762376"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why are `best_score_` and the score above different? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `n_jobs=-1`\n",
    "- Note the `n_jobs=-1` above.\n",
    "- Hyperparameter optimization can be done _in parallel_ for each of the configurations.\n",
    "- This is very useful when scaling up to large numbers of machines in the cloud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The `__` syntax \n",
    "\n",
    "- Above: we have a nesting of transformers.\n",
    "- We can access the parameters of the \"inner\" objects by using __ to go \"deeper\":\n",
    "- `svc__gamma`: the `gamma` of the `svc` of the pipeline\n",
    "- `svc__C`: the `C` of the `svc` of the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "pipe_svm = make_pipeline(StandardScaler(), SVC())\n",
    "\n",
    "param_grid = {\n",
    "    \"svc__gamma\": [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
    "    \"svc__C\": [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    pipe_svm, param_grid, cv=5, n_jobs=-1, return_train_score=True\n",
    ")\n",
    "# grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Range of `C`\n",
    "\n",
    "- Note the exponential range for `C`. This is quite common.\n",
    "- There is no point trying $C=\\{1,2,3\\ldots,100\\}$ because $C=1,2,3$ are too similar to each other.\n",
    "- Often we're trying to find an order of magnitude, e.g. $C=\\{0.01,0.1,1,10,100\\}$. \n",
    "- We can also write that as $C=\\{10^{-2},10^{-1},10^0,10^1,10^2\\}$. \n",
    "- Or, in other words, $C$ values to try are $10^n$ for $n=-2,-1,0,1,2$ which is basically what we have above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Visualizing the parameter grid as a heatmap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_heatmap(param_grid, pipe, X_train, y_train):\n",
    "    grid_search = GridSearchCV(\n",
    "        pipe, param_grid, cv=5, n_jobs=-1, return_train_score=True\n",
    "    )\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    results = pd.DataFrame(grid_search.cv_results_)\n",
    "    scores = np.array(results.mean_test_score).reshape(6, 6)\n",
    "\n",
    "    # plot the mean cross-validation scores\n",
    "    mglearn.tools.heatmap(\n",
    "        scores,\n",
    "        xlabel=\"gamma\",\n",
    "        xticklabels=param_grid[\"svc__gamma\"],\n",
    "        ylabel=\"C\",\n",
    "        yticklabels=param_grid[\"svc__C\"],\n",
    "        cmap=\"viridis\",\n",
    "    );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Note that the range we pick for the parameters play an important role in hyperparameter optimization. \n",
    "- For example, consider the following grid and the corresponding results.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mtaemzgEDBhAeHm6wf/LkyXz55Zf8+uuvaLVaHBxabrVv3jkliXgHmx7m8wpy1CvbFGUaHQfXXQCg7yTDXnRpcQXF+dXJ00RbvIKU7UV5Wso0OhxdmjYvnZamTIeEtDJ9fHCIXXXZpidpjaaSNauVVe633l5/L7qkpIqZLyq9V7W6iqQTFaSk6PDzt2XBx164uzf/Gjn7nDIq4BtsPEEr+1R6ZZuiVKNj929KMhxyi3+T62kpwc7KqEFWab7JMtllyr4g54Z7c8HOvrjYK5/FTp7hPNVuYu3vNR4qH8usI9+RcNG8BWA3BPcGWqYXDRAUroxeZKXlmiyTXb3YKSi8cf9Hna9pR+dr9NedlJWU892cVSx9f22j6pr0xEjs7O3IzbjI0d3NXywXFBkAQFaq6VG/7HPKexIcEdCoumMGdiBmYAe9bWUl5Sx67SeWvLWqkS29PK6aJJ2UlIRGo8wPxMbGGi0TGxvLtm3bSEjQHx6r+b2+4+qWM4e5dRYXF3Py5Ek6depkdt0NKStWEpaqnoTn6KL815cWm3/bwt8dXJdJmUaHu5+KjoMNhz/Lii8lQ5Wz8bbUTcplxRVNTtLFRcriFBcX00PINfuK1E1fyLLm11KKi6vwD7DluqGmEyOAtryK5cv0b1tr3dqOue94GCw2a6rSYmVYtr75XydXZV9JUdP/r3evzaO0uBIvfwe6X+vV5Hpaioud8t6X6kzP65dU7zNnTtrD4dJI1/MdbuVIQQofn/qVVE0WrZx9mRY1mn5+nZjdZSoP7p3H+ZL6p4m6e7WhlbMf5TotGzLjzQmpQc5uShxlGtMXWyVFyufN3DnpvKwCfnj7V3atTSAzORtteQWhbYO48aHhDLv9Gu7/v1uwtbNhybtrzKqvx5BO3PzESAA+f2kJFdqmf+ZquLgrsZQWt2Dcmfl8//pydv2yj4wzF9CWaQlrH8L4x0Yx/O7BTHvzLmztbPlhzspmt7+5LHa4u7GSk5XFBF5eXri7Gx+uCAsL0ysLyurr3FzlKqx1a+NDRDXHZWdnU1xs3pBhzWuYqtPDwwMPDw+D9vxdWVkZhYWFej+WYvcKZcFS7PgQ7Oyvmo9SvX6qHuqeOMkZe/v655Q9PG1JTgsiOS2IPfv9+fpbb3x8bbnj1ou8Mdty/h/NsaV6qHvgTX7YNRD3v5ENl2LKKSvgPwc+I0l9jlJdOaeLMnjp0FecKcrAxd6JyeEN35UxOkRZMLYj5wjqiqaPVl1u8RsTWThrBUnxyRTmFVFSVMrJhBTeffgLvnhJWSF+5wvj8fJv+I6IiE6hvLhwOnb2dqz6ZANblrXMCMLlsG/9Ab6Z+SMn9p6iMFdNSVEpSfFneHvqgtoV4nfNvBmvAM8r3NKrKEmr1crcp6urq8kyNbdf1U10NcfVd2zd27bMTZJNbc/fzZkzB09Pz9qfmguG+ji6Kr3Rco3pq9gyjdLLdXJtWs81K6WY5P35APQ1sqpbacelgZryEuNtKavTxrrlG8vVTTnJajSme8k1+9zcm5ZkzpypYN9eZfj+FiOruuvj72/HkOscWbLMh06d7fn8Uw2bNjb8cJiG1PSSyzSmFzrV9Lad3Zr2f52RXEJSfBEA1958ZReM1dDolF5VffPEztX7inUNv8819QGsy9iHtkr/81pJFb+e3wVQuxjMFFc7Jwb5dwFabqgbLvUWHV1Mj+DU9LY1hc2/MPj54w3k56hROTnQa1jnesuGtg1izi/P4e7tyvpF2/j4P4ub/fo1NGolFifXfybulR+sJT+7EJWTitgRl+8hPOa6apL01SouLo6CgoLan7S0hh8O4NNKSSD5maZPTvmZyknJu1XTbtXYU92LjuzpRUCk8QsRJ1d7XDyVufb8DONtqWmjq7dDk4e6AUJDlWMz0k1fmNTsqynbWDULxmJ7O9CmTdMuKFQqG8ZXP8Tk93XNW2UO4N9KOXHlZpoe9s3NKNcr21h/LlOGdtvHuhESdXlv7TFXZoky9xrg6GWyjH/1vpqy9dZXmkdl9YrujFLjc741231V9fcqhwb2wMlORWZpHvF5xh8Q0hQ1i8XqWzHt30rZd6Ge+VtzVVZWkX5aWXPiF2L6NVtFBzJ39X/wDvBkw+IdvP/Y181+7boupCijOP5hhncv1PAPVfZlns1u9utVVlZy/qRyS6hfC6xOb66rJknXDHHXNxxdVKT0BmqGmeseV9+xNcf9/djL0Z6/c3R0rB0arztEXp9WHatfO19LromFYWmJSu89tFPjVzJW6qrYt0pJ0n0n1b9ytqb+mtczaMeRmnaY976a0jlGuRi4eLGKtFTjC8MOH1J6wTExjV+kp9NVsWKZ8l7e1sCCsYbUzI3n5Db/Np/wTsoFUtHFCrLSjCf95ETlMxjR2fSojimVuiq2/ayc8K+9+covGKtRc1+0p8qVICfjJ9L21Q8eqXsPtSmlunLSNMoJ3tPB+PtUs72knnlwgFEhfQBYl7GXKpr/II8apw8p92h7+roTGG58RKNt9UrouvdQN4eHjxKzpsj4RXZIm0Dmrn4B32BvNv24k/ce+ZKqqpaLGai9L9rTz4MgEwvD2sUqjyI+tb9lnnhXs8K7RN380a7mumqSdEREBAD5+fl6Q9h11fRCa8qCkkx9fJQ/8tRU4w8qqDnOz8+v3uFrY+0xVWfd+eW67WkJXkFOhHVRkt7+1YYPCTkTf5H8zFLsVbZGF3w15NjWHAqzy3F0taPbDYH1lu0yXPmjSvgt0+DJQ5WVVRz47YJeuaYKDrajazcl+a762fAPa++ectLTK1E5wpAGFnwZ88fmMrKyKnFzs2H0WPMejmHKjh1KMo2MbN4T1kBZuR3VVflM7vzVsAd4Yp+a3IxyHFQ2dB/S+Pm1A1vyyc/S4uRqS99RV75XUSOnrKD2wSLDAg3vjY3xjCTQyZtynZbdOcfMqrPmiWC9vI0PZ/fyUVY/H6/ngSYRrkF09GhNZVUl6zL2mvW65spJv8iJeCUJXXdLP4P9nfu1JSDMl/JSLXt+b/7zF6K7hRPaVnnOQs3r1hUc6c/c1f/BL0RJ0O889EWLJ2hQHs95fI+ySnzo5IEG+zsP6EBAaz/KS8vZvdb8xb2mRPeIJKy9MoVX87pX0lWTpNu3b197L/K+ffuMlqnZ3rNnT73tNb839rj6mFunq6sr7dqZ92jOxhj+gHK/4OYvUjh39FIvtji/nOWzlPvEB04Ow9n9Uq/y0MYs3hyzg4+n1r8atebe6B6jgxocou59UwgeAY5kp2hY9+FpvX3rPjxNdooGryBHYscbPnSlsR59TElWn3xUTOLhS09Su3ixkpkvKe/BlHtc8PC49LFf/1spw4Zkc+ft9Q+J1gx1j73RCZcGnqT11RfFpBsZdi8pqeJ/HxSxbm0Z9vZwy60tM3Q8/mHlhPLrZ+kkH7k0cqO+qOXr/6YAcP3dgbi4Xxqi3/t7Hs/dcIg3phh/bnGNP5crvehrxvri1IzpiMvh+5RNAEwOH6p3L7SHvQtPtVeeULXy/A69OemBfjEs7PsC73Y3fKjR8nPbKdRq6OfXiXEh+knwuoDuDA9U/qZXnNtmsk2jg5VedPzFk1ww8Tzv5vjxndUA3Pr0aKK7Xbq1093HlcfeUx749Mtnm/TmZvuP7cnn+95gzi/P69Xl6Kxi3ANDa+dz64rp346XFylfQJG4M4mkeP3FrYHhfsxd/QL+rXzY+MOOy5aga/wwR3mC3G0v3ER0nXuh3X3ceGKB8gUhqxasQ1N46QlgA27qw5dH3+etDa/o1eXorOLG6TcYjbvLoI68suw5AA5vO8aJvVc+SV81t2CpVCrGjBnD0qVLWbx4Mdddd53e/rNnz7Jz504AJkyYoLdvwoQJbNy4kR9//JFXX31V717pyspKlixRVjlOnDjR7PbcdNNNvPzyy+zYsYPU1FSDVd6LFysLK8aNG9ei90jX6DI8gEF3hbHtuzQ+uH0PbfspX7Bx8q88SgoriOzpxcgn9L+tqlRdQVayBm2Z6WFYdW45x/5UTtx9Jzb8kAiVsx33vNeVTx+IZ+NnyST+kU1wW1cyThaTebIIlbMdU+Z1bfZzuwFGjHTi3vtc+OYrDRPH59J/gApnFxt27iinsKCK2N4OPPuc/vC+Wl3JmdM6yspMn2BycnT8Uf2UsobujQb46stiZv+fmui29kRF2eHoaENWViXHjmopKKhC5Qhz5nrSvkPL/L/HXu/NDVMCWf/tBV695Sidr/HA0dmWI7sK0RTqaNfLjVueCtU7RqPWkXGmtN7/64JcLQlb8gEYYuZQ97zpJ7mYpQwH19zylZdZzis3H6ktc92t/lx3a/NGTgB25CSyPG0rk8IGsyD2CfZfPEmprpye3m1xd3DhcH4yX535Te8YV3tnWrsGoLI1PPUVaov5v8RFvN71Pp7pcAsTQgdyVpNFiLMv7dyV9+/b5N/ZnWv8wsbOxpbrg3oByjdeXQ671iTw88cbuOmR65m36WUO/HmU0uIyul/bCXdvV47sSuLb2Sv0jnH1cCasXTAqR/3Pm73KjkffvZsHXr+d04fOkpWWh529La2ig4jsrMSbnJjGG/d8ZNCOmYseq+21Azzz0X1G27tu4VaO/NX8efmdq/ay8sO1THhiNB/ueoOETYcpLS6jx7AY3L3dSNx+nG9m6j+v3NXThdYdWqFy+nvc9jw+fxoPvTOFUwkpZKXmYGdvS2i7ECK7KOfpM4fOMvu294y25fEF02hbfaHgUP2e2tnb8eHO12vL7F67n+9nL2923HAVJWmAGTNmsGzZMr7++msmTZrEyJHK/XoajYb7778fnU7HpEmT6NBB/+b1e++9l9dff52kpCRmzpzJ669ferNnzpxJUlISoaGhTJkyxeA1a+r69ttv6dOnT+32zp07M378eFatWsX999/PL7/8grOzcoL/7bff+Oabb7C1tdV7qEpLm/BiByJ7eLH9hzRSDhSgq6jEN8yFodOCuHZKOPaqxg+kxP+Sjq6iiqBoV8K7mTd8GtnTi2dXXMOGT5Svqjz0exauPipixwcz4pEo/Fq33PN2X33Ng16xDixaqGF/vBattorwcHseme7EfdNcm/QozpXLS9FqoV07e3r0aPj+5uf/4862rWUcPlTBnt3lFBZW4eJiQ+twO269XcVdd7vQOrxl//SmzAynXS83NnyXxcn9Regqqgho7ciND/oyampQk/6vt/+cg05bRWhbZ6K7m/f0qZSjxQZfc6ktr+L0wUs9/G6DW+62lvknV5FYkMJNrQbQ2TMCexs70ktyWXx2M8vStlJR1bj7dOMvJjFtzzvcGTGcXt5tGeDXGU1FGX/lHGX5uW3syzP9hSED/DrjpXKjQFvM9uzDzQ3NpE9eWMyRv04y7oGhdOoTjZ2DHRnJ2fw0by0rF6w3+97kMk05389dRdsekYS1DaJ1h1Y4OjtQlK9h/+YjbPt5Lxu+3260PndvZdRK5eTA8DsGmHyNQ9uOt0iSBvjoqa9J3HGc8dNH0rl/eyXu0xdYMvdnls9bQ4XWvIcUlWnK+W7WMtr1iiKsQyvCO4fi6KxCfbGY+A2H2LpsF79/s8VkfeEdQ+nYz3D0s+62tOPpTQvSCJuqyzlG0Qz79+9n+vTptb+fPn2anJwcQkNDadXqUg9u5cqVBAdfGiqdN28ezzzzDDY2Nlx77bUEBASwbds2MjIyaN++Pdu3b8fPz3AedseOHYwYMQKNRkNMTAwxMTEkJiaSmJiIq6srGzdupF8/w3kgGxvlpP/HH38wZMgQvX1ZWVkMHDiQkydPEhwczKBBg8jKyuLPP/+kqqqKDz74gCeeeKJR70thYSGenp68sec6nNyuqmusek1wv3wnPUu2s7Rxj3e8GnyeajjvaA0cb2p4FfrVSFdnYa61qKjSsoVVFBQUNLgY2GLP8oWFhezebXiP4blz5zh37tJzcMvK9Fe0Pv3003Tp0oV333239qsqW7duTVxcHHFxcSYfdDJgwAAOHjzIrFmz2LhxI8uXL8ff358pU6bwyiuv0KZNG6PH1ScgIIB9+/bVflXlqlWrcHV15YYbbuC5555j2LBhja5TCCGE9bDYnrQwTnrS1kV60tZDetLWozE96atmdbcQQghxtZEkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhbK/0g0QTbMtLxqHMtWVbsY/ppvz2SvdhCuiu+O5K90EIcQVJD1pIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslP2VboC4vPr79mB0yGAiXVthb2NHRmk2f2bt45f0zeiqKptUZx+fLlwf2J+27uG42btQXFFCZmk2+y8eZUnaOoPytthyQ9AABvvH0to1GCdbRzS6EpKLz/NH1h62ZO2hiqrmhlrrzzVqVi3K5/SxMiq0VYSEOzBsvAc33++NvYON2fVMHniGC+crGix3z9O+THnCt/b3tNPl7PmzmH3bijl9rIyCPB0qR1vCohwYeIM7E+7xwtm15a+P168u4YdvizlxVItWC2ERdoy9yZkp09xwaETc1/e/QPo5XYPlHn3GnelPudf+fipJy9LFGo4lajl/Tkd+XiVVVVUEBNnRu58jd9/vSrsODk2KrT7X+nflptABtHELwd7WjnRNLhsv7Gdp2p9N/owP8OvMqOC+dPQIw93BhaKKEs6X5LI39zjfpmzQKzuvxyN0945usM7f0vfw1vElTWrP3w26KZZxDwwjMiYMB5U96WcusPmnv1i54Hd0FQ3/39W4fvIAnv1kWr1lXpr4LvEbE/W2+Yf60Pv6rvQaFkN093C8Az3RaXVkJGexZ/0hVi74nYJcdZNiq8/gm/tx4/SRRHULx15lT/qpTDYv3sbyeWsaFfeIe4bw/NeP1lsmbtTr7Ft/QG+bra0tAyb0oW2vKNr1jKJtryg8fN3RVegYqbq9KSE1SJL0Vez+yEnc2Oo6Kip1HC5IokRXRlevdtwbeRO9fWL475EFlFdqza7P3saOp9vdw0D/npTpyjmuTia/XI23yp3WLsGMDRlikKTtbex5LeZRYjzboq3UcrTwDIXaIvwcveji2ZZuXu3p59uVOcc+b5GYF/xfFiu+zsfOHnpc44Kzqy0JOzV8PjeHXZuKeWtRKxydzEuQg0e5U3DR+B++Ol/Hrk3FAHTv56y377m7zpGTWYHK0YZ2XZzo2seeizkVHN1fyolDOfz2UwHvfB9KYKuWS1hz/lvAd18VY28Pffo74uJqw54dZbw3R82WjWV8/r0vTk7mJeoRo524mGc8uRXkV7JlYxkAfa9R6e07EF/Od18V4+tvS2SUPd17OlBSUsXJExWsWKLhl+Ua3pjnxZjxLs0Lto5H247n5rDBVFTqSLh4ihJdGT28o3koeizX+HXi+QOfUl7Z8IVWDXsbO17qNJkhgd0p1ZVztOAsF8vVeDu6E+EaxMTQQQZJek/eCTJLLxqtz8HGjmFBPQFIyD/V9EDreOjNO5gwfQQV2goObj1OSVEp3QZ3ZNqsW+k3qjsv3vQO5aXm/10DpJ+5wJFdJ43uy03PN9g248uH6HxNOyq0FZw+lMqxvadx93alQ68obn9uLCPvGcyLN73DmcNpTQnRqEfm3cvEJ8dQoa3gwOZESopK6T40hgfm3k2/sbHMuGE25aXljarz/KlMjmw/bnRfzvk8g23O7k68svTZJrW/qa66JH3ixAl+//134uPjiY+P59ixY+h0OmbNmsXLL79c77EbN27kvffeY8+ePRQXFxMeHs6kSZOIi4vDzc2tSe05deoUs2fPZuPGjWRnZ+Pv78/w4cN55ZVXiIqKalKd5ujr05UbW11HSUUpLx5+nzPF5wBwt3dldpcn6OwZzeTWY/kmZaXZdT4aPZmB/j35K/cg808uRl1RXLvPBhvauocbHDM6eBAxnm25UJrLi4ffJ6fs0sks2q01s2Iep59vNwb59WJbTnwzIobtvxex4ut8nF1teO/HMNrFOAFQkKfj2clpJO4r4et3c3n4JX+z6quv3I+f5LFrUzGhkQ507aOfdMKiHLj3aV+GjHHX6zFnntPy0v3nSUkq563nM3l3cVgTojS0aX0J331VjIurDQt/8qVTFyV5XszTcd/tuezfW87/3ink+Zc9zaqvvnJffqwk/YgoO3r1ddTbd81AR37d7E9UtP7FR2VlFQs/L+ad1wt59T8FDLzWCU+v5o8kDPCL4eawwWgqSnlq/0ecLDoPgIeDK+/1eJiuXlHcFzWKT079anadz3W4hSGB3dmWfZh3ji+lUKv/Ge/gYfh/9sPZzSbrGxLQjWFBPSnSlvBn1sFGRGfcNWN6MGH6CDTqEv4zei6nDp4FwMPHjbmr/0NM/3ZMeXkiX7zcuB77kV0nefeRL80un5OezyczFrPpx52o8y69R56+7ry48BG6De7Iiwun82Dsi1RWNn+UrP/43kx8cgwadQnPDnmVUwnJAHj4uvP2plfpMqgj9866jc+eX9Soeo9sP87b9y0wu7xOq2Pjd1s5fSCZk/uTUecV8emBdxr1mo111c1Jf/zxxzzxxBMsXLiQxMREdDrzhkDmzZvH9ddfz7p16+jcuTPjxo2joKCAN954g9jYWHJychrdlh07dtCtWzcWLlyIl5cXEyZMwMvLi4ULF9K1a1f++uuvRtdprpvDRgCw/NyG2gQNoK4o5pPTyh/wmJDBuNg5mVVfV892DA3sS0pxOm8d/1IvQQNUUUWSOsXguC5e7QD4LWOrXoIGOFWUyrac/QC094gwqx31WbwgF4A7HvapTdAAnj52PDkrEICfv82nqND8YTFT1i0tAGDkrYYJ7Z3vwxh1q6fBkHZQqANPzVbacWBXCdkZjevtmPLZ/CIApk13q03QAN4+drw8W2nf4oXFqAubNvRb18qfSgCYcKthb7hVmL1BggawtbVh6kNuhLW2o6Skiv17G9fbMeXOiGEALD67uTZBAxRqi3n/xAqlna0G4GrmZ7ynd1tuCO7NmaIMXkv8Vi9Bg/IZP1aY2qg2jg7uC8CmCwmN6tGbcvtzYwH4ad7a2gQNUJhXxPxnlAR144PDcPFwNnp8S5kz9WN+/miDXoIGKMhV8/aDyqhYaHQQHfs0PA1gjjviJgKwZO7PtQkaoDBXzYePfgHA+EdH4uLRcqM0xpRqypg75X8se281B7ccobhAc1lfD67CJB0TE8Nzzz3H999/z7Fjx7j77rsbPCYhIYFnn30WOzs71qxZw59//slPP/3E6dOnGTZsGCdOnODhhx9uVDs0Gg233norGo2GuLg4EhMT+fHHH0lMTCQuLo7i4mJuvfVWSkpKmhqqST4qT9q5RwDwZ/Y+g/3HCs+QXZqHytaBXt6dzapzTMi1APya/kej5vm0Zg6n//2E2FjZmVpOHFKGYYeO9zDY36W3MwEh9mjLq9izpXmvlbivhLQzWuzs4YZJhq9Vn+jOl3qfWRnNP2lfyNSReFB5j8eMNzwx9+rjSFCIHeVlsPWP0ma91v69ZSSfrsDeHsbf3PiToV31uJ3Ksf5y5vBTedDRozWgJMC/SyxI5kLpRVR2DvT162hWnRNCBwKwPG1rk+ey6/J39KKnT1sA1mbsbnZ9vsFetO+ljL79sdTwAv/IXyfJSstF5eRAnxFdm/16TZWTfpH8HGU+2j/Up9n1+Yb40KE62W9evN1g/5Edx8lKzUHlpKLv6B7Nfj1Lc9UNd0+bpr8Iwta24euQOXPmUFVVxdSpUxk1alTtdhcXF7788kuioqJYvnw5x48fp0OHDma145tvviE9PZ127doxe/ZsvX2zZ89m+fLlJCUl8e233/LQQw+ZVae5olyVIblCbTFZZblGy5wqSsXfyYcot9AGh5ltsaGbZ3sAjhScwsvBnUH+vWjlHIi2soIzxefYlZNAaaVhDyn+4lEG+PVkVPBgtuXs1+tNt3ENY5CfMr+9JWtPU8NV4jmiJGh3L1uCw4zP9bbr4kRWehEnj5Qx9Mamv9Zv1b3ovkNc8fFv3J/Q+ZRL75FvQPP//I4lKgna08uG0NbG64vp6kBmuo5jiVrGjG/6a9X0ogdd54R/gF2jjv3p+2JSzujw9bOlWw9Vwwc0INq9FQAF2mIySw3nDgFOFKYR6ORNW7dWbDaSyOuyxYae1Yu/DuafwVvlztCA7oS5BKCtquCU+jx/Zh+iVGf+KMDI4FjsbGw5rU4nSX2u4QMa0KabMp1UmFfEhbPGR/ZOJqQQEOZLm66t2bLM/AuDkKgA7pk5EU8/d0qLy0g5ep6/1iZQmFfU6HZ6+Ljh7qVcxOVl5jf6+L+L7hEBKL3mzJQso2WS9p0moLUfbXpE8sePO8yuOyQ6iHtn3Y5XgCclRaWkJKay65d9FF6GRW9NddUl6cYqLy9nzZo1AEyePNlgf3h4OAMGDGDbtm2sXLmSuLg4s+pduVKZ67399tsNLhRsbW257bbbmDVrFitWrGjxJB3opKw0zikzfvICyCm/qFe2/vr8cLZXhgzbu0fwcJvban+vcW/ETbxz4msOFyTpbd98YTcxHm0ZGtiXT3q9wtHCMxRo1fipvOngEclZTTofn1pCVj1tNUdmmpKsAkNML8byD7bXK9sUJZpK/lyj/AGPMjLU3ZAfPlbe97YxjgSFNn/h2Pk0pTce3Mp00gwKtqsu2/Rhfo2mknWrlSQ98bb6e9ElJZXMekm5kClSV3HyhJbUFB2+/rbM+8gbN/fmD+AFOyuf26zSfJNlssuUfUHODffmgp19can+THfyDOepdhNrf6/xUPlYZh35joSL5i0AuyG4N9AyvWiAoHA/ALLSjF94A2RXL3YKCjdv3UWNzte0o/M17fS2lZWU892cVSx9f22j6pr0xEjs7O3IzbjI0d3NXywXFBkAQFaq6SnH7HPKexIcEdCoumMGdiBmoH7Hq6yknEWv/cSSt1Y1sqWXh9Un6aSkJDQaZV4hNjbWaJnY2Fi2bdtGQkL9V+N11ZStr8665VqSs50ynljfVX/NPhe7hueu3B1ca//9WNs7Oa5O5pvklZzTZBLk7M/d4eOI9YnhpY4P8vSBuWSUZteWr6KKD04uIkVznrvDb6SbV/s6bSjjYP4JMuuUbypNsTI86eRiegWzs4uSHDRFTR/K/HONmpLiKnz87eh7nWvDB9SxblkBW1arsbWDR19p3MnElOJiZVGOs7PpxOfiqrwnRUVNX8CzfnUpmuIq/PxtGTy0/vFqbTmsWqY/jRPW2o7/e9vLYLFZU7mY8Rkvqd5nzpy0h8OlC4/nO9zKkYIUPj71K6maLFo5+zItajT9/Doxu8tUHtw7j/Ml9a9R6e7VhlbOfpTrtGzIbN6CyBrObkocZZoyk2VKipQpDXPnpPOyCvjh7V/ZtTaBzORstOUVhLYN4saHhjPs9mu4//9uwdbOhiXvrjGrvh5DOnHzEyMB+PylJVRom7/+w8VdiaW0uAXjzszn+9eXs+uXfWScuYC2TEtY+xDGPzaK4XcPZtqbd2FrZ8sPc8xfWHu5XHVz0o2VnKwsQvDy8sLd3d1ombCwML2yDVGr1eTmKld2rVu3rrfO7OxsioubN0d6udlwKfHllefz38QFnCpKpbSynJTi87x+9FNSitNxtndiUuj1esc62zkxs9PD3BtxE2sztvLwvte4ZefTPL7/dXbnHuKmVsN4u9vz+Km8/uGomua3n5Qe4vUTPbCzN//e4/07NLz/kjJU9+AMf7r0vrwLe1ra8h+VC9kbJ7lg30DcHp62HEkN4UhqCH/GB/LJQh+8fW2Zelsu77xe8E80t9HqfsZzygr4z4HPSFKfo1RXzumiDF469BVnijJwsXdicvjQBusbHaIsGNuRcwR1RcuvO2kp8RsTWThrBUnxyRTmFVFSVMrJhBTeffgLvnhJWWB65wvj8fJveO1FRKdQXlw4HTt7O1Z9sqFRw+3/tH3rD/DNzB85sfcUhblqSopKSYo/w9tTF9SuEL9r5s14BTR+tKylWX2SVquVoUtXV9O9oprbrwoLCxtVZ3311r2lq756y8rKKCws1PtpSIlOueJ0sjM991ezT6Nr+ARSoru04GjThd1UVOkveKqkivWZyoKObl76Q0f3RU4g1ieGdRnb+Sp5BRml2ZRXaknVZPBe0kL2XzxKoJMvd4aPa7Ad9XGpXkldqjHdWyzRKD1oF7emfezTzpSTuE95L0bdYv4f7+G9Jcx88Dza8iqmPOnDLdO8m/T6xrhW95JLSkyPDmiqe9tubuZfVNSVcqaChH1Kr7Shoe6/8/O3Y9B1Tixc6keHzvZ8/WkxWzY2bwEbgMaMz7hz9b5iXcOvV1MfwLqMfWir9HuAlVTx6/ldALWLwUxxtXNikH8XoOWGuuFSb9HRxfRoRE1vW1PY/AuDnz/eQH6OGpWTA72G1b/ANLRtEHN+eQ53b1fWL9rGx/9Z3OzXr6FRK7E4uf4zca/8YC352YWonFTEjujW7Pqay+qTtKWbM2cOnp6etT81PfD61CwW83M0nQz8VMq+LBOLbvTqK82lsnq164VS48N8Ndu9VZeuuG2xYUhAHwC25hiuMgfYWr36vO4weFMEVs/vZtVzW1N29Wrqps4F19x2FRPrTFgb8xY/HYkv4cX7zlOqqeLOR3245ym/Jr22KSGh1fPs6aaTdGaGknBahTVusVeNFUuUXnTP3ioi2zRthkylsmHsTUqC37S++Uk6s0T53AY4epks41+9r6ZsvfWV5tV+xjNKjc/51mz3VdXfqxwa2AMnOxWZpXnE5xl/QEhT1CwWq2/FtH8rZd+FeuZvzVVZWUX66QsA+IWYfs1W0YHMXf0fvAM82bB4B+8/9nWzX7uuCynKdJh/mOn1M/6hyr7Ms82fOqusrOT8yQwA/FpgdXpzWX2Srhnirm/IuahIWeHo4WHe7TZ1h81N1VtTZ0P1xsXFUVBQUPuTltbwE3zOFCkrST0c3AhwNP7BjnZThuFPFzVcX2llOedLsmrrNKZme2mdHomngzsqWyUhaiqMn5iLq4cC3eybd39j2+pbmwovVpJhYmFY0mGlDW1jGj8vqtNVsWGFMoox6jbzPgdHE0qYce95NEWVTJ7uw33PtWyCBugYo7y/+RcrOZdq/JauxENavbKNodNV8ctyJUlPvL15/0fO1esFcnOaP09Zc1+0p8qVICfjJ9L21Q8eqXsPtSmlunLSNMoJ3tPB+OhXzfaSBlZ4jwpRLkzXZext0cfdnj6k3KPt6etOYLjxz1Lb6pXQde+hbg4PHyVmTZHxv9+QNoHMXf0CvsHebPpxJ+898iVVVS0XM1B7X7SnnwdBJhaGtYtto5Tdf6ZFXtPDVzmHl6ibf0HZXFafpCMiIgDIz8/XG6auqyYx1pRtiLu7Oz4+yokjNdX4ww9q6vTz86t3qN3R0REPDw+9n4bklufXPljkWn/DhWsdPaLwd/KhvFJL/MUjDdYHsDNHWeBmqsdbM8x9sujSyUFdUVz72NGa+7b/rr17JIDJW8XM5R/sQPuuSvLdvMpwSuDw3hKy0itwUNnQZ0jjFnwB7P6jmNwsHS5utlw72vjahbqOHyhhxj2XEvT9z7d8ggZl5XZMNyX5rlllONQXv6eMzHQdKkcYfJ15D/Woa+vmMrKzKnF1s+GGMY0/vq6/tisXcBFRzV+vmlNWUPtgkWGBhvfGxnhGEujkTblOy+6cY2bVWfNEsF7exoeze/koq5+P1/NAkwjXIDp6tKayqpJ1GXvNel1z5aRf5ES8koSuu6Wfwf7O/doSEOZLeamWPb8favbrRXcLJ7RtMEDt69YVHOnP3NX/wS9ESdDvPPRFiydoUB7PeXyPskp86OSBBvs7D+hAQGs/ykvL2b22+Qtxo3tEEtY+BKD2da8kq0/S7du3x8VF6SHs22d8SLZme8+ePc2ut6ZsS9bZGMvSfgdgUuj1RLmG1m53t3fl4Ta3AbAmfSuaOvN1/Xy7sqDny/xfzOMG9f2avgW1tphYnxhuCBqgt2+QX6/ai4HV6Vtqt1dU6diTexiAO8PHEu4SondcV892jAsZAhh/6EpjTX5UGTX44ZM8khIvxVVwUccHM5Vhu5umeOHmcWnYd/t6NfcOS+a5O+sfUagZ6r5urHvtKnFTThwq5T9TzlOsvrwJusaDjymjGF98VMTRw5d6efkXK5n9stLuyfe44u5xqd0b15Uw9ros7ru9/mHRlT8pvehR45xxaSDub78oIiPdsJdcUlLJJx+q2fBbKfb2xp9W1hTfp2wCYHL4UNq6tard7mHvwlPtlSdUrTy/Q29OeqBfDAv7vsC73Q0fTrT83HYKtRr6+XViXIh+ErwuoDvDA5W/1RXntpls0+hgpRcdf/EkF0w8z7s5fnxnNQC3Pj2a6G6XHsPr7uPKY+8pD2765bNNenOz/cf25PN9bzDnl+f16nJ0VjHugaG187l1xfRvx8uLlC+gSNyZRFK8/qLZwHA/5q5+Af9WPmz8YcdlS9A1fpijPEHuthduIrpHZO12dx83nligPBtj1YJ1aAovPQFswE19+PLo+7y14RW9uhydVdw4/QajcXcZ1JFXlj0HwOFtxzix98onaau/BUulUjFmzBiWLl3K4sWLue666/T2nz17lp07dwIwYcIEs+udMGECGzdu5Mcff+TVV1/Vu1e6srKSJUuUlZMTJ05sgSgM7c47xK/pfzAu5Dre6vYch/JPUFZZTlev9rjZu3C04DSLU1frHeNi50yoSxAOtobDouqKYt4+8TUvdXyQ6dF3MCb4Ws6VZBLk5E8bN2VYcUnqb8RfPKp33JfJy2njFkawsz/zerzAicIU8srzCXTyq33W98H8E/x8blOzYx44wo0J93qx8pt8Hp+YSo/+Ljg5K1+wUVRYSUysE1Of1R/+L1JXknZGS3mZ6RPMxZwKdv+hTFuMurXhkYwXppyjWF2Jm4ctORcqmPtcptFydzziQ2sz57brM+wGZ+6aWs53Xxdzx0059BvgiLOzDbt3lFFYWEWPWBWPP6ffbrW6iuTTFZSVmZ6nzs3R8edmJcFNMmOoe9FXxbw1q5CoaHsi29ijcrQhO0vHiaNaCguqUDnCa2960bZ9y3yxyI6cRJanbWVS2GAWxD7B/osnKdWV09O7Le4OLhzOT+arM7/pHeNq70xr1wBUtoanvkJtMf+XuIjXu97HMx1uYULoQM5qsghx9qWdu3Kh+23y7+zONf6FDHY2tlwf1AtQvvHqcti1JoGfP97ATY9cz7xNL3Pgz6OUFpfR/dpOuHu7cmRXEt/OXqF3jKuHM2HtglE56r/v9io7Hn33bh54/XZOHzpLVloedva2tIoOIrKzEm9yYhpv3PORQTtmLnqsttcO8MxH9xlt77qFWznyV/Pn5Xeu2svKD9cy4YnRfLjrDRI2Haa0uIwew2Jw93Yjcftxvpmp/7xyV08XWndohcrp73Hb8/j8aTz0zhROJaSQlZqDnb0toe1CiOyiTAOeOXSW2be9Z7Qtjy+YRtvqCwWH6vfUzt6OD3e+Xltm99r9fD97ebPjBknSAMyYMYNly5bx9ddfM2nSJEaOVO7z02g03H///eh0OiZNmmTwtLE9e/YwZcoUAI4f1//Dvffee3n99ddJSkpi5syZvP76pf/AmTNnkpSURGhoaO3xl8MXZ5ZzrPAMo4MH08EjqvqrKnNYnvY7v6T/QUVV4+YGD+Yf56mEN7k5bATdvNrTx6crJbpS9uUl8mv6Fg7kG5688soLePrAm4wNHkIf3y6Eu4bQ3iOC4opSDhecZGvWPjZe2EllC83dPfZqADG9nFm1KJ+j+0trv6ry9kc8uPk+bxxUjV/hvGFlIRVaiGinomOPhm+dUhcoC5CKCiv5fbnp1fg33OzRIkkaIO41T3rEqvjh22IOxJdToYWwcDvun658VaWqCXH/uqKECi1Et7OnqxlPCXvyP+7s3FrGkUNa9u0uR11YibOLDWHh9ky6XcVtd7kSFt6yp5z5J1eRWJDCTa0G0NkzAnsbO9JLcll8djPL0rY2+jMefzGJaXve4c6I4fTybssAv85oKsr4K+coy89tY19eksljB/h1xkvlRoG2mO3Zh5sbmkmfvLCYI3+dZNwDQ+nUJxo7BzsykrP5ad5aVi5Yb/a9yWWacr6fu4q2PSIJaxtE6w6tcHR2oChfw/7NR9j28142fL/daH3u3sqUkcrJgeF3DDDYX+PQtuMtkqQBPnrqaxJ3HGf89JF07t9eifv0BZbM/Znl89ZQoTXvMbtlmnK+m7WMdr2iCOvQivDOoTg6q1BfLCZ+wyG2LtvF799sMVlfeMdQOvZrZ7C97ra04+lNC9IIm6rLOUZxBezfv5/p06fX/n769GlycnIIDQ2lVatLQ2IrV64kODi49vd58+bxzDPPYGNjw7XXXktAQADbtm0jIyOD9u3bs337dvz89Ictt2zZUtvzNvY27tixgxEjRqDRaIiJiSEmJobExEQSExNxdXVl48aN9OtnOLdUn8LCQjw9PRm17gEcXFvmBP9v8GTohoYLXYWC7Br/WMZ/u0dPXp7v5bV0jjc176l7/1a6Iuv7jFdUadnCKgoKChpcZ3TV9aQLCwvZvdvw3sRz585x7tyl5+eWlek/vebpp5+mS5cuvPvuu7VfVdm6dWvi4uKIi4sz+aCT+gwYMICDBw8ya9YsNm7cyPLly/H392fKlCm88sortGnTpvEBCiGEsBpXXZIeMmRIkxcwDB8+nOHDh7foa0VHR7Nw4cImtUcIIYR1s/rV3UIIIYSlkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWChJ0kIIIYSFkiQthBBCWCj7K90A0TQnsvyxc3G60s34x/zh2elKN+GKeM3/yJVuwj9OW2l3pZtwRajKy690E4QFkp60EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWyv5KN0BcXiNbdeTONrF08AzEwdaO1KKL/JJ2mG9O7qaiqrJJdQ4LbsfNET3o6hOCp8oZdXkpZ4vz2JZ5mgXHt+mV7eMXznfXTqm3vlf2r+HH5P1NaosxR38/z74fz5B1ohCdthLv1q7EjA6j791tsHMw/7r0fyPXU5Be0mC5wdM7MPjhDgbb81KL2P7ZCZL/ykZzsRwXbxWR/fwZ9HAHvENdGxWTOZb+qubjrws4eLSM8vIqoiMdmDzRnace9MbBwcbseqJ6J3P2XEWD5f77vA8zn/Gtt8zBI2X0HZWKVgttIhxI2hVhdjvMdV1AFyaG9SfaLRh7W3vOa3L4PTOBJanb0DXxMz7QvxNjQ/rQ0SMMDwdniipKOafJYXduEt8kb9Qr+79eD9HDu02Dda5J38uco0ub1J6/GzSxD+Mevp6oLuE4qOxIP32BzT/uZMWHv6Gr0Jldz/V3D+a5zx+qt8xL4+ayb8MhvW22tjb0H9+btj0jadsjkrY9IvDwdUdXoWO0W/1/780x+OZ+3Dh9JFHdwrFX2ZN+KpPNi7exfN6aRsU94p4hPP/1o/WWiRv1OvvWH9DbZmtry4AJfWjbK4p2PaNo2yuqNu6RqtubElKDJElfxV7sOoJ72/ZFW6njr+wUNBXl9POP4D9dhjM0uB1Tt31PWWXDJ+MaDja2vN1nAqNDO1FSoeVA3jlySovxc3KlrYc/U6L7GCTpGtmlRWzLPG10X3JRbpPiM+b3uYfY8/0ZbO1tiOjjj8rZjpQ9OWx+/wgn/8xk8qf9cXCyM6uujsNbockvM7qvpEDLyT8zAYjo7WewPy0hl8UP7URbqsO/jTthPX3JPlnIoV/SOLYhnTs/G0BoN5+mB/o3T8/M5sMv8rG3h+sGuODmasMfO0qYMTuX1b8Xs+7HVjg7m3eBMmmsGzl5xk94efmVrP69GIAh/V3qrae8vIp7n8ikwvyPWKM90W4ct7YeREWljviLpyjRldPLuw3T245hgF8nnk74nPJGfMbtbex4JeZ2hgZ2o1RXTmLBWS6WF+GjcifSNZCbwwYYJOndOSfIKLlotD4HWzuuD+oBwP4845//xnr47buY8PgoKrQVHNhylNKiUroN6cy0N+6g35gexI15k/JSbaPqTD+dSeLOJKP7ctINY3N2d2bmD082qf1N9ci8e5n45Bgl7s2JlBSV0n1oDA/MvZt+Y2OZccNsykvLG1Xn+VOZHNl+3Oi+nPN5Btuc3Z14ZemzTWp/U0mSrnbixAl+//134uPjiY+P59ixY+h0OmbNmsXLL7/c5Ho3btzIe++9x549eyguLiY8PJxJkyYRFxeHm5tbC0agb3hIe+5t25cibRl3bf2Wo/lKQvFWObNw8N3E+rXmqc5DmHt4YwM1XTK711hGh3Ziw/njvLx/NRfLL/UybYCuPq1MHntGncOM+F+aHI85TmxOZ8/3Z1C52HP3VwMJ7uQFgOZiGd9N20FaQi5/zj/G8OdizKqvvnI7vzrJyT8z8Ql3o3Uv/SStLalgxfN70Zbq6H9/O4Y+2al23+YPjrLzyyRWPL+XR34ZbvYFQ31+/q2ID7/IVxLzilB6dnUCICdXx/BbzrF9TymvvJXL26/6m1VffeXeXpDH6t+LadfGgUH9nOutZ9Z7uRw6Ws6j93my4KsC8wMy0yD/ztzaehCaijIei/+EJPV5ADwdXPig50N0847kgTY3sODkGrPrfKHjJIYGdmNrViJzjy2jQKup3WeDDZ08wgyO+e7sFpP1DQ3oyvVBPVBrS/gj65DJcua6ZlwvJjw+Co26hOevn82pAykAePi6MXfdS8QM6MA9r97C53GLG1Vv4s4k3n3gU7PLV2h1bPphO6cPnOXUgRTUeUV8vHdOo16zMfqP783EJ8egUZfw7JBXOZWQDICHrztvb3qVLoM6cu+s2/js+UWNqvfI9uO8fd8Cs8vrtDo2freV0weSObk/GXVeEZ8eeKdRr9lYMidd7eOPP+aJJ55g4cKFJCYmotOZP3Riyrx587j++utZt24dnTt3Zty4cRQUFPDGG28QGxtLTk5OC7TcuIfbDwDgsxM7axM0wMXyEl5L+A2Au9r0xs3e0az6rvGPYEJ4N04UZPHk7uV6CRqgCjiYd75lGt9EO75QegL972tbm6ABXLwdGflSNwD2/niGUnXjehnGHPz5LADdJ7Q23LcqFXVWKT7hblz3eEe9fdc93hGfcDcKM0s49Gtqs9sB8OaHyhX/C4/51CZoAD9fO+bPCQBgwdcFFBQ2/zP99Q+FAEy93aPecnsPlDJ3/kVuHufGxDGX52L07ojrAPgu5Y/aBA1QoNXw3vGVAEwM7Y+rnZPR4/+ul3c0o0JiOV2UwczD3+klaIAqqjhS2Lj/szGtegOw8cKBRvXoTbnjP+MB+OmdX2sTNEBhbhHzn/wagBsfuR4Xj/ovoJqrTFPGW1M/ZvkHazn451GKCzUNH9QMd8RNBGDJ3J9rEzRAYa6aDx/9AoDxj47ExaP+0Z3mKtWUMXfK/1j23moObjlCccHljRskSdeKiYnhueee4/vvv+fYsWPcfffdzaovISGBZ599Fjs7O9asWcOff/7JTz/9xOnTpxk2bBgnTpzg4YcfbqHW6wt0cq/t1a5OSzTYH5+bRrqmAEc7e64Nijarzruj+wCw8FTT57Ivp8ILJaQn5gPQeXSowf7WPX3xCHJGV17JqW0XmvVaaQm55KYUYWtvQ9cbDZP0ic0ZSjtGtsLGVn8u2MbWhk4jlf+bE5symtUOgPMZFew9oAzJ3zHR3WD/wL7OhIXYU1ZWxdpNzTuh7NhTwonTWuztYcqtppN0aWklU5+4gLenHf97w7zee2P5OXrQyVN57zdkJhjsP1SQwoXSizjaOXCNn+F6AWMmhfUH4KfU7U2ey64rwNGTWJ+2AKw+v7fZ9fmGeNO+tzL3vXnJToP9R3YmkZWWg8pJRZ+R3Zv9epbCN8SHDn2U89TmxdsN9h/ZcZysVCXuvqN7/NPNu+xkuLvatGnT9H63tW3e9cucOXOoqqpi6tSpjBo1qna7i4sLX375JVFRUSxfvpzjx4/ToYN5JxFzdfQKAuBimYZzmnyjZRIvZhDi4kknryDWnDtSb3222NDPPwKAvTmp+Dm6MiasM5HuvpTrdBzLz2T9+WNodKZ7qH6ObjzaYRCBzu6UVVZwRp3LloyTZJQUNinGv7twXBlOdfZ0MLkoK7izF4WZJVw4nk+MkURurgPVvejogYG4+Rn20jKr2xLc2cvo8SHVvfzMY/lNbkONhMRSAHy8bYls7WC0TK9ujqSlV3AgsYw7JhgmcnN9/aPyfzVqmCtBAaZPHa+8lcuxk+V891EQAX72HE1q3DyhOdq5Kxc6BeXFZJQanw8+XniOQCdv2rqHsPHCgXrrs8WmNqEevJiMj8qNYYHdae3qj7aygiR1On9mHaZEZ34so0JisbOx5ZQ6nRPqc2YfZ0p0twhA6T1eSMk2WiYpPpmAMD/adItgy0+7zK47pE0g9/z3Frz8PSgpKiXl6Dn+Wh1PYW5Rs9vdXNE9IgAl7syULKNlkvadJqC1H216RPLHjzvMrjskOoh7Z92OV4CnEndiKrt+2Udhrrolmt4iJElfBuXl5axZo8yDTZ482WB/eHg4AwYMYNu2baxcuZK4uLgWff0wVy+AehNgRomSSEKry9Zbn5s3bg7KsHh3n1a82n1U7e81/tNlOE/vWcFf2SlG62jj4ceTnYfobdN20/Hd6b28dXgjuqqqBttRn/zzymImj2DTw10egc7VZZveoyzXVHBsfToA3SeEG+wvK9ZSkq+cyD1NtMUjSGmH5mI55ZoKVC5N/zNMSVWGUFu3Mp6gAcJClH3JqU0f5i/WVLL0F+XEdd8dpnvRO/eWMO/TfG68wbVZFwQNCXb2BuBCWb7JMlmlymc8xLnhBXohzr64VE/9dPZszbMdJtT+XuPRtmN49fD37L9o3gKw0cGxAKxOb34vGiAwQhmVyEozvdAy+5yyLyiicSMYMf3bE9O/vd62spJyvpu9nJ/eXd3IlrasoEhlyiYr1fT0YE3cwREBjao7ZmAHYgbqd5LKSspZ9NpPLHlrVSNbenlIkr4MkpKS0GiURBAbG2u0TGxsLNu2bSMhwXCorrlc7VUAlFSYvurXVCgn7L8nW2O8VJfmt17vNY6E3DTmHtrIGXUOrd18eKbzdQwJbstH19zKhM1fcLbo0qpItbaMr0/+xYb0E6SocymqKKO1qw+TIrpxZ5veTG3bDxd7FTP3m7+4x5iyYiVZOTibXohVkwxryjbF0d/PU66pwM3PkehBgQb7y+vUbaotdZNyWXHzkrS6SBmWdXU2fYuVm6uyr1Dd9CHcn35RU1RcRVCAHaOHGR+p0Ggque+pC3h62PLR3MadLBvLxU753JbW07Mt0SnTAH9PtsZ4Oly6oJrR6WYSC84yP2kNqZosWjn78mD0SPr7deTNbvdy3+4POFdS/3qSHt5taOXiS5lOy/qMlrm90MVdGbUp1Ri/4wCgtFgZWTF3TvpiZj6L3/yZv1bHk5GchbasgtB2wYyfPoJhkwdy/+t3YGtny49vXd5Fn/VxcVdiKS02HXdJUePizsvM5/vXl7Prl31knLmAtkxLWPsQxj82iuF3D2bam3dha2fLD3NWNj+AZpI56csgOVlZ2ODl5YW7u/HeRFhYmF5ZU8rKyigsLNT7+afVPf1fKCnkvu2LSczPQKPTcrzgAg/vXMKJgizcHBx5sF1/vWOPFWQy59AG9uWkklNWTKmugqTCLOYc2sAzu1cAcFtkTzp6GiY8S3RgpTLU3WVca2ztrefP5+vFyufu7ls8sLc3fkEQ90YOJ89oee81f4ID/2XX/3VCyi4r5Jn9X3BCfY4SXTmnijKYceAbThdl4GLvyF0RQxqsbmyIsmBse/ZR1BUN32t/pezbcIiF/13KiX1nKMwtoqSolJP7k3ln2qd8PkNZIX7nixPwCqh/oeC/zb71B/hm5o+c2HuKwlw1JUWlJMWf4e2pC2pXiN8182a8AjyvcEslSV8WarUyLOjqavqBFTW3XzWUdOfMmYOnp2ftT01yr09xdQ/aubpHbYyLvTIEWqQ1fXX69/oAVp49hLZSf5VwJVUsSY4HoH9AZIP11fg9/XjtyvPrgtuZfZwxjq5KUtCWmF7BXK6p0CvbWLkpRZxLUEYJut9kuGAMQFWnblNtqWlHc9pSw91N+RMuLjE9XVBUrOzzcG/an3vS6XJ27FV6KqZWdW/ZqWHBVwWMGuZS76KylqKp7iU72Zn+jDtX97Y1FQ1/xuuW+S19H9oqw8/4qnO7AWrnrk1xtXPi2gDl9r3V6XsafG1zadTK/4GTi+mRASdXpbetKWz+hcHP89eRn12IyklFr+Fdm11fU2nUSixOrqbjdnZrubhXfrC2Nu7YEd2aXV9z/csud61PXFwczzzzTO3vhYWFDSbqmsViwc6mT5bBzsoV4nkTC8vqOl+cT2VVFbY2NqQVG1+kk1as1OPv3Lh5yNOFOXTyCiKokcf9nWeIMlxZmGn6j7TwQole2caque0qrIcvvpHG2+vo6oCzpwMlBVoKMjQEtje8Eq9po4u3qllD3QDhYcrxaedNzzenpSv7IsKa9lo1C8YG9nGifbTxpLjqt2KqqiDtfAVDJ+ovksqvvvXrfOalfe/9nz/dY8y7/c+YzOqHhwQ4epksE+CkvPemHjSiV1/pRSqrKrG1sSW9xPAhFkDtdl/H+j+rw4O642SnIrPkIvvyTjX42ua6cFZZLOYfavopbzX7aso2R2VlFemnMvHy98CvVcs9eKexahbJ+Yc1HHdmi8RdyfmTGUrcoVcu7hqSpC+DmiHu4uJik2WKipRVkx4e9fc6HB0dcXRs3Mms9sElji6EungZXeEd4x0MwJGLmQb7/k6j05KszqWNhx/eKuMJrma7pp55cGO8HJU5pOJGHvd3QR28ACjJL+fiuWKjK7wzjuQrZTt6Nbr+Sl0Vh35NA6D7ROO96Nq2dPQi+a9sMo7k025IsMH+9KNNb8ff9ahOdLkXK0lO1Rpd4R1/UOkl9uhi3v3Cdel0VSxaWn1v9OSGh/4Sj5v+fywtreLPXcoFSn4z79lOUiuL97xUrgQ7eRtd4d3BI7S6bMP375foyknVZBPhGoinyvgImFf1Z7yhFd5jQpR1KGsz9lFF8xZE1nX6oHKR6OnnTmCEv9EV3u16KSNZde+hbg53X2XEr0R95Ybsa+6L9vTzICgiwOgK73axyq1pp/afaZHX9PBVzuEl1aMXV5IMd18GERERAOTn59cOff9dWlqaXtmWdKFEzaHqB4uMDTN8alYv3zBCXDwp01XwZ6Z5V/q/nT8KmB7OHlC9/VAjHmgS6OROrG/r6uPSzT7OGI8gZ0JivAA4stbwdpfU/bkUZpZgp7I1uuCrIae2ZVKUXYrK1Z6O15t+shpA+6HVF0DrzlNVqX+Srqqs4ug65T1qP8wwgTdWaIgDvbsrifqHFYafte27S0hLr8DR0YbRwxo/grB2UzEZF3S4u9lyyzjTDyWZN8sfXUZboz+blivvV5sIh9ptDT1StCHZZQUcLVAeLFLz2M26unpGEOjkTZlOy64c4499/Ls/LhwGINbH+LMDaoa5jxWkmawj0jWQTp6tqayqZE0LrequkXM+jxN7lZXlQ2/rb7C/c/92BIT5UV5azp51B5r9etHdIwhrFwLAiX0t80jTpsg5n8fxPcp5aujkgQb7Ow/oQEBrJe7da5u/EDe6RyRh7ZW4a173SpIkfRm0b98eFxflJLRv3z6jZWq29+zZ87K04ZMTyr2CD7bvT6fq+6ZBWan9ag/lvu3vTu+lqM5c3PUh7Vk34hEWDrrLoL5Fp/aQX17CkOC23Bap3+YxoZ0Z17qLUu60/olpSnQfvFWGKy7bewTwSf/bcLZ34GxRHhszTjQx0ksGTFPmtXd+dZKM6t4qgCa/nHWvHwSg9+1ROLlf6m0e35TOxzdu5Ltphg9JqOvgz0pC6DyyVYND1N3Gt8Y9wIm8s0VsmX9Mb9+W+cfIO1uEe6AzXcfV3yM314wnlCG5ufPz2H/o0pV/bp6Ox+KUXsejUz3x9Li02nzl2iI6DUzh+lvqv3+35gljt413w9XFsk4Xi1L+AOCuiOtq75sG8HBw4ZkOEwBYcW4nxbpL78lg/858f81zvN/zAYP6lqVtp1Crob9fR8a36qu3b1hgN0ZUXwwsTTN9H+7YVspDf/blneJCaX7TAqvHD9W3Bd363Diiu0fUbnf3ceOxD6YC8MvHG/TmZvvfGMsXB9/mzd/0b/V0dFYx7qHra+dz64oZ2IGZPyrP5k7ccZwT+1qmh9pUP8ypXmT6wk1E97jUUXD3ceOJBcozLlYtWIemzpPPBtzUhy+Pvs9bG17Rq8vRWcWN028wGneXQR15ZdlzABzedowTe698kpbh7stApVIxZswYli5dyuLFi7nuuuv09p89e5adO5UnBk2YMOGytGFj+gkWntrNPdF9+em6+/grKxlNhZZrAiLwVDkTn5PK+0e26B3j5uBIlLsfKlvDj8XF8hKe2r2cT/rfxqyeY7i7TW9Oq3No7epN5+qh8/nHthr0zJ/oeC0zulzPsYJMzhXnU1VVRZibN528grCzseV8cT4P71xisBitKdoPDaH35Cj2Lj7D13f9SWRffxyc7UnZnU2pWktoDx+ufUz/MZ1lRVpyU4qoKDf9+sW5ZZzcqkwLGLs3+u8cnO2Z+HZvFj+0kx1fJJG0JRP/aHeyT6nJPlWIg7Mdk97p3SLP7Qa4aZQbj9/vxf++zKf/2DSGDnTB1cWWzds15BdUMqC3E//3H/35vAJ1JSdOayktMz0cm5VTwdpNypTNfWYMdf/TtmUfYWnqdm5pPZBPez9KfJ7yBRuxPtG4O7hwKD+Zz0+v1zvG1d6JcNcAo5/xAq2GVw5/z9xu9/J8x0lMChvA2eIsQpx9ae+hXAR8fWYjf+Ua75nb2djWJvKWujf673b9Gs/K+euY8NhI3t/6Ggf+OEJpcRndr+uMu7criTtPsPA1/W/acvV0Iax9CA5O+lMh9ip7HvvgXh6cO5lTB86SnZaLnb0trdoGExmjrHs5cziV1+/8n9G2PPbBvUR3VxKmg6PyftrZ2/H+n6/VltmzLoHFc35udtw7V+1l5YdrmfDEaD7c9QYJmw5TWlxGj2ExuHu7kbj9ON/MXGIQd+sOrVAZifvx+dN46J0pnEpIISs1Bzt7W0LbhRDZRblwPnPoLLNve89oWx5fMI22PWridqiN+8Odr9eW2b12P9/PXt7suEGSdLPMnz+f+fPn06dPH7799lu9fTNmzGDZsmV8/fXXTJo0iZEjRwKg0Wi4//770el0TJo0qcWfNlbX6wd/Z3/OOe5sE0sP31DsbexILb7IZyd28s3Jv9A28tGHO7OSuXHjZzzcfiD9AyIZFtKeYm0ZWzJOsvDUHnZkGV5tf3xiO718w4j28GNAQBTO9g4UactIyD3HxvQTLEne3+z56LpumNGVsB6+7PvxDOcO5qGrqMI71JX+97el793RjfqqyhqHV6dSWVGFfxt3WnU1byFJWA9fHlh2Hds+PUHK7myOb0zH1ceRLuPCGPRwB3zCWvarKt+f7U//Pk58/HUBu/aVotVW0SbCgRceU76qUqUy/6sqa3y3TI1WC53bq+jbs/Hz2f+ED5J+4XB+ChPC+hPjFY69jR3nS3L5LmULS1K3UVHVuIu/fXknuXf3PO6OGEqsTzQD/TtRXFHGzpxjLE3dzt68kyaPHejfCW+VGwXlxWzLMnwcb0v55LlFHN2VxLiHr6dTv7bYOdiRcSaLn975hRUf/kaF1ryYyzRlfP/GCtr2jCKsfQjhnUJxdHag6GIx+zcdZuuK3Wz4dqvJ+lp3bEXHvoZTA3W3pSU1bxqrro+e+prEHccZP30knfu3V+I+fYElc39m+bw1VGjNe/5Bmaac72Yto12vKMI6tCK8cyiOzirUF4uJ33CIrct28fs3W0zWF94xlI79DO9Gqbst7XjLxW1TVdXMRz1dJfbv38/06dNrfz99+jQ5OTmEhobSqtWlobSVK1cSHKz0HP/73//y2muvce2117JlyxaDOufNm8czzzyDjY0N1157LQEBAWzbto2MjAzat2/P9u3b8fMz/JrD+hQWFuLp6UnUty9i52KZJ87L4bZ2Lfd90/8mr/nX/8jWq9HgxJuudBOuCNexV/YLaq6UyvKWf2yspauo0rKFVRQUFDS4eFh60tUKCwvZvXu3wfZz585x7tylebuysobvuazx9NNP06VLF959993ar6ps3bo1cXFxxMXFmXzQiRBCCAHSk/7XkZ60dZGetPWQnrT1aExP2rKWawohhBCiliRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslCRpIYQQwkJJkhZCCCEslH1jCu/du5eMjAw6duxI27Zt6y2blJTE8ePHCQkJITY2tlmNFEIIIayR2Uk6JyeHYcOG4e7uzoEDBxos7+3tzfTp09FoNJw5cwYvL69mNFMIIYSwPmYPd3/33XcUFRXx2muv4e/v32B5f39//u///o/8/Hy+++67ZjVSCCGEsEZmJ+m1a9fi6urKPffcY3bld999N25ubqxevbpJjRNCCCGsmdnD3YmJifTt2xcHBwezK3dwcKBPnz4cPny4SY0TprWecgR7G/P/L/7tEreFXOkmXBHv2WuudBP+cc4jkq90E66IyivdAGGRzO5J5+XlERQU1OgXCAwMJDc3t9HHCSGEENbO7CTt6OhIcXFxo19Ao9Hg6OjY6OOEEEIIa2d2kg4KCuLQoUONfoFDhw41qQcuhBBCWDuzk3T//v1JSUlh586dZle+Y8cOkpOT6d+/f5MaJ4QQQlgzs5P0nXfeSVVVFQ8++CAFBQUNls/Pz+fBBx/ExsaGO+64o1mNFEIIIayR2Ul6+PDhDBs2jKNHj9KrVy9++eUXqqqqDMpVVVWxatUqYmNjOX78OEOGDGHEiBEt2mghhBDCGjTqsaA//vgjAwYMICkpiQkTJuDl5UXPnj0JCAgAICsri/3795Ofn09VVRXR0dEsWbLksjRcCCGEuNo1Kkn7+vqyZ88eHnvsMX744QcuXrzIpk2bsLGxAajtWdva2jJ58mT+97//yeNAhRBCiCZqVJIG8PDw4Ntvv+W1115j9erV7Nu3j+zsbEB5FGivXr0YO3YsUVFRLd5YIYQQwpo0OknXiIyM5PHHH2/JtgghhBCiDvk+aSGEEMJCSZIWQgghLJQkaSGEEMJCSZIWQgghLJQkaSGEEMJCSZIWQgghLJQkaSGEEMJCSZIWQgghLJQkaSGEEMJCSZIWQgghLJQkaSGEEMJCNfnZ3eLfYfDN/bhx+kiiuoVjr7In/VQmmxdvY/m8NegqdGbXM+KeITz/9aP1lokb9Tr71h/Q22Zra8uACX1o2yuKdj2jaNsrCg9fd3QVOkaqbm9KSA3q79uDUcGDiXANxd7GjozSbLZm7+XX9M3oqiqbVGcfn64MC7yGtm4RuNm7oKkoIaM0m4T8o/yU9ptBeVtsGRE0gMH+vQlzCcbJ1hGNroSU4vNsydrNluw9VGH4fezNcXBdJjt/SCPjhJoKbSV+rV3oMTaYwVPCsXMw/3r8jeFbuZhe2mC5EY+14frpbQy255zVsPHTM5zalUtRXjluPiqir/Hl+kei8A1zaVRMDbHGzzdI3NYUtyTpq9gj8+5l4pNjqNBWcGBzIiVFpXQfGsMDc++m39hYZtwwm/LS8kbVef5UJke2Hze6L+d8nsE2Z3cnXln6bJPa3xT3RU5iXMhQKip1HC44QamujC5e7bknYgK9vbvw2tH5lFdqza7P3saOp9rdywC/npTpyjmhTiZfW4i3gwdhLsGMCR5ikKTtbez5b+fH6OzZFm2llmOFZyjQqvFz9CbGsy1dvdrTx7crc49/3mJxr5pznO2LUrG1tyG6rw8qFztO785j7bsnOfZHNg980QsHJzuz6uoyIpDifOPvUUmBlqN/KN9616aPj8H+5P0X+fyBeLQllQRGuxLR05vMk2rif07n8PoLPPhVL8K7eTU5zrqs8fMNEre1xS1J2oilS5eyYMECDh48SHl5OdHR0dx55508/fTTODg4mF1Pbm4uv/76K/Hx8cTHx3PgwAFKSkoYNmwYGzduvIwRQP/xvZn45Bg06hKeHfIqpxKSAfDwdeftTa/SZVBH7p11G589v6hR9R7Zfpy371tgdnmdVsfG77Zy+kAyJ/cno84r4tMD7zTqNc3Vx6cr40KGUqIr5eXD73OmOA0Ad3tX/i/mSTp5RnNH67EsTFlpdp3ToyczwK8nf+Ue4KNTi1FXFNfus8GGtm7hBseMCh5EZ8+2ZJXm8tLheeSUX6zd18atNf/X+Qn6+XZnoF8vtufENyNiReLGLLYvSsXRxY6Hv+1NaCcPAIovlvPJ1H0k789n3YenGPef9mbVV1+5P75M5ugf2fhHuBAV6623r7xEx3fPHEJbUsnQByIZ9XTb2n2/zTvJ5s+T+e7pQ/xn7QCzLxhMscbPN0jc1hY3yJy0gaeeeopbb72VHTt20KdPH0aOHElqaiovvPACQ4cOpaSkxOy6tm3bxtSpU5k/fz67du1q1LHNdUfcRACWzP259gMNUJir5sNHvwBg/KMjcfFo2eHHvyvVlDF3yv9Y9t5qDm45QnGB5rK91s2hNwCw4tzvtQkaQF1RzGenlwAwOvhaXOyczKqvi2d7rgvox9ni87xz4ku9BA1QRRVJRSlGjwP4LXOrXoIGOF2UWpuY27tHmhdYAzZ/fgaA66ZF1iZoAFdvFRNndgRg5+I0StTmjyCYsnfFeQB6T2xlsG/fz+cpzCrDP8KFG56M1tt3w5PR+Ee4kJ9Zyr5V6c1uhzV+vkHitra4QZK0np9//pkPPvgANzc3du/ezfr161m+fDknT56kS5cubN++nZkzZ5pdX2BgIA899BCffvope/fu5ZNPPrmMrb/EN8SHDn2Uk+TmxdsN9h/ZcZys1BxUTir6ju7xj7TpcvNRedLWPQKArdn7DPYfU58muywPla0DPb07m1XnmOBrAfg1/Y9GzWVrzRxOL9QWmV2nKQUXSkk7XAhAj7FBBvsje3njFeRERXklx7fmNOu1kvdfJDtZg629Db3GhxjsT9yYBUC3UUHY2tro7bO1taHbqCC9ck1ljZ9vkLjBuuKuIcPddbzxxhsAzJgxg549e9Zu9/Pz46OPPmLQoEHMnz+fmTNn4unp2WB911xzDddcc03t74mJiS3faCOie0QAylVmZorxE2LSvtMEtPajTY9I/vhxh9l1h0QHce+s2/EK8KSkqJSUxFR2/bKPwlx1SzS9yaJcwwBQa4vIKss1WuZ0USr+jj5EuYY1OMxsiw1dvZQe8dHCU3g5eDDQrxetnAPRVmlJLjrHrtwDlFaWGRy7/+JR+vv1ZFTQYLZnx+v1pqNcwxjo14syXTlbsvc0Ndxa548p77uLpwM+ocZ7EaExHuRnlnL+mJoeY4Kb/Fp7Vyg94A6D/PDwdzTZlrAYD4N9AKGdParLFTa5DWCdn2+QuK0t7hqSpKudP3+evXv3AjB58mSD/QMHDiQsLIy0tDTWrl3LHXfc8U830WxBkQEAZKWa7jlln1MSWXBEQKPqjhnYgZiBHfS2lZWUs+i1n1jy1qpGtrTlBDj5ApBddtFkmZzqfYHVZesT6OSHc/WweDv3SB5qc1vt7zXuKZ/Au0lfcbggSW/75qy/6OwZzXUB/fio16t6C8fau0dyVpPOp6d/JLvMcGFKY+WdU6ZQvIJND+F7BSn7Lp5r+nRLuaaCQ+syAegzyXCou7S4Ak31YjOvYOd621Gcp6VcU4HKpWmnH2v8fIPEbW1x15AkXS0hIQEAHx8fIiONzxXGxsaSlpZGQkKCRSdpF3flJFlabNjLq1FSpNxi4+Jh/IT6d3mZ+Xz/+nJ2/bKPjDMX0JZpCWsfwvjHRjH87sFMe/MubO1s+WGO+YuyWlJNAi2rNL26s1RXple2Pu72rrX/fjT6Tk6oz/BNykrOay4Q5OTHneE3EusTQ1zHh3j2wJtklGbXlq+iig9PLiKlOJ27wm+s7ZHXtOFQ/gky65RvjrLiCgBULqYXYtXsK60u2xQH112gTKPD3U9Fh8F+JtsBoHI23pa6bSwt1jU5SVvj5xskbmuLu4Yk6WrJycpihNatW5ssExYWplf2n1BWVkZZ2aUPZ2Fh84YKm2rf+gMG9wwmxZ/h7akLOHPoLA+/ew93zbyZ377cTH5WwRVpY0uysbk0p5pXns9rRxZQUaUkohTNeeYc+4T3uscR7tqKiaEjWHDq+9ryznZOPNNuKj29O7E6/Q/WZW4jtzyfICc/JobewPhWw7jGtzsvHn6P3PL8fzq0JtlTvWCs1/gQ7OyvvqUs1vb5riFxX2KpcV99f21NpFYrcxCurq4my7i5uQH/bKKcM2cOnp6etT81Fwr10aiVYU0nV8N5wxrObkpvUlPY/BXnKz9YS352ISonFbEjujW7vqYo0SlX0o62KpNlnOwc9cqaUx/A5qzdtQm6RiVVrM9U5r66euoPl02NmEisTwzrM7fxdcoKMkqzKa/UkqrJ4P2kb9h/8SgBTr7cGT7OvODq4eiqXGeXa0w/yKFmn5Nr067Js1OKSdmfDxhf1V23HaDcilVfO5S2NP0WLGv8fIPEbW1x15AkbeHi4uIoKCio/UlLS2vwmAspylCqf5jpuVf/UGVf5tnmD7tWVlZy/mQGAH6hhg+4+CdklSpzUn6O3ibL1OzLMmMuOKs0j8rqFd0XSo3PhV0oU7Z7qy4tlLLFhmsD+gCwLdv44rRt2crah78n96bwaaWcnPIzTV941OzzbmXerWd/V3PbVWRPLwIijV/EOrna4+KpPEMgP8P4ibKmHa7eDk0e6gbr/HyDxG1tcdeQJF3N3d0dgOLiYpNlioqUW2Y8PIyvXr0cHB0d8fDw0PtpSM19hJ5+HgSZWEjRLlZ5nOOp/WdapJ0evsr7V6JuuJd6OSQXn1Pa4eBGgKPxP+Y2bspUxumi1AbrK60sI70kq7pO44nJo3reumauG8DTwR2VrZKsNDrjyUpT3Ut3s2/+PZ0hHZXPgyZfS9454/dsnktURn5adWr857ZSV0X8KuWE1dvIgrG6WnVSPgNpicZHms4daXo76rLGzzdI3NYWdw1J0tUiIiIA6u2p1uyrKWupcs7ncXzPKQCGTh5osL/zgA4EtPajvLSc3WsTmv160T0iCWuv3Ddb87r/tNzyfE6qUwAY7B9rsL+jexv8HX0or9Sy/+IRs+rcmbsfMN3j7ealPCjkZNHZ2m3qiuLax462q75v++9qtpu6VawxvIKcCOuiJL2E1ZkG+5PjL5KfWYq9ytbogq+GHN+aTWF2GY6udnS7IbDesjHDlRPowd8yqazUfy55ZWUVB3/L1CvXVNb4+QaJG6wr7hqSpKv16KHcBJ+bm2tyYdi+fcpDMureQ22pfpizAoDbXriJ6B6XVqu7+7jxxIJpAKxasA5N4aXe14Cb+vDl0fd5a8MrenU5Oqu4cfoNtfM+dXUZ1JFXlj0HwOFtxzix98p9qJedWw/AxNARtfdNg7JS+8E2twGwNuPP2p4sQF+fbvyvx0xe6/yEQX2r07eg1hYT6xPDiED9k8NAv161FwNrMrbUbq+o0rE37zAAd7QeR7iL/kM/uni2Z2zwdYDxh640xdAHogD444tkzh291Istzi9nxaxjAPSfHIaz+6VH2h7eeIG3xmzn06n1t2FP9b3R3UcHNThEHXtTKzwCHMlO0bD+Q/3PwfoPT5GdosEzyJFYIw9CaSxr/HyDxG1tcYOs7q4VGhpK79692bt3L4sXL+all17S2799+3bS0tJwdHRk9OjRV6iV5tu5ai8rP1zLhCdG8+GuN0jYdJjS4jJ6DIvB3duNxO3H+WbmEr1jXD1daN2hFSon/eeT26vseXz+NB56ZwqnElLISs3Bzt6W0HYhRHZRhpDPHDrL7NveM9qWxxdMo231H5aDo1K3nb0dH+58vbbM7rX7+X728mbFvCfvEKvT/2BsyHW82fW56i/YKKerV3vc7F04VniaH1JX6x3jYu9MqEtQ7RB1XeqKYt498RVxHR/ikeg7GBN8LedKMgl08qsdOv8pba1Bz/yr5GW0cQ0jyNmfd7vPIEmdQm55PoGOfrR1V571fSj/BKvOt8zz22OGBzDwrtZs/y6V+bfvJrqf8gUbp/7Ko6SwgoieXox8Qv8xnaXqCrKTNVSUmX6SWlFuGcf/VOb4+phYMFaXytmOu97ryucPxLP5M+UZ30Ft3cg8WUTmySJUznbcPa9bs5/bDdb5+Za4rS9ukCSt58UXX2TChAm8+eabjBo1qrbHnJuby/Tp0wF47LHH9J42tnLlSuLi4mjVqhWbNm26Iu025aOnviZxx3HGTx9J5/7tsXOwI+P0BZbM/Znl89ZQoTXvvtkyTTnfzVpGu15RhHVoRXjnUBydVagvFhO/4RBbl+3i92+2mKwvvGMoHfu1M9hed1va8eY/zxngy+RlHCs8w+jgwbR3j8Lexo7M0mxWnPudX9M3U1Fl/tfZARwsOM4zB+YwKfQGunq1p7dPV0p0pezLS2R1xh8czDf8Bp288gKePjiHscFD6OPTldYuIbRzj0BTUUJiQRJbs/ex6cJOKlvwqyrHv9iBiB5e7PwhjbMHCtBVVOIb5sJ104IZNCUce1XjB83if8lAV1FFYLQrrc385qrInt48s6I/Gz85zcldeRz+/QKuPip6jQ9m+CNt8Gvdcs9WtsbPN0jc1ha3TVVVVct+qe2/3JNPPsmHH36Ig4MDw4YNw9XVlU2bNpGfn8+AAQPYsGEDzs6Xbpj/5ptvmDp1KuHh4aSkpBjU169fv9p/Z2dnc+bMGTw8POjYsWPt9pkzZzJmzBiz2ldYWIinpydDGI+9jfnfyPVv57atefOY/1YDfK78cNs/bX3nf25hphBXQkWVli2soqCgoMHFwNKT/psPPviAAQMGsGDBAnbu3IlWq6VNmzbMmDGDp59+GpXK9H24xuzevdtgW2Fhod727OyWefqUEEKIq4v0pP9lpCdtXaQnLcTVpzE9aVndLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgoSdJCCCGEhZIkLYQQQlgo+yvdACHMUTQo60o34YpYj8eVboIQ4gqSnrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBZKkrQQQghhoSRJCyGEEBbK/ko3QFxeg2/ux43TRxLVLRx7lT3ppzLZvHgby+etQVehM7ueEfcM4fmvH623TNyo19m3/oDeNltbWwZM6EPbXlG06xlF215RePi6o6vQMVJ1e1NCapA1xgzWGbc1xgwStzXFLUn6KvbIvHuZ+OQYKrQVHNicSElRKd2HxvDA3LvpNzaWGTfMpry0vFF1nj+VyZHtx43uyzmfZ7DN2d2JV5Y+26T2N4U1xgzWGbc1xgwSt7XFfdUm6aVLl7JgwQIOHjxIeXk50dHR3HnnnTz99NM4ODg0ur74+HjefPNNtm7dSkFBAcHBwYwdO5aZM2cSEBBgUF6n07Fy5Uri4+Nrf/Ly8rCzs6OioqIlQqxX//G9mfjkGDTqEp4d8iqnEpIB8PB15+1Nr9JlUEfunXUbnz2/qFH1Htl+nLfvW2B2eZ1Wx8bvtnL6QDIn9yejzivi0wPvNOo1zWWNMYN1xm2NMYPEbW1xw1U6J/3UU09x6623smPHDvr06cPIkSNJTU3lhRdeYOjQoZSUlDSqvmXLltGvXz+WLVtGeHg448ePx9bWlvnz59O1a1dOnTplcIxareaWW27hzTffZMOGDeTlGV6VXU53xE0EYMncn2s/0ACFuWo+fPQLAMY/OhIXD5fL2o5STRlzp/yPZe+t5uCWIxQXaC7ba1ljzGCdcVtjzCBxW1vccBUm6Z9//pkPPvgANzc3du/ezfr161m+fDknT56kS5cubN++nZkzZ5pdX3p6Ovfccw8VFRV8+umn7NmzhyVLlpCUlMRdd93FhQsXmDx5MlVVVXrHOTg4cOedd/LOO++wefNmDhw40MKRmuYb4kOHPtEAbF683WD/kR3HyUrNQeWkou/oHv9Yuy4na4wZrDNua4wZJG6wrrhrXHXD3W+88QYAM2bMoGfPnrXb/fz8+Oijjxg0aBDz589n5syZeHp6Nljf+++/j0ajYfjw4Tz44IO12+3s7Pj444/59ddf2bt3L7///js33HBD7X5XV1e+++672t9TUlJaIDrzRPeIAJSrzMyULKNlkvadJqC1H216RPLHjzvMrjskOoh7Z92OV4AnJUWlpCSmsuuXfRTmqlui6U1mjTGDdcZtjTGDxG1tcde4qpL0+fPn2bt3LwCTJ0822D9w4EDCwsJIS0tj7dq13HHHHQ3WuXLlSpP1ubm5ceONN7Jo0SJWrFihl6SvpKBIZY48KzXHZJnsc7kABEcYzqfXJ2ZgB2IGdtDbVlZSzqLXfmLJW6sa2dKWY40xg3XGbY0xg8RtbXHXuKqSdEJCAgA+Pj5ERkYaLRMbG0taWhoJCQkNJmm1Wl073xwbG2uyvkWLFtW+tiVwcXcGoLS4zGSZkqJSpayHs1l15mXm8/3ry9n1yz4yzlxAW6YlrH0I4x8bxfC7BzPtzbuwtbPlhzkrmx9AE1hjzGCdcVtjzCBxW1vcNa6qJJ2crCwoaN26tckyYWFhemXrU3eI2lSdjamvKcrKyigru/ThLCwsvCyv05B96w8Y3DOYFH+Gt6cu4Myhszz87j3cNfNmfvtyM/lZBVekjS3NGmMG64zbGmMGibsuS437qlo4plYr8wiurq4my7i5uQHmJbua+uqrszH1NcWcOXPw9PSs/am5KKiPRq2sXndydTRZxtnNSSlb2LiV7sas/GAt+dmFqJxUxI7o1uz6msIaYwbrjNsaYwaJ29rirnFVJemrUVxcHAUFBbU/aWlpDR5zISUbAP8wX5Nl/EOVfZlns5vdxsrKSs6fzADAL9Sn2fU1hTXGDNYZtzXGDBK3tcVd46pK0u7u7gAUFxebLFNUVASAh4eH2fXVV2dj6msKR0dHPDw89H4aUnMfoaefB0EmFlK0i22jlN1/pkXa6eGrvFcl6tIWqa+xrDFmsM64rTFmkLitLe4aV1WSjoiIAKi3t1mzr6ZsfcLDw2v/nZqa2uz6/ik55/M4vkdZ8DZ08kCD/Z0HdCCgtR/lpeXsXtv8BW/RPSIJax8CUPu6/zRrjBmsM25rjBkkbrCuuGtcVUm6Rw/lRvbc3FyTC7n27dsHoHcPtSkeHh5ER0frHdec+v5JP8xZAcBtL9xEdI9LK93dfdx4YsE0AFYtWIem8NITcwbc1Icvj77PWxte0avL0VnFjdNvqJ33qavLoI68suw5AA5vO8aJvVfuQ22NMYN1xm2NMYPEbW1xw1W2ujs0NJTevXuzd+9eFi9ezEsvvaS3f/v27aSlpeHo6Mjo0aPNqnPChAm8/fbbLF68mKlTp+rtKyoq4tdffwVg4sSJLRNEC9m5ai8rP1zLhCdG8+GuN0jYdJjS4jJ6DIvB3duNxO3H+WbmEr1jXD1daN2hFSon/Web26vseXz+NB56ZwqnElLISs3Bzt6W0HYhRHZRVr2fOXSW2be9Z7Qtjy+YRtvqPywHR6VuO3s7Ptz5em2Z3Wv38/3s5RKzxC0xS9wSd932tkgtFuTFF19kwoQJvPnmm4waNaq2h5ubm8v06dMBeOyxx/SeNrZy5Uri4uJo1aoVmzZt0qvvqaeeYsGCBWzcuJHPP/+cBx54AFC+QGP69Onk5+fTu3dvRowY8Q9FaL6PnvqaxB3HGT99JJ37t8fOwY6M0xdYMvdnls9bQ4XWvC/6KNOU892sZbTrFUVYh1aEdw7F0VmF+mIx8RsOsXXZLn7/ZovJ+sI7htKxXzuD7XW3pR1Pb1qQf2ONMYN1xm2NMYPEbW1x21T9/aHTV4Enn3ySDz/8EAcHB4YNG4arqyubNm0iPz+fAQMGsGHDBpydL930/s033zB16lTCw8ONPr5z6dKl3HHHHeh0Ovr27UtERAR79+7lzJkzBAYGsn379tph8bqmT5/O/v37AeV+55rnd/ft27e2zJgxYxr1LPHCwkI8PT0ZwnjsbRr/bV5CCCGurIoqLVtYRUFBQYOLga+6njTABx98wIABA1iwYAE7d+5Eq9XSpk0bZsyYwdNPP41KpWpUfbfccgtRUVG88cYbbNu2jYSEBIKDg3n00UeZOXMmgYGBRo87evQou3fvNthed1uHDh0M9gshhBBwlfakr2bSkxZCiH+3xvSkr6rV3UIIIcTVRJK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWSpK0EEIIYaEkSQshhBAWyv5KN0BcXoNv7seN00cS1S0ce5U96acy2bx4G8vnrUFXoTO7nhH3DOH5rx+tt0zcqNfZt/6A3jZbW1sGTOhD215RtOsZRdteUXj4uqOr0DFSdXtTQmqQNcYM1hm3NcYMErc1xS1J+ir2yLx7mfjkGCq0FRzYnEhJUSndh8bwwNy76Tc2lhk3zKa8tLxRdZ4/lcmR7ceN7ss5n2ewzdndiVeWPtuk9jeFNcYM1hm3NcYMEre1xW3xSXrp0qUsWLCAgwcPUl5eTnR0NHfeeSdPP/00Dg4Oja4vPj6eN998k61bt1JQUEBwcDBjx45l5syZBAQEmDzuwoULzJo1izVr1pCeno6XlxeDBw8mLi6Onj17Gj1m69atbN++nfj4eOLj4zl79iwA27ZtY+DAgY1ue2P0H9+biU+OQaMu4dkhr3IqIRkAD1933t70Kl0GdeTeWbfx2fOLGlXvke3Hefu+BWaX12l1bPxuK6cPJHNyfzLqvCI+PfBOo17TXNYYM1hn3NYYM0jc1hY3WPic9FNPPcWtt97Kjh076NOnDyNHjiQ1NZUXXniBoUOHUlJS0qj6li1bRr9+/Vi2bBnh4eGMHz8eW1tb5s+fT9euXTl16pTR45KSkujatSsLFizA1taWm266ifDwcJYtW0bfvn1ZuXKl0eOeeOIJXnrpJVasWFGboP8pd8RNBGDJ3J9rP9AAhblqPnz0CwDGPzoSFw+Xy9qOUk0Zc6f8j2XvrebgliMUF2gu22tZY8xgnXFbY8wgcVtb3GDBSfrnn3/mgw8+wM3Njd27d7N+/XqWL1/OyZMn6dKlC9u3b2fmzJlm15eens4999xDRUUFn376KXv27GHJkiUkJSVx1113ceHCBSZPnkxVVZXecVVVVdx+++1kZWVx9913k5SUxJIlS9izZw+ffvopFRUVTJkyhczMTIPXvP766/nvf//LL7/8wrlz5wgPD2/2+2IO3xAfOvSJBmDz4u0G+4/sOE5Wag4qJxV9R/f4R9p0uVljzGCdcVtjzCBxg3XFXcNih7vfeOMNAGbMmKE3nOzn58dHH33EoEGDmD9/PjNnzsTT07PB+t5//300Gg3Dhw/nwQcfrN1uZ2fHxx9/zK+//srevXv5/fffueGGG2r3//bbbyQkJODl5cVHH32EnZ1d7b4HH3yQn376iU2bNvHBBx8wZ84cvdd8++23mxx/c0T3iACUq8zMlCyjZZL2nSagtR9tekTyx487zK47JDqIe2fdjleAJyVFpaQkprLrl30U5qpboulNZo0xg3XGbY0xg8RtbXHXsMgkff78efbu3QvA5MmTDfYPHDiQsLAw0tLSWLt2LXfccUeDddYMSRurz83NjRtvvJFFixaxYsUKvSRdc9yNN96Im5ubwbGTJ09m06ZNrFixwiBJXylBkcrcelZqjsky2edyAQiOMD0Pb0zMwA7EDOygt62spJxFr/3EkrdWNbKlLccaYwbrjNsaYwaJ29rirmGRSTohIQEAHx8fIiMjjZaJjY0lLS2NhISEBpO0Wq2unW+OjY01Wd+iRYtqX/vvbanvOICTJ09SXFyMq6trvW35J7i4OwNQWlxmskxJUalS1sPZrDrzMvP5/vXl7PplHxlnLqAt0xLWPoTxj41i+N2DmfbmXdja2fLDHOPz85ebNcYM1hm3NcYMEre1xV3DIuekk5OVhQGtW7c2WSYsLEyvbH1SUlJq/22qTlP1NdSWmuOqqqr0XqellJWVUVhYqPdzJexbf4BvZv7Iib2nKMxVU1JUSlL8Gd6euqB2ReVdM2/GK6DhqYd/C2uMGawzbmuMGSTuf0PcFpmk1WplPqC+XmnN0LM5SaumvvrqNFVfQ22pOwR+ORLonDlz8PT0rP2puSioj0atrHp3cnU0WcbZzUkpW9i4FfLGrPxgLfnZhaicVMSO6Nbs+prCGmMG64zbGmMGidva4q5hkUlaXBIXF0dBQUHtT1paWoPHXEjJBsA/zNdkGf9QZV/m2exmt7GyspLzJzMA8Av1aXZ9TWGNMYN1xm2NMYPEbW1x17DIJO3u7g5AcXGxyTJFRUUAeHh4mF1ffXWaqq+httQcZ25bGsvR0REPDw+9n4bU3Efo6edBkImFFO1i2yhl959pkXZ6+CrvU4m6tEXqayxrjBmsM25rjBkkbmuLu4ZFJumIiAiAenuNNftqytan7v3Jqampjaqv5veGjrOxsfnH7oNuSM75PI7vURbKDZ1s+GSzzgM6ENDaj/LScnavTTDY31jRPSIJax8CUPu6/zRrjBmsM25rjBkkbrCuuGtYZJLu0UO5IT03N9fkwrB9+/YBmHwkZ10eHh5ER0frHWdufTW/N3Rc27Ztjd6idaX8MGcFALe9cBPRPS6tkHf3ceOJBdMAWLVgHZrCS0/MGXBTH748+j5vbXhFry5HZxU3Tr+hdt6nri6DOvLKsucAOLztGCf2XrkPtTXGDNYZtzXGDBK3tcUNFnoLVmhoKL1792bv3r0sXryYl156SW//9u3bSUtLw9HRkdGjR5tV54QJE3j77bdZvHgxU6dO1dtXVFTEr7/+CsDEiRMNjvviiy/45ZdfjN5itXjxYqPHXWk7V+1l5YdrmfDEaD7c9QYJmw5TWlxGj2ExuHu7kbj9ON/MXKJ3jKunC607tELlpP9MdHuVPY/Pn8ZD70zhVEIKWak52NnbEtouhMguyqr3M4fOMvu294y25fEF02hb/Yfl4KjUbWdvx4c7X68ts3vtfr6fvVxilrglZolb4q7b3hap5TJ48cUXmTBhAm+++SajRo2q7dHm5uYyffp0AB577DG9p42tXLmSuLg4WrVqxaZNm/Tqe+qpp1iwYAEbN27k888/54EHHgBAp9Mxffp08vPz6d27NyNGjNA7btSoUfTo0YOEhASmT5/OV199VfvUsc8++4xNmzbh5ubGk08+ednei6b66KmvSdxxnPHTR9K5f3vsHOzIOH2BJXN/Zvm8NVRoK8yqp0xTznezltGuVxRhHVoR3jkUR2cV6ovFxG84xNZlu/j9my0m6wvvGErHfu0MttfdlnY8vWlB/o01xgzWGbc1xgwSt7XFbVP194dVW5Ann3ySDz/8EAcHB4YNG4arqyubNm0iPz+fAQMGsGHDBpydL928/s033zB16lTCw8ON3rO8dOlS7rjjDnQ6HX379iUiIoK9e/dy5swZAgMD2b59e+2weF0nTpxg0KBBZGdnExUVRe/evUlOTmbPnj3Y29vz008/MWHCBIPjvvjiC7744ova3xMSEigvL6dTp061C9KCg4NNfkGHMYWFhXh6ejKE8djbNP5bwIQQQlxZFVVatrCKgoKCBhcDW2xPGuCDDz5gwIABLFiwgJ07d6LVamnTpg0zZszg6aefRqVSNaq+W265haioKN544w22bdtGQkICwcHBPProo8ycOZPAwECjx7Vv355Dhw4xe/ZsVq9ezcqVK/H09GTixIm89NJLJufFz507x+7duw22Hz16tPbflrLYTAghhOWx6J60MCQ9aSGE+HdrTE/aIld3CyGEEEKStBBCCGGxJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGhJEkLIYQQFkqStBBCCGGh7K90A0TjVFVVAVCBFqqucGOEEEI0WgVa4NL5vD6SpP9l1Go1ANtZe4VbIoQQojnUajWenp71lrGpMieVC4tRWVlJeno67u7u2NjY/KOvXVhYSFhYGGlpaXh4ePyjr30lWWPc1hgzWGfc1hgzXNm4q6qqUKvVhISEYGtb/6yz9KT/ZWxtbQkNDb2ibfDw8LCqP+Ya1hi3NcYM1hm3NcYMVy7uhnrQNWThmBBCCGGhJEkLIYQQFkqStDCbo6Mjr776Ko6Ojle6Kf8oa4zbGmMG64zbGmOGf0/csnBMCCGEsFDSkxZCCCEslCRpIYQQwkJJkhZCCCEslCTpq8DSpUsZMmQI3t7euLq60q1bN9566y20Wm2T6ouPj+eWW24hMDAQJycnIiMjefzxx8nKyqr3uAsXLvDYY48RGRmJo6MjgYGB3HLLLezfv9/kMVu3buWNN95g0qRJREREYGNjg42NDdu3b29S2+HKvx86nY5ly5YRFxfHiBEj8PX1xcbGBnv7K/dYgpZ6T3Jzc/nmm294/PHH6d+/Py4uLtjY2DB8+PDL1PKmOXHiBP/73/+499576dKlC/b29tjY2DB79uxm1btx40ZGjx6N3/+3d+8xTd1tHMC/LVDuiBW8gRRxUKcQRQeG4MBXUZkKE1gp03mLM246dcYrmEznLpnLsjg3E5l/OI0z8zLdZOCWecHpMEzRuXkdbuAE1HBRKHWi0Of9w7UvpYVe6JHTt88nIRnnd35Pn+e3nj720J4TFARvb28MHToU69atQ3Nzs4My71p36hIi9xs3bmDu3LkIDQ2Fp6cnQkNDMXfuXPz11182xemJuhyVe3sajQZ5eXlQKpXw9vZGUFAQpk6diuPHj9sdE8Sc2rJlywgAubu706RJkygzM5MCAwMJAI0dO5YePHhgU7z9+/eTu7s7AaC4uDjKzs6miIgIAkD9+vWj8vJys/OuX79Offv2JQAUERFB2dnZFBcXZ8jt4MGDZueNGDGC8OQq5EY/p06dsnktiMSxHvfu3TNbk5ubm101dZcj1+TQoUNma5swYYKAFdhOX3PHn3feecfumB9//DEBIIlEQklJSaRSqah///4EgJRKJdXW1jqwAvPsrUuI3E+fPk0+Pj4EgIYPH05qtZqGDx9OAMjX15fOnDkj2rocmbve3bt3KSoqigDQgAEDSKVSUVJSEkkkEpJIJLRlyxabYxIRcZN2YvoXTD8/PyorKzNsr62tpZiYGAJAK1assDpedXW14Ymbn59v2N7a2kqvvPKKoVHpdDqjeTqdjmJjYwkAzZo1i1pbWw1j+fn5hhxv375t8pgrV66kDRs20OHDh6mqqooUCoXdTVos69Hc3EwzZ86kjz76iI4fP06//vprjzVpR69JSUkJLVy4kPLz8+ns2bO0bds2UTbp7du308qVK+nLL7+kq1ev0qxZs7rVpM+fP08SiYTc3NyoqKjIsF2r1dKECRMIAGVlZTkq/U7ZU5cQuWu1Who4cCABoNzcXKOx3NxcAkCDBg2y+h+AT7MuR+eu9+KLLxqOBa1Wa9heWFhIbm5uJJVK6eLFizbFJOIm7dT071Tfffddk7FTp04RAPL09KT79+9bFW/VqlUEgFJSUkzGNBoN9erViwDQ999/bzRWWFhIACgwMJA0Go3JXP0Bs3btWos5dKdJi2U9OqqoqOixJu3oNelox44domzSHc2ZM6dbTVqlUhEAevXVV03GKisrSSqVEgC6evVqd1O1iTV1CZH71q1bCQBFRUVRW1ub0VhbW5vhHeW2bdusL6YdIesSIvfLly8bjvHKykqT8fnz5xMAysnJsTqmHv9N2klVV1fj7NmzAIAZM2aYjI8dOxaDBg1CS0sLioqsu2PWoUOHOo3n5+eH9PR0AMDBgwfNzktPT4efn5/JXH28jvMcSUzrIRZCrIkrevToEQoLCwGYX0eFQoHExEQA/3vOiIVQuev3zcnJMblBhFQqhVqtBiDcsdGduoTIXR8zMTERCoXCZFyfY0FBgc2fA+Em7aQuXLgAAJDL5Rg8eLDZfZ577jmjfbui0Whw48YNo3nWxtP/bmleeXk5tFqtxVzsIab1EAtHr4mr+uOPP/DgwQMAzvdcECp3a495odajO3UJkbu1MbVaLcrLy62OC3CTdloVFRUAgLCwsE73GTRokNG+XamsrDT8d2cxO4tnKRf9PCIyehxHEtN6iIWj18RV6dcmMDAQ/v7+ZvcR6zoKkbtGo0F9fT0Ay8dGbW2tIP8wt7cuoXK3dKy1v9OWrc8RbtJOSqPRAAB8fX073Ud/6rmpqcnqeF3F7CyepVzanwK3Jhd7iGk9xMLRa+KqnHkdhcjdlmPDlri2sLcuoXIX8jnCTZoxxhgTKW7STkp/iqer0zH6L/Nbc0Pz9qeMOovZWTxLubS/qIBQN1cX03qIhaPXxFU58zoKkbstx4YtcW1hb11C5S7kc4SbtJMKDw8HANy6davTffRj+n270v4TiX///bdN8fS/W5onkUjMfvLREcS0HmLh6DVxVfq1uX//vtHp0vbEuo5C5O7v7w+5XA7A8rERFBTU5Slge9lbl1C5W3oNbGpqMpzmtvU5wk3aScXGxgJ4cpnGzj6IcO7cOQDAqFGjLMYLCAjAM888YzTP2nj63y3Ni4yMNPsVLUcQ03qIhaPXxFUplUr4+PgAcL7nglC5W3vMC7Ue3alLiNytjenr64uoqCir4wLcpJ1WaGgo4uLiAAB79uwxGT99+jRu3boFT09PTJkyxaqYGRkZncZrbm5GQUEBACAzM9PsvMOHD5s93aOP13GeI4lpPcRCiDVxRTKZDFOnTgVgfh1v3ryJkpISAP97zoiFULnr9/3qq6+g0+mMxnQ6Hfbu3QtAuGOjO3UJkfv06dMBAD///LPZd9P6HNPS0uDh4WF1XAB87W5n1tklH+vq6jq95OPBgwdJqVTS+PHjTeK1vwzm559/btje2tpquEyfpcuCzp4926bLgnYkxGVBn/Z6dNSTVxxz9Jp09P90xbFPP/2UlEolzZo1y2SsrKzMcAnKI0eOGLY/7cuCdmRNXfbmXlpaSkqlkpRKpclY+0tr5uXlGY3l5eURAAoNDbX50ppPo67u5K5fj9LSUpMx/WVBU1JSjOYWFRXxZUFd2dKlSwkAeXh4UGpqKmVlZRlunpCYmGjyRNO/qCoUCrPx9u3bR25ubgSAxowZQ2q12qobbFy7do2Cg4MJeHKDDbVaTfHx8QR0fYON7du305gxYww/MpmMANCwYcMM26ZPn+506/H6668b8h85ciTh3xsGtK9148aNVtfVHY5ek/Y16NciICDAaPt33333FCrrXFlZmVE+QUFBhhff9ttramoMc9avX08AKDk52WzM9jdzGDduHGVnZ9OAAQMIT/EGG/bUZW/uJ06cMDxvzWl/k4ro6GjKycmh6Ohogh03qXiadXUnd/16nDhxwmTs7t27FBkZSfj3BhvZ2dk0btw4kkgkBIA++eQTq9fD6DHtmsVEZe/evZSUlEQBAQHk7e1N0dHR9MEHH1BLS4vJvpZegImIzp07R5mZmRQcHEwymYwUCgUtXryY7ty502Uet2/fpsWLF5NCoSCZTEbBwcGUmZlp9A6uI/0LY1c/XeVqjhjWIzk52WJdc+bMsamu7nDkmliqCwDt2LFD2IIsaN9guvqpqKgwzLHUpImIfvzxR0pNTSW5XE6enp4UGRlJubm51NTUJHxRZF9d9uZuqUkTEZWXl9Ps2bNp4MCB5OHhQQMHDqTZs2fTjRs3RFtXd3LvqkkTETU2NtLatWspMjKSPD09SS6XU2pqKh09etSW5TAi+feBGWOMMSYy/MExxhhjTKS4STPGGGMixU2aMcYYEylu0owxxphIcZNmjDHGRIqbNGOMMSZS3KQZY4wxkeImzRhjjIkUN2nGGGNMpLhJM8YYYyLFTZoxxhgTKW7SjDHGmEhxk2aMMcZEips0Yy7q0qVLyMrKQlBQEHx8fBATE4PNmzdDp9MhPDwcEokElZWVhv2vXLmC9evXIzExESEhIZDJZOjTpw9SUlKwb98+s49RXFwMiUSCcePGoaWlBW+//TaioqLg5eWFsLAwrFmzBg8fPgQANDY2YuXKlYiIiICXlxfCw8OxYcMGtLa2msSdO3cuJBIJvvjiC1y/fh1qtRp9+/aFr68v4uLi8O233xr2LS0tRXp6OoKDg+Ht7Y2EhAQcO3bMbL6//PILVq9ejfj4ePTv3x8ymQz9+vVDWloajh492o3VZsxOdt/kkjHmtIqLi8nb25sA0JAhQygnJ4cmTpxIMpmM1Go1KRQKk/v3zp8/nwDQ0KFDafLkyaRWqykhIYGkUikBoOXLl5s8jv4+wQkJCZScnEwBAQGUnp5O06ZNo169ehEAmjZtGtXX15NSqaTg4GDKysqiSZMmkZeXFwGg1157zSTunDlzCAAtWbKEfH19SalUUk5ODiUkJBAAkkgktH//fjp06BB5eHhQbGwsqdVqGjFiBAEgd3d3OnXqlEncCRMmkFQqpZiYGJoyZQqpVCoaNWqU4T7Cmzdvduj/B8Ys4SbNmIt58OABhYSEEABasWIFtbW1GcYuX75M/fr1MzSl9k26uLiY/vzzT5N4165do9DQUAJApaWlRmP6Jg2A4uPjqa6uzjBWWVlJvXv3JgAUExNDaWlppNVqDeNnz54ld3d3kkqldPPmTaO4+iYNgN59913S6XSGsS1bthAACg0Npd69e9OuXbuM5r755psEgFJSUkxqKSoqopqaGpPtJSUlFBAQQB4eHlRVVWUyzphQuEkz5mJ27dpFAEihUNCjR49Mxj/77DOzTbor+fn5BIBWrVpltF3fpCUSCf3+++8m85YuXUoAyM/Pj+7evWsynpaWRgBo586dRtv1TTo+Pt6oQRMRPX78mORyOQEglUplErOuro4AkEwmM1t/Z3JzcwkAbd261eo5jHWXu9Cn0xlj4nLy5EkAgEqlgoeHh8n4zJkz8cYbb5id29zcjCNHjuDChQuoq6vDo0ePAAC3b98GAFy/ft3svLCwMERHR5tsj4yMBACMHj0affv27XS8pqbGbNwXXngBEonEaJu7uzsGDx6MhoYGTJkyxWROnz59IJfL0dDQgPr6evTv399ovL6+HoWFhbh06RLu3buHx48fAwDKy8u7rJExIXCTZszFVFVVAQDCw8PNjgcGBqJXr15obGw02l5QUIB58+ahvr6+09hNTU1mt4eFhZnd7ufn1+W4v78/ABg+XObIuA0NDSZxt2/fjuXLl0Or1ZqdB3ReI2NC4E93M+aiOr4D7WqsuroaarUa9fX1WL16NS5evIjGxka0tbWBiPDDDz8AAIjIbDyptOuXGkvj9s6zJW5ZWRkWLlyIlpYWbNq0CVeuXEFzczN0Oh2ICPn5+QA6r5ExIfA7acZcTEhICAAYfb2qvcbGRty/f99oW0FBAf755x9kZGRg06ZNJnP0p4Kd2f79+0FEWLJkCVavXm0y/v9QI3M+/E6aMReTlJQE4ElTMvcd5D179phsa2hoAAAoFAqTMSIyO8fZdFXjw4cP8fXXXz/tlBjjJs2Yq1GpVBgwYAAqKyuxbt066HQ6w9i1a9ewceNGkznPPvssAODAgQOGD4kBQFtbG9566y2UlJQIn7jA9DXu3LkTGo3GsP3hw4dYtGgRKioqeio15sK4STPmYnx8fLB79254eXnhww8/hFKpxMsvv4zJkydjxIgReP755w0fuJLJZACAtLQ0jB49GlVVVYiKisK0adOgVqsxZMgQbNq0CWvWrOnJkhxi3rx5UCgUuHDhAgYPHoyMjAy89NJLUCgUOHDgAJYtW9bTKTIXxE2aMRc0fvx4lJaWIiMjAw0NDfjmm29QVVWF9957D7t378adO3cglUohl8sBPPlaU3FxMfLy8hASEoJjx46huLgYsbGxOHPmDFJTU3u4ou4LDAzEuXPnsGjRIgQGBuLIkSM4c+YMJk2ahPPnz2PkyJE9nSJzQRLijyoyxtr56aefkJycjJiYGPz22289nQ5jLo3fSTPmgmpra83+jfXSpUtYsGABgCenfxljPYvfSTPmgoqLi/Gf//wHw4YNQ0REBLy9vVFRUYHz589Dp9Nh4sSJKCoqgrs7f0uTsZ7ETZoxF1RTU4P3338fJ0+eRHV1NTQaDfz9/TF8+HDMmDEDCxYs4AbNmAhwk2aMMcZEiv8mzRhjjIkUN2nGGGNMpLhJM8YYYyLFTZoxxhgTKW7SjDHGmEhxk2aMMcZEips0Y4wxJlLcpBljjDGR+i/s2NxTkWrQTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_grid1 = {\n",
    "    \"svc__gamma\": 10.0**np.arange(-3, 3, 1), \n",
    "    \"svc__C\": 10.0**np.arange(-3, 3, 1)\n",
    "}\n",
    "display_heatmap(param_grid1, pipe_svm, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Each point in the heat map corresponds to one run of cross-validation, with a particular setting\n",
    "- Colour encodes cross-validation accuracy. \n",
    "    - Lighter colour means high accuracy\n",
    "    - Darker colour means low accuracy\n",
    "- SVC is quite sensitive to hyperparameter settings.\n",
    "- Adjusting hyperparameters can change the accuracy from 0.51 to 0.74! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Bad range for hyperparameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10.        ,  15.84893192,  25.11886432,  39.81071706,\n",
       "        63.09573445, 100.        ])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(1, 2, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.2, 1.4, 1.6, 1.8, 2. ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 2, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_grid2 = {\"svc__gamma\": np.round(np.logspace(1, 2, 6), 1), \"svc__C\": np.linspace(1, 2, 6)}\n",
    "display_heatmap(param_grid2, pipe_svm, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Different range for hyperparameters yields better results! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-3, 2, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.2, 1.4, 1.6, 1.8, 2. ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 2, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_grid3 = {\"svc__gamma\": np.logspace(-3, 2, 6), \"svc__C\": np.linspace(1, 2, 6)}\n",
    "\n",
    "display_heatmap(param_grid3, pipe_svm, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "It seems like we are getting even better cross-validation results with `C` = 2.0 and `gamma` = 0.1 \n",
    "\n",
    "How about exploring different values of `C` close to 2.0? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_grid4 = {\"svc__gamma\": np.logspace(-3, 2, 6), \"svc__C\": np.linspace(2, 3, 6)}\n",
    "\n",
    "display_heatmap(param_grid4, pipe_svm, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "That's good! We are finding some more options for `C` where the accuracy is 0.75. \n",
    "The tricky part is we do not know in advance what range of hyperparameters might work the best for the given problem, model, and the dataset.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```{note}\n",
    "`GridSearchCV` allows the param_grid to be a list of dictionaries. Sometimes some hyperparameters are applicable only for certain models. \n",
    "For example, in the context of `SVC`, `C` and `gamma` are applicable when the kernel is `rbf` whereas only `C` is applicable for `kernel=\"linear\"`.    \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Problems with exhaustive grid search \n",
    "\n",
    "- Required number of models to evaluate grows exponentially with the dimensionally of the configuration space. \n",
    "- Example: Suppose you have\n",
    "    - 5 hyperparameters \n",
    "    - 10 different values for each hyperparameter\n",
    "    - You'll be evaluating $10^5=100,000$ models! That is you'll be calling `cross_validate` 100,000 times!\n",
    "- Exhaustive search may become infeasible fairly quickly. \n",
    "- Other options? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Randomized hyperparameter search\n",
    "\n",
    "- Randomized hyperparameter optimization \n",
    "    - [`sklearn.model_selection.RandomizedSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)\n",
    "- Samples configurations at random until certain budget (e.g., time) is exhausted "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid size: 36\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'svc__gamma': [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
       " 'svc__C': array([2. , 2.2, 2.4, 2.6, 2.8, 3. ])}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "param_grid = {\n",
    "    \"svc__gamma\": [0.001, 0.01, 0.1, 1.0, 10, 100],\n",
    "    \"svc__C\": np.linspace(2, 3, 6),\n",
    "}\n",
    "\n",
    "print(\"Grid size: %d\" % (np.prod(list(map(len, param_grid.values())))))\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(\n",
    "    pipe_svm, param_distributions=param_grid, n_jobs=-1, n_iter=20, cv=5, random_state=42\n",
    ")\n",
    "random_search.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>rank_test_score</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>13</th>\n",
       "      <th>13</th>\n",
       "      <th>16</th>\n",
       "      <th>16</th>\n",
       "      <th>16</th>\n",
       "      <th>16</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.745826</td>\n",
       "      <td>0.743349</td>\n",
       "      <td>0.713578</td>\n",
       "      <td>0.711103</td>\n",
       "      <td>0.710482</td>\n",
       "      <td>0.709863</td>\n",
       "      <td>0.672044</td>\n",
       "      <td>0.672044</td>\n",
       "      <td>0.669567</td>\n",
       "      <td>0.667686</td>\n",
       "      <td>0.66645</td>\n",
       "      <td>0.661493</td>\n",
       "      <td>0.51519</td>\n",
       "      <td>0.51519</td>\n",
       "      <td>0.51519</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__gamma</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__C</th>\n",
       "      <td>2.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.062474</td>\n",
       "      <td>0.049992</td>\n",
       "      <td>0.060257</td>\n",
       "      <td>0.047394</td>\n",
       "      <td>0.05273</td>\n",
       "      <td>0.063017</td>\n",
       "      <td>0.078304</td>\n",
       "      <td>0.08291</td>\n",
       "      <td>0.071162</td>\n",
       "      <td>0.073954</td>\n",
       "      <td>0.059467</td>\n",
       "      <td>0.06007</td>\n",
       "      <td>0.096729</td>\n",
       "      <td>0.087505</td>\n",
       "      <td>0.077712</td>\n",
       "      <td>0.069697</td>\n",
       "      <td>0.065534</td>\n",
       "      <td>0.068643</td>\n",
       "      <td>0.074247</td>\n",
       "      <td>0.06792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "rank_test_score         1         2         3         4         5         6   \\\n",
       "mean_test_score   0.745826  0.743349  0.713578  0.711103  0.710482  0.709863   \n",
       "param_svc__gamma       0.1       0.1      0.01      0.01      0.01      0.01   \n",
       "param_svc__C           2.8       2.2       3.0       2.0       2.6       2.4   \n",
       "mean_fit_time     0.062474  0.049992  0.060257  0.047394   0.05273  0.063017   \n",
       "\n",
       "rank_test_score         7         7         9         10        11        12  \\\n",
       "mean_test_score   0.672044  0.672044  0.669567  0.667686   0.66645  0.661493   \n",
       "param_svc__gamma       1.0       1.0       1.0     0.001     0.001     0.001   \n",
       "param_svc__C           2.6       2.4       2.2       3.0       2.4       2.0   \n",
       "mean_fit_time     0.078304   0.08291  0.071162  0.073954  0.059467   0.06007   \n",
       "\n",
       "rank_test_score         13        13        13        16        16        16  \\\n",
       "mean_test_score    0.51519   0.51519   0.51519  0.508371  0.508371  0.508371   \n",
       "param_svc__gamma        10        10        10       100       100       100   \n",
       "param_svc__C           2.4       3.0       2.0       2.0       3.0       2.2   \n",
       "mean_fit_time     0.096729  0.087505  0.077712  0.069697  0.065534  0.068643   \n",
       "\n",
       "rank_test_score         16        16  \n",
       "mean_test_score   0.508371  0.508371  \n",
       "param_svc__gamma       100       100  \n",
       "param_svc__C           2.8       2.4  \n",
       "mean_fit_time     0.074247   0.06792  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(random_search.cv_results_)[\n",
    "    [\n",
    "        \"mean_test_score\",\n",
    "        \"param_svc__gamma\",\n",
    "        \"param_svc__C\",\n",
    "        \"mean_fit_time\",\n",
    "        \"rank_test_score\",\n",
    "    ]\n",
    "].set_index(\"rank_test_score\").sort_index().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `n_iter`\n",
    "\n",
    "- Note the `n_iter`, we didn't need this for `GridSearchCV`.\n",
    "- Larger `n_iter` will take longer but it'll do more searching.\n",
    "  - Remember you still need to multiply by number of folds!\n",
    "- I have set the `random_state` for reproducibility but you don't have to do it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Optional) Passing probability distributions to random search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Another thing we can do is give probability distributions to draw from:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import expon, lognorm, loguniform, randint, uniform, norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SUlJSnNueffZZrV69WtOnT9eJEycUFBTk7lsBAACGcjvgPPbYYy6vAwKarxvP+vXrdf78eQ0YMKDGLbBVq1YpLS1Nubm52rBhg+bPn99sdQAAAP/i052M09PTJUkzZ86sEaQCAgI0Y8YMSdKOHTtavDYAAOC73L6C0xRHjhzR8uXLdfnyZUVERGjYsGGaNGmSwsLCat0/OztbkhQfH1/rdsd6x34AAABSCwec3bt3a/fu3S7rIiIitGbNGs2ZM8dlfUlJiQoLCyVJPXv2rPV4MTExkqRLly6prKxMISEhzVA1AADwNy1yi6pfv35KSUlRdna2Ll++rMuXL+vAgQOaOHGiioqKNHfuXP3pT39yaVNSUuL8uq7gEhoa6vy6uLi4zvNXVFSouLjYZQEAAOZqkYAze/ZsJSUl6cc//rE6duyojh07KiEhQbt379aTTz4pSVqyZImuXbvWLOdfvXq1IiIinIvjyg8AADCT1zsZr1ixQm3atNGlS5d06NAh5/rq/XLKyspqbVtaWur8Ojw8vM5zJCUlqaioyLnk5+d7oHIAAOCrvB5wOnXqpDvuuEOSdPbsWef6sLAwderUSZKUl5dXa1tHUImKiqq3/01gYKDCw8NdFgAAYC6vB5wbN26oqKhIkmqMpoqLi5MkZWVl1drWsd6xHwAAgOQDAWfXrl0qLy+XzWarMRw8MTFRkrR582ZVVVW5bKuqqtKWLVskSVOmTGmZYgEAgF9o9oCTl5enjRs36rvvvquxbefOnc6ZkR9++GF169bNZbvdblePHj2Um5ur5ORkl23JycnKzc1VdHR0jSHmAACgdXN7HpwjR47oiSeecL7++uuvJUnvvfeePvzwQ+f69PR0de/eXZcvX9bs2bP1r//6rxo2bJjuvPNOXb16Vf/zP/+jr776SpJ03333ae3atTXOFRwcrK1bt2rs2LFKSUnRrl27NHjwYOXk5CgnJ0chISHatm0bz6ECAAAu3A44xcXFLqOdHM6ePevSSbiiokLSzcn4fvnLX+rw4cM6efKkjhw5omvXrikqKkoTJ07UrFmzNGPGjDqfaZWQkKCjR49q5cqVyszMVFpamrp06aI5c+bohRdeUL9+/dx9CwAAwHBuB5yRI0fKsqwG79+5c2f96le/cvc0Lvr376/U1NQmHQMAALQeXu9kDAAA4GkEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHLcDzokTJ/TWW2/JbrdryJAhatu2rWw2m1atWnXbtpmZmRo/fryioqIUFBSkgQMH6rnnnlNpaWm97U6ePCm73a7o6GgFBgYqOjpadrtdp06dcrd8AADQCrgdcNauXauFCxcqNTVVOTk5unHjRoPavfHGG7r//vv10UcfadCgQZo0aZKKioqUkpKi+Ph4FRQU1Nru4MGDGjp0qFJTUxUZGanExERFRkYqNTVVd911lz799FN33wIAADCc2wFn8ODBeuqpp/SnP/1Jx44d0+zZs2/bJjs7W8uWLVObNm2UkZGhjz/+WFu3btXXX3+t0aNH68SJE1qwYEGNduXl5Zo+fbrKy8uVlJSknJwcbd68WTk5OUpKSlJZWZmmT5+uq1evuvs2AACAwdq62+Cxxx5zeR0QcPuMtHr1almWpUcffVTjxo1zrg8ODta6devUt29fpaWl6fjx4xo4cKBz+/r163X+/HkNGDCgxi2wVatWKS0tTbm5udqwYYPmz5/v7lsBAACGavZOxteuXVNGRoYkadasWTW29+rVSwkJCZKk9PR0l22O1zNnzqwRpAICAjRjxgxJ0o4dOzxeNwAA8F/NHnByc3NVXl4uSYqPj691H8f67Oxsl/WO1+62AwAArVuzB5zTp09LkiIjIxUWFlbrPjExMS77SlJJSYkKCwslST179qy33aVLl1RWVuaxmgEAgH9zuw+Ou0pKSiRJISEhde4TGhoqSSouLq7Rrr62jnaOtnXtV1FRoYqKCpd9AQCAuVrFRH+rV69WRESEc3Fc+QEAAGZq9oDjuC1V3y0kx0R/4eHhNdrV17b6BIHV294qKSlJRUVFziU/P79hxQMAAL/U7AGnd+/ekqQrV6643HaqzhE4HPtKNwNOp06dJEl5eXn1touKiqr3FlhgYKDCw8NdFgAAYK5mDzixsbEKDg6WJGVlZdW6j2N9XFycy3rHa3fbAQCA1q3ZA0779u01YcIESdKmTZtqbP/mm2/097//XZKUmJjoss3xevPmzaqqqnLZVlVVpS1btkiSpkyZ4vG6AQCA/2qRTsbLly+XzWbT+++/r48++si5vry8XPPmzdONGzc0depUl1mMJclut6tHjx7Kzc1VcnKyy7bk5GTl5uYqOjpac+bMaYm3AQAA/ITbw8SPHDmiJ554wvn666+/liS99957+vDDD53r09PT1b17d0k3byG9/vrrWrp0qcaPH697771Xd9xxh/bv369vv/1WsbGx+t3vflfjXMHBwdq6davGjh2rlJQU7dq1S4MHD1ZOTo5ycnIUEhKibdu2KSgoyO03DgAAzOV2wCkuLtahQ4dqrD979qzOnj3rfF193hlJWrJkiYYMGaLXX39dn332mcrKytSzZ08lJSUpKSmpzkkAExISdPToUa1cuVKZmZlKS0tTly5dNGfOHL3wwgvq16+fu28BAAAYzu2AM3LkSFmW1aiTjRkzRmPGjHG7Xf/+/ZWamtqocwIAgNanVUz0BwAAWhcCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwTosGHLvdLpvNVu/y3Xff1dr2888/17Rp09S1a1d16NBBffr00ZNPPqmLFy+25FsAAAB+oK03TpqQkKD+/fvXuq1NmzY11m3fvl0PPfSQrl+/ruHDh6tPnz7KysrS22+/rW3btunAgQN1Hg8AALQ+Xgk4jz32mOx2e4P2PX/+vObOnavr16/rvffe0+OPPy5JunHjhux2uzZu3KhZs2bp0KFDstlszVg1AADwFz7fB+e3v/2tysvLNWbMGGe4kW5e6Vm7dq0iIiJ0+PBh7dmzx4tVAgAAX+LzASc9PV2SNGvWrBrbQkNDNXnyZEnSjh07WrQuAADgu7xyi+qvf/2rvvzyS5WUlKhz5866++67NX78eAUGBrrsV1JSopMnT0qS4uPjaz1WfHy8PvjgA2VnZzd73QAAwD94JeBs2LChxrru3bvrj3/8ox544AHnujNnzji/7tmzZ63HiomJkSSdPn3as0UCAAC/1aK3qIYOHao333xTOTk5Ki4u1v/+7/9qz549+tnPfqZvv/1WkydP1t/+9jfn/iUlJc6vQ0JCaj1maGioJKm4uLjO81ZUVKi4uNhlAQAA5mrRgLNkyRItXLhQgwYNUlhYmO644w7df//9OnDggH7+85+rsrJSixcv9vh5V69erYiICOfiuOoDAADM5BOdjG02m1566SVJ0tGjR5Wfny9JCgsLc+5TVlZWa9vS0lJJUnh4eJ3HT0pKUlFRkXNxHB8AAJjJJwKOJP3whz90fn327FlJUq9evZzr8vLyam3nCCu9e/eu89iBgYEKDw93WQAAgLl8JuAUFhY6v3ZcuQkPD3fOUJyVlVVrO8f6uLi4Zq4QAAD4C58JOJs3b5Z0M9TExsY61ycmJkqSNm3aVKNNaWmpdu/eLUmaMmVKC1QJAAD8QYsFnC+++EK7du3S9evXXdZXVVVp3bp1evbZZyVJCxcuVLt27ZzbFy9erODgYGVmZuoPf/iDc/2NGzf0xBNP6MqVKxo+fLjGjh3bMm8EAAD4vBabB+fMmTNKTExUx44dFRcXp65du+rKlSvKyclx9q956KGH9OKLL7q069Gjh9avX6+HHnpIjz/+uNatW6fevXvr8OHDOnXqlLp27apNmzbxHCoAAODUYldwhg4dqsWLF2vQoEE6fvy4duzYoX379kmS/vmf/1kZGRnatGmT2ratmbmmTZumQ4cOacqUKTp16pTS09N148YN/eIXv9DRo0d5kjgAAHDRYldw+vTpozfeeKPR7X/yk58oLS3NgxUBAABT+UwnYwAAAE8h4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcfwq4Gzbtk0jR45Ux44dFRISoqFDh+rVV19VZWWlt0sDAAA+xG8CzuLFizV9+nQdPHhQd999tx544AHl5eXpl7/8pUaNGqWrV696u0QAAOAj/CLg7Ny5U2+++aZCQ0N16NAh/eUvf1FaWpq++uorDRkyRAcOHFBycrK3ywQAAD7CLwJOSkqKJGn58uWKi4tzro+KitK7774rSXr77bdVVFTklfoAAIBv8fmAc+7cOR0+fFiSNGvWrBrbR4wYoZiYGFVUVOjPf/5zS5cHAAB8kM8HnOzsbElSp06d1KdPn1r3iY+Pd9kXAAC0bm29XcDtnD59WpLUs2fPOveJiYlx2fdWFRUVqqiocL523MoqLi72VJkuLly4uTgEBEhVVY1/7Ylj+MM5faEGd9ucOHHzvxUXpKprbVRZWCWpuMGvJbndpqmvvXFOX6ihtZzTF2po0DkvS1KxPv9cKi1t3M+rL/w/wxdq8NVzdut2c/Ekx+9ty7Juv7Pl415++WVLkpWQkFDnPs8++6wlyRo7dmyt21988UVLEgsLCwsLC4sBS35+/m3zg89fwfGEpKQkLV261Pm6qqpKly9fVufOnWWz2bxYmXcUFxcrJiZG+fn5Cg8P93Y5fo3P0nP4LD2Dz9Fz+Cw9x1OfpWVZKikpUY8ePW67r88HnLCwMElSWVlZnfuU/t/1zbo+tMDAQAUGBrqsi4yM9EyBfiw8PJwfWg/hs/QcPkvP4HP0HD5Lz/HEZxkREdGg/Xy+k3Hv3r0lSfn5+XXu49jm2BcAALRuPh9whg0bJkkqLCyssxNxVlaWJLnMkQMAAFovnw840dHRGj58uCRp06ZNNbYfOHBA+fn5CgwM1Pjx41u6PL8UGBioF198scZtO7iPz9Jz+Cw9g8/Rc/gsPccbn6XNshoy1sq7du7cqcTERIWGhurjjz92XqkpLCzUfffdpy+//FLLli3Ta6+95uVKAQCAL/CLgCNJixYt0po1a9SuXTuNHj1aISEh2rdvn65cuaKEhATt3btXQUFB3i4TAAD4AL8JOJK0detWvfPOO/riiy9UWVmpfv366ZFHHtGSJUvUvn17b5cHAAB8hF8FHAAAgIbw+U7GaH4XL17Uhg0bNGvWLP3gBz9Qhw4dFBwcrIEDB2rhwoU6c+aMt0v0Odu2bdPIkSPVsWNHhYSEaOjQoXr11VdVWVnp7dL8QmVlpfbt26enn35aw4cPV2RkpNq1a6du3bpp8uTJysjI8HaJfu2ZZ56RzWaTzWbTqlWrvF2OX7p27ZrWrFmjESNGqFOnTurQoYOio6M1btw4bdmyxdvl+YW8vDz927/9m2JjYxUUFKQOHTqoT58+mjt3ro4ePdr8Bbj76ASY5+GHH7YkWQEBAdZdd91lTZs2zRo/frzVpUsXS5IVEhJi7dmzx9tl+oxFixZZkqy2bdtaY8eOtaZMmWJFRkZakqwRI0ZY5eXl3i7R5+3du9c55Xq3bt2sCRMmWNOnT7cGDx7sXP/4449bVVVV3i7V7xw8eNAKCAiwbDabJclauXKlt0vyO/n5+daPfvQjS5IVFRVlTZw40ZoxY4b1s5/9zAoODramTp3q7RJ93qeffmqFhYVZkqw777zTmjx5spWYmGj16dPH+f/PrVu3NmsNBBxYTz75pPXSSy9ZZ8+edVlfUlJizZw505JkderUybp8+bKXKvQd6enpliQrNDTU+vzzz53rL126ZA0ZMsSSZC1btsyLFfqHffv2WVOnTrX+8z//s8a2zZs3W23atLEkWampqV6ozn+VlZVZP/jBD6w777zTevDBBwk4jVBeXm4NHDjQkmStWLHCunbtmsv2srIyKzs72zvF+ZG77rrL+YdK9c/wxo0b1vPPP29JsiIjI62rV682Ww0EHNSrrKzMmcI/+OADb5fjdcOHD7ckWatWraqxbf/+/ZYkKzAw0Lpy5YoXqjPHvHnzLEnW6NGjvV2KX1m4cKElycrIyLDmzp1LwGmE5ORk5y9mNE5BQYHzSuzFixdrbL9+/boVFBRkSbKOHDnSbHXQBwf1Cg4OVmxsrKT6H5fRGpw7d06HDx+WJM2aNavG9hEjRigmJkYVFRX685//3NLlGcUxg3lr/zfnjr/97W966623NGfOHCY9baTKykqtXbtWkvT00097uRr/5c5kflFRUc1WBwEH9aqsrHR2Mu7evbt3i/Gy7OxsSVKnTp3Up0+fWveJj4932ReN89VXX0ni31xDlZaW6l/+5V/UtWtX/fa3v/V2OX7ryJEjKigoUI8ePdS/f399+eWXeumllzR//nwtX75cGRkZqqqq8naZPi80NFT33HOPJOn55593GXxRVVWlFStW6OrVqxo3bpxiYmKarQ6ff5o4vGvdunUqKChQUFCQxo0b5+1yvMrxLLSePXvWuY/jh7Wu56bh9i5cuKD169dLkqZOnerdYvzEU089pdOnTys9PV0dO3b0djl+67/+678k3XxE0PLly/Xqq6/KqjaTyiuvvKJhw4Zp586d9f5/ANIf/vAHjR8/Xr///e+VkZGh+Ph4tWnTRtnZ2Tp37pxmz56tt99+u1lr4AoO6vTll186L9MmJyera9euXq7Iu0pKSiRJISEhde4TGhoqSSouLm6Rmkxz/fp1PfLIIyoqKtKQIUM0f/58b5fk8/bs2aP33ntPM2fO1IMPPujtcvxaYWGhpJtXYF955RU98cQTOnHihIqKirR3714NGDBA2dnZmjBhAlNC3EZsbKw++eQTjR07VufOndN//Md/aMeOHTp9+rT69++vkSNHKjw8vFlr4AqOn3vmmWe0a9cut9v9+7//u0aMGFHn9rNnz2rSpEkqLS3V5MmTtXz58qaUCTTIggULtG/fPnXu3Fnbt29nhvLbKCoq0rx589SlSxe99dZb3i7H7zmu1lRWVuqhhx5yucIwZswY7d27V7GxscrJydHmzZs1e/Zsb5Xq8w4ePKgpU6aobdu22rRpk0aNGqX27dvr4MGDWrp0qebNm6eDBw9q3bp1zVYDAcfPnT9/XidOnHC7XWlpaZ3bLly4oNGjR+ubb77RP/3TP2nr1q2y2WxNKdMIYWFhkqSysrI693F8rs39l4mJFi1apHXr1qljx47Ov5ZRv8WLF+vs2bPasmVLs3bWbC0cP+OSar162LNnT02YMEFpaWnKzMwk4NThypUrSkxMVEFBgT755BP99Kc/dW6bOHGifvSjH2nIkCH64x//qEceeUT33Xdfs9RBwPFzGzdu1MaNGz12vIsXL2rUqFHKzc3VmDFjtHPnzhZ9vL0v6927t6T6R/Y4tjn2RcMsW7ZMa9asUWRkpPbs2eMcRYX6paenq23btnr33Xf17rvvumw7fvy4pJv96DIzM9WtWzdt3rzZG2X6jb59+9b6dW37fPvtty1Skz/KyMjQpUuX1K9fP5dw49C3b1/99Kc/1V//+ldlZmYScND8Ll26pFGjRunYsWMaPXq0du3apQ4dOni7LJ/h+KVbWFio06dP1zqSKisrS5IUFxfXorX5s2eeeUa/+c1vFBERoT179jhHoqFhrl+/ro8//rjO7WfOnNGZM2fUq1evFqzKP8XFxclms8myLBUUFNQ6wqegoEDS9/3tUFNeXp6k+q9kR0RESJIuX77cbHXQyRiSbv7Qjho1Sv/93/+t0aNHa/fu3QoKCvJ2WT4lOjpaw4cPlyRt2rSpxvYDBw4oPz9fgYGBzEPSQMuXL9evf/1rRUREaO/evc7PFw1z5coVWTcnbK2xzJ07V5K0cuVKWZbFM+UaoFu3bs6+iZmZmTW2V1ZWOsPk3Xff3aK1+ZM777xT0s2riEVFRTW2V1ZW6siRI5JU55QbHtFsUwjCbxQWFjqn1R4zZgzPUqpHXY9qKCgo4FENbnruueec07V/9tln3i7HOMxk3DiZmZmWJKtjx47WJ5984lxfWVlpPfnkk5YkKywszLpw4YIXq/RtFy9etEJCQixJ1rRp06ySkhLntoqKCusXv/iFJclq166d9fXXXzdbHTbLqjbIH63SlClTlJ6eLpvNpmnTptV55ebBBx9kGKpudoZds2aN2rVrp9GjRyskJET79u3TlStXlJCQoL1793L16zZ27dqln//855JuTo44aNCgWveLiorSa6+91pKlGcNutys1NVUrV67U888/7+1y/MqqVauUnJystm3b6u6771a3bt105MgRnTlzRkFBQdq2bZsmTJjg7TJ92saNG/Xoo4/q+vXr6tKli4YPH6527dopKytL586dU0BAgN555x0tWLCg+YpotugEv3Hvvfc6nxtS3/Liiy96u1SfsWXLFusf//EfrfDwcCsoKMgaPHiw9atf/cqqqKjwdml+4f3332/Qv7levXp5u1S/xRWcpvnLX/5ijRs3zurUqZPVrl07KyYmxrLb7daxY8e8XZrf+OKLLyy73W717dvXCgwMtNq3b2/16tXLevjhh61Dhw41+/m5ggMAAIxDJ2MAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxvn/w6QqgHIZ6/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "\n",
    "y = uniform.rvs(0, 5, 10000)\n",
    "bin = np.arange(-3,8,0.1)  \n",
    "\n",
    "plt.hist(y, bins=bin, edgecolor='blue') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = norm.rvs(0, 1, 10000)\n",
    "\n",
    "#creating bin\n",
    "bin = np.arange(-4,4,0.1)  \n",
    "\n",
    "plt.hist(y, bins=bin, edgecolor='blue') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = expon.rvs(0, 1, 10000)\n",
    "\n",
    "#creating bin\n",
    "bin = np.arange(-1,10,0.1)  \n",
    "\n",
    "plt.hist(y, bins=bin, edgecolor='blue') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_dist = {\n",
    "    \"svc__C\": uniform(0.1, 1e4),  # loguniform(1e-3, 1e3),\n",
    "    \"svc__gamma\": loguniform(1e-5, 1e3),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(\n",
    "    pipe_svm, param_dist, n_iter=100, verbose=1, n_jobs=-1, random_state=123\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
       "                                              StandardScaler()),\n",
       "                                             (&#x27;svc&#x27;, SVC())]),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={&#x27;svc__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x14a253550&gt;,\n",
       "                                        &#x27;svc__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x1486dd420&gt;},\n",
       "                   random_state=123, verbose=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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
       "                                              StandardScaler()),\n",
       "                                             (&#x27;svc&#x27;, SVC())]),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={&#x27;svc__C&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x14a253550&gt;,\n",
       "                                        &#x27;svc__gamma&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x1486dd420&gt;},\n",
       "                   random_state=123, verbose=1)</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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;svc&#x27;, SVC())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(estimator=Pipeline(steps=[('standardscaler',\n",
       "                                              StandardScaler()),\n",
       "                                             ('svc', SVC())]),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'svc__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x14a253550>,\n",
       "                                        'svc__gamma': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x1486dd420>},\n",
       "                   random_state=123, verbose=1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7383804780493433"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>rank_test_score</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.73838</td>\n",
       "      <td>0.7359</td>\n",
       "      <td>0.735277</td>\n",
       "      <td>0.733415</td>\n",
       "      <td>0.731556</td>\n",
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       "      <td>0.728454</td>\n",
       "      <td>0.72845</td>\n",
       "      <td>0.72845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.508371</td>\n",
       "      <td>0.50713</td>\n",
       "      <td>0.50713</td>\n",
       "      <td>0.50713</td>\n",
       "      <td>0.50713</td>\n",
       "      <td>0.50713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__gamma</th>\n",
       "      <td>0.00271</td>\n",
       "      <td>0.001946</td>\n",
       "      <td>0.00283</td>\n",
       "      <td>0.003148</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.015524</td>\n",
       "      <td>0.00067</td>\n",
       "      <td>0.005593</td>\n",
       "      <td>0.00089</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>...</td>\n",
       "      <td>33.017913</td>\n",
       "      <td>51.754341</td>\n",
       "      <td>126.722678</td>\n",
       "      <td>84.511472</td>\n",
       "      <td>120.952433</td>\n",
       "      <td>766.440217</td>\n",
       "      <td>739.685456</td>\n",
       "      <td>918.053216</td>\n",
       "      <td>263.398099</td>\n",
       "      <td>806.79529</td>\n",
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       "      <th>param_svc__C</th>\n",
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       "      <td>6964.791856</td>\n",
       "      <td>2865.466167</td>\n",
       "      <td>4258.402903</td>\n",
       "      <td>7224.533826</td>\n",
       "      <td>1511.374523</td>\n",
       "      <td>3617.986556</td>\n",
       "      <td>2408.658977</td>\n",
       "      <td>7636.928414</td>\n",
       "      <td>9053.515757</td>\n",
       "      <td>...</td>\n",
       "      <td>5826.810879</td>\n",
       "      <td>7087.073954</td>\n",
       "      <td>6648.824488</td>\n",
       "      <td>6007.085678</td>\n",
       "      <td>957.225166</td>\n",
       "      <td>4830.442643</td>\n",
       "      <td>9019.213727</td>\n",
       "      <td>7049.688305</td>\n",
       "      <td>3370.763834</td>\n",
       "      <td>26.980646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.270511</td>\n",
       "      <td>0.390626</td>\n",
       "      <td>0.250277</td>\n",
       "      <td>0.362036</td>\n",
       "      <td>0.60281</td>\n",
       "      <td>0.421078</td>\n",
       "      <td>0.120652</td>\n",
       "      <td>0.334508</td>\n",
       "      <td>0.233567</td>\n",
       "      <td>0.171383</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081538</td>\n",
       "      <td>0.080531</td>\n",
       "      <td>0.059941</td>\n",
       "      <td>0.06476</td>\n",
       "      <td>0.059756</td>\n",
       "      <td>0.058091</td>\n",
       "      <td>0.055657</td>\n",
       "      <td>0.057711</td>\n",
       "      <td>0.064806</td>\n",
       "      <td>0.059273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>4 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rank_test_score            1            2            3            4   \\\n",
       "mean_test_score       0.73838       0.7359     0.735277     0.733415   \n",
       "param_svc__gamma      0.00271     0.001946      0.00283     0.003148   \n",
       "param_svc__C      3427.738338  6964.791856  2865.466167  4258.402903   \n",
       "mean_fit_time        0.270511     0.390626     0.250277     0.362036   \n",
       "\n",
       "rank_test_score            5            6            7            8   \\\n",
       "mean_test_score      0.731556     0.729716     0.729068     0.728454   \n",
       "param_svc__gamma     0.003834     0.015524      0.00067     0.005593   \n",
       "param_svc__C      7224.533826  1511.374523  3617.986556  2408.658977   \n",
       "mean_fit_time         0.60281     0.421078     0.120652     0.334508   \n",
       "\n",
       "rank_test_score            9            9   ...           82           82  \\\n",
       "mean_test_score       0.72845      0.72845  ...     0.508371     0.508371   \n",
       "param_svc__gamma      0.00089     0.000458  ...    33.017913    51.754341   \n",
       "param_svc__C      7636.928414  9053.515757  ...  5826.810879  7087.073954   \n",
       "mean_fit_time        0.233567     0.171383  ...     0.081538     0.080531   \n",
       "\n",
       "rank_test_score            82           82          82           96  \\\n",
       "mean_test_score      0.508371     0.508371    0.508371      0.50713   \n",
       "param_svc__gamma   126.722678    84.511472  120.952433   766.440217   \n",
       "param_svc__C      6648.824488  6007.085678  957.225166  4830.442643   \n",
       "mean_fit_time        0.059941      0.06476    0.059756     0.058091   \n",
       "\n",
       "rank_test_score            96           96           96         96  \n",
       "mean_test_score       0.50713      0.50713      0.50713    0.50713  \n",
       "param_svc__gamma   739.685456   918.053216   263.398099  806.79529  \n",
       "param_svc__C      9019.213727  7049.688305  3370.763834  26.980646  \n",
       "mean_fit_time        0.055657     0.057711     0.064806   0.059273  \n",
       "\n",
       "[4 rows x 100 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(random_search.cv_results_)[\n",
    "    [\n",
    "        \"mean_test_score\",\n",
    "        \"param_svc__gamma\",\n",
    "        \"param_svc__C\",\n",
    "        \"mean_fit_time\",\n",
    "        \"rank_test_score\",\n",
    "    ]\n",
    "].set_index(\"rank_test_score\").sort_index().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- This is a bit fancy. What's nice is that you can have it concentrate more on certain values by setting the distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### Advantages of `RandomizedSearchCV`\n",
    "\n",
    "- Faster compared to `GridSearchCV`.\n",
    "- Adding parameters that do not influence the performance does not affect efficiency.\n",
    "- Works better when some parameters are more important than others. \n",
    "- In general, I recommend using `RandomizedSearchCV` rather than `GridSearchCV`.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Advantages of `RandomizedSearchCV`\n",
    "\n",
    "![](img/randomsearch_bergstra.png)\n",
    "    \n",
    "Source: [Bergstra and Bengio, Random Search for Hyper-Parameter Optimization, JMLR 2012](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The yellow on the left shows how your scores are going to change when you vary the unimportant hyperparameter.\n",
    "- The green on the top shows how your scores are  going to change when you vary the important hyperparameter.\n",
    "- You don't know in advance which hyperparameters are important for your problem.\n",
    "- In the left figure, 6 of the 9 searches are useless because they are only varying the unimportant parameter.\n",
    "- In the right figure, all 9 searches are useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Searching for optimal parameters with successive halving¶\n",
    "\n",
    "- Successive halving is an iterative selection process where all candidates (the parameter combinations) are evaluated with a small amount of resources (e.g., small amount of training data) at the first iteration.\n",
    "- Checkout [successive halving with grid search](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html) and [random search](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## (Optional) Fancier methods \n",
    "- Both `GridSearchCV` and `RandomizedSearchCV` do each trial independently.\n",
    "- What if you could learn from your experience, e.g. learn that `max_depth=3` is bad?\n",
    "  - That could save time because you wouldn't try combinations involving `max_depth=3` in the future.\n",
    "- We can do this with `scikit-optimize`, which is a completely different package from `scikit-learn`\n",
    "- It uses a technique called \"model-based optimization\" and we'll specifically use \"Bayesian optimization\".\n",
    "  - In short, it uses machine learning to predict what hyperparameters will be good.\n",
    "  - Machine learning on machine learning!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- This is an active research area and there are sophisticated packages for this.\n",
    "\n",
    "Here are some examples \n",
    "- [hyperopt-sklearn](https://github.com/hyperopt/hyperopt-sklearn)\n",
    "- [auto-sklearn](https://github.com/automl/auto-sklearn)\n",
    "- [SigOptSearchCV](https://sigopt.com/docs/overview/scikit_learn)\n",
    "- [TPOT](https://github.com/rhiever/tpot)\n",
    "- [hyperopt](https://github.com/hyperopt/hyperopt)\n",
    "- [hyperband](https://github.com/zygmuntz/hyperband)\n",
    "- [SMAC](http://www.cs.ubc.ca/labs/beta/Projects/SMAC/)\n",
    "- [MOE](https://github.com/Yelp/MOE)\n",
    "- [pybo](https://github.com/mwhoffman/pybo)\n",
    "- [spearmint](https://github.com/HIPS/Spearmint)\n",
    "- [BayesOpt](https://github.com/rmcantin/bayesopt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### (iClicker) Exercise 8.1 \n",
    "\n",
    "**iClicker cloud join link: https://join.iclicker.com/3DP5H**\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "- (A) If you get best results at the edges of your parameter grid, it might be a good idea to adjust the range of values in your parameter grid.   \n",
    "- (B) Grid search is guaranteed to find best hyperparameters values. \n",
    "- (C) It is possible to get different hyperparameters in different runs of `RandomizedSearchCV`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Questions for class discussion (hyperparameter optimization)\n",
    "\n",
    "- Suppose you have 10 hyperparameters, each with 4 possible values. If you run `GridSearchCV` with this parameter grid, how many cross-validation experiments will be carried out? \n",
    "- Suppose you have 10 hyperparameters and each takes 4 values. If you run `RandomizedSearchCV` with this parameter grid with `n_iter=20`, how many cross-validation experiments will be carried out? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Optimization bias/Overfitting of the validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Overfitting of the validation error \n",
    "\n",
    "- Why do we need to evaluate the model on the test set in the end?\n",
    "- Why not just use cross-validation on the whole dataset? \n",
    "- While carrying out hyperparameter optimization, we usually try over many possibilities.  \n",
    "- If our dataset is small and if your validation set is hit too many times, we suffer from **optimization bias** or **overfitting the validation set**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Optimization bias of parameter learning\n",
    "- Overfitting of the training error\n",
    "- An example: \n",
    "    - During training, we could search over tons of different decision trees.\n",
    "    - So we can get \"lucky\" and find a tree with low training error by chance.\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Optimization bias of hyper-parameter learning\n",
    "\n",
    "- Overfitting of the validation error\n",
    "- An example: \n",
    "    - Here, we might optimize the validation error over 1000 values of `max_depth`.\n",
    "    - One of the 1000 trees might have low validation error by chance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### (Optional) Example 1: Optimization bias\n",
    "\n",
    "Consider a multiple-choice (a,b,c,d) \"test\" with 10 questions:\n",
    "- If you choose answers randomly, expected grade is 25% (no bias).\n",
    "- If you fill out two tests randomly and pick the best, expected grade is 33%.\n",
    "    - Optimization bias of ~8%.\n",
    "- If you take the best among 10 random tests, expected grade is ~47%.\n",
    "- If you take the best among 100, expected grade is ~62%.\n",
    "- If you take the best among 1000, expected grade is ~73%.\n",
    "- If you take the best among 10000, expected grade is ~82%.\n",
    "    - You have so many \"chances\" that you expect to do well.\n",
    "    \n",
    "**But on new questions the \"random choice\" accuracy is still 25%.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expected grade among the best of 1 tests is : 0.25\n",
      "The expected grade among the best of 2 tests is : 0.33\n",
      "The expected grade among the best of 10 tests is : 0.47\n",
      "The expected grade among the best of 100 tests is : 0.62\n",
      "The expected grade among the best of 1000 tests is : 0.73\n",
      "The expected grade among the best of 10000 tests is : 0.83\n"
     ]
    }
   ],
   "source": [
    "# (Optional) Code attribution: Rodolfo Lourenzutti\n",
    "number_tests = [1, 2, 10, 100, 1000, 10000]\n",
    "for ntests in number_tests:\n",
    "    y = np.zeros(10000)\n",
    "    for i in range(10000):\n",
    "        y[i] = np.max(np.random.binomial(10.0, 0.25, ntests))\n",
    "    print(\n",
    "        \"The expected grade among the best of %d tests is : %0.2f\"\n",
    "        % (ntests, np.mean(y) / 10.0)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### (Optional) Example 2: Optimization bias \n",
    "\n",
    "- If we instead used a 100-question test then:\n",
    "    - Expected grade from best over 1 randomly-filled test is 25%.\n",
    "    - Expected grade from best over 2 randomly-filled test is ~27%.\n",
    "    - Expected grade from best over 10 randomly-filled test is ~32%.\n",
    "    - Expected grade from best over 100 randomly-filled test is ~36%.\n",
    "    - Expected grade from best over 1000 randomly-filled test is ~40%.\n",
    "    - Expected grade from best over 10000 randomly-filled test is ~43%.\n",
    "\n",
    "- The optimization bias **grows with the number of things we try**.\n",
    "    - “Complexity” of the set of models we search over.\n",
    "- But, optimization bias **shrinks quickly with the number of examples**.\n",
    "    - But it’s still non-zero and growing if you over-use your validation set!    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The expected grade among the best of 1 tests is : 0.25\n",
      "The expected grade among the best of 2 tests is : 0.27\n",
      "The expected grade among the best of 10 tests is : 0.32\n",
      "The expected grade among the best of 100 tests is : 0.36\n",
      "The expected grade among the best of 1000 tests is : 0.40\n",
      "The expected grade among the best of 10000 tests is : 0.43\n"
     ]
    }
   ],
   "source": [
    "# (Optional) Code attribution: Rodolfo Lourenzutti\n",
    "number_tests = [1, 2, 10, 100, 1000, 10000]\n",
    "for ntests in number_tests:\n",
    "    y = np.zeros(10000)\n",
    "    for i in range(10000):\n",
    "        y[i] = np.max(np.random.binomial(100.0, 0.25, ntests))\n",
    "    print(\n",
    "        \"The expected grade among the best of %d tests is : %0.2f\"\n",
    "        % (ntests, np.mean(y) / 100.0)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Optimization bias on the Spotify dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tiny, X_test_big, y_train_tiny, y_test_big = train_test_split(\n",
    "    X_spotify, y_spotify, test_size=0.99, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 13)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tiny.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0.055100</td>\n",
       "      <td>0.547</td>\n",
       "      <td>251093</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2670</td>\n",
       "      <td>-8.904</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2270</td>\n",
       "      <td>143.064</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.420</td>\n",
       "      <td>210240</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>-3.899</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>127.204</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>0.314000</td>\n",
       "      <td>0.430</td>\n",
       "      <td>193427</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0808</td>\n",
       "      <td>-10.043</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>133.992</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>0.082100</td>\n",
       "      <td>0.725</td>\n",
       "      <td>246653</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0931</td>\n",
       "      <td>-4.544</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>93.003</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>0.286000</td>\n",
       "      <td>0.616</td>\n",
       "      <td>236960</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.2770</td>\n",
       "      <td>-6.079</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>81.856</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      acousticness  danceability  duration_ms  energy  instrumentalness  key  \\\n",
       "130       0.055100         0.547       251093   0.643          0.000000    1   \n",
       "1687      0.000353         0.420       210240   0.929          0.000747    7   \n",
       "871       0.314000         0.430       193427   0.734          0.000286    9   \n",
       "1123      0.082100         0.725       246653   0.711          0.000000   10   \n",
       "1396      0.286000         0.616       236960   0.387          0.000000    9   \n",
       "\n",
       "      liveness  loudness  mode  speechiness    tempo  time_signature  valence  \n",
       "130     0.2670    -8.904     1       0.2270  143.064             4.0   0.1870  \n",
       "1687    0.1220    -3.899     0       0.1210  127.204             4.0   0.3180  \n",
       "871     0.0808   -10.043     0       0.1020  133.992             4.0   0.0537  \n",
       "1123    0.0931    -4.544     1       0.0335   93.003             4.0   0.4760  \n",
       "1396    0.2770    -6.079     0       0.0335   81.856             4.0   0.4700  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tiny.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(StandardScaler(), SVC())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid size: 900\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'svc__gamma': array([1.e-20, 1.e-19, 1.e-18, 1.e-17, 1.e-16, 1.e-15, 1.e-14, 1.e-13,\n",
       "        1.e-12, 1.e-11, 1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05,\n",
       "        1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
       "        1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08, 1.e+09]),\n",
       " 'svc__C': array([1.e-20, 1.e-19, 1.e-18, 1.e-17, 1.e-16, 1.e-15, 1.e-14, 1.e-13,\n",
       "        1.e-12, 1.e-11, 1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05,\n",
       "        1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,\n",
       "        1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08, 1.e+09])}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    \"svc__gamma\": 10.0 ** np.arange(-20, 10),\n",
    "    \"svc__C\": 10.0 ** np.arange(-20, 10),\n",
    "}\n",
    "print(\"Grid size: %d\" % (np.prod(list(map(len, param_grid.values())))))\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(\n",
    "    pipe, param_distributions=param_grid, n_jobs=-1, n_iter=900, cv=5, random_state=123\n",
    ")\n",
    "random_search.fit(X_train_tiny, y_train_tiny);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>rank_test_score</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>20</th>\n",
       "      <th>900</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__gamma</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1000000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_svc__C</th>\n",
       "      <td>1000000000.0</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>1000000000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000000000.0</td>\n",
       "      <td>1000000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.000974</td>\n",
       "      <td>0.001891</td>\n",
       "      <td>0.003131</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>0.000991</td>\n",
       "      <td>0.001699</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.001782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00129</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.000963</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.00098</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.002592</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>0.001115</td>\n",
       "      <td>0.00093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 900 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rank_test_score            1            1             3         3    \\\n",
       "mean_test_score            0.8          0.8          0.75      0.75   \n",
       "param_svc__gamma           0.0          0.0         0.001     0.001   \n",
       "param_svc__C      1000000000.0  100000000.0  1000000000.0   10000.0   \n",
       "mean_fit_time         0.000974     0.001891      0.003131  0.000929   \n",
       "\n",
       "rank_test_score         3           3         3         3           3    \\\n",
       "mean_test_score        0.75        0.75      0.75      0.75        0.75   \n",
       "param_svc__gamma      0.001         0.0     0.001     0.001       0.001   \n",
       "param_svc__C      1000000.0  10000000.0  100000.0    1000.0  10000000.0   \n",
       "mean_fit_time      0.001277    0.001868  0.000991  0.001699    0.000961   \n",
       "\n",
       "rank_test_score           3    ...      20        20        20        20   \\\n",
       "mean_test_score          0.75  ...     0.65      0.65      0.65      0.65   \n",
       "param_svc__gamma        0.001  ...      0.0       0.0       0.0       0.0   \n",
       "param_svc__C      100000000.0  ...      0.0       0.0       0.0       0.0   \n",
       "mean_fit_time        0.001782  ...  0.00129  0.003133  0.000963  0.000936   \n",
       "\n",
       "rank_test_score       20        20        20        20            20   \\\n",
       "mean_test_score      0.65      0.65      0.65      0.65          0.65   \n",
       "param_svc__gamma      0.0       0.0       0.0  100000.0  1000000000.0   \n",
       "param_svc__C          0.0       0.0       0.0       0.0  1000000000.0   \n",
       "mean_fit_time     0.00098  0.000961  0.002592  0.000955      0.001115   \n",
       "\n",
       "rank_test_score            900  \n",
       "mean_test_score           0.55  \n",
       "param_svc__gamma           0.0  \n",
       "param_svc__C      1000000000.0  \n",
       "mean_fit_time          0.00093  \n",
       "\n",
       "[4 rows x 900 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(random_search.cv_results_)[\n",
    "    [\n",
    "        \"mean_test_score\",\n",
    "        \"param_svc__gamma\",\n",
    "        \"param_svc__C\",\n",
    "        \"mean_fit_time\",\n",
    "        \"rank_test_score\",\n",
    "    ]\n",
    "].set_index(\"rank_test_score\").sort_index().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the results: one might claim that we found a model that performs with 0.8 accuracy on our dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Do we really believe that 0.80 is a good estimate of our test data?\n",
    "- Do we really believe that `gamma`=0.0 and C=1_000_000_000 are the best hyperparameters? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's find out the test score with this best model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6163366336633663"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The results above are overly optimistic. \n",
    "    - because our training data is very small and so our validation splits in cross validation would be small. \n",
    "    - because of the small dataset and the fact that we hit the small validation set 900 times and it's possible that we got lucky on the validation set! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- As we suspected, the best cross-validation score is not a good estimate of our test data; it is overly optimistic. \n",
    "- We can trust this test score because the test set is of good size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1997, 13)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_big.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Overfitting of the validation data\n",
    "\n",
    "The following plot demonstrates what happens during overfitting of the validation data.  \n",
    "![](img/optimization-bias.png)\n",
    "<!-- <center>\n",
    "<img src='./img/optimization-bias.png' width=\"600\">\n",
    "</center>\n",
    " -->\n",
    "\n",
    "[Source](https://amueller.github.io/COMS4995-s20/slides/aml-03-supervised-learning/#20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Thus, not only can we not trust the cv scores, we also cannot trust cv's ability to choose of the best hyperparameters. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Why do we need a test set? \n",
    "- This is why we need a test set.\n",
    "- The frustrating part is that if our dataset is small then our test set is also small 😔. \n",
    "- But we don't have a lot of better alternatives, unfortunately, if we have a small dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### When test score is much lower than CV score\n",
    "- What to do if your test score is much lower than your cross-validation score:\n",
    "    - Try simpler models and use the test set a couple of times; it's not the end of the world.\n",
    "    - Communicate this clearly when you report the results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Large datasets solve many of these problems\n",
    "- With infinite amounts of training data, overfitting would not be a problem and you could have your test score = your train score.\n",
    "    - Overfitting happens because you only see a bit of data and you learn patterns that are overly specific to your sample.\n",
    "    - If you saw \"all\" the data, then the notion of \"overly specific\" would not apply.\n",
    "- So, more data will make your test score better and robust. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you\n",
    "\n",
    "Attribution: From Mark Schmidt's notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Would you trust the model?  \n",
    "\n",
    "- You have a dataset and you give me half of it. I build a model using all the data you have given me and I tell you that the model accuracy is 0.99. Would it classify the rest of the data with similar accuracy? \n",
    "\n",
    "1. Probably \n",
    "2. Probably not "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Would you trust the model?  \n",
    "\n",
    "- You have a dataset and you give me half of it. I build a model using 80% of the data given to me and report the accuracy of 0.95 on the remaining 20% of the data. Would it classify the rest of the data with similar accuracy? \n",
    "\n",
    "1. Probably \n",
    "2. Probably not "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Would you trust the model?  \n",
    "\n",
    "- You have a dataset and you give me 1/10th of it. The dataset given to me is rather small and so I split it into 96% train and 4% validation split. I carry out hyperparameter optimization using a single 4% validation split and report validation accuracy of 0.97. Would it classify the rest of the data with similar accuracy? \n",
    "\n",
    "1. Probably \n",
    "2. Probably not "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final comments and summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Automated hyperparameter optimization\n",
    "\n",
    "- Advantages \n",
    "    - reduce human effort\n",
    "    - less prone to error and improve reproducibility\n",
    "    - data-driven approaches may be effective\n",
    "- Disadvantages\n",
    "    - may be hard to incorporate intuition\n",
    "    - be careful about overfitting on the validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often, especially on typical datasets, we get back `scikit-learn`'s default hyperparameter values. This means that the defaults are well chosen by `scikit-learn` developers!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The problem of finding the best values for the important hyperparameters is tricky because \n",
    "    - You may have a lot of them (e.g. deep learning). \n",
    "    - You may have multiple hyperparameters which may interact with each other in unexpected ways.    \n",
    "- The best settings depend on the specific data/problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional readings and resources\n",
    "\n",
    "- [Preventing \"overfitting\" of cross-validation data](http://www.robotics.stanford.edu/~ang/papers/cv-final.pdf) by Andrew Ng"
   ]
  }
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