02 Introduction to Scikit-Learn and Model Evaluation.ipynb

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   "cell_type": "markdown",
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   "source": [
    "\n",
    "## Introduction to SciKit-Learn and Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1339be2",
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     "text": [
      "The Python version is 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)].\n",
      "\n",
      "The Numpy version is 1.24.3.\n",
      "\n",
      "The Pandas version is 2.0.3.\n",
      "\n",
      "The Matplotlib version is 3.7.2.\n",
      "\n",
      "The Scikit-Learn version is 1.3.0.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import packages\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "mpl.rcParams['figure.dpi'] = 200\n",
    "mpl.rcParams['font.size'] = 12 \n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# needed for activity 2\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "# import the data\n",
    "df = pd.read_csv(r'C:\\Users\\Lappy\\Data Science Projects with Python\\00 data\\ch01_cleaned_data.csv')\n",
    "\n",
    "# which versions?\n",
    "print('The Python version is {}.\\n'.format(sys.version))\n",
    "print('The Numpy version is {}.\\n'.format(np.__version__))\n",
    "print('The Pandas version is {}.\\n'.format(pd.__version__))\n",
    "print('The Matplotlib version is {}.\\n'.format(mpl.__version__))\n",
    "print('The Scikit-Learn version is {}.\\n'.format(sklearn.__version__))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d62122ab",
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       "0.2217971797179718"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['default payment next month'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14886639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default payment next month\n",
       "0    20750\n",
       "1     5914\n",
       "Name: ID, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('default payment next month')['ID'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "911f13ef",
   "metadata": {},
   "source": [
    "## Intro to Scikit-Learn\n",
    "\n",
    "Switching to the 2nd edition because the version differnce in Scikit-Learn is quite large between 1st and 2nd ed. \n",
    "\n",
    "https://github.com/PacktPublishing/Data-Science-Projects-with-Python-Second-Ed/blob/main/Chapter02/Example_Scikit_Learn/Example_Scikit_Learn.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "LogisticRegression()"
      ]
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "# create and examine a logistic regression object\n",
    "my_lr = LogisticRegression()\n",
    "my_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77538a8",
   "metadata": {},
   "source": [
    "## parameters of LogisticRegression() in sklearn\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "\n"
   ]
  },
  {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_new_lr = LogisticRegression(penalty='l2', dual=False,\n",
    "                              tol=0.0001, C=1.0, fit_intercept=True,\n",
    "                              intercept_scaling=1, class_weight=None,\n",
    "                              random_state = None, solver='lbfgs',\n",
    "                              max_iter=100, multi_class='auto',\n",
    "                              verbose=0, warm_start=False,\n",
    "                              n_jobs=None, l1_ratio=None)\n",
    "my_new_lr"
   ]
  },
  {
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   "execution_count": 6,
   "id": "1103cd42",
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   "outputs": [
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      ],
      "text/plain": [
       "LogisticRegression(C=0.1, solver='liblinear')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# altering some parameters\n",
    "my_new_lr.C = 0.1\n",
    "my_new_lr.solver = 'liblinear'\n",
    "my_new_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43eb7ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2],\n",
       "       [2],\n",
       "       [2],\n",
       "       [2],\n",
       "       [2],\n",
       "       [1],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [3]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df['EDUCATION'][0:10].values.reshape(-1,1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33bc42bd",
   "metadata": {},
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    {
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       "array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['default payment next month'][0:10].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "968f9ac3",
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       "<style>#sk-container-id-4 {color: black;}#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>LogisticRegression(C=0.1, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.1, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.1, solver='liblinear')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_new_lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87afd4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [1],\n",
       "       [2],\n",
       "       [2],\n",
       "       [1],\n",
       "       [3],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [3]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X = df['EDUCATION'][10:20].values.reshape(-1,1)\n",
    "new_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4e5537f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_new_lr.predict(new_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c91e1fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['default payment next month'][10:20].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f6e3509",
   "metadata": {},
   "source": [
    "# Activity 2\n",
    "\n",
    "## logistic regression with a new feature and creating a precision-recall curve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b47cf505",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up the parameters of the logistic regression.\n",
    "# todo: comment w/meanings\n",
    "\n",
    "example_lr = LogisticRegression(C = 0.1,\n",
    "                               class_weight = None, \n",
    "                               dual = False,\n",
    "                               fit_intercept = True,\n",
    "                               intercept_scaling = 1, \n",
    "                               max_iter = 100,\n",
    "                               multi_class = 'auto',\n",
    "                               n_jobs = None,\n",
    "                               penalty = 'l2',\n",
    "                               random_state = None,\n",
    "                               solver = 'liblinear',\n",
    "                               tol = 0.0001,\n",
    "                               verbose = 0,\n",
    "                               warm_start = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcecbcd",
   "metadata": {},
   "source": [
    "## Using `train_test_split`\n",
    "\n",
    " * Two inputs from the data: features `LIMIT_BAL`, and response `default payment next month`\n",
    " * `test_size` (x% for the training set, 1-x% for the training set is understood)\n",
    " * `random_state` is the random seed\n",
    " * there are four (4) X and y training, and X and y test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92c76ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(\n",
    "df['LIMIT_BAL'].values.reshape(-1,1), # first argument is features\n",
    "df['default payment next month'].values, # second argument is the response\n",
    "test_size = 0.2, # 20% test (and therefore 80% train)\n",
    "random_state = 24) # random seed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c240c9ab",
   "metadata": {},
   "source": [
    "Note that the test to train ratio is a 20/80 split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7175f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21331, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cc97de24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5333, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d182468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.200007500750075"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Out[16][0]/(Out[15][0]+Out[16][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1107578d",
   "metadata": {},
   "source": [
    "Note that the mean of the train and test sets are nearly identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "df03ec70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.223102526838873"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ba5efd6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21657603600225014"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_test_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902fb0e7",
   "metadata": {},
   "source": [
    "## Training and fitting steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e1c7975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.1, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.1, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.1, solver='liblinear')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the training step\n",
    "example_lr.fit(X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd33c1a",
   "metadata": {},
   "source": [
    "# Difference between `predict` and `predict_proba`\n",
    "\n",
    "> The `predict` function returns the prediction as True/False value, whereas `predict_proba` function returns probabilities, values between one and zero and this is the reason for the difference.\n",
    "> AUC means \"area under the curve\" which is indeed different if the curve is a 0/1 step function or a curve made of continuous values.\n",
    "> Let's imagine you have only one example, it should be classified as False. If your classifier yields the probability of 0.7 (`predict_proba`), the ROC-AUC value is 1.0-0.7=0.3. If you used `predict`, the prediction will be True = 1.0, so the ROC-AUC will be 1.0-1.0=0.0.\n",
    "\n",
    "source\n",
    "https://stackoverflow.com/a/56397922"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2e6a8b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the fitting step\n",
    "# predict_proba is used\n",
    "y_test_2_pred_proba = example_lr.predict_proba(X_test_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f42f639",
   "metadata": {},
   "source": [
    "## How accurate is our model?\n",
    "\n",
    "Compare the predicted outcome with the testing data set outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3c4e7183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7834239639977498"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_2 = example_lr.predict(X_test_2) # predict is used here\n",
    "is_correct = y_pred_2 == y_test_2\n",
    "np.mean(is_correct) # the model is 78% accurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "39fea9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7834239639977498"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_lr.score(X_test_2, y_test_2) # same accuracy reading as above (78%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b194d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7834239639977498"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test_2, y_pred_2) # this uses the metrics library in sklearn\n",
    "# and the accuracy agrees with the two methods above (78%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b134ea20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1155"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P = sum(y_test_2)\n",
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "245769c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate true positives\n",
    "TP = sum ((y_test_2 ==1) & (y_pred_2 ==1))\n",
    "TP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "43f56da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# True Positive Rate\n",
    "TPR = TP / P\n",
    "TPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2416fd64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1155"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# false negative\n",
    "FN = sum ((y_test_2 == 1) & (y_pred_2 == 0))\n",
    "FN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bd267efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# False negative rate\n",
    "FNR = FN/P\n",
    "FNR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8cbfe98",
   "metadata": {},
   "source": [
    "## What does this mean?\n",
    "\n",
    "The model is acting as a *majority class null model* where every positive sample was predicted negative. (None were correctly predicted.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6e63159b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4178"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Negatives\n",
    "N = sum(y_test_2 == 0)\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7fb24c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4178"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# true negatives\n",
    "TN = sum((y_test_2 ==0) & (y_pred_2 == 0))\n",
    "TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "08be85b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# false positives\n",
    "FP = sum((y_test_2 == 0) & (y_pred_2 ==1))\n",
    "FP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e34f6a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The true negative rate is 1.0 and the false positive rate is 0.0.\n"
     ]
    }
   ],
   "source": [
    "# compute True Negative Rate and Falst Positive Rate in a single line of output\n",
    "# for no real reason I added in line breaks using \\ in the print command\n",
    "TPR = TN/N\n",
    "FPR = FP/N\n",
    "print(\\\n",
    "      'The true negative rate is {} and the false positive rate is {}.'\\\n",
    "      .format(TPR,FPR)\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a612326",
   "metadata": {},
   "source": [
    "## Confusion matrix using scikit-learn\n",
    "\n",
    "We've already computed the confusion matrix manually, let's do it in a single line using scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "743a4924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4178,    0],\n",
       "       [1155,    0]], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test_2,y_pred_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fcbbc8",
   "metadata": {},
   "source": [
    "## Vocabulary\n",
    "\n",
    " * **TPR** True Positive Rate, or Sensitivity, or Recall\n",
    " * **Precision** TP/(TP + FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0a18bdab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.74826924, 0.25173076],\n",
       "       [0.584297  , 0.415703  ],\n",
       "       [0.79604453, 0.20395547],\n",
       "       ...,\n",
       "       [0.584297  , 0.415703  ],\n",
       "       [0.82721498, 0.17278502],\n",
       "       [0.66393434, 0.33606566]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the predictdd probabilities of our data\n",
    "y_pred_2_proba = example_lr.predict_proba(X_test_2)\n",
    "y_pred_2_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c57ea9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_sum = np.sum(y_pred_2_proba,1)\n",
    "prob_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bf0a933d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5333,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_sum.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "697dc7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(prob_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "faf05664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25173076, 0.415703  , 0.20395547, ..., 0.415703  , 0.17278502,\n",
       "       0.33606566])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_proba = y_pred_2_proba[:,1]\n",
    "pos_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0789ba8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.size'] = 12\n",
    "plt.hist(pos_proba)\n",
    "plt.xlabel('Predicted probability of positive cclass for testing data')\n",
    "plt.ylabel('Number of samples');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "292d60bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_sample_pos_proba = pos_proba[y_test_2 == 1]\n",
    "neg_sample_pos_proba = pos_proba[y_test_2 == 0]\n",
    "plt.hist(\n",
    "    [pos_sample_pos_proba, \n",
    "     neg_sample_pos_proba], \n",
    "    histtype='barstacked')\n",
    "plt.legend(['Positive samples', 'negative samples'])\n",
    "plt.xlabel('Predicted prob of a positive case')\n",
    "plt.ylabel('number of samples');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a0b9a8",
   "metadata": {},
   "source": [
    "## Meaning of ROC AUC\n",
    "\n",
    "### Receiver Operating Characteristic Curve\n",
    "\n",
    "> The roc_auc_score function, denoted by ROC-AUC or AUROC, computes the area under the ROC curve. By doing so, the curve information is summarized in one number.\n",
    "\n",
    "source\n",
    "https://scikit-learn.org/stable/modules/model_evaluation.html#receiver-operating-characteristic-roc\n",
    "\n",
    "> receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the performance of a binary classifier model (can be used for multi class classification as well) at varying threshold values.\n",
    "\n",
    "> The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting. \n",
    "\n",
    "> The ROC curve is thus the sensitivity or recall as a function of false positive rate. \n",
    "\n",
    "Wikipedia\n",
    "https://en.wikipedia.org/wiki/Receiver_operating_characteristic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "387d2f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6201990844642832"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.roc_auc_score(y_test_2,y_test_2_pred_proba[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72c4d38",
   "metadata": {},
   "source": [
    " * FPR = False Positive Rate\n",
    " * TPR = True Positive Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bbb712ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_2, tpr_2, thresholds_2 = metrics.roc_curve( y_test_2, y_test_2_pred_proba[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e4f7125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr_2, tpr_2, '*-')\n",
    "plt.plot([0,1],[0,1], 'r--')\n",
    "plt.legend(['Logistic regression', 'Random chance'])\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.title('ROC curve for logistic regression with LIMIT_BAL feature');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba894c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall, thresh_3 = \\\n",
    "metrics.precision_recall_curve(y_test_2, y_test_2_pred_proba[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37e98849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z//e//93lffn7+xsvvPCCU/W9lEK69PR0Y8CAAU7vx2KxGNOmTSs2IC3K9vguKCgw7r//frf+T0vj6vshOR/Svfbaa8UGREVvgYGBxhdffOF0nQsKCozHH3+82B8LHN1at25tHD582K33qDTuhHSvvPKKU+9N4a1t27Yu1X/RokVOn6+nTZvm8utwZd0333zT8Pf3d/k427Rpk2k/vg7pvv76ayMiIsKlstu3b+/Uvp3hKKRbvHixER0dXWz5jkK6EydOGFdeeaVLr+OBBx5w6txlGBfD4csuu8yp/fbv399ITU0tU0jn7vlx7ty5Lv1/1qxZ01i6dGmpr3/v3r1OX9MUvd1///3F7nPNmjVOhXPO/P8Xciek+/nnn0s83mxvUVFRTr1njj6TaWlpxqBBg5wqp2XLliX+ED137lzT+j/88EOJ9Xn00Uftynj66adL3GbWrFmm9ZcvX+5wPV+GdJ58T8uiIod0hmEYbdq0MZ37SvLiiy+ajvfCHwQJ6f6nLPUqKCiwO+e4GiC3atXKum2NGjVMjWj27t1r2veoUaNc2ndFDekqbVtA24Ego6OjS1w/KytLI0aM0NatWyVdnJWkW7duio2N1blz57Rjxw6H21x33XWm5pWSVKNGDV1xxRWqU6eOsrOztWvXLu3cudP6/M8//6yEhAStXr261K5kv/zyi6677jprP/hCYWFh6ty5s+rUqSPDMJSUlKTNmzfbrVcWO3bs0JgxY5STk2N9zGKxqFWrVmratKlq1KihrKwspaSkaMeOHTp58qTHyi7qww8/1C233KKCggLT4506dVKTJk2Um5ur7du3W5uyShe7pfXp00dLly51qQtsRkaGhg4dqi1btki6+H/ZtWtX1a5dW+fOndOaNWuss8RI0q+//qpp06bpnXfeKeOrtGf7euvUqaNWrVopKipK1apVU1pamnbu3KkDBw5Y10lJSdG1116rtWvXutwFbPny5ZowYYLy8vIkSZdddplatmypsLAwHT58WGvXrrU+J0nTp09Xly5d1Lt37xL3e+LECfXv399ufIG4uDh16NBBISEhOnDggDZs2CDDMLRgwQLdfffdLtW9rKrS59/2uKpevbq1q3p4eLgyMzO1f/9+7dixw/r/nZ+fr+nTpyssLEx/+tOfSi3jUnDu3DklJCTYDTIbFRWlLl26KCoqytolvHD6eMMw9PLLLyslJcXlGfWee+45a/cLSWrdurUuu+wyBQYG6vDhw1q/fn1ZX5LHffrpp7rvvvusy23atFGzZs0UHBys/fv3Wz/T0sWhA6ZMmaKOHTvqsssuK3G/hmFo8uTJdrN7hYSE6IorrlBsbKwMw7B2eSssY/v27erRo4fWrVununXrevjVuubpp5/W448/bnrM399fXbp0UaNGjZSRkaHNmzfr6NGj1ue3bdumnj176tdff1XDhg1L3P+qVas0bNgwXbhwwfR4y5Yt1bx5cwUEBGjfvn3W4/fll19W48aNPfPibCxevFh33XWX6TF/f3+1a9dO8fHxCgsLU2Zmpk6fPq3t27eburRUJK+88ooefPBBuy7ydevWVbt27VSrVi1lZmbq6NGj2rZtm7Kzs71ep71792r69OnWwbCjo6PVuXNnRUVF6fTp09q0aZPdNvv379eAAQPsvnPr1aun9u3bKzIyUunp6dqwYYPpuu2VV17RuXPnSr2eOXPmjPr166f9+/ebHo+NjVXHjh1Vo0YNHTt2TGvXrlVOTo6WLl2qW2+9tUzd69w5P7799tuaOnWq6TvPz89P7dq1U+PGjRUcHKzjx49r7dq11v/L9PR0XXPNNfruu+80YMAAh3XJysrSNddcYzfLYsOGDa3XaPn5+UpPT9fevXt14MCBUoddSEpK0jXXXKPU1FTT45dddpmaN2+umjVrKicnR2lpadq1a5eOHDlS4v7ctXjxYl1//fXW77RCrVq1UvPmzeXn56c9e/Zo27Zt1udSUlJ0zTXX6JtvvtHgwYOdLis/P1/jxo3TwoULJV08v3fr1k1169bVhQsXtH79etPfcjt37tTkyZO1ePFih/vr27ev/P39lZ+fL0lasmRJifVZsmSJw8f+9re/ObVNSEiIrrzyypJfZDnz9Ht6Kbvxxhut3VxnzZqlF154odhhg4p2dR0/fryCgoLKpY5VxY8//qjk5GTrcvv27V3qTrxu3TrT32kjR45USEiIdblZs2bq1q2b1q5dK0lasGCBUlNTFRkZ6YHa+5Cv0kHDcL8lXV5entGgQQPTth9//HGJ+y78NTooKMj45z//6bCZdGJiomn5zjvvNO2jYcOGxqxZsxx2Wdm8ebPRs2dP0/q33357ia/j6NGjRkxMjGmbxo0bG7NmzXLYraegoMBYs2aNcd999xlhYWFlbkk3evRoU9mTJk0yjhw5Uuz6iYmJxuuvv260a9fOYy3pdu7cadeSb9CgQca+ffvs1v3555+N5s2bm9a9/PLLS+z6apv8Fyb5UVFRxrvvvmv3f5mbm2s888wzdl1Htm/fXuLrcEffvn2NK6+80njjjTdKfN+3b99ujBw50lSfNm3alPqLuO0vrIWvvXfv3g6bGR8+fNiudV/Xrl1LfR3Dhw83bRMbG2t88803dvXbv3+/cfXVV1vXK/rrt7db0lWlz/+MGTOM+Ph446mnnjK2bNlS7HFy8uRJ4y9/+YupJVC1atWMQ4cOlVhvX7eky83NNQ4ePGi9Fe0aVb9+fdNzhTdHn68pU6aY6lajRg3jP//5j5GTk2NaLz093Zg+fbrh5+dnWv+dd94psZ5Fz4PVqlWztkIaPHiwsXPnTrv1jx496lZ3SMMwrK/z+eefN9Vx1qxZDt+PgwcPGqdPny6xzmFhYUa1atUMScbw4cONvXv32q2/Y8cOu9apY8eOLbW+zzzzjGmbqKgo46233rL+Al7U/v37jeuvv960/sCBA51uEeQsV1qVLV261O47YtKkScaJEydM6xUUFBjffPONXXe5Pn36lNgtPjMz02jWrJlpm44dOxrr16+3W3f37t1G//79DUlGcHCwadgKT7Wks215/uCDDxpnzpwpdv2dO3ca//rXv4wmTZrYtaRLTU21HoP169e37rNbt27FHqsHDx60+2y4em758ccf7T7Dffr0MVauXOnwWMrKyjLmz59vjBo1yujUqVOJ+3aFbWuLwu+m2NhY44svvrB2HS2UnZ1tHD9+3FQv2675vXv3NlauXOmwvG+++cb0PksyPvnkkxLrOHHiRNP6MTExxuzZs+2O2ZSUFFMXRNuWEiUp6/nxl19+MbXs9Pf3Nx555BHj5MmTdtump6cbf/7zn02f2djYWIfnQMMwjNdff930Ojp37lxil+fU1FRj1qxZxpAhQ6wtWm1Nnz7dtM9BgwY5fJ2FTp48abz33ntGz549jVdeeaXY9VxpSZeUlGR3zdGlSxeHXQG3bNli1+24Vq1adue4omw/k4XHQ7Vq1Yznn3/ebviYgoIC47333rPrPv/9998XW0bRc1HHjh2LXS81NdVhy9/g4OASh7Fp0qSJdd2rr7662PVKO3eeO3fOpWuUwpvtd2B5vKfuqugt6Y4dO2Y653/33XcOt9+6datp+6LDmtCS7n/crdeePXvsMpv33nvPpbLvvvtu0/ZLliyxW8f2vP3GG284vf+K2pKuUoZ0H3zwgd2J17Y5r6MuUX5+fsV+SG39+OOPpm07duxopKSklLhNdna2MXDgQNN227ZtK3b9a6+91rRujx49Si2j0JkzZ4r9g86ZkCw/P9/UvbR///5OlVvI0R9TrpRfyLar2dixY0v84+XMmTNGy5YtTds88cQTxa5ve1KRLv5BWNLFkWEYxl/+8hfTNg8//HCJ67ujtDDE1gMPPODSl67txZt08Y9t2xCiqMzMTNNFilRyQLl48WK7i7g9e/YUu35eXp7dH9yufPadVZU//0eOHLH7Q68ktl1IHnnkkRLX93VIZ8udbtO//PKLqV5hYWGljjf3xhtvmLapXr263XhbxdWr8HbDDTe4NWahs4ob78pZjup89913lxiInTx50hS4BwUFlXgc//7776Yurg0bNiz1XFhQUGDcfPPNpnq5M25mSZwNrPLz8+26AU6fPr3Efe/fv99uHMySLlJtx17p1KlTicMu5ObmGkOGDLH7v/NESHfy5EnTPm+55ZYS91lUfn5+ieMIujsOoGG4dm7JyMiwCyfuvvtupz+LjoIfdznqBhcbG2scOHDAqe3//Oc/m7a99dZbS30dR48eNQV19erVK/Y6YM2aNab9R0REGL///nuJ+//nP/9p95ok50M6V8+P2dnZpmMnKCjIWLRoUanb2b73xX1uBw8ebF0nOjrapWEIirs2LjqkRYsWLVwaX7Ok621XQrpbb73VtG7v3r1LDKwuXLhg9OnTx7TNTTfdVOz6jq67goODjV9++aXEer399tumbcaMGVPsukW7sFoslmJ/LCh6XdOiRQvT2G2LFy92qv7PPPNMsfVw5dxVlqFdyuM9dVdFD+kMwzA1DBg/frzD7R966CHrOpdffrnpOUK6/7GtV3E/rB04cMDYunWrMXfuXONPf/qT3VBWo0ePdulH1uzsbNMPQPXr13f4PXH69GlTo4Nu3bo5XQYhnQPuhHTLli2zG6Nl6NChpe5bknHvvfc6XbeiX0yhoaFOjyNz6tQp0wFZXGuazZs3m+oWExNjnDp1yun6lcSZL4SkpCRT+f/+9789Uraz5RvGxT/Witahfv36xrlz50rd/4YNG0y/jsTExBhZWVkO13UU0s2aNavUMtLS0qytSCTnWpR5W1ZWlqlFRmlfALYXbzExMSWGCoVsv9xL+jVixIgRpnVtW7Q6cubMGbtxWcojpKsqn393jBo1yun/i0shpLNtRfzSSy85td2wYcNM25U0VpDthWO9evW8Nr5lIU+HdG3atCkx1C9k+6NGST8g3HTTTdb1LBaLsW7dOqfqduHCBdOvsSW1cnCHs390LViwwPRaO3bs6FSwYBuGFzfOWUFBgemHkoCAgBLD/kInT560m3TJEyHdb7/9Ztrnt99+W2pdnFVeId0rr7xiWrdfv34eb4npLEch3VdffeXUtunp6ab/4w4dOjj9g4ztcfvpp586XM/2uuGtt94qdd8FBQVG9+7d7V5XScpyfnzvvfdM27oyfm7Rc39ERITDnhhFxz5ydWyj4hS9LnjooYc8sk/DcD6kO3PmjOmaNiwszKnrmiNHjpj+5goKCio2tHZ03VVS0FUoPz/fFCLHxMQUu+6iRYtM+y86aU5RRVt43n333aYfef785z873Oadd94x7buk1pO+DOk8/Z66qzKEdB9//LH18ZCQECM9Pd20bV5enmnCGdsxCwnp/sfR39Ou3GJiYoznnnvOpUYEhmEYX331lWk/JTWaGTp0qGndkibdKaqihnTlO7d8KfLy8pSYmGh3+/333/XVV1/phhtu0FVXXaXz589btwkJCdE///nPUvdtsVj04IMPOlWPHTt2aMWKFdblO+64w+mp0GNiYjR+/Hjr8nfffedwvf/+97+m5ccee0wxMTFOleENRcdhKy+ffvqpafmhhx5SeHh4qdt16tRJw4YNsy4nJSU5Pd5CfHy8xo4dW+p6NWvWVM+ePa3LW7duLXXMEW8LDg7WoEGDrMuFfe+ddeedd6pmzZqlrjdkyBDTcuH4fbbOnTunBQsWWJfj4+M1ceLEUvcfHR2tqVOnlrqeJ/H5L9n1119vvX/o0CGdOnXKZ3XxtoyMDH3zzTfW5Vq1aumee+5xatt//OMfpuVPPvnE6XLvvPPOMo3Z5AvTpk1TYGBgqes5e85IS0vTrFmzrMvDhg1T586dnapLtWrVdMcdd1iXly1bpszMTKe29STb763HH39cfn6lX0oNHz7cNH7qli1b9Pvvv9utt2HDBtM4pEOGDFGbNm1K3X+dOnV00003lbpeWfniWqGsbM+3r776qiwWi49qY9akSRONGDHCqXU/+eQT69h1kvTkk08WO8aSrWuvvVZNmjSxLjv6biooKNDXX39tXa5Vq5ZuueWWUvdtsVg0ffp0p+pRHFfOj2+++ab1fkxMjO69916nyyk6xmZaWppWrlxZ4vreON598Rn66quvTOPQ3XrrrU5d1zRo0EC33367dTknJ0dz5sxxqsywsDCnxh/28/MzjS2XlJRU7BjYvXr1UnBwsHXZ0bhzkrR06VLr/QEDBpjGH3Rmm5o1a6pTp06l1r28eeM9vZSNHDnS+nflhQsX9OWXX5qeX7x4sU6cOCHp4nls0qRJ5V7HqqBJkyb6xz/+oXvuucfp76xCH374oWm5pP8j2+dst61sKlRId+zYMTVu3Nju1rZtW40ePVqzZs2yDhgqSYGBgfr444/Vrl27Uvfdtm1bxcfHO1WPZcuWmZZHjx7t0usoOtD+8ePH7QaftS0jMDBQkydPdqmMsqpVq5Zp0Ma33nrL9IdBeVi1apVpuWi4UZobbrihxH0V5+qrr3bqDypJatGihfV+VlaWKRz2ptzcXKWkpOjw4cN2gXVYWJh1vT179thNElASZwf8bdCggamc06dPO1xv/fr1ys3NtS6PGjXK6T98nAlKPYnP/8U/wNLT03X06FG748r2S3PXrl1er4+vrFu3zjRByqhRo5wKoqSLx1Hr1q2ty1u2bHE6KLruuutcq2gF4Ow5o+i5Uir+nLFy5UrTOaMsn628vDz99ttvLm3vCUW/a6pXr65rr73W6W2d+d5as2aNaXnkyJFO73/UqFFOr+usZs2amb4zn332WdMA0BVd4WQWhbp166a2bdv6sEZmQ4cOdfp7s+j3RmhoqK655hqXyir6+XEUTu3YsUPnzp0z1c3Zc+PQoUPLNNi6s+fHs2fPauPGjdbl66+/3uk6SlL37t1N6zt6Hy6//HLr/V9//VXz5893ev/FKbrPOXPmlPu5y/ZcM2HCBKe3dfd6u0ePHk4Hr85+h4SEhKh79+7WZUeB28mTJ62DzPv5+alv377q37+/9fmNGzcqLS3NbruiIV3hJBUVjTfe00tZaGio6XvRdrKqohNGJCQkqFGjRuVWt6rkwIEDuvPOOxUfH6+vvvrK6e3OnDmjH374wbrctm3bEjOf66+/XjVq1LAuf/LJJy79nVzRVNrZXdu3b693331XV1xxhVPrd+jQwel9235p16xZ0+Ef2sWxveA6ePCgKSA4e/asaTbIDh06mA6q8mCxWDRmzBi9/fbbkqRTp06pffv2mjx5ssaNG6crr7xSAQHePTw2bNhgvd+wYUPFxsY6vW3RL2nbfZWkZcuWTpdh2+rs7NmzXmkJk5ycrDlz5ui7777Tli1bnJ7Vq6CgQGfPnlVERIRT67v62jMyMiTJ9Mt9UbazznXp0sXp/bdu3VohISF2Mxd6S1X8/GdnZ+u7777TV199pU2bNmnPnj2mHzlKYjsD3aXE9lzRrVs3l7bv3r279Y/+vLw8bdmypdQZ4Pz9/Z1qDVWR1KhRQ/Xq1XNqXUfnSkdsP1vR0dEufbZsj9+DBw+qb9++Tm9fVsnJyaYZNTt27OhSMOHM91bRWRUludSaw5WZzp0VGRmpq6++2jqb4K5du9S8eXPddtttGjNmjDp16lRhWqU5YtvivE+fPj6qiWPufjc1btxYx48fd6msorPhHT58WPn5+aYgoizHXnBwsFq1amU3W7YzXDk/rlmzxnQeqF+/vkvnEOni+aqwNdvBgwftnh83bpy1tXV+fr6GDx+u4cOH68Ybb9TVV1/tVI8PR/ssfG8yMzPVq1cv3XDDDZowYYL69u1rah3mDUXPNYGBgS6dKzp06KBq1apZW+KV1/V2cQYMGKCff/5Z0sXZkY8cOWJqFVg0bOvUqZN1hseWLVtq586dys/P188//2zqkbNjxw5TS7OioV5F4q339FJ24403WltU/fzzzzp06JAaNWqkc+fOad68eab14LyEhAQtX77c4XPZ2dlKSUnR1q1bNXv2bH300UfKy8tTUlKSRo8erddff92pFqGfffaZ6Yfd0lo6hoSEaOTIkfrggw8kSUeOHNGyZcuKncm7oqsUIV1oaKhq1qypJk2aqFu3bho2bJjLF1q1a9d2et2jR4+alsv6q2tKSoppOSkpydR1slWrVmXav7ueeuopff/999ZQ6Pz585o5c6Zmzpyp8PBwXXnllerVq5f69u2r7t27e3RK6uzsbFPLtGbNmrm0fYMGDUwhj7O/EDnT3bOQ7a+zRU8UnlBQUKAXX3xRTz31lNut9FwJ6dx97cW9btsukU2bNnV6/35+fmrcuLFpSm1vqmqf/++++0733HOPy3+8FLqUL+ZszxWunnuKtohwtD9HIiIiXGrtURF441xp+9lytSWQLdvPlreVx7Fj20qtQYMGTu+/Zs2aql69uqk1lCe88sor6t69u9LT0611fPbZZ/Xss88qMjJSvXr1sl4rXHHFFRWqBYptFy9fXW8Vx9nvpvz8fNNr2b59uxo3bux2uYZhKC0tTdHR0dbHynLsSVJcXJxbIZ0r50fbc8jf//53/f3vf3e5zEKOziFjx47VRx99ZG3BYRiG5s6dq7lz5yogIECdOnVSr169lJCQoD59+jh1DXbfffdp9uzZ1h83c3Nz9eGHH+rDDz9UcHCwunXrZt1nr169FBoa6vZrcqTouSYuLs6lUDAgIECNGze2/rjo6+vtAQMG6IknnrAuL1myRFOmTDEtF1236P3C17BkyRJTSGfbIq+i/lFfkf6GqSz69eunuLg4HTlyRIZh6NNPP9Vf//pXzZkzx9oTIiQkRGPGjPFxTS8dwcHBio2NVWxsrAYNGqQ77rhDAwcOtP5tcf/996tbt26lDndStLuqn5+fXateRyZNmmQN6Qr3UVE/z6WpUN1dGzVqJOPiZBamW0ZGho4fP65ff/1VL774olu/hLryy5enL/xtAxjbCyFnQxZPi4mJ0erVq3X11VfbPXf+/HktXrxYM2bMUEJCgurUqaPbb79de/bs8UjZti113GlJVPTLytmWP852dfU2wzB066236pFHHilTN1pXmvF6+rUX/sFWyNX/w/JsPVqVPv/vvfeerrvuOrcDOsm146qyse3m4upxaHuR7My5x52WF77mjXOltz9b3lYex47tedXV1tveOK+2aNFCq1atcnhBnZqaqm+//VaPPvqounXrpvr16+vBBx/UsWPHPF4Pd1SU663iOHtuSE1N9fi4uLafH18dexXp+1m6eO6bO3eu7r33XrseJYXd7F966SUNGzZMMTExuu666/TTTz+VWE5oaKiWLl2q8ePH27U8zc7O1ooVK/R///d/GjRokGrXrq0JEyZo/fr1ZX+Bfyh67irr9XZ6erpTx6K3rre7du1qOmZsAzbb8egc3bfdpuhynTp1TMNaVCQV5W+YysTPz880XnZhl9eiXV2HDx9e6cYMrky6deum559/3rqcn5+vxx9/vMRtfv/9d9PQBgkJCU79cNSvXz/Tel9//XW5Xyt6Cp92Bzz9a0NpX2a+7CpSv359LVq0SKtWrdKdd95ZbH/8tLQ0vfPOO2rVqpWeeuopj9fDnffA1xM5lMVHH31kSvqlixcQ//73v7Vq1SodOXJE586dU15enimwnjFjhm8q7EBZ3/+K+v9XmT//e/fu1V133WUqs2XLlnr66af1008/ae/evUpPT1d2drbpuLIdh68qcfX9r6jHbWVQ3p8tb/PGd7dti3VX37Ps7GxPVseqVatW+u2337Rw4UJNmjRJdevWdbjeqVOn9PLLL+uyyy7TO++845W6lEVF7ppbEm+0grH9/FTUY6+o8jqHBAcH67XXXtOePXv02GOPqUOHDg6PndzcXC1YsEBXX321Ro0aVWIr1oiICM2aNUtbtmzRtGnT7MYMK5SZmanPP/9cXbp00dSpUz3+miv79XZAQICpsUbRgO3AgQPWHyiDgoLUq1cv63NFx5nbsWOHdcKAgoICa/dZqeJ2dYX7ik6qtGvXLn355ZemCeLKY9Klqu7GG280fccsXLiwxB9dbCd9WLZsmSwWS6k3f39/U4vrjIwMuwlDKotK0d21vBWdUKFmzZoOBxj11P6lijH+05VXXmkdV+nIkSNauXKlVqxYoYULF5omlMjPz9eMGTMUFBSkP//5z26XVzhGRCHbX3CdUbRLnu3+Krqnn37aet/Pz0+zZ892atBvT3djKgvbFgmudpGsSK+lqMr8+X/22WeVk5NjXZ42bZpeeOGFUn99raj/F95ge9y6eu6xPc4r27nHl2yP/ZSUlEr1/pX12LFd39Frt30sNTXVNJZYSQrHKfUWi8WigQMHauDAgZIu/ihQ9Fqh6BhpFy5c0O23366wsDCXBqn3tIp4veUO29cxbNgw0yzVnuDo2HOFp78rHbF9H+bOnavhw4d7rbzGjRvrH//4h/7xj38oNTVVa9as0YoVK7RkyRKtX7/eFF59/fXXysjI0I8//ljiPtu2bauXXnpJL730kk6dOqVVq1ZpxYoV+umnn+xmfH7zzTdVUFCgt956q0yvIyIiwjpESVmvt2vWrOnzsHvAgAH6/vvvJUknTpzQjh071KpVK1Ng16NHD9O5MyIiQp06ddK6deskXWxxN3HiRG3YsMF07FbWrnEoXsuWLdW5c2dr69Tbb7/d+tmNjY112KMMnhUSEqLmzZtbxz41DEMbN27UVVddZbdufn6+Pv30U4+V/dFHH5m6xFcWtKRzICYmxno/PT3d4zPi1KlTx/QFV17jcjkrLi5O48eP1xtvvKH9+/dr3bp1djNv/eMf/yjTDG/BwcGm5ur79u1zafujR4+aJh1wZcwxX9u9e7fp9d58881Oz8pXkaZQL/o5kaT9+/c7vW1BQYHDAZsrgsr8+f/uu++s9y+//HKnAjqpYh1X3mZ7rnD13GPb5b8ynXt8zfac4ep772vlcewUHQBdkmlm0tLs27fPFNJ722WXXaYpU6bovffe09GjR7V06VL17NnTtM706dOdnrDGG2xb/FW06y1nBQcHm7op7t271+NllOXYc2d9d9ieQ7zxPhQnMjJS11xzjZ555hn99ttvOnTokB566CHTGIwLFy40zUZYmjp16mjEiBF6+eWXtW3bNu3atUs333yzaZ3//Oc/ZT5ui55rjhw54lKrx7y8PNP1WkX4zrMN0grDueLGoytUtJWco21s18Glo2hruaKh7A033FChxlG9lNl2KS6cwMfWokWLrC1dPWH58uWmSb8qC0I6B2xnYHM0xXdZ1KhRwzR48ebNmyv0QO2dO3fWN998Y/31XLrYHL+s70vRmXkPHz7s0gdyzZo1xe6rorP9w27w4MFOb2v7un3Jdua3wl8nnfH777+X28yurqqsn//MzExT2Hb11Vc7PX5JRTquvM32XGE7+2Npir5XAQEBJU4HX9583bqhNN7+bHlbdHS0aUiITZs2uRSKOfO91bVrV9Ny0S45pXFlXU+zWCzq16+fFi9ebJps5/jx48XOBlkex2v37t1N5fjyPSqrop+fnTt3evSPGMl+hnZX3qt9+/Z5vD6OVKRzSFxcnF544QW7iSu+/fZbt/fZvHlzvffee7rtttusjxmGoQULFri9T8l8rsnNzbVOYOGMzZs3W2d2td2Xr7Rr1061atWyLi9ZssRu6A5HIV3RxwrHris6hl3jxo3LNCGLIxX9e7mqmDBhgsMJaujqWn5su7cW10vAtqvrV199pYMHD7p0e/31163bG4ZhHYuwMiGkc8C26aXt2GGe0K9fP+v9wlmeKjI/Pz+76anLMjC9dLEpelFffPGF09t+9tlnpuXCrrqVgbsTLqxZs8bU9djXOnfubBpU+auvvnJ63JLZs2d7q1plVlk//+4Oap+ZmenxblMVWZcuXeyOW2fH/Nm2bZuptUi7du0UFhbm8Tq6y3bGvvJsVeWM/v37m/5g+eijjyrdJCVFv7fOnTtn7XLlDGe+t3r16mU6Pj/99FPl5eU5tX9vnKtcFRISorFjx5oeK+5aoejx6q1jtXbt2qZB4NeuXWvtblPZFP1uMgzD49eNUVFRat++vXV55cqVTrcWLa9jLzY21vQj17Jly3T48OFyKbs4kydPNi2X9drYG/u0vd7+/PPPnd62Il5vF/4oUGj58uXasmWLkpKSJF1ssWMbOksXz6+F551Dhw5px44d+vXXX63Pe6MVXXmc51C6WrVq2c0o3759+wr1Q+ulLC0tza7lc7169ezWS09P17x586zLsbGxGj58uOLj41263XTTTapWrZp1P0UnCqksCOkc6NKli6mV0MKFC03dyDzhjjvuMC0//fTT1i+Xisr2j37bQYZdZTuV8gsvvODUDCwbN240fYBr165tauVX0dmOa+TMjLmGYeixxx7zUo3cU716dQ0dOtS6nJiY6NQYAsnJyXrzzTe9WbUyqayff3eOK+ni587TM+ZVZGFhYRo2bJh1+cyZM6Zf3EpiOxvVpEmTPFq3srKdPbSidWOuU6eOafyonTt3VuhzgSO231tPPfWUU0HjN998Y2q90q5dO1OLs0K1a9fW9ddfb10+ePCgXnnllVL3P2fOHK1cubLU9cqDs9cKRY9Xbx6rtufbBx54oEINhO+sm266ydTy4LnnnjMNkO0Jt956q/W+YRiaNm1aqdscOnRIL7/8skfrUZI777zTej8nJ0fTp08vt7Id8fS1sTf2OXLkSNMfrO+8845Tx86xY8f03//+11SPMWPGlKkunlK0VVx6erqee+4563JCQoLdzLzSxR8RioaMTz/9tKlXhzfGoyt6njtz5oxPu/9XdTNnztQvv/xivX311Ve+rlKVMXPmTNOxHxERoY4dO9qt98UXX5ha7o4ePdqtWY2rV69u6qm2d+9erVq1yuX9+BIhXTFsZ9GcOHGiy/+5+/fv1/Llyx0+17ZtW1PAkZSUpBEjRjg98G5ycrLTv647snv3bs2ZM8elLwvbX9OaN2/udvmS1KZNG9MvYceOHdPtt99e4h88ycnJmjRpkmmdO++8064FSUVm+4fZG2+8YTohOfLXv/61QnYNu+uuu0zLDz74YIm/vOfn5+vWW2+t8KFQZfz8h4aGqkmTJtblBQsWlDpez4IFC/SPf/zDqTIvJffdd59p+fHHHy+1u/abb75p+nEgPDzcbuwgX7M9J1fEWXufeOIJ0wXXtGnTXG7JeeLECZdasHnSkCFD1LRpU+vypk2bSp1E6cCBA3bnSttjsKhHHnnE9B795S9/KbHly7Jly7x2LK5Zs0aLFi1yev2CggK7VvHFXSsUfTwxMdFr45TeeuutqlOnjnV56dKluvfee51uxVk44L6v1alTR3/605+sy6mpqRo6dKhpwg5n/PLLL8X+iDN58mTFxsZalxcsWKAHHnig2GvFI0eOaPDgwcrMzHSpDmVxxx13mFpgzJkzR48++qhLrXIvXLhgd01b6KWXXnJpMqXSro3PnDmjd99916Vx4Dx9vV2rVi3TBC4ZGRmaOHFiideeWVlZmjhxounH87FjxxY7s3N5sw3Uip4jSwrbiraWsz2veqMlXdH/u9zcXFPLPZSvBg0aqFevXtZb0e9yeM/s2bPthgWYNGmSwyDdtoW4bct8V9huW9la0xHSFeP66683/fqanp6uhIQEPfTQQyUOPnj69Gl9+OGHuu6663T55ZeXOMvT22+/bRqAddWqVbriiiv0xRdfOGwSbRiG1q5dq/vuu0+NGjVyqtVZcU6cOKGxY8eqWbNm+tvf/qaNGzcWexF28uRJ3XHHHaYL7zp16jickcVVr7/+uunXvc8//1xDhw512K1zxYoV6tmzp3bu3Gl97LLLLivTLLO+EBcXZxp3aOfOnRo6dKjD4+rAgQMaM2aM/vWvf0mSaQyOimDgwIGmVkmnT59WQkKC5s+fb9dS4cCBA7r22mutQYdty6+KpLJ+/kePHm29n52drYEDBzq8IExPT9fjjz+uESNGKC8vr8IdV97Wp08fU/f9jIwMXX311Xr33Xftur6ePXtWjzzyiO655x7T4y+99FKFO4abNGmiBg0aWJc/+ugjPfzww/rll1+0b98+JSYmWm/FDdjrbR06dDDNbp2bm6sRI0bolltuKXFw9PT0dM2ZM0fjx49XfHy8zy62/Pz89Pbbb5u67T7//POaPHmyXZhjGIbmzZun3r17m1qK9erVq8RQrVu3bqbjLS8vTxMmTNCwYcM0Z84cbdu2TTt27ND8+fM1adIkDRgwQBkZGbryyitVv359D75aadeuXRo0aJDatGmj//u//9POnTuLbYV24MABjRw50vSDRqdOndSyZUuH6/fp08d63zAMDR8+XJ999pl+//1307GamJhYph8lQ0ND9dFHH5mCz5kzZ6p///7F/viSnZ2tb7/9VqNGjdKQIUPcLtvTnn76aXXo0MG6vGXLFnXo0EGvvvpqiWOb7t27Vy+88II6d+6sPn36FDt0Ro0aNTRz5kzTY6+++qo6d+6st99+Wxs2bNDu3bu1bNkyPfroo2rdurV27dqlqKiochtwv1q1apo1a5ZpfKnnnntO/fr105IlS4oN63JycrRs2TLdd999iouL04MPPuhwvYceekhxcXG64447tGjRomLHz71w4YJeeeUVPfDAA9bHLBaLXWvb8+fP67bbblOjRo30wAMP6Ndffy12iIX09HT97W9/0/PPP296vc5OLlaSZ555xnTNsWLFCvXt21ebN2+2W3fr1q3q27evfv75Z+tj0dHRptZqvtasWTM1bNjQulz0vFRSSFf0uaLbtG7d2hTme0rR85x0MQh/9913tXnzZh08eNB0nivtB/uK7OTJk3bnbWduZflbFr6TlZVV7P/pnj17tGbNGr399tsaMGCAxo0bZzrn1a1b1y60ky6ObVr0O7l+/fp2k1G54rrrrjO1PrdtpVectLQ0t45lj19XGz508OBBQ5L11qhRI6/te8aMGS7vIzs72xg6dKhpP4W3Zs2aGddee60xceJEY9SoUUb//v2N+vXr26336KOPlljGzz//bNSoUcNuu/DwcKNv377GuHHjjLFjxxoJCQlGzZo1TeukpqY63GdCQkKp7+myZcvsygwNDTW6d+9uDBs2zJg0aZIxatQoo0OHDoafn59pPYvFYsydO7fY1+RM+UW9++67hsVisSvjiiuuMMaMGWMMHz7caNasmV19a9asaaxfv77Efdu+zvfff7/U+hSaMWOGaduDBw86vW1pfvrpJ7vX7O/vb3Tr1s2YMGGCMXLkSKN9+/am56+88krjr3/9q9N1mjx5smldVzRq1Mi6XUJCQonrHjt2zIiLi7P7/2nYsKFx/fXXG2PHjjW6dOlier0333yzy8eJK6rq5z8pKcmIjo6221+LFi2M0aNHG+PGjTN69eplBAYGWp+rVauW8f777zv9OXHlM+WJ/4fSuHscpaenG+3atbN7r6Kjo41rrrnGmDBhgtGvXz+jWrVqdutMnDjRa/Uqq3/9618Oj1nb2+TJkz1a59L2bevOO+90WK+4uDhj8ODBxg033GCMGTPGuPrqq43GjRvbnS/HjRvnUv1K48o5zzDsvx8Kz+E9evQwxo8fb1x33XVGgwYN7NapX7++U98l2dnZxrXXXuvU/2XhcXvw4EHT6+jbt2+ZX7PtuaHwu7dXr17GiBEjjEmTJhnDhw83WrVqZbdeUFCQ8dtvvxVb/tmzZ43atWs79fps3zN3zi0vvfSS3XEkyYiNjTUGDRpk3HDDDcaIESOMLl26GMHBwdbn27dvX+q+nWX7fi5btszlfSQmJjq8JvL39zc6depkvYYbNmyY0bNnTyMiIsJu3R9++KHEMp5++mmnjz0/Pz/jm2++cemawxPnxw8++MD0XVZ4i4iIMPr372+MHTvWmDBhgjFkyBCjbdu2RkBAgGm9OnXqONyv7f4CAgKMtm3bGkOGDDEmTpxojB8/3ujVq5cREhJit+4DDzxgtz/bY1WSERwcbFxxxRXGddddZ0yaNMkYM2aM0bVrV4ev55VXXin2PXD1Ou+HH34wHduFt9atWxsjR440Ro0aZbRt29bhZ3nBggUl7rss3/fufi6mTJliV9eYmBijoKCg2G1yc3ON6tWr22137733Ol1fV74vCgoKHJ4fHd1sX7cv3lNnFf0Ml+Vmew1p+91a2mu2rceFCxc89hrr1Knj9HnK9v125jrIEdtrbGeuR8qDo9zAnVtMTIzx+++/OyzjscceK/V86qpRo0aZ9vnFF1/YrePoes6dm7v/58Wxb2cIq6CgIM2bN09PPPGEnn32WdMvufv27XNqQN3SWlr06dNHK1eu1IgRI0z7O3/+fLFd5bwlMzOz1FkeQ0ND9Z///Mc0rlBZ3XLLLQoNDdUtt9xi/cXSMAxt2LCh2FnhGjRooAULFpgGOa5MBgwYoJdeekkPPvig9Ze8/Px8rV271uFsk926ddO3336rf//73+Vd1VLVq1dPS5cu1VVXXWVqZXb48GGHAzoPHjxYb775pgYNGlSe1XRZZfz8165dW3PnztXQoUNNrSp27dqlXbt22a1fp04dLViwoEr+klmjRg39/PPPGjlypKlbaHJysn744Ydit7v33nudGiPMV6ZPn64NGzZozpw5vq5Kid566y21bdtWDz/8sKmlypEjR3TkyJFSt4+MjPRm9Ur15JNPqnr16vrzn/9sPTfk5+dr1apVxbbOat26tb7//ntT64/iBAUF6auvvtLDDz+smTNnltiVr0OHDvr6668VHx9v+pU4PDzcxVflnPT09FK7bEVFRWn27NkOB28vVL16dc2ePVujRo0qlyEQpk2bpvr16+u2224zdWc8ceJEucxM6imNGjXSunXrNHnyZM2fP9/6eH5+vjZu3KiNGzeWuH1AQECpx8bf/vY31a5dWw8//HCJLfSio6M1a9YsXX311Zo1a5b1cW8de0VNnjxZTZo00YQJE3Ts2DHr42lpaaYZO4vj7DkkLy9P27ZtK3HCEYvFovvvv18vvviiU/vMzs4u8RpXuvj/9H//93+6//77ndqnMwYPHqwffvhBo0ePNn3mtm/fbpoUqaiIiAjNmTPHI71nPG3AgAF2k5bYTlBkKyAgQH369LEba9gb49FJF4+NL774Qtdee63PJzkBfMXf31/jx4/XSy+9pJiYGLvnDQczsJalq2vRfRQde/DDDz/0yH7LA91dS+Hn56enn35ae/bs0R133KHo6OgS17dYLGrXrp0eeeQRbdu2zamumG3atNGOHTv0+uuvF9stpFBYWJiGDx+uBQsW2A0S7oorr7xS33zzjW699VanphuPjIzUnXfeqV27dmnixIlul1uc8ePHa+/evbr11ltVvXr1YteLjY3Vk08+qV27dlXagK7QAw88oB9//LHE19G0aVP961//0ooVK0o99nypWbNm2rZtm+69995iZ7uMj4/Xiy++qAULFlSaMQQr4+e/d+/eWr9+vYYOHVrshWpkZKSmTp2qrVu3qnPnzqXW8VIVERGhJUuW6LPPPitxhi9/f38NGDBAq1at0muvvebWILblxd/fX7Nnz9bixYt1yy23qH379oqMjDR1Daso7r77bh08eFDTp093qpvm5ZdfrnvuuUerVq2qEBNOPPTQQ9q2bZtGjx5tGrbBVuPGjfXqq69q06ZNTgV0hYKDg/Xaa69p06ZNmjZtmtq0aaOIiAiFhoaqWbNmuv766/Xll1/qt99+s36PF509vCzXCIVGjRqlzz77TDfccIPDmdhsxcbG6uGHH9bevXud+qO3b9++2rlzp5577jkNHDhQDRo0UGhoaIl/ZJfF2LFjrcdcaV3b6tSpozvuuMPjs6h6QkREhObNm6cVK1Zo6NChpi49jgQFBalv3756/vnndeTIEfXq1avUMu644w7t3r1bzzzzjLp3766YmBgFBQUpLi5OCQkJ+ve//619+/bp6quvlmSeYdwTx54zevfurX379unf//632rRpU+r6MTExuuGGG/T1119r69atDtf59ddf9fDDD6tDhw7y9/cvcX9BQUG6/vrrtXLlSr388ssOvxvi4uK0aNEi3XPPPWrZsmWpx3ZYWJgmTpyoLVu26OGHHy71NbmqX79+2rt3rx588MESr2mioqJ0//33a9++fRUyoJMcjyHnTLdr23OTn5+fEhISPFYvW23atNG2bds0c+ZMDR06VPHx8QoPD/faeQ7wpYCAAEVFRenyyy/X6NGj9cILL+jQoUP65JNPHAZ00sUZmos29IiLi1P37t3LXJehQ4ea/i5duHBhhZtUrTgWo7AZD5xiGIa2bNmiHTt26MyZMzp79qxCQ0MVGRmpyy67TK1atVJUVFSZyjh06JB+++03JSUlKS0tTdWqVVPt2rXVokULdejQwSMzR9k6efKkduzYoYMHDyolJUXZ2dkKDQ1VrVq11KZNG7Vt27bc/sjLzc3VypUrdeDAAZ0+fVoBAQGKiYlR69at1bFjx0vyS2379u1au3atTp8+rcDAQMXGxuqyyy6rlAFKZmamli5dah1rovC1XHnllZX+/66yff6PHz+uX375RUePHlVeXp7q1q2ruLg49erVyyvnkcru8OHDWrNmjU6dOqVz584pKipK9erVU+/evX3ecqsq2LVrl7Zs2aIzZ84oLS1NwcHBioiIUNOmTdWqVSuvjBfkKZmZmfrll190+PBhnTlzRiEhIYqJiVHHjh1LDd89JTEx0fSj27Rp0/TSSy95tIzDhw9r165dSkxMVFpamnJychQeHq46deqobdu2atWqVYUOsYsqPJ9v375dp0+f1vnz5xUeHq769eurdevWTgUqFUV2drbWrFljHRcnKytL4eHh1u+Oli1blhrklVV8fLz1j6yOHTuW2qLPG06ePKk1a9YoKSlJycnJ8vPzU40aNRQXF6dWrVqpcePGLv2fnjt3Tr///rv279+vpKQkZWZmWs9LzZs3V8eOHUv8YdmRlJQU/f777zpw4ICSk5OVmZmp0NBQRUVFqWXLlmrfvr3X/68KFRQUaO3atdq9e7dOnz4t6WKL/Msvv1zdunUrNaQEAHgHIR0AAEAl98knn5gmRPnoo49My4C3HD16VHFxcdblW265Re+++64PawQAQOVVOX7uBAAAQLFsQ5Gis4gD3sSxBwCA59CSDgAAoBKbNWuWbrjhButyp06dShyUHvCUffv2qUOHDsrIyJB0cZy2Y8eOqVatWj6uGQAAlRMt6QAAACqQlJQUjR8/Xrt37y513VmzZumWW24xPXb33Xd7q2qoAm655Rb99NNPpa63efNmDRgwwBrQSdK4ceMI6AAAKANa0jmQn5+v7du3a926dVq/fr3WrVunrVu3Kjc3V5KUkJCg5cuXe7UO58+f18cff6zZs2dr7969On36tHUw17Fjx2rSpEnlMsU9AAAoX2fOnFHt2rVlsVjUu3dvDR06VFdccYXq1KmjgIAAJScna/369fr888+1evVq07YJCQlatmxZpZn0ABVPmzZttH37djVv3lyjR49Wt27d1LBhQ4WFhSktLU07duzQggUL9NVXX6mgoMC6Xa1atbR9+/ZiZ/ADAAClI6Sz8c0332jixInKzMwsdh1vh3SrV6/WxIkTdfDgwWLXadKkiT777DN169bNa/UAAADlrzCkc1WbNm30448/qn79+l6oFaqKwpDOFTVr1tS8efOUkJDgpVoBABxJS0tTWlqaR/dZt25dVatWzaP7hPMCfF2BiiYtLa3EgM7btm7dqkGDBuncuXOSpMDAQPXv318NGjTQkSNHtHTpUuXl5enAgQMaOHCgVq5cqTZt2visvgAAwLMCAwNVvXp167VAafz9/XXjjTfqlVdeUc2aNb1cO1zqoqOjXVo/ISFBb7zxhlq1auWlGgEAivPKK6/o73//u0f3uWzZMvXt29ej+4TzCOmKUadOHXXp0sV6W7hwoV599VWvlpmbm6uRI0daL8rbt2+vefPmqVGjRtZ1EhMTNXz4cG3ZskVnz57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      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(recall, precision, '-x')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision and recall for the logistic regression'\n",
    "         + 'with LIMIT_BAL')\n",
    "plt.xlim([0,1])\n",
    "plt.ylim([0,1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7c11f754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31566964427378624"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.auc(recall, precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a673545",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_2_pred_proba = example_lr.predict_proba(X_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6ff8deed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6182918113358344"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.roc_auc_score(y_train_2, y_train_2_pred_proba[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1643bd72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63c040ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}