ScoringModels/KDD2009_resample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an supervised classification example taken from the KDD 2009 cup.  A copy of the data and details can be found here: [https://github.com/WinVector/PDSwR2/tree/master/KDD2009](https://github.com/WinVector/PDSwR2/tree/master/KDD2009).  The problem was to predict account cancellation (\"churn\") from very messy data (column names not given, numeric and categorical variables, many missing values, some categorical variables with a large number of possible levels).  In this example we show how to quickly use `vtreat` to prepare the data for modeling.  `vtreat` takes in `Pandas` `DataFrame`s and returns both a treatment plan and a clean `Pandas` `DataFrame` ready for modeling.\n",
    "\n",
    "The results shown here are for only one data set, one modeling method, and one family of hyper-parameters, and mild imbalance. However, the results are typical of what we see in production: once one builds probability models and looks closely, resampling is less attractive and may be a ritual instead of a useful tool."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load our packages/modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import xgboost\n",
    "import vtreat\n",
    "import vtreat.cross_plan\n",
    "import numpy.random\n",
    "import wvpy.util\n",
    "import scipy.sparse\n",
    "import sklearn.metrics\n",
    "import matplotlib\n",
    "# https://imbalanced-learn.org/stable/over_sampling.html\n",
    "import imblearn.over_sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in explanatory variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 230)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data from https://github.com/WinVector/PDSwR2/tree/master/KDD2009\n",
    "dir = \"../../PracticalDataScienceWithR2nd/PDSwR2/KDD2009/\"\n",
    "d = pandas.read_csv(dir + 'orange_small_train.data.gz', sep='\\t', header=0)\n",
    "vars = [c for c in d.columns]\n",
    "d.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in dependent variable we are trying to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn = pandas.read_csv(dir + 'orange_small_train_churn.labels.txt', header=None)\n",
    "churn.columns = [\"churn\"]\n",
    "churn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    46328\n",
       " 1     3672\n",
       "Name: churn, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn[\"churn\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arrange test/train split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "numpy.random.seed(2020)\n",
    "n = d.shape[0]\n",
    "# https://github.com/WinVector/pyvtreat/blob/master/Examples/CustomizedCrossPlan/CustomizedCrossPlan.md\n",
    "split1 = vtreat.cross_plan.KWayCrossPlanYStratified().split_plan(n_rows=n, k_folds=10, y=churn.iloc[:, 0])\n",
    "train_idx = set(split1[0]['train'])\n",
    "is_train = [i in train_idx for i in range(n)]\n",
    "is_test = numpy.logical_not(is_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(The reported performance runs of this example were sensitive to the prevalance of the churn variable in the test set, we are cutting down on this source of evaluation variarance by using the stratified split.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "d_train = d.loc[is_train, :].copy()\n",
    "d_train.reset_index(inplace=True, drop=True)\n",
    "churn_train = numpy.asarray(churn.loc[is_train, :][\"churn\"]==1)\n",
    "d_test = d.loc[is_test, :].copy()\n",
    "d_test.reset_index(inplace=True, drop=True)\n",
    "churn_test = numpy.asarray(churn.loc[is_test, :][\"churn\"]==1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the dependent variables.  They are a mess, many missing values.  Categorical variables that can not be directly used without some re-encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "d_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45000, 230)"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "d_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's quickly prepare a data frame with none of these issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by building our treatment plan, this has the `sklearn.pipeline.Pipeline` interfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "plan = vtreat.BinomialOutcomeTreatment(\n",
    "    outcome_target=True,\n",
    "    params=vtreat.vtreat_parameters({\n",
    "        'filter_to_recommended': True,\n",
    "        'sparse_indicators': False\n",
    "    }))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `.fit_transform()` to get a special copy of the treated training data that has cross-validated mitigations againsst nested model bias. We call this a \"cross frame.\" `.fit_transform()` is deliberately a different `DataFrame` than what would be returned by `.fit().transform()` (the `.fit().transform()` would damage the modeling effort due nested model bias, the `.fit_transform()` \"cross frame\" uses cross-validation techniques similar to \"stacking\" to mitigate these issues)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "cross_frame = plan.fit_transform(d_train, churn_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the new data.  This frame is guaranteed to be all numeric with no missing values, with the rows in the same order as the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
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       "      <td>0.003867</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>-3.435875e+00</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.701267</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089244</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>-1.069223e-02</td>\n",
       "      <td>0.509111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.069933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 234 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Var84_is_bad  Var49_is_bad  Var7_is_bad  Var38_is_bad  Var44_is_bad  \\\n",
       "0           1.0           1.0          0.0           0.0           0.0   \n",
       "1           1.0           1.0          0.0           0.0           0.0   \n",
       "2           1.0           1.0          0.0           0.0           0.0   \n",
       "3           1.0           1.0          0.0           0.0           0.0   \n",
       "4           1.0           1.0          0.0           0.0           0.0   \n",
       "\n",
       "   Var201_is_bad  Var184_is_bad  Var183_is_bad  Var75_is_bad  Var150_is_bad  \\\n",
       "0            1.0            1.0            1.0           1.0            1.0   \n",
       "1            1.0            1.0            1.0           1.0            1.0   \n",
       "2            0.0            1.0            1.0           1.0            1.0   \n",
       "3            0.0            1.0            1.0           1.0            1.0   \n",
       "4            1.0            1.0            1.0           1.0            1.0   \n",
       "\n",
       "   ...  Var227_lev_nIGXDli  Var197_prevalence_code  Var217_prevalence_code  \\\n",
       "0  ...                 0.0                0.089244                0.000067   \n",
       "1  ...                 0.0                0.008489                0.013956   \n",
       "2  ...                 0.0                0.002311                0.001867   \n",
       "3  ...                 0.0                0.003867                0.002333   \n",
       "4  ...                 0.0                0.089244                0.001556   \n",
       "\n",
       "   Var200_logit_code  Var200_prevalence_code  Var200_lev__NA_  \\\n",
       "0      -3.839145e-03                0.509111              1.0   \n",
       "1      -4.197609e-03                0.509111              1.0   \n",
       "2       2.220446e-16                0.000044              0.0   \n",
       "3      -3.435875e+00                0.000089              0.0   \n",
       "4      -1.069223e-02                0.509111              1.0   \n",
       "\n",
       "   Var207_prevalence_code  Var207_lev_me75fM6ugJ  \\\n",
       "0                0.701267                    1.0   \n",
       "1                0.701267                    1.0   \n",
       "2                0.069933                    0.0   \n",
       "3                0.701267                    1.0   \n",
       "4                0.069933                    0.0   \n",
       "\n",
       "   Var207_lev_7M47J5GA0pTYIFxg5uy  Var207_lev_DHn_WUyBhW_whjA88g9bvA64_  \n",
       "0                             0.0                                   0.0  \n",
       "1                             0.0                                   0.0  \n",
       "2                             0.0                                   1.0  \n",
       "3                             0.0                                   0.0  \n",
       "4                             0.0                                   1.0  \n",
       "\n",
       "[5 rows x 234 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45000, 234)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_frame.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick a recommended subset of the new derived variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .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>variable</th>\n",
       "      <th>orig_variable</th>\n",
       "      <th>treatment</th>\n",
       "      <th>y_aware</th>\n",
       "      <th>has_range</th>\n",
       "      <th>PearsonR</th>\n",
       "      <th>R2</th>\n",
       "      <th>significance</th>\n",
       "      <th>vcount</th>\n",
       "      <th>default_threshold</th>\n",
       "      <th>recommended</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Var84_is_bad</td>\n",
       "      <td>Var84</td>\n",
       "      <td>missing_indicator</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.016325</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>2.253129e-04</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Var151_is_bad</td>\n",
       "      <td>Var151</td>\n",
       "      <td>missing_indicator</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.014288</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>1.169199e-03</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Var49_is_bad</td>\n",
       "      <td>Var49</td>\n",
       "      <td>missing_indicator</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.016358</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>2.184160e-04</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Var7_is_bad</td>\n",
       "      <td>Var7</td>\n",
       "      <td>missing_indicator</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.026297</td>\n",
       "      <td>0.001427</td>\n",
       "      <td>6.437306e-09</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Var190_is_bad</td>\n",
       "      <td>Var190</td>\n",
       "      <td>missing_indicator</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.006103</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>1.756498e-01</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable orig_variable          treatment  y_aware  has_range  \\\n",
       "0   Var84_is_bad         Var84  missing_indicator    False       True   \n",
       "1  Var151_is_bad        Var151  missing_indicator    False       True   \n",
       "2   Var49_is_bad         Var49  missing_indicator    False       True   \n",
       "3    Var7_is_bad          Var7  missing_indicator    False       True   \n",
       "4  Var190_is_bad        Var190  missing_indicator    False       True   \n",
       "\n",
       "   PearsonR        R2  significance  vcount  default_threshold  recommended  \n",
       "0  0.016325  0.000576  2.253129e-04   193.0           0.001036         True  \n",
       "1  0.014288  0.000446  1.169199e-03   193.0           0.001036        False  \n",
       "2  0.016358  0.000579  2.184160e-04   193.0           0.001036         True  \n",
       "3 -0.026297  0.001427  6.437306e-09   193.0           0.001036         True  \n",
       "4  0.006103  0.000078  1.756498e-01   193.0           0.001036        False  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plan.score_frame_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "234"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_vars = numpy.asarray(plan.score_frame_[\"variable\"][plan.score_frame_[\"recommended\"]])\n",
    "model_vars.sort()\n",
    "len(model_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def fit_model(*, explanatory_variables, dependent_variable, tree_depth=3):\n",
    "    x_parameters = {\n",
    "        \"max_depth\":tree_depth, \n",
    "        \"objective\":'binary:logistic',\n",
    "        \"eval_metric\": 'logloss'}\n",
    "    # cross validate for good number of trees on simulated out of sample data\n",
    "    cross_dmatrix = xgboost.DMatrix(\n",
    "        data=explanatory_variables, \n",
    "        label=dependent_variable)\n",
    "    cv = xgboost.cv(x_parameters, cross_dmatrix, num_boost_round=100, verbose_eval=False)\n",
    "    best = cv.loc[cv[\"test-logloss-mean\"]<= min(cv[\"test-logloss-mean\"]), :]\n",
    "    ntree = best.index.values[0] + 1\n",
    "    # refit with this number of trees\n",
    "    model = xgboost.XGBClassifier(\n",
    "        n_estimators=ntree, \n",
    "        max_depth=tree_depth, \n",
    "        objective='binary:logistic',\n",
    "        eval_metric = 'logloss')\n",
    "    model.fit(explanatory_variables, dependent_variable)\n",
    "    return(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_9/lib/python3.9/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "model = fit_model(\n",
    "    explanatory_variables=cross_frame.loc[:, model_vars],\n",
    "    dependent_variable=churn_train\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',\n",
       "              gamma=0, gpu_id=-1, importance_type='gain',\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=3, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=39, n_jobs=6,\n",
       "              num_parallel_tree=1, random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "              scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the quality of the model on training data (a biased measure of performance)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7744362249604722"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf_train = pandas.DataFrame({\"churn\":churn_train})\n",
    "pf_train[\"pred\"] = model.predict_proba(cross_frame.loc[:, model_vars])[:, 1]\n",
    "wvpy.util.plot_roc(pf_train[\"pred\"], pf_train[\"churn\"], title=\"Model on Train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the data transform to our held-out data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_processed = plan.transform(d_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the quality of the model score on the held-out data.  This AUC is not great, but in the ballpark of the original contest winners."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7478334705610067"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf = pandas.DataFrame({\"churn\": churn_test})\n",
    "pf[\"pred\"] = model.predict_proba(test_processed.loc[:, model_vars])[:, 1]\n",
    "wvpy.util.plot_roc(\n",
    "    prediction=pf[\"pred\"], \n",
    "    istrue=pf[\"churn\"], \n",
    "    title=\"Model on Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wvpy.util.threshold_plot(\n",
    "    pf,\n",
    "    pred_var=\"pred\", \n",
    "    truth_var=\"churn\", \n",
    "    plotvars=(\"sensitivity\", \"specificity\"),\n",
    "    title=\"Model on Test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice we dealt with many problem columns at once, and in a statistically sound manner. More on the `vtreat` package for Python can be found here: [https://github.com/WinVector/pyvtreat](https://github.com/WinVector/pyvtreat).  Details on the `R` version can be found here: [https://github.com/WinVector/vtreat](https://github.com/WinVector/vtreat)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare this to the [R solution (link)](https://github.com/WinVector/PDSwR2/blob/master/KDD2009/KDD2009vtreat.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make the deliberate mistake of calling `.predict()` insteadl of `.predictd_proba()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf[\"hard_decision_rule\"] = model.predict(test_processed.loc[:, model_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>hard_decision_rule</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>4625</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>360</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "hard_decision_rule  False  True \n",
       "churn                           \n",
       "False                4625      6\n",
       "True                  360      9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas.crosstab(pf.churn, pf.hard_decision_rule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This *superficially* looks very bad, few examples are classified as positive. Our point is: if we don't like how many positives were return simply call `.predict_proba()` and pick threshold more to your liking later.\n",
    "\n",
    "This model is good as, the distribution of score conditioned on outcomes differ. We can see that in the following graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83112565b0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wvpy.util.dual_density_plot(\n",
    "    probs=pf[\"pred\"], \n",
    "    istrue=pf[\"churn\"], \n",
    "    title=\"Model on Test\",\n",
    "    show=False\n",
    ")\n",
    "matplotlib.pyplot.plot([0.5, 0.5], [0, 8], \n",
    "                       color='DarkGrey', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is just the case that the model did not place many scores above 0.5. 0.5 being the hard-coded decision treshold\n",
    "if you call `.predict()`. However at another threshold, say 0.2 one sees a non-negigble fraction of the positives and\n",
    "a lesser fraction of the negatives. That would in fact be a useful threshold.\n",
    "\n",
    "Our strong advice: popluate the model score in your system, and leave the threshold and even the possibility of\n",
    "varying the threshold to the business stakeholders!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>tpr</th>\n",
       "      <th>fpr</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02439</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>Naive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tpr       fpr  label\n",
       "0  0.02439  0.001296  Naive"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn, fp, fn, tp = sklearn.metrics.confusion_matrix(\n",
    "        y_true=pf[\"churn\"], \n",
    "        y_pred=pf[\"hard_decision_rule\"]).ravel()\n",
    "specificity = tn / (tn + fp)\n",
    "sensitivity = tp / (tp + fn)\n",
    "\n",
    "decision_rule_performance = pandas.DataFrame({\n",
    "    'tpr': [sensitivity],\n",
    "    'fpr': [1 - specificity],\n",
    "    'label': ['Naive']\n",
    "})\n",
    "\n",
    "decision_rule_performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary of how a probability score behaves with respect to every possible thershold is usually summarized in an \"ROC plot\".\n",
    "Though, we advise using the unwound thresholds plot for clarity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at rebalanced solutions attempt to work around this mistake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_resampling_method(*, method, name=None):\n",
    "    if name is None:\n",
    "        name = str(method)\n",
    "    # resample the prepared training data\n",
    "    x_resampled, y_resampled = method.fit_resample(\n",
    "        cross_frame.loc[:, model_vars],\n",
    "        churn_train)\n",
    "    # build a new model\n",
    "    model_resampled = fit_model(\n",
    "        explanatory_variables=x_resampled,\n",
    "        dependent_variable=y_resampled\n",
    "    )\n",
    "    # evaluate the model on train\n",
    "    pf_resampled_test = pandas.DataFrame({\"churn\": churn_test})\n",
    "    pf_resampled_test[\"pred\"] = model_resampled.predict_proba(test_processed.loc[:, model_vars])[:, 1]\n",
    "    pf_resampled_test[\"hard_decision_rule\"] = model_resampled.predict(test_processed.loc[:, model_vars])\n",
    "    # summarize the performance\n",
    "    tn, fp, fn, tp = sklearn.metrics.confusion_matrix(\n",
    "        y_true=pf_resampled_test[\"churn\"], \n",
    "        y_pred=pf_resampled_test[\"hard_decision_rule\"]).ravel()\n",
    "    specificity = tn / (tn + fp)\n",
    "    sensitivity = tp / (tp + fn)\n",
    "    perf_frame = pandas.DataFrame({\n",
    "        'tpr': [sensitivity],\n",
    "        'fpr': [1 - specificity],\n",
    "        'label': [name]\n",
    "    })\n",
    "    return({\n",
    "        'pf': pf_resampled_test, \n",
    "        'specificity': specificity, \n",
    "        'sensitivity': sensitivity,\n",
    "        'perf_frame': perf_frame})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# https://imbalanced-learn.org/stable/introduction.html\n",
    "method_evals = [\n",
    "    evaluate_resampling_method(\n",
    "        method=imblearn.over_sampling.RandomOverSampler(),\n",
    "        name='over'),\n",
    "    evaluate_resampling_method(\n",
    "        method=imblearn.over_sampling.RandomOverSampler(sampling_strategy=1/3),\n",
    "        name='over(1/3)'),\n",
    "    evaluate_resampling_method(\n",
    "        method=imblearn.over_sampling.RandomOverSampler(sampling_strategy=2/3),\n",
    "        name='over(2/3)'),\n",
    "    evaluate_resampling_method(\n",
    "        method=imblearn.under_sampling.RandomUnderSampler(),\n",
    "        name='under'),\n",
    "    evaluate_resampling_method(\n",
    "        method=imblearn.over_sampling.SMOTE()),\n",
    "    evaluate_resampling_method(\n",
    "        method=imblearn.over_sampling.ADASYN()),\n",
    "    evaluate_resampling_method(\n",
    "        method=imblearn.combine.SMOTEENN())\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>tpr</th>\n",
       "      <th>fpr</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>Naive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.574526</td>\n",
       "      <td>0.241417</td>\n",
       "      <td>over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.241192</td>\n",
       "      <td>0.050097</td>\n",
       "      <td>over(1/3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.422764</td>\n",
       "      <td>0.127402</td>\n",
       "      <td>over(2/3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.677507</td>\n",
       "      <td>0.334917</td>\n",
       "      <td>under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.021680</td>\n",
       "      <td>0.000864</td>\n",
       "      <td>SMOTE()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.021680</td>\n",
       "      <td>0.001080</td>\n",
       "      <td>ADASYN()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.203252</td>\n",
       "      <td>0.048370</td>\n",
       "      <td>SMOTEENN()</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        tpr       fpr       label\n",
       "0  0.024390  0.001296       Naive\n",
       "0  0.574526  0.241417        over\n",
       "0  0.241192  0.050097   over(1/3)\n",
       "0  0.422764  0.127402   over(2/3)\n",
       "0  0.677507  0.334917       under\n",
       "0  0.021680  0.000864     SMOTE()\n",
       "0  0.021680  0.001080    ADASYN()\n",
       "0  0.203252  0.048370  SMOTEENN()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_frame = pandas.concat(\n",
    "    [decision_rule_performance] +\n",
    "    [ei['perf_frame'] for ei in method_evals]\n",
    ")\n",
    "perf_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7478334705610067"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wvpy.util.plot_roc(\n",
    "    prediction=pf[\"pred\"], \n",
    "    istrue=pf[\"churn\"], \n",
    "    title=\"Original Model on Test\",\n",
    "    extra_points=perf_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The re-sampling plans are, at best, just laborious ways to try and trace out the efficient frontier of the ROC plot. Re-sampling's primary\n",
    "contribuition in this sort of application is: it almost undoes the mistake of calling `.predict()` (instisting on using decision rules for decision\n",
    "problems) instead of taking the trouble to call `.predict_proba()`. Our position is, take the simple step\n",
    "of calling `.predict_proba()` and keeping the probabilities around and you don't need any of the resamping\n",
    "machinery.\n",
    "\n",
    "This study was inspired by by Dr. Nina Zumel's earlier observation that rebalancing just approximates the ROC curve: [*Does Balancing Classes Improve Classifier Performance?*](https://win-vector.com/2015/02/27/does-balancing-classes-improve-classifier-performance/).\n",
    "\n",
    "Some follow up material includes:\n",
    " \n",
    "   * [Against Accuracy](https://win-vector.com/2020/12/27/against-accuracy/) (why not to use accuarcy as a measure).\n",
    "   * [Don’t Use Classification Rules for Classification Problems](https://win-vector.com/2020/08/07/dont-use-classification-rules-for-classification-problems/) (why to use scores and not hard decision rules for classification problems).\n",
    "   * [The Shift and Balance Fallacies](https://win-vector.com/2020/10/15/the-shift-and-balance-fallacies/) (some notes on rebalancine logistic regression models).\n",
    "   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix: Plotting the ROCs\n",
    "\n",
    "In this appendix we plot the ROCs of the additional re-balanced models. This is to demonstrate, that in\n",
    "addition to not generarting any new regions of performance for decision rules, they also are not \n",
    "themselves new regions of performance as probabilty models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get ROC curve for base probability model\n",
    "fpr, tpr, _ = sklearn.metrics.roc_curve(\n",
    "    y_true=pf[\"churn\"], \n",
    "    y_score=pf[\"pred\"])\n",
    "\n",
    "# run through all resampled model evals\n",
    "for i in range(len(method_evals)):\n",
    "    me = method_evals[i]\n",
    "    # plot ROC curve for resampled model\n",
    "    wvpy.util.plot_roc(\n",
    "        prediction=me['pf'][\"pred\"], \n",
    "        istrue=me['pf'][\"churn\"], \n",
    "        title=\"resample: \" + me['perf_frame'].label[0],\n",
    "        extra_points=perf_frame,\n",
    "        show=False)\n",
    "    # overlay ROC curve for base probability model\n",
    "    matplotlib.pyplot.plot(\n",
    "        fpr, tpr, linestyle=\"--\", color='DarkGrey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice in all cases the new organge ROC curve is pretty much under (dominated by) the grey ROC curve from the original model prior to re-balancing! This is illustrating the rebalancing achieved nothing (in this case, for these rebalances, these hyper parameters, for this specific problem, and machine learning algorithm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.4"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}