3_Modelling_and_Evaluating_RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48b31c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import Parallel,delayed,dump\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68e0bf2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cust_funcs import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00bd045b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Data/training.csv')\n",
    "X,weights,y=prepare_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a35bed65",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters={\n",
    "    'min_samples_leaf':[1,2,5,10],\n",
    "    'n_estimators':[100,150,200,250]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60fea149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean ams = 3.360861202014717, slf = 1, n = 100\n",
      "Mean ams = 3.364382303000128, slf = 1, n = 150\n",
      "Mean ams = 3.3901989732495252, slf = 1, n = 200\n",
      "Mean ams = 3.3866780136272645, slf = 1, n = 250\n",
      "Mean ams = 3.3789260610477196, slf = 2, n = 100\n",
      "Mean ams = 3.4049642789157417, slf = 2, n = 150\n",
      "Mean ams = 3.401500564070928, slf = 2, n = 200\n",
      "Mean ams = 3.418786421437465, slf = 2, n = 250\n",
      "Mean ams = 3.4205201532534804, slf = 5, n = 100\n",
      "Mean ams = 3.4362833897339686, slf = 5, n = 150\n",
      "Mean ams = 3.44731927273579, slf = 5, n = 200\n",
      "Mean ams = 3.4562364620559225, slf = 5, n = 250\n",
      "Mean ams = 3.445780472444389, slf = 10, n = 100\n",
      "Mean ams = 3.44561602165918, slf = 10, n = 150\n",
      "Mean ams = 3.443898796804069, slf = 10, n = 200\n",
      "Mean ams = 3.446434018889242, slf = 10, n = 250\n",
      "BEST SCORES: ams = 3.4562364620559225, slf = 5, n = 250\n"
     ]
    }
   ],
   "source": [
    "random_states=np.random.randint(10,100,8)\n",
    "AMS_vals=[];N=[];LR=[]\n",
    "for slf in parameters['min_samples_leaf']:\n",
    "    for n in parameters['n_estimators']:\n",
    "        RF=RandomForestClassifier(n_estimators=n,min_samples_leaf=slf,n_jobs=2)\n",
    "        trial_vals=Parallel(n_jobs=4)(delayed(ams_curve_tuning)(X,y,weights,slf,n,random_states[i],RF) for i in range(8))\n",
    "        AMS_vals.append(np.mean(trial_vals));N.append(n);LR.append(slf) \n",
    "        \n",
    "        print('Mean ams = {}, slf = {}, n = {}'.format(np.mean(trial_vals),slf,n))\n",
    "        \n",
    "LR=np.array(LR);AMS_vals=np.array(AMS_vals);N=np.array(N)        \n",
    "print('BEST SCORES: ams = {}, slf = {}, n = {}'.format(max(AMS_vals),\n",
    "                                                      LR[AMS_vals==max(AMS_vals)][0],N[AMS_vals==max(AMS_vals)][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f655bed1",
   "metadata": {},
   "source": [
    "### Fitting and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4a3a853",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test,weights,y_test=prepare_dataset(pd.read_csv('Data/testing.csv',index_col=0))\n",
    "\n",
    "X_train,_,y_train=prepare_dataset(pd.read_csv('Data/training.csv'))\n",
    "\n",
    "X_test=prepare_training_features(X_test,X_dep=X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ecb5e27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_leaf=5, n_estimators=250, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_leaf=5, n_estimators=250, n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(min_samples_leaf=5, n_estimators=250, n_jobs=-1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF=RandomForestClassifier(n_estimators=250,min_samples_leaf=5,n_jobs=-1)\n",
    "RF.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ee0371a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_proba=RF.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56f31c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ams_dat,max_ams=ams_curve(prediction_proba,y_test,weights,test_ratio=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bbb97a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('Data/RF_curve.csv',ams_dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5fdca2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final AMS = 3.236380156037992\n"
     ]
    }
   ],
   "source": [
    "print('Final AMS = {}'.format(max_ams))"
   ]
  }
 ],
 "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}