RFtest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-11 01:58:26.881266: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import helpers\n",
    "import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tensorflow_decision_forests as tfdf\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "truthPath = './processed_truth/'\n",
    "liePath = './processed_lie/'\n",
    "\n",
    "personList = [3, 24, 28, 35, 46]\n",
    "testRatio = 0.2\n",
    "\n",
    "Train, Xtrain, Ytrain, Xtest, Ytest = dataset.createDatasetGeneral(truthPath, liePath, testRatio, byPerson=True, personlst=personList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23709,)\n",
      "(46960,)\n"
     ]
    }
   ],
   "source": [
    "print(Ytrain.shape)\n",
    "print(Ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5128010460162807\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "for i in Ytrain:\n",
    "    if i == 0:\n",
    "        counter += 1\n",
    "\n",
    "print(counter / len(Ytrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4436541737649063\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "for i in Ytest:\n",
    "    if i == 0:\n",
    "        counter += 1\n",
    "\n",
    "print(counter / len(Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTFRF():\n",
    "\n",
    "    dataset = tfdf.keras.pd_dataframe_to_tf_dataset(Train, label=\"Result\")\n",
    "\n",
    "    #https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel\n",
    "    TFmodel = tfdf.keras.RandomForestModel() #add params\n",
    "    TFmodel.compile()\n",
    "    TFmodel.fit(dataset)\n",
    "\n",
    "    print(TFmodel.summary())\n",
    "\n",
    "    res = pd.DataFrame(TFmodel.predict(tfdf.keras.pd_dataframe_to_tf_dataset(Xtest)))\n",
    "\n",
    "    for i in range(res.shape[0]):\n",
    "        if res.iloc[i,0] > 0.5: \n",
    "            res.iloc[i] = 1 \n",
    "        else:\n",
    "            res.iloc[i] = 0\n",
    "\n",
    "    helpers.displayConfusion(Ytest, res)\n",
    "\n",
    "    #return TFmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSKRF():\n",
    "\n",
    "    #https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
    "    SKRF = RandomForestClassifier(verbose=2) #add params\n",
    "    SKRF.fit(Xtrain, Ytrain)\n",
    "\n",
    "    helpers.displayConfusion(Ytest, SKRF.predict(Xtest))\n",
    "\n",
    "    return SKRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use /var/folders/66/pn8653k51_75x5ksbpsy8hm80000gn/T/tmpv5_e8z7v as temporary training directory\n",
      "Reading training dataset...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/frank/opt/anaconda3/lib/python3.8/site-packages/tensorflow_decision_forests/keras/core_inference.py:873: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  features_dataframe = dataframe.drop(label, 1)\n",
      "2022-11-11 01:59:07.474976: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset read in 0:00:03.263862. Found 23709 examples.\n",
      "Training model...\n",
      "Model trained in 0:00:00.788392\n",
      "Compiling model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO kernel.cc:1176] Loading model from path /var/folders/66/pn8653k51_75x5ksbpsy8hm80000gn/T/tmpv5_e8z7v/model/ with prefix db68db3f7a6742ec\n",
      "[INFO decision_forest.cc:639] Model loaded with 300 root(s), 15534 node(s), and 17 input feature(s).\n",
      "[INFO abstract_model.cc:1249] Engine \"RandomForestOptPred\" built\n",
      "[INFO kernel.cc:1022] Use fast generic engine\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f8537387dc0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: could not get source code\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f8537387dc0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: could not get source code\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f8537387dc0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: could not get source code\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Model compiled.\n",
      "Model: \"random_forest_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      "=================================================================\n",
      "Total params: 1\n",
      "Trainable params: 0\n",
      "Non-trainable params: 1\n",
      "_________________________________________________________________\n",
      "Type: \"RANDOM_FOREST\"\n",
      "Task: CLASSIFICATION\n",
      "Label: \"__LABEL\"\n",
      "\n",
      "Input Features (17):\n",
      "\tAU01_r\n",
      "\tAU04_r\n",
      "\tAU10_r\n",
      "\tAU12_r\n",
      "\tAU45_r\n",
      "\tdgaze_0_x\n",
      "\tdgaze_0_y\n",
      "\tdgaze_angle_y\n",
      "\tgaze_0_x\n",
      "\tgaze_0_y\n",
      "\tgaze_0_z\n",
      "\tgaze_angle_x\n",
      "\tgaze_angle_y\n",
      "\tpose_Ry\n",
      "\tpose_Tx\n",
      "\tpose_Ty\n",
      "\tpose_Tz\n",
      "\n",
      "No weights\n",
      "\n",
      "Variable Importance: MEAN_MIN_DEPTH:\n",
      "    1.       \"__LABEL\"  5.576476 ################\n",
      "    2.        \"AU01_r\"  5.507788 ###############\n",
      "    3.        \"AU45_r\"  5.487448 ###############\n",
      "    4.     \"dgaze_0_y\"  5.467776 ###############\n",
      "    5. \"dgaze_angle_y\"  5.447217 ###############\n",
      "    6.     \"dgaze_0_x\"  5.281733 ##############\n",
      "    7.      \"gaze_0_z\"  5.143411 #############\n",
      "    8.      \"gaze_0_y\"  4.985424 ############\n",
      "    9.  \"gaze_angle_y\"  4.958161 ###########\n",
      "   10.       \"pose_Ty\"  4.666452 #########\n",
      "   11.        \"AU04_r\"  4.490868 ########\n",
      "   12.       \"pose_Ry\"  3.916101 ####\n",
      "   13.        \"AU10_r\"  3.841616 ####\n",
      "   14.        \"AU12_r\"  3.831033 ####\n",
      "   15.      \"gaze_0_x\"  3.710368 ###\n",
      "   16.       \"pose_Tx\"  3.507748 ##\n",
      "   17.  \"gaze_angle_x\"  3.258443 \n",
      "   18.       \"pose_Tz\"  3.185293 \n",
      "\n",
      "Variable Importance: NUM_AS_ROOT:\n",
      "    1. \"gaze_angle_x\" 95.000000 ################\n",
      "    2.     \"gaze_0_x\" 75.000000 ############\n",
      "    3.      \"pose_Ry\" 47.000000 #######\n",
      "    4.       \"AU12_r\" 34.000000 #####\n",
      "    5.       \"AU10_r\" 21.000000 ###\n",
      "    6.       \"AU04_r\" 11.000000 #\n",
      "    7.      \"pose_Tx\"  7.000000 #\n",
      "    8.      \"pose_Tz\"  7.000000 #\n",
      "    9.     \"gaze_0_z\"  2.000000 \n",
      "   10.      \"pose_Ty\"  1.000000 \n",
      "\n",
      "Variable Importance: NUM_NODES:\n",
      "    1.       \"pose_Tz\" 1518.000000 ################\n",
      "    2.       \"pose_Tx\" 1064.000000 ##########\n",
      "    3.       \"pose_Ty\" 647.000000 ######\n",
      "    4.        \"AU10_r\" 550.000000 #####\n",
      "    5.        \"AU04_r\" 529.000000 ####\n",
      "    6.       \"pose_Ry\" 510.000000 ####\n",
      "    7.  \"gaze_angle_x\" 487.000000 ####\n",
      "    8.      \"gaze_0_x\" 426.000000 ###\n",
      "    9.  \"gaze_angle_y\" 364.000000 ###\n",
      "   10.        \"AU12_r\" 338.000000 ##\n",
      "   11.      \"gaze_0_z\" 306.000000 ##\n",
      "   12.      \"gaze_0_y\" 257.000000 #\n",
      "   13.     \"dgaze_0_x\" 201.000000 #\n",
      "   14. \"dgaze_angle_y\" 116.000000 \n",
      "   15.        \"AU01_r\" 107.000000 \n",
      "   16.        \"AU45_r\" 101.000000 \n",
      "   17.     \"dgaze_0_y\" 96.000000 \n",
      "\n",
      "Variable Importance: SUM_SCORE:\n",
      "    1.       \"pose_Tz\" 1185834.818916 ################\n",
      "    2.  \"gaze_angle_x\" 793411.685276 ##########\n",
      "    3.      \"gaze_0_x\" 646166.931694 ########\n",
      "    4.       \"pose_Tx\" 637089.227395 ########\n",
      "    5.       \"pose_Ry\" 471672.538560 ######\n",
      "    6.        \"AU12_r\" 392575.739875 #####\n",
      "    7.        \"AU10_r\" 342664.088832 ####\n",
      "    8.       \"pose_Ty\" 116369.644932 #\n",
      "    9.        \"AU04_r\" 105737.527043 #\n",
      "   10.      \"gaze_0_y\" 96657.805074 #\n",
      "   11.  \"gaze_angle_y\" 81343.014448 #\n",
      "   12.      \"gaze_0_z\" 33679.485954 \n",
      "   13.     \"dgaze_0_x\" 9877.969487 \n",
      "   14.        \"AU45_r\" 3023.808021 \n",
      "   15.        \"AU01_r\" 2588.719928 \n",
      "   16. \"dgaze_angle_y\" 2403.450449 \n",
      "   17.     \"dgaze_0_y\" 1683.162678 \n",
      "\n",
      "\n",
      "\n",
      "Winner take all: true\n",
      "Out-of-bag evaluation: accuracy:0.999831 logloss:0.00149226\n",
      "Number of trees: 300\n",
      "Total number of nodes: 15534\n",
      "\n",
      "Number of nodes by tree:\n",
      "Count: 300 Average: 51.78 StdDev: 14.2891\n",
      "Min: 21 Max: 99 Ignored: 0\n",
      "----------------------------------------------\n",
      "[ 21, 24)  3   1.00%   1.00% #\n",
      "[ 24, 28)  8   2.67%   3.67% ##\n",
      "[ 28, 32) 13   4.33%   8.00% ####\n",
      "[ 32, 36) 19   6.33%  14.33% #####\n",
      "[ 36, 40) 17   5.67%  20.00% #####\n",
      "[ 40, 44) 34  11.33%  31.33% #########\n",
      "[ 44, 48) 27   9.00%  40.33% #######\n",
      "[ 48, 52) 36  12.00%  52.33% ##########\n",
      "[ 52, 56) 37  12.33%  64.67% ##########\n",
      "[ 56, 60) 35  11.67%  76.33% #########\n",
      "[ 60, 64) 16   5.33%  81.67% ####\n",
      "[ 64, 68) 13   4.33%  86.00% ####\n",
      "[ 68, 72) 15   5.00%  91.00% ####\n",
      "[ 72, 76)  8   2.67%  93.67% ##\n",
      "[ 76, 80)  8   2.67%  96.33% ##\n",
      "[ 80, 84)  4   1.33%  97.67% #\n",
      "[ 84, 88)  4   1.33%  99.00% #\n",
      "[ 88, 92)  1   0.33%  99.33%\n",
      "[ 92, 96)  0   0.00%  99.33%\n",
      "[ 96, 99]  2   0.67% 100.00% #\n",
      "\n",
      "Depth by leafs:\n",
      "Count: 7917 Average: 5.7249 StdDev: 1.82134\n",
      "Min: 1 Max: 14 Ignored: 0\n",
      "----------------------------------------------\n",
      "[  1,  2)    3   0.04%   0.04%\n",
      "[  2,  3)  214   2.70%   2.74% #\n",
      "[  3,  4)  593   7.49%  10.23% ####\n",
      "[  4,  5) 1314  16.60%  26.83% ########\n",
      "[  5,  6) 1590  20.08%  46.91% ##########\n",
      "[  6,  7) 1609  20.32%  67.24% ##########\n",
      "[  7,  8) 1297  16.38%  83.62% ########\n",
      "[  8,  9)  744   9.40%  93.02% #####\n",
      "[  9, 10)  383   4.84%  97.85% ##\n",
      "[ 10, 11)  115   1.45%  99.31% #\n",
      "[ 11, 12)   39   0.49%  99.80%\n",
      "[ 12, 13)   13   0.16%  99.96%\n",
      "[ 13, 14)    1   0.01%  99.97%\n",
      "[ 14, 14]    2   0.03% 100.00%\n",
      "\n",
      "Number of training obs by leaf:\n",
      "Count: 7917 Average: 898.408 StdDev: 2087.02\n",
      "Min: 5 Max: 8916 Ignored: 0\n",
      "----------------------------------------------\n",
      "[    5,  450) 6080  76.80%  76.80% ##########\n",
      "[  450,  896)  428   5.41%  82.20% #\n",
      "[  896, 1341)  163   2.06%  84.26%\n",
      "[ 1341, 1787)   66   0.83%  85.10%\n",
      "[ 1787, 2233)   69   0.87%  85.97%\n",
      "[ 2233, 2678)  289   3.65%  89.62%\n",
      "[ 2678, 3124)   83   1.05%  90.67%\n",
      "[ 3124, 3569)   94   1.19%  91.85%\n",
      "[ 3569, 4015)   47   0.59%  92.45%\n",
      "[ 4015, 4461)    2   0.03%  92.47%\n",
      "[ 4461, 4906)    0   0.00%  92.47%\n",
      "[ 4906, 5352)    2   0.03%  92.50%\n",
      "[ 5352, 5797)    4   0.05%  92.55%\n",
      "[ 5797, 6243)   11   0.14%  92.69%\n",
      "[ 6243, 6689)   32   0.40%  93.09%\n",
      "[ 6689, 7134)   69   0.87%  93.96%\n",
      "[ 7134, 7580)  104   1.31%  95.28%\n",
      "[ 7580, 8025)  167   2.11%  97.39%\n",
      "[ 8025, 8471)  120   1.52%  98.90%\n",
      "[ 8471, 8916]   87   1.10% 100.00%\n",
      "\n",
      "Attribute in nodes:\n",
      "\t1518 : pose_Tz [NUMERICAL]\n",
      "\t1064 : pose_Tx [NUMERICAL]\n",
      "\t647 : pose_Ty [NUMERICAL]\n",
      "\t550 : AU10_r [NUMERICAL]\n",
      "\t529 : AU04_r [NUMERICAL]\n",
      "\t510 : pose_Ry [NUMERICAL]\n",
      "\t487 : gaze_angle_x [NUMERICAL]\n",
      "\t426 : gaze_0_x [NUMERICAL]\n",
      "\t364 : gaze_angle_y [NUMERICAL]\n",
      "\t338 : AU12_r [NUMERICAL]\n",
      "\t306 : gaze_0_z [NUMERICAL]\n",
      "\t257 : gaze_0_y [NUMERICAL]\n",
      "\t201 : dgaze_0_x [NUMERICAL]\n",
      "\t116 : dgaze_angle_y [NUMERICAL]\n",
      "\t107 : AU01_r [NUMERICAL]\n",
      "\t101 : AU45_r [NUMERICAL]\n",
      "\t96 : dgaze_0_y [NUMERICAL]\n",
      "\n",
      "Attribute in nodes with depth <= 0:\n",
      "\t95 : gaze_angle_x [NUMERICAL]\n",
      "\t75 : gaze_0_x [NUMERICAL]\n",
      "\t47 : pose_Ry [NUMERICAL]\n",
      "\t34 : AU12_r [NUMERICAL]\n",
      "\t21 : AU10_r [NUMERICAL]\n",
      "\t11 : AU04_r [NUMERICAL]\n",
      "\t7 : pose_Tz [NUMERICAL]\n",
      "\t7 : pose_Tx [NUMERICAL]\n",
      "\t2 : gaze_0_z [NUMERICAL]\n",
      "\t1 : pose_Ty [NUMERICAL]\n",
      "\n",
      "Attribute in nodes with depth <= 1:\n",
      "\t169 : pose_Tz [NUMERICAL]\n",
      "\t122 : pose_Tx [NUMERICAL]\n",
      "\t121 : gaze_angle_x [NUMERICAL]\n",
      "\t111 : gaze_0_x [NUMERICAL]\n",
      "\t109 : AU12_r [NUMERICAL]\n",
      "\t109 : AU10_r [NUMERICAL]\n",
      "\t86 : pose_Ry [NUMERICAL]\n",
      "\t25 : pose_Ty [NUMERICAL]\n",
      "\t16 : gaze_0_z [NUMERICAL]\n",
      "\t13 : AU04_r [NUMERICAL]\n",
      "\t12 : gaze_0_y [NUMERICAL]\n",
      "\t4 : gaze_angle_y [NUMERICAL]\n",
      "\n",
      "Attribute in nodes with depth <= 2:\n",
      "\t416 : pose_Tz [NUMERICAL]\n",
      "\t286 : pose_Tx [NUMERICAL]\n",
      "\t190 : AU10_r [NUMERICAL]\n",
      "\t175 : gaze_angle_x [NUMERICAL]\n",
      "\t153 : gaze_0_x [NUMERICAL]\n",
      "\t143 : AU12_r [NUMERICAL]\n",
      "\t139 : pose_Ry [NUMERICAL]\n",
      "\t106 : pose_Ty [NUMERICAL]\n",
      "\t66 : gaze_angle_y [NUMERICAL]\n",
      "\t61 : AU04_r [NUMERICAL]\n",
      "\t59 : gaze_0_y [NUMERICAL]\n",
      "\t55 : gaze_0_z [NUMERICAL]\n",
      "\t16 : dgaze_0_x [NUMERICAL]\n",
      "\t4 : dgaze_angle_y [NUMERICAL]\n",
      "\t4 : dgaze_0_y [NUMERICAL]\n",
      "\t2 : AU45_r [NUMERICAL]\n",
      "\t2 : AU01_r [NUMERICAL]\n",
      "\n",
      "Attribute in nodes with depth <= 3:\n",
      "\t705 : pose_Tz [NUMERICAL]\n",
      "\t512 : pose_Tx [NUMERICAL]\n",
      "\t281 : AU10_r [NUMERICAL]\n",
      "\t233 : pose_Ty [NUMERICAL]\n",
      "\t229 : gaze_angle_x [NUMERICAL]\n",
      "\t227 : gaze_0_x [NUMERICAL]\n",
      "\t220 : pose_Ry [NUMERICAL]\n",
      "\t188 : AU12_r [NUMERICAL]\n",
      "\t163 : gaze_angle_y [NUMERICAL]\n",
      "\t142 : AU04_r [NUMERICAL]\n",
      "\t111 : gaze_0_y [NUMERICAL]\n",
      "\t105 : gaze_0_z [NUMERICAL]\n",
      "\t60 : dgaze_0_x [NUMERICAL]\n",
      "\t30 : dgaze_angle_y [NUMERICAL]\n",
      "\t17 : AU01_r [NUMERICAL]\n",
      "\t11 : dgaze_0_y [NUMERICAL]\n",
      "\t10 : AU45_r [NUMERICAL]\n",
      "\n",
      "Attribute in nodes with depth <= 5:\n",
      "\t1252 : pose_Tz [NUMERICAL]\n",
      "\t865 : pose_Tx [NUMERICAL]\n",
      "\t505 : pose_Ty [NUMERICAL]\n",
      "\t444 : AU10_r [NUMERICAL]\n",
      "\t392 : pose_Ry [NUMERICAL]\n",
      "\t374 : gaze_angle_x [NUMERICAL]\n",
      "\t372 : AU04_r [NUMERICAL]\n",
      "\t338 : gaze_0_x [NUMERICAL]\n",
      "\t306 : gaze_angle_y [NUMERICAL]\n",
      "\t276 : AU12_r [NUMERICAL]\n",
      "\t216 : gaze_0_z [NUMERICAL]\n",
      "\t180 : gaze_0_y [NUMERICAL]\n",
      "\t138 : dgaze_0_x [NUMERICAL]\n",
      "\t85 : dgaze_angle_y [NUMERICAL]\n",
      "\t62 : AU01_r [NUMERICAL]\n",
      "\t61 : AU45_r [NUMERICAL]\n",
      "\t48 : dgaze_0_y [NUMERICAL]\n",
      "\n",
      "Condition type in nodes:\n",
      "\t7617 : HigherCondition\n",
      "Condition type in nodes with depth <= 0:\n",
      "\t300 : HigherCondition\n",
      "Condition type in nodes with depth <= 1:\n",
      "\t897 : HigherCondition\n",
      "Condition type in nodes with depth <= 2:\n",
      "\t1877 : HigherCondition\n",
      "Condition type in nodes with depth <= 3:\n",
      "\t3244 : HigherCondition\n",
      "Condition type in nodes with depth <= 5:\n",
      "\t5914 : HigherCondition\n",
      "Node format: NOT_SET\n",
      "\n",
      "Training OOB:\n",
      "\ttrees: 1, Out-of-bag evaluation: accuracy:0.999771 logloss:0.00824232\n",
      "\ttrees: 11, Out-of-bag evaluation: accuracy:0.999235 logloss:0.00650113\n",
      "\ttrees: 22, Out-of-bag evaluation: accuracy:0.999663 logloss:0.00341663\n",
      "\ttrees: 32, Out-of-bag evaluation: accuracy:0.999663 logloss:0.00182188\n",
      "\ttrees: 42, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00184332\n",
      "\ttrees: 52, Out-of-bag evaluation: accuracy:0.999789 logloss:0.00181203\n",
      "\ttrees: 62, Out-of-bag evaluation: accuracy:0.999789 logloss:0.00175098\n",
      "\ttrees: 72, Out-of-bag evaluation: accuracy:0.999873 logloss:0.00171874\n",
      "\ttrees: 82, Out-of-bag evaluation: accuracy:0.999747 logloss:0.00170809\n",
      "\ttrees: 92, Out-of-bag evaluation: accuracy:0.999747 logloss:0.00170276\n",
      "\ttrees: 102, Out-of-bag evaluation: accuracy:0.999789 logloss:0.00168543\n",
      "\ttrees: 112, Out-of-bag evaluation: accuracy:0.999789 logloss:0.00165828\n",
      "\ttrees: 122, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00168082\n",
      "\ttrees: 132, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00164929\n",
      "\ttrees: 142, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00160654\n",
      "\ttrees: 152, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00159124\n",
      "\ttrees: 162, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00158533\n",
      "\ttrees: 172, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00156161\n",
      "\ttrees: 182, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00155513\n",
      "\ttrees: 192, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00153319\n",
      "\ttrees: 202, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00154189\n",
      "\ttrees: 212, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00153272\n",
      "\ttrees: 222, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00151207\n",
      "\ttrees: 232, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00149788\n",
      "\ttrees: 242, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00148462\n",
      "\ttrees: 252, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00149336\n",
      "\ttrees: 262, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00150803\n",
      "\ttrees: 272, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00150493\n",
      "\ttrees: 282, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00150971\n",
      "\ttrees: 292, Out-of-bag evaluation: accuracy:0.999831 logloss:0.0014955\n",
      "\ttrees: 300, Out-of-bag evaluation: accuracy:0.999831 logloss:0.00149226\n",
      "\n",
      "None\n",
      "47/47 [==============================] - 1s 11ms/step\n",
      "Accuracy is  69.96 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "getTFRF()\n",
    "# run the models here\n",
    "# dataset.perdictSingleVideo('./singleVidCSV/', \"tf\", getTFRF())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 ('base')",
   "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": "3.8.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "24b17b925cb255c3f5f9d44279af6acbce2f518dd20c49d83bc1f6775a53d6cb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}