{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67febe95-dd8e-4564-8a5e-e641bb16906e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antonsruberts/miniconda/envs/blog/lib/python3.9/site-packages/tensorflow_addons/utils/ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.4.0 and strictly below 2.7.0 (nightly versions are not supported). \n",
      " The versions of TensorFlow you are currently using is 2.7.0 and is not supported. \n",
      "Some things might work, some things might not.\n",
      "If you were to encounter a bug, do not file an issue.\n",
      "If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n",
      "You can find the compatibility matrix in TensorFlow Addon's readme:\n",
      "https://github.com/tensorflow/addons\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "import tensorflow_addons as tfa\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "from tabtransformertf.models.fttransformer import FTTransformerEncoder, FTTransformer\n",
    "from tabtransformertf.utils.preprocessing import df_to_dataset\n",
    "\n",
    "import catboost as cb\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a44d4949-4d7f-4f55-abcf-5184756d4814",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.rcParams.update({'font.size': 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e3a3d374-bcc2-4af9-b972-0fa4f7e2f346",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f89b597-3f3a-4079-bc7c-ad3fbf16dba8",
   "metadata": {},
   "source": [
    "## Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "03901e64-09df-4625-87f1-4c36e16457de",
   "metadata": {},
   "outputs": [],
   "source": [
    "dset = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "233c9d44-5bcf-438d-a918-9ddd1eadae1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dset['data']\n",
    "y = dset['target']\n",
    "LABEL = dset['target_names'][0]\n",
    "\n",
    "NUMERIC_FEATURES = ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Longitude', 'Latitude']\n",
    "\n",
    "data = pd.DataFrame(data, columns=dset['feature_names'])\n",
    "data[LABEL] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c346bb4b-4970-4a98-b4a1-520cadf0e6cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(data, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "72159882-5ba1-4784-a71a-f4e5c093f96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset shape: (16512, 9)\n",
      "Test dataset shape: (4128, 9)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train dataset shape: {train_data.shape}\")\n",
    "print(f\"Test dataset shape: {test_data.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89bff333-3750-46c9-b687-3f06a8f43845",
   "metadata": {},
   "source": [
    "## Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "00ed6d33-d0d7-4e43-bff3-87c92649ad09",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val = train_test_split(train_data, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f1a1647-31e7-4ad8-b851-92107d12868a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train.loc[:, NUMERIC_FEATURES] = sc.fit_transform(X_train[NUMERIC_FEATURES])\n",
    "X_val.loc[:, NUMERIC_FEATURES] = sc.transform(X_val[NUMERIC_FEATURES])\n",
    "test_data.loc[:, NUMERIC_FEATURES] = sc.transform(test_data[NUMERIC_FEATURES])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "662fc59b-baf0-46db-8060-65a98760c15d",
   "metadata": {},
   "outputs": [],
   "source": [
    "FEATURES = NUMERIC_FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e038953e-06d7-4670-9408-cc63fa89941a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/66/1klxbkpn5vdgpvqwt_hmtn5c0000gn/T/ipykernel_65933/141569203.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(X_train[LABEL])\n",
      "/var/folders/66/1klxbkpn5vdgpvqwt_hmtn5c0000gn/T/ipykernel_65933/141569203.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(X_val[LABEL])\n",
      "/var/folders/66/1klxbkpn5vdgpvqwt_hmtn5c0000gn/T/ipykernel_65933/141569203.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(test_data[LABEL])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MedHouseVal', ylabel='Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(X_train[LABEL])\n",
    "sns.distplot(X_val[LABEL])\n",
    "sns.distplot(test_data[LABEL])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5a5c46-f4bd-4c52-9556-666eabafc51f",
   "metadata": {},
   "source": [
    "# Baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ebbed2eb-bee6-4fcf-8e41-cfef0eeaeb86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=20)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100, max_depth=20)\n",
    "rf.fit(X_train[FEATURES], X_train[LABEL])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e7853225-f417-4981-89f5-97341d4a9096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5111468321095857\n"
     ]
    }
   ],
   "source": [
    "rf_preds = rf.predict(test_data[FEATURES])\n",
    "rf_rms = mean_squared_error(test_data[LABEL], rf_preds, squared=False)\n",
    "print(rf_rms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65b7f4d-a77f-4d6d-9e7c-e641b139ee77",
   "metadata": {},
   "source": [
    "## CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cb051074-05f5-4f5c-bc5d-ed565bdfa090",
   "metadata": {},
   "outputs": [],
   "source": [
    "catb_train_dataset = cb.Pool(X_train[FEATURES], X_train[LABEL]) \n",
    "catb_val_dataset = cb.Pool(X_val[FEATURES], X_val[LABEL]) \n",
    "catb_test_dataset = cb.Pool(test_data[FEATURES], test_data[LABEL])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "440f05f9-7d75-4fad-acbf-59ef7bed2659",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.076361\n",
      "0:\tlearn: 1.1095583\ttest: 1.0881120\tbest: 1.0881120 (0)\ttotal: 12.5ms\tremaining: 12.5s\n",
      "1:\tlearn: 1.0665468\ttest: 1.0453537\tbest: 1.0453537 (1)\ttotal: 14.4ms\tremaining: 7.19s\n",
      "2:\tlearn: 1.0256526\ttest: 1.0062932\tbest: 1.0062932 (2)\ttotal: 16.9ms\tremaining: 5.61s\n",
      "3:\tlearn: 0.9892363\ttest: 0.9709483\tbest: 0.9709483 (3)\ttotal: 18.4ms\tremaining: 4.58s\n",
      "4:\tlearn: 0.9547325\ttest: 0.9376612\tbest: 0.9376612 (4)\ttotal: 19.9ms\tremaining: 3.96s\n",
      "5:\tlearn: 0.9241432\ttest: 0.9081350\tbest: 0.9081350 (5)\ttotal: 22.5ms\tremaining: 3.72s\n",
      "6:\tlearn: 0.8956798\ttest: 0.8803798\tbest: 0.8803798 (6)\ttotal: 25ms\tremaining: 3.54s\n",
      "7:\tlearn: 0.8700884\ttest: 0.8555606\tbest: 0.8555606 (7)\ttotal: 26.8ms\tremaining: 3.32s\n",
      "8:\tlearn: 0.8476829\ttest: 0.8335607\tbest: 0.8335607 (8)\ttotal: 28.3ms\tremaining: 3.12s\n",
      "9:\tlearn: 0.8265173\ttest: 0.8135861\tbest: 0.8135861 (9)\ttotal: 29.9ms\tremaining: 2.96s\n",
      "10:\tlearn: 0.8076136\ttest: 0.7953894\tbest: 0.7953894 (10)\ttotal: 31.4ms\tremaining: 2.83s\n",
      "11:\tlearn: 0.7892390\ttest: 0.7776602\tbest: 0.7776602 (11)\ttotal: 32.9ms\tremaining: 2.71s\n",
      "12:\tlearn: 0.7722510\ttest: 0.7618619\tbest: 0.7618619 (12)\ttotal: 34.4ms\tremaining: 2.61s\n",
      "13:\tlearn: 0.7576047\ttest: 0.7481415\tbest: 0.7481415 (13)\ttotal: 36ms\tremaining: 2.53s\n",
      "14:\tlearn: 0.7452769\ttest: 0.7364133\tbest: 0.7364133 (14)\ttotal: 38ms\tremaining: 2.49s\n",
      "15:\tlearn: 0.7327560\ttest: 0.7245772\tbest: 0.7245772 (15)\ttotal: 39.6ms\tremaining: 2.44s\n",
      "16:\tlearn: 0.7197274\ttest: 0.7123585\tbest: 0.7123585 (16)\ttotal: 41.3ms\tremaining: 2.39s\n",
      "17:\tlearn: 0.7069121\ttest: 0.7004626\tbest: 0.7004626 (17)\ttotal: 42.9ms\tremaining: 2.34s\n",
      "18:\tlearn: 0.6976305\ttest: 0.6914005\tbest: 0.6914005 (18)\ttotal: 45.7ms\tremaining: 2.36s\n",
      "19:\tlearn: 0.6867908\ttest: 0.6811619\tbest: 0.6811619 (19)\ttotal: 47.4ms\tremaining: 2.32s\n",
      "20:\tlearn: 0.6790106\ttest: 0.6740170\tbest: 0.6740170 (20)\ttotal: 48.9ms\tremaining: 2.28s\n",
      "21:\tlearn: 0.6718886\ttest: 0.6673920\tbest: 0.6673920 (21)\ttotal: 50.6ms\tremaining: 2.25s\n",
      "22:\tlearn: 0.6658073\ttest: 0.6618017\tbest: 0.6618017 (22)\ttotal: 52.3ms\tremaining: 2.22s\n",
      "23:\tlearn: 0.6584829\ttest: 0.6554214\tbest: 0.6554214 (23)\ttotal: 54.8ms\tremaining: 2.23s\n",
      "24:\tlearn: 0.6492555\ttest: 0.6468283\tbest: 0.6468283 (24)\ttotal: 56.5ms\tremaining: 2.21s\n",
      "25:\tlearn: 0.6434679\ttest: 0.6417883\tbest: 0.6417883 (25)\ttotal: 58.1ms\tremaining: 2.18s\n",
      "26:\tlearn: 0.6385035\ttest: 0.6377783\tbest: 0.6377783 (26)\ttotal: 59.9ms\tremaining: 2.16s\n",
      "27:\tlearn: 0.6329988\ttest: 0.6324359\tbest: 0.6324359 (27)\ttotal: 61.7ms\tremaining: 2.14s\n",
      "28:\tlearn: 0.6269642\ttest: 0.6268466\tbest: 0.6268466 (28)\ttotal: 63.4ms\tremaining: 2.12s\n",
      "29:\tlearn: 0.6234592\ttest: 0.6239189\tbest: 0.6239189 (29)\ttotal: 65.4ms\tremaining: 2.11s\n",
      "30:\tlearn: 0.6179250\ttest: 0.6181557\tbest: 0.6181557 (30)\ttotal: 67.9ms\tremaining: 2.12s\n",
      "31:\tlearn: 0.6144073\ttest: 0.6144330\tbest: 0.6144330 (31)\ttotal: 70.6ms\tremaining: 2.13s\n",
      "32:\tlearn: 0.6086309\ttest: 0.6090642\tbest: 0.6090642 (32)\ttotal: 72.4ms\tremaining: 2.12s\n",
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      "954:\tlearn: 0.3135709\ttest: 0.4446423\tbest: 0.4445681 (951)\ttotal: 1.89s\tremaining: 89ms\n",
      "955:\tlearn: 0.3134693\ttest: 0.4445852\tbest: 0.4445681 (951)\ttotal: 1.89s\tremaining: 87ms\n",
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      "963:\tlearn: 0.3126102\ttest: 0.4444677\tbest: 0.4444677 (963)\ttotal: 1.91s\tremaining: 71.2ms\n",
      "964:\tlearn: 0.3125010\ttest: 0.4444442\tbest: 0.4444442 (964)\ttotal: 1.91s\tremaining: 69.2ms\n",
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      "987:\tlearn: 0.3104023\ttest: 0.4442578\tbest: 0.4442578 (987)\ttotal: 1.95s\tremaining: 23.7ms\n",
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      "989:\tlearn: 0.3101750\ttest: 0.4443024\tbest: 0.4442578 (987)\ttotal: 1.95s\tremaining: 19.7ms\n",
      "990:\tlearn: 0.3100911\ttest: 0.4443136\tbest: 0.4442578 (987)\ttotal: 1.96s\tremaining: 17.8ms\n",
      "991:\tlearn: 0.3100110\ttest: 0.4443248\tbest: 0.4442578 (987)\ttotal: 1.96s\tremaining: 15.8ms\n",
      "992:\tlearn: 0.3098664\ttest: 0.4442948\tbest: 0.4442578 (987)\ttotal: 1.96s\tremaining: 13.8ms\n",
      "993:\tlearn: 0.3097941\ttest: 0.4442559\tbest: 0.4442559 (993)\ttotal: 1.96s\tremaining: 11.8ms\n",
      "994:\tlearn: 0.3096655\ttest: 0.4441859\tbest: 0.4441859 (994)\ttotal: 1.96s\tremaining: 9.87ms\n",
      "995:\tlearn: 0.3095558\ttest: 0.4442223\tbest: 0.4441859 (994)\ttotal: 1.97s\tremaining: 7.89ms\n",
      "996:\tlearn: 0.3094865\ttest: 0.4442231\tbest: 0.4441859 (994)\ttotal: 1.97s\tremaining: 5.92ms\n",
      "997:\tlearn: 0.3094134\ttest: 0.4442271\tbest: 0.4441859 (994)\ttotal: 1.97s\tremaining: 3.95ms\n",
      "998:\tlearn: 0.3093425\ttest: 0.4442830\tbest: 0.4441859 (994)\ttotal: 1.97s\tremaining: 1.97ms\n",
      "999:\tlearn: 0.3092760\ttest: 0.4442812\tbest: 0.4441859 (994)\ttotal: 1.97s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.4441859469\n",
      "bestIteration = 994\n",
      "\n",
      "Shrink model to first 995 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7f9b71ddbfa0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_catb = cb.CatBoostRegressor()\n",
    "tuned_catb.fit(catb_train_dataset, eval_set=catb_val_dataset, early_stopping_rounds=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1e590998-c6b8-42e7-8166-161c503047f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "catb_preds = tuned_catb.predict(catb_test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "55c09369-04b9-4214-a296-1b56312591a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "catb_rms = mean_squared_error(test_data[LABEL], catb_preds, squared=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a07ad92f-f1b9-46ad-9b61-831cbc903e22",
   "metadata": {},
   "source": [
    "## Modelling Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9bfdb641-4fec-4f2e-a84f-149a41e2a02b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antonsruberts/personal/TabTransformerTF/tabtransformertf/utils/preprocessing.py:21: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  dataset[key] = value[:, tf.newaxis]\n",
      "/Users/antonsruberts/personal/TabTransformerTF/tabtransformertf/utils/preprocessing.py:21: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  dataset[key] = value[:, tf.newaxis]\n",
      "/Users/antonsruberts/personal/TabTransformerTF/tabtransformertf/utils/preprocessing.py:27: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  dataset[key] = value[:, tf.newaxis]\n"
     ]
    }
   ],
   "source": [
    "# To TF Dataset\n",
    "train_dataset = df_to_dataset(X_train[FEATURES + [LABEL]], LABEL, shuffle=True)\n",
    "val_dataset = df_to_dataset(X_val[FEATURES + [LABEL]], LABEL, shuffle=False)  # No shuffle\n",
    "test_dataset = df_to_dataset(test_data[FEATURES + [LABEL]], shuffle=False) # No target, no shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe05de6-639b-48b6-8387-e44be0d8db0c",
   "metadata": {},
   "source": [
    "# FTTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9342c4cd-d309-420f-b966-c6377030d6d4",
   "metadata": {},
   "source": [
    "## FT Transformer - Linear Numerical Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d46c4f93-579e-4de1-8ff2-a57334ac5ca0",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "26/26 [==============================] - 10s 230ms/step - loss: 1.2817 - output_loss: 1.2817 - output_rmse: 1.1321 - val_loss: 0.7904 - val_output_loss: 0.7904 - val_output_rmse: 0.8890\n",
      "Epoch 2/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.7574 - output_loss: 0.7574 - output_rmse: 0.8703 - val_loss: 0.6370 - val_output_loss: 0.6370 - val_output_rmse: 0.7981\n",
      "Epoch 3/1000\n",
      "26/26 [==============================] - 5s 190ms/step - loss: 0.6220 - output_loss: 0.6220 - output_rmse: 0.7887 - val_loss: 0.5082 - val_output_loss: 0.5082 - val_output_rmse: 0.7129\n",
      "Epoch 4/1000\n",
      "26/26 [==============================] - 5s 192ms/step - loss: 0.5493 - output_loss: 0.5493 - output_rmse: 0.7412 - val_loss: 0.4677 - val_output_loss: 0.4677 - val_output_rmse: 0.6839\n",
      "Epoch 5/1000\n",
      "26/26 [==============================] - 5s 205ms/step - loss: 0.5040 - output_loss: 0.5040 - output_rmse: 0.7099 - val_loss: 0.4803 - val_output_loss: 0.4803 - val_output_rmse: 0.6931\n",
      "Epoch 6/1000\n",
      "26/26 [==============================] - 6s 219ms/step - loss: 0.4777 - output_loss: 0.4777 - output_rmse: 0.6912 - val_loss: 0.3875 - val_output_loss: 0.3875 - val_output_rmse: 0.6225\n",
      "Epoch 7/1000\n",
      "26/26 [==============================] - 6s 215ms/step - loss: 0.4410 - output_loss: 0.4410 - output_rmse: 0.6641 - val_loss: 0.4442 - val_output_loss: 0.4442 - val_output_rmse: 0.6665\n",
      "Epoch 8/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.4280 - output_loss: 0.4280 - output_rmse: 0.6542 - val_loss: 0.3708 - val_output_loss: 0.3708 - val_output_rmse: 0.6089\n",
      "Epoch 9/1000\n",
      "26/26 [==============================] - 6s 217ms/step - loss: 0.4045 - output_loss: 0.4045 - output_rmse: 0.6360 - val_loss: 0.3603 - val_output_loss: 0.3603 - val_output_rmse: 0.6002\n",
      "Epoch 10/1000\n",
      "26/26 [==============================] - 6s 220ms/step - loss: 0.4109 - output_loss: 0.4109 - output_rmse: 0.6410 - val_loss: 0.3524 - val_output_loss: 0.3524 - val_output_rmse: 0.5937\n",
      "Epoch 11/1000\n",
      "26/26 [==============================] - 6s 219ms/step - loss: 0.3795 - output_loss: 0.3795 - output_rmse: 0.6160 - val_loss: 0.3377 - val_output_loss: 0.3377 - val_output_rmse: 0.5811\n",
      "Epoch 12/1000\n",
      "26/26 [==============================] - 6s 213ms/step - loss: 0.3790 - output_loss: 0.3790 - output_rmse: 0.6156 - val_loss: 0.3414 - val_output_loss: 0.3414 - val_output_rmse: 0.5843\n",
      "Epoch 13/1000\n",
      "26/26 [==============================] - 6s 234ms/step - loss: 0.3726 - output_loss: 0.3726 - output_rmse: 0.6104 - val_loss: 0.3403 - val_output_loss: 0.3403 - val_output_rmse: 0.5834\n",
      "Epoch 14/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.3663 - output_loss: 0.3663 - output_rmse: 0.6053 - val_loss: 0.3329 - val_output_loss: 0.3329 - val_output_rmse: 0.5770\n",
      "Epoch 15/1000\n",
      "26/26 [==============================] - 6s 232ms/step - loss: 0.3619 - output_loss: 0.3619 - output_rmse: 0.6015 - val_loss: 0.3834 - val_output_loss: 0.3834 - val_output_rmse: 0.6192\n",
      "Epoch 16/1000\n",
      "26/26 [==============================] - 6s 223ms/step - loss: 0.3679 - output_loss: 0.3679 - output_rmse: 0.6065 - val_loss: 0.3405 - val_output_loss: 0.3405 - val_output_rmse: 0.5835\n",
      "Epoch 17/1000\n",
      "26/26 [==============================] - 6s 223ms/step - loss: 0.3493 - output_loss: 0.3493 - output_rmse: 0.5910 - val_loss: 0.3257 - val_output_loss: 0.3257 - val_output_rmse: 0.5707\n",
      "Epoch 18/1000\n",
      "26/26 [==============================] - 6s 222ms/step - loss: 0.3610 - output_loss: 0.3610 - output_rmse: 0.6008 - val_loss: 0.3143 - val_output_loss: 0.3143 - val_output_rmse: 0.5606\n",
      "Epoch 19/1000\n",
      "26/26 [==============================] - 6s 229ms/step - loss: 0.3433 - output_loss: 0.3433 - output_rmse: 0.5859 - val_loss: 0.3179 - val_output_loss: 0.3179 - val_output_rmse: 0.5638\n",
      "Epoch 20/1000\n",
      "26/26 [==============================] - 6s 215ms/step - loss: 0.3510 - output_loss: 0.3510 - output_rmse: 0.5924 - val_loss: 0.3436 - val_output_loss: 0.3436 - val_output_rmse: 0.5862\n",
      "Epoch 21/1000\n",
      "26/26 [==============================] - 6s 211ms/step - loss: 0.3435 - output_loss: 0.3435 - output_rmse: 0.5861 - val_loss: 0.3177 - val_output_loss: 0.3177 - val_output_rmse: 0.5636\n",
      "Epoch 22/1000\n",
      "26/26 [==============================] - 6s 217ms/step - loss: 0.3351 - output_loss: 0.3351 - output_rmse: 0.5789 - val_loss: 0.3282 - val_output_loss: 0.3282 - val_output_rmse: 0.5729\n",
      "Epoch 23/1000\n",
      "26/26 [==============================] - 6s 232ms/step - loss: 0.3405 - output_loss: 0.3405 - output_rmse: 0.5835 - val_loss: 0.3122 - val_output_loss: 0.3122 - val_output_rmse: 0.5588\n",
      "Epoch 24/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.3441 - output_loss: 0.3441 - output_rmse: 0.5866 - val_loss: 0.3124 - val_output_loss: 0.3124 - val_output_rmse: 0.5589\n",
      "Epoch 25/1000\n",
      "26/26 [==============================] - 6s 221ms/step - loss: 0.3348 - output_loss: 0.3348 - output_rmse: 0.5786 - val_loss: 0.3270 - val_output_loss: 0.3270 - val_output_rmse: 0.5718\n",
      "Epoch 26/1000\n",
      "26/26 [==============================] - 6s 242ms/step - loss: 0.3371 - output_loss: 0.3371 - output_rmse: 0.5806 - val_loss: 0.3164 - val_output_loss: 0.3164 - val_output_rmse: 0.5625\n",
      "Epoch 27/1000\n",
      "26/26 [==============================] - 6s 227ms/step - loss: 0.3281 - output_loss: 0.3281 - output_rmse: 0.5728 - val_loss: 0.3086 - val_output_loss: 0.3086 - val_output_rmse: 0.5555\n",
      "Epoch 28/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.3348 - output_loss: 0.3348 - output_rmse: 0.5786 - val_loss: 0.3193 - val_output_loss: 0.3193 - val_output_rmse: 0.5651\n",
      "Epoch 29/1000\n",
      "26/26 [==============================] - 6s 211ms/step - loss: 0.3365 - output_loss: 0.3365 - output_rmse: 0.5801 - val_loss: 0.3190 - val_output_loss: 0.3190 - val_output_rmse: 0.5648\n",
      "Epoch 30/1000\n",
      "26/26 [==============================] - 6s 216ms/step - loss: 0.3348 - output_loss: 0.3348 - output_rmse: 0.5786 - val_loss: 0.3177 - val_output_loss: 0.3177 - val_output_rmse: 0.5636\n",
      "Epoch 31/1000\n",
      "26/26 [==============================] - 6s 224ms/step - loss: 0.3304 - output_loss: 0.3304 - output_rmse: 0.5748 - val_loss: 0.3098 - val_output_loss: 0.3098 - val_output_rmse: 0.5566\n",
      "Epoch 32/1000\n",
      "26/26 [==============================] - 6s 225ms/step - loss: 0.3479 - output_loss: 0.3479 - output_rmse: 0.5898 - val_loss: 0.3129 - val_output_loss: 0.3129 - val_output_rmse: 0.5594\n",
      "Epoch 33/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.3259 - output_loss: 0.3259 - output_rmse: 0.5709 - val_loss: 0.3039 - val_output_loss: 0.3039 - val_output_rmse: 0.5513\n",
      "Epoch 34/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.3330 - output_loss: 0.3330 - output_rmse: 0.5770 - val_loss: 0.3092 - val_output_loss: 0.3092 - val_output_rmse: 0.5561\n",
      "Epoch 35/1000\n",
      "26/26 [==============================] - 6s 222ms/step - loss: 0.3190 - output_loss: 0.3190 - output_rmse: 0.5648 - val_loss: 0.3019 - val_output_loss: 0.3019 - val_output_rmse: 0.5495\n",
      "Epoch 36/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.3228 - output_loss: 0.3228 - output_rmse: 0.5682 - val_loss: 0.3086 - val_output_loss: 0.3086 - val_output_rmse: 0.5555\n",
      "Epoch 37/1000\n",
      "26/26 [==============================] - 6s 217ms/step - loss: 0.3171 - output_loss: 0.3171 - output_rmse: 0.5631 - val_loss: 0.2924 - val_output_loss: 0.2924 - val_output_rmse: 0.5408\n",
      "Epoch 38/1000\n",
      "26/26 [==============================] - 6s 219ms/step - loss: 0.3190 - output_loss: 0.3190 - output_rmse: 0.5648 - val_loss: 0.3220 - val_output_loss: 0.3220 - val_output_rmse: 0.5674\n",
      "Epoch 39/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.3208 - output_loss: 0.3208 - output_rmse: 0.5664 - val_loss: 0.3051 - val_output_loss: 0.3051 - val_output_rmse: 0.5523\n",
      "Epoch 40/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.3145 - output_loss: 0.3145 - output_rmse: 0.5608 - val_loss: 0.2910 - val_output_loss: 0.2910 - val_output_rmse: 0.5395\n",
      "Epoch 41/1000\n",
      "26/26 [==============================] - 6s 216ms/step - loss: 0.3074 - output_loss: 0.3074 - output_rmse: 0.5544 - val_loss: 0.3110 - val_output_loss: 0.3110 - val_output_rmse: 0.5577\n",
      "Epoch 42/1000\n",
      "26/26 [==============================] - 6s 224ms/step - loss: 0.3123 - output_loss: 0.3123 - output_rmse: 0.5589 - val_loss: 0.2943 - val_output_loss: 0.2943 - val_output_rmse: 0.5425\n",
      "Epoch 43/1000\n",
      "26/26 [==============================] - 6s 218ms/step - loss: 0.3201 - output_loss: 0.3201 - output_rmse: 0.5657 - val_loss: 0.3230 - val_output_loss: 0.3230 - val_output_rmse: 0.5683\n",
      "Epoch 44/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.3139 - output_loss: 0.3139 - output_rmse: 0.5602 - val_loss: 0.3747 - val_output_loss: 0.3747 - val_output_rmse: 0.6121\n",
      "Epoch 45/1000\n",
      "26/26 [==============================] - 6s 218ms/step - loss: 0.3220 - output_loss: 0.3220 - output_rmse: 0.5674 - val_loss: 0.2979 - val_output_loss: 0.2979 - val_output_rmse: 0.5458\n",
      "Epoch 46/1000\n",
      "26/26 [==============================] - 6s 221ms/step - loss: 0.3058 - output_loss: 0.3058 - output_rmse: 0.5530 - val_loss: 0.3060 - val_output_loss: 0.3060 - val_output_rmse: 0.5531\n",
      "Epoch 47/1000\n",
      "26/26 [==============================] - 6s 217ms/step - loss: 0.3079 - output_loss: 0.3079 - output_rmse: 0.5549 - val_loss: 0.2886 - val_output_loss: 0.2886 - val_output_rmse: 0.5372\n",
      "Epoch 48/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.3170 - output_loss: 0.3170 - output_rmse: 0.5630 - val_loss: 0.3127 - val_output_loss: 0.3127 - val_output_rmse: 0.5592\n",
      "Epoch 49/1000\n",
      "26/26 [==============================] - 6s 217ms/step - loss: 0.3095 - output_loss: 0.3095 - output_rmse: 0.5563 - val_loss: 0.2861 - val_output_loss: 0.2861 - val_output_rmse: 0.5349\n",
      "Epoch 50/1000\n",
      "26/26 [==============================] - 6s 224ms/step - loss: 0.3108 - output_loss: 0.3108 - output_rmse: 0.5575 - val_loss: 0.3061 - val_output_loss: 0.3061 - val_output_rmse: 0.5533\n",
      "Epoch 51/1000\n",
      "26/26 [==============================] - 6s 239ms/step - loss: 0.3160 - output_loss: 0.3160 - output_rmse: 0.5622 - val_loss: 0.2884 - val_output_loss: 0.2884 - val_output_rmse: 0.5370\n",
      "Epoch 52/1000\n",
      "26/26 [==============================] - 6s 239ms/step - loss: 0.2997 - output_loss: 0.2997 - output_rmse: 0.5474 - val_loss: 0.2876 - val_output_loss: 0.2876 - val_output_rmse: 0.5363\n",
      "Epoch 53/1000\n",
      "26/26 [==============================] - 6s 234ms/step - loss: 0.3173 - output_loss: 0.3173 - output_rmse: 0.5633 - val_loss: 0.2901 - val_output_loss: 0.2901 - val_output_rmse: 0.5386\n",
      "Epoch 54/1000\n",
      "26/26 [==============================] - 6s 224ms/step - loss: 0.3011 - output_loss: 0.3011 - output_rmse: 0.5487 - val_loss: 0.2853 - val_output_loss: 0.2853 - val_output_rmse: 0.5342\n",
      "Epoch 55/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.3008 - output_loss: 0.3008 - output_rmse: 0.5485 - val_loss: 0.2865 - val_output_loss: 0.2865 - val_output_rmse: 0.5352\n",
      "Epoch 56/1000\n",
      "26/26 [==============================] - 6s 236ms/step - loss: 0.3011 - output_loss: 0.3011 - output_rmse: 0.5487 - val_loss: 0.2912 - val_output_loss: 0.2912 - val_output_rmse: 0.5396\n",
      "Epoch 57/1000\n",
      "26/26 [==============================] - 6s 233ms/step - loss: 0.3042 - output_loss: 0.3042 - output_rmse: 0.5515 - val_loss: 0.3066 - val_output_loss: 0.3066 - val_output_rmse: 0.5537\n",
      "Epoch 58/1000\n",
      "26/26 [==============================] - 6s 245ms/step - loss: 0.3006 - output_loss: 0.3006 - output_rmse: 0.5482 - val_loss: 0.2879 - val_output_loss: 0.2879 - val_output_rmse: 0.5366\n",
      "Epoch 59/1000\n",
      "26/26 [==============================] - 7s 253ms/step - loss: 0.2987 - output_loss: 0.2987 - output_rmse: 0.5465 - val_loss: 0.2935 - val_output_loss: 0.2935 - val_output_rmse: 0.5418\n",
      "Epoch 60/1000\n",
      "26/26 [==============================] - 6s 232ms/step - loss: 0.2940 - output_loss: 0.2940 - output_rmse: 0.5422 - val_loss: 0.2889 - val_output_loss: 0.2889 - val_output_rmse: 0.5375\n",
      "Epoch 61/1000\n",
      "26/26 [==============================] - 6s 210ms/step - loss: 0.2978 - output_loss: 0.2978 - output_rmse: 0.5457 - val_loss: 0.2999 - val_output_loss: 0.2999 - val_output_rmse: 0.5476\n",
      "Epoch 62/1000\n",
      "26/26 [==============================] - 6s 210ms/step - loss: 0.2953 - output_loss: 0.2953 - output_rmse: 0.5434 - val_loss: 0.2903 - val_output_loss: 0.2903 - val_output_rmse: 0.5388\n",
      "Epoch 63/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2953 - output_loss: 0.2953 - output_rmse: 0.5434 - val_loss: 0.2846 - val_output_loss: 0.2846 - val_output_rmse: 0.5335\n",
      "Epoch 64/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.3032 - output_loss: 0.3032 - output_rmse: 0.5506 - val_loss: 0.3062 - val_output_loss: 0.3062 - val_output_rmse: 0.5534\n",
      "Epoch 65/1000\n",
      "26/26 [==============================] - 6s 210ms/step - loss: 0.2966 - output_loss: 0.2966 - output_rmse: 0.5446 - val_loss: 0.3080 - val_output_loss: 0.3080 - val_output_rmse: 0.5550\n",
      "Epoch 66/1000\n",
      "26/26 [==============================] - 5s 209ms/step - loss: 0.2928 - output_loss: 0.2928 - output_rmse: 0.5411 - val_loss: 0.2776 - val_output_loss: 0.2776 - val_output_rmse: 0.5268\n",
      "Epoch 67/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2995 - output_loss: 0.2995 - output_rmse: 0.5473 - val_loss: 0.2823 - val_output_loss: 0.2823 - val_output_rmse: 0.5313\n",
      "Epoch 68/1000\n",
      "26/26 [==============================] - 5s 209ms/step - loss: 0.2863 - output_loss: 0.2863 - output_rmse: 0.5351 - val_loss: 0.2817 - val_output_loss: 0.2817 - val_output_rmse: 0.5307\n",
      "Epoch 69/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.2896 - output_loss: 0.2896 - output_rmse: 0.5381 - val_loss: 0.2852 - val_output_loss: 0.2852 - val_output_rmse: 0.5340\n",
      "Epoch 70/1000\n",
      "26/26 [==============================] - 6s 211ms/step - loss: 0.2837 - output_loss: 0.2837 - output_rmse: 0.5326 - val_loss: 0.2743 - val_output_loss: 0.2743 - val_output_rmse: 0.5238\n",
      "Epoch 71/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2926 - output_loss: 0.2926 - output_rmse: 0.5409 - val_loss: 0.2821 - val_output_loss: 0.2821 - val_output_rmse: 0.5311\n",
      "Epoch 72/1000\n",
      "26/26 [==============================] - 5s 209ms/step - loss: 0.2861 - output_loss: 0.2861 - output_rmse: 0.5348 - val_loss: 0.2715 - val_output_loss: 0.2715 - val_output_rmse: 0.5210\n",
      "Epoch 73/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2831 - output_loss: 0.2831 - output_rmse: 0.5320 - val_loss: 0.2919 - val_output_loss: 0.2919 - val_output_rmse: 0.5402\n",
      "Epoch 74/1000\n",
      "26/26 [==============================] - 5s 209ms/step - loss: 0.2868 - output_loss: 0.2868 - output_rmse: 0.5356 - val_loss: 0.2917 - val_output_loss: 0.2917 - val_output_rmse: 0.5401\n",
      "Epoch 75/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.2926 - output_loss: 0.2926 - output_rmse: 0.5409 - val_loss: 0.2867 - val_output_loss: 0.2867 - val_output_rmse: 0.5354\n",
      "Epoch 76/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2915 - output_loss: 0.2915 - output_rmse: 0.5399 - val_loss: 0.2742 - val_output_loss: 0.2742 - val_output_rmse: 0.5237\n",
      "Epoch 77/1000\n",
      "26/26 [==============================] - 6s 209ms/step - loss: 0.2879 - output_loss: 0.2879 - output_rmse: 0.5366 - val_loss: 0.2768 - val_output_loss: 0.2768 - val_output_rmse: 0.5261\n",
      "Epoch 78/1000\n",
      "26/26 [==============================] - 5s 209ms/step - loss: 0.2785 - output_loss: 0.2785 - output_rmse: 0.5277 - val_loss: 0.2732 - val_output_loss: 0.2732 - val_output_rmse: 0.5227\n",
      "Epoch 79/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2785 - output_loss: 0.2785 - output_rmse: 0.5277 - val_loss: 0.2835 - val_output_loss: 0.2835 - val_output_rmse: 0.5324\n",
      "Epoch 80/1000\n",
      "26/26 [==============================] - 6s 210ms/step - loss: 0.2798 - output_loss: 0.2798 - output_rmse: 0.5290 - val_loss: 0.2820 - val_output_loss: 0.2820 - val_output_rmse: 0.5311\n",
      "Epoch 81/1000\n",
      "26/26 [==============================] - 6s 213ms/step - loss: 0.2763 - output_loss: 0.2763 - output_rmse: 0.5256 - val_loss: 0.2888 - val_output_loss: 0.2888 - val_output_rmse: 0.5374\n",
      "Epoch 82/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2827 - output_loss: 0.2827 - output_rmse: 0.5317 - val_loss: 0.2874 - val_output_loss: 0.2874 - val_output_rmse: 0.5361\n",
      "Epoch 83/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2865 - output_loss: 0.2865 - output_rmse: 0.5353 - val_loss: 0.2784 - val_output_loss: 0.2784 - val_output_rmse: 0.5276\n",
      "Epoch 84/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2749 - output_loss: 0.2749 - output_rmse: 0.5243 - val_loss: 0.2664 - val_output_loss: 0.2664 - val_output_rmse: 0.5162\n",
      "Epoch 85/1000\n",
      "26/26 [==============================] - 6s 209ms/step - loss: 0.2778 - output_loss: 0.2778 - output_rmse: 0.5271 - val_loss: 0.2678 - val_output_loss: 0.2678 - val_output_rmse: 0.5175\n",
      "Epoch 86/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.2792 - output_loss: 0.2792 - output_rmse: 0.5284 - val_loss: 0.2776 - val_output_loss: 0.2776 - val_output_rmse: 0.5269\n",
      "Epoch 87/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.2834 - output_loss: 0.2834 - output_rmse: 0.5324 - val_loss: 0.2963 - val_output_loss: 0.2963 - val_output_rmse: 0.5443\n",
      "Epoch 88/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2807 - output_loss: 0.2807 - output_rmse: 0.5298 - val_loss: 0.2928 - val_output_loss: 0.2928 - val_output_rmse: 0.5411\n",
      "Epoch 89/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2777 - output_loss: 0.2777 - output_rmse: 0.5270 - val_loss: 0.2773 - val_output_loss: 0.2773 - val_output_rmse: 0.5266\n",
      "Epoch 90/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2705 - output_loss: 0.2705 - output_rmse: 0.5201 - val_loss: 0.2669 - val_output_loss: 0.2669 - val_output_rmse: 0.5166\n",
      "Epoch 91/1000\n",
      "26/26 [==============================] - 6s 209ms/step - loss: 0.2737 - output_loss: 0.2737 - output_rmse: 0.5232 - val_loss: 0.2689 - val_output_loss: 0.2689 - val_output_rmse: 0.5185\n",
      "Epoch 92/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2624 - output_loss: 0.2624 - output_rmse: 0.5122 - val_loss: 0.2681 - val_output_loss: 0.2681 - val_output_rmse: 0.5178\n",
      "Epoch 93/1000\n",
      "26/26 [==============================] - 5s 208ms/step - loss: 0.2838 - output_loss: 0.2838 - output_rmse: 0.5327 - val_loss: 0.2762 - val_output_loss: 0.2762 - val_output_rmse: 0.5256\n",
      "Epoch 94/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2718 - output_loss: 0.2718 - output_rmse: 0.5213 - val_loss: 0.2665 - val_output_loss: 0.2665 - val_output_rmse: 0.5162\n",
      "Epoch 95/1000\n",
      "26/26 [==============================] - 6s 210ms/step - loss: 0.2682 - output_loss: 0.2682 - output_rmse: 0.5179 - val_loss: 0.2737 - val_output_loss: 0.2737 - val_output_rmse: 0.5232\n",
      "Epoch 96/1000\n",
      "26/26 [==============================] - 6s 209ms/step - loss: 0.2665 - output_loss: 0.2665 - output_rmse: 0.5163 - val_loss: 0.2667 - val_output_loss: 0.2667 - val_output_rmse: 0.5164\n",
      "Epoch 97/1000\n",
      "26/26 [==============================] - 6s 212ms/step - loss: 0.2639 - output_loss: 0.2639 - output_rmse: 0.5137 - val_loss: 0.2759 - val_output_loss: 0.2759 - val_output_rmse: 0.5253\n",
      "Epoch 98/1000\n",
      "26/26 [==============================] - 5s 207ms/step - loss: 0.2618 - output_loss: 0.2618 - output_rmse: 0.5116 - val_loss: 0.2595 - val_output_loss: 0.2595 - val_output_rmse: 0.5094\n",
      "Epoch 99/1000\n",
      "26/26 [==============================] - 6s 216ms/step - loss: 0.2694 - output_loss: 0.2694 - output_rmse: 0.5191 - val_loss: 0.2705 - val_output_loss: 0.2705 - val_output_rmse: 0.5201\n",
      "Epoch 100/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.2881 - output_loss: 0.2881 - output_rmse: 0.5367 - val_loss: 0.2834 - val_output_loss: 0.2834 - val_output_rmse: 0.5324\n",
      "Epoch 101/1000\n",
      "26/26 [==============================] - 6s 213ms/step - loss: 0.2700 - output_loss: 0.2700 - output_rmse: 0.5196 - val_loss: 0.2768 - val_output_loss: 0.2768 - val_output_rmse: 0.5262\n",
      "Epoch 102/1000\n",
      "26/26 [==============================] - 6s 227ms/step - loss: 0.2642 - output_loss: 0.2642 - output_rmse: 0.5140 - val_loss: 0.2635 - val_output_loss: 0.2635 - val_output_rmse: 0.5133\n",
      "Epoch 103/1000\n",
      "26/26 [==============================] - 6s 238ms/step - loss: 0.2681 - output_loss: 0.2681 - output_rmse: 0.5178 - val_loss: 0.2597 - val_output_loss: 0.2597 - val_output_rmse: 0.5096\n",
      "Epoch 104/1000\n",
      "26/26 [==============================] - 6s 223ms/step - loss: 0.2697 - output_loss: 0.2697 - output_rmse: 0.5193 - val_loss: 0.2888 - val_output_loss: 0.2888 - val_output_rmse: 0.5374\n",
      "Epoch 105/1000\n",
      "26/26 [==============================] - 6s 229ms/step - loss: 0.2658 - output_loss: 0.2658 - output_rmse: 0.5156 - val_loss: 0.2638 - val_output_loss: 0.2638 - val_output_rmse: 0.5136\n",
      "Epoch 106/1000\n",
      "26/26 [==============================] - 6s 219ms/step - loss: 0.2675 - output_loss: 0.2675 - output_rmse: 0.5172 - val_loss: 0.2707 - val_output_loss: 0.2707 - val_output_rmse: 0.5203\n",
      "Epoch 107/1000\n",
      "26/26 [==============================] - 6s 221ms/step - loss: 0.2633 - output_loss: 0.2633 - output_rmse: 0.5132 - val_loss: 0.2788 - val_output_loss: 0.2788 - val_output_rmse: 0.5280\n",
      "Epoch 108/1000\n",
      "26/26 [==============================] - 6s 216ms/step - loss: 0.2694 - output_loss: 0.2694 - output_rmse: 0.5190 - val_loss: 0.2733 - val_output_loss: 0.2733 - val_output_rmse: 0.5228\n",
      "Epoch 109/1000\n",
      "26/26 [==============================] - 6s 216ms/step - loss: 0.2664 - output_loss: 0.2664 - output_rmse: 0.5161 - val_loss: 0.2653 - val_output_loss: 0.2653 - val_output_rmse: 0.5151\n",
      "Epoch 110/1000\n",
      "26/26 [==============================] - 6s 219ms/step - loss: 0.2604 - output_loss: 0.2604 - output_rmse: 0.5103 - val_loss: 0.2609 - val_output_loss: 0.2609 - val_output_rmse: 0.5108\n",
      "Epoch 111/1000\n",
      "26/26 [==============================] - 6s 218ms/step - loss: 0.2578 - output_loss: 0.2578 - output_rmse: 0.5078 - val_loss: 0.2690 - val_output_loss: 0.2690 - val_output_rmse: 0.5187\n",
      "Epoch 112/1000\n",
      "26/26 [==============================] - 6s 223ms/step - loss: 0.2656 - output_loss: 0.2656 - output_rmse: 0.5154 - val_loss: 0.2613 - val_output_loss: 0.2613 - val_output_rmse: 0.5112\n",
      "Epoch 113/1000\n",
      "26/26 [==============================] - 6s 215ms/step - loss: 0.2643 - output_loss: 0.2643 - output_rmse: 0.5141 - val_loss: 0.2546 - val_output_loss: 0.2546 - val_output_rmse: 0.5046\n",
      "Epoch 114/1000\n",
      "26/26 [==============================] - 6s 214ms/step - loss: 0.2584 - output_loss: 0.2584 - output_rmse: 0.5084 - val_loss: 0.2638 - val_output_loss: 0.2638 - val_output_rmse: 0.5136\n",
      "Epoch 115/1000\n",
      "26/26 [==============================] - 6s 220ms/step - loss: 0.2625 - output_loss: 0.2625 - output_rmse: 0.5123 - val_loss: 0.2825 - val_output_loss: 0.2825 - val_output_rmse: 0.5315\n",
      "Epoch 116/1000\n",
      "26/26 [==============================] - 6s 225ms/step - loss: 0.2584 - output_loss: 0.2584 - output_rmse: 0.5084 - val_loss: 0.2645 - val_output_loss: 0.2645 - val_output_rmse: 0.5143\n",
      "Epoch 117/1000\n",
      "26/26 [==============================] - 6s 233ms/step - loss: 0.2523 - output_loss: 0.2523 - output_rmse: 0.5023 - val_loss: 0.2572 - val_output_loss: 0.2572 - val_output_rmse: 0.5072\n",
      "Epoch 118/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.2657 - output_loss: 0.2657 - output_rmse: 0.5155 - val_loss: 0.2690 - val_output_loss: 0.2690 - val_output_rmse: 0.5187\n",
      "Epoch 119/1000\n",
      "26/26 [==============================] - 6s 230ms/step - loss: 0.2577 - output_loss: 0.2577 - output_rmse: 0.5076 - val_loss: 0.2618 - val_output_loss: 0.2618 - val_output_rmse: 0.5117\n",
      "Epoch 120/1000\n",
      "26/26 [==============================] - 6s 223ms/step - loss: 0.2579 - output_loss: 0.2579 - output_rmse: 0.5078 - val_loss: 0.2770 - val_output_loss: 0.2770 - val_output_rmse: 0.5263\n",
      "Epoch 121/1000\n",
      "26/26 [==============================] - 6s 235ms/step - loss: 0.2567 - output_loss: 0.2567 - output_rmse: 0.5066 - val_loss: 0.2685 - val_output_loss: 0.2685 - val_output_rmse: 0.5182\n",
      "Epoch 122/1000\n",
      "26/26 [==============================] - 6s 209ms/step - loss: 0.2599 - output_loss: 0.2599 - output_rmse: 0.5098 - val_loss: 0.2630 - val_output_loss: 0.2630 - val_output_rmse: 0.5128\n",
      "Epoch 123/1000\n",
      "26/26 [==============================] - 6s 220ms/step - loss: 0.2672 - output_loss: 0.2672 - output_rmse: 0.5169 - val_loss: 0.2754 - val_output_loss: 0.2754 - val_output_rmse: 0.5248\n",
      "Epoch 124/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.2718 - output_loss: 0.2718 - output_rmse: 0.5214 - val_loss: 0.2589 - val_output_loss: 0.2589 - val_output_rmse: 0.5088\n",
      "Epoch 125/1000\n",
      "26/26 [==============================] - 6s 227ms/step - loss: 0.2494 - output_loss: 0.2494 - output_rmse: 0.4994 - val_loss: 0.2640 - val_output_loss: 0.2640 - val_output_rmse: 0.5138\n",
      "Epoch 126/1000\n",
      "26/26 [==============================] - 6s 227ms/step - loss: 0.2479 - output_loss: 0.2479 - output_rmse: 0.4979 - val_loss: 0.2630 - val_output_loss: 0.2630 - val_output_rmse: 0.5129\n",
      "Epoch 127/1000\n",
      "26/26 [==============================] - 6s 233ms/step - loss: 0.2484 - output_loss: 0.2484 - output_rmse: 0.4984 - val_loss: 0.2596 - val_output_loss: 0.2596 - val_output_rmse: 0.5095\n",
      "Epoch 128/1000\n",
      "26/26 [==============================] - 6s 221ms/step - loss: 0.2516 - output_loss: 0.2516 - output_rmse: 0.5016 - val_loss: 0.2547 - val_output_loss: 0.2547 - val_output_rmse: 0.5047\n",
      "Epoch 129/1000\n",
      "26/26 [==============================] - 6s 221ms/step - loss: 0.2540 - output_loss: 0.2540 - output_rmse: 0.5040 - val_loss: 0.2630 - val_output_loss: 0.2630 - val_output_rmse: 0.5128\n"
     ]
    }
   ],
   "source": [
    "ft_linear_encoder = FTTransformerEncoder(\n",
    "    numerical_features = NUMERIC_FEATURES,\n",
    "    categorical_features = [],\n",
    "    numerical_data = X_train[NUMERIC_FEATURES].values,\n",
    "    categorical_data =None, # No categorical data\n",
    "    y = None,\n",
    "    numerical_embedding_type='linear',\n",
    "    embedding_dim=64,\n",
    "    depth=3,\n",
    "    heads=6,\n",
    "    attn_dropout=0.3,\n",
    "    ff_dropout=0.3,\n",
    "    explainable=True\n",
    ")\n",
    "\n",
    "# Pass th encoder to the model\n",
    "ft_linear_transformer = FTTransformer(\n",
    "    encoder=ft_linear_encoder,\n",
    "    out_dim=1,\n",
    "    out_activation=\"relu\",\n",
    ")\n",
    "\n",
    "LEARNING_RATE = 0.001\n",
    "WEIGHT_DECAY = 0.0001\n",
    "NUM_EPOCHS = 1000\n",
    "\n",
    "optimizer = tfa.optimizers.AdamW(\n",
    "        learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY\n",
    "    )\n",
    "\n",
    "ft_linear_transformer.compile(\n",
    "    optimizer = optimizer,\n",
    "    loss = {\"output\": tf.keras.losses.MeanSquaredError(name='mse'), \"importances\": None},\n",
    "    metrics= {\"output\": [tf.keras.metrics.RootMeanSquaredError(name='rmse')], \"importances\": None},\n",
    ")\n",
    "\n",
    "early = EarlyStopping(monitor=\"val_output_loss\", mode=\"min\", patience=16, restore_best_weights=True)\n",
    "callback_list = [early]\n",
    "\n",
    "ft_linear_history = ft_linear_transformer.fit(\n",
    "    train_dataset, \n",
    "    epochs=NUM_EPOCHS, \n",
    "    validation_data=val_dataset,\n",
    "    callbacks=callback_list\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56969cbe-ad46-4ae5-b7df-dbfce2a1053c",
   "metadata": {},
   "source": [
    "## FT Transformer - Periodic Numerical Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e29156d1-7d28-439b-9e00-2a98e38f1fb1",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "26/26 [==============================] - 11s 259ms/step - loss: 1.2407 - output_loss: 1.2407 - output_rmse: 1.1139 - val_loss: 0.6024 - val_output_loss: 0.6024 - val_output_rmse: 0.7761\n",
      "Epoch 2/1000\n",
      "26/26 [==============================] - 6s 245ms/step - loss: 0.5353 - output_loss: 0.5353 - output_rmse: 0.7316 - val_loss: 0.4126 - val_output_loss: 0.4126 - val_output_rmse: 0.6424\n",
      "Epoch 3/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.4323 - output_loss: 0.4323 - output_rmse: 0.6575 - val_loss: 0.3607 - val_output_loss: 0.3607 - val_output_rmse: 0.6006\n",
      "Epoch 4/1000\n",
      "26/26 [==============================] - 6s 238ms/step - loss: 0.3879 - output_loss: 0.3879 - output_rmse: 0.6228 - val_loss: 0.3430 - val_output_loss: 0.3430 - val_output_rmse: 0.5857\n",
      "Epoch 5/1000\n",
      "26/26 [==============================] - 6s 241ms/step - loss: 0.3541 - output_loss: 0.3541 - output_rmse: 0.5951 - val_loss: 0.3129 - val_output_loss: 0.3129 - val_output_rmse: 0.5594\n",
      "Epoch 6/1000\n",
      "26/26 [==============================] - 6s 236ms/step - loss: 0.3288 - output_loss: 0.3288 - output_rmse: 0.5734 - val_loss: 0.3024 - val_output_loss: 0.3024 - val_output_rmse: 0.5499\n",
      "Epoch 7/1000\n",
      "26/26 [==============================] - 6s 238ms/step - loss: 0.3163 - output_loss: 0.3163 - output_rmse: 0.5624 - val_loss: 0.3017 - val_output_loss: 0.3017 - val_output_rmse: 0.5493\n",
      "Epoch 8/1000\n",
      "26/26 [==============================] - 6s 239ms/step - loss: 0.2997 - output_loss: 0.2997 - output_rmse: 0.5474 - val_loss: 0.2832 - val_output_loss: 0.2832 - val_output_rmse: 0.5322\n",
      "Epoch 9/1000\n",
      "26/26 [==============================] - 6s 232ms/step - loss: 0.2930 - output_loss: 0.2930 - output_rmse: 0.5413 - val_loss: 0.2840 - val_output_loss: 0.2840 - val_output_rmse: 0.5329\n",
      "Epoch 10/1000\n",
      "26/26 [==============================] - 6s 246ms/step - loss: 0.2797 - output_loss: 0.2797 - output_rmse: 0.5289 - val_loss: 0.2738 - val_output_loss: 0.2738 - val_output_rmse: 0.5233\n",
      "Epoch 11/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.2741 - output_loss: 0.2741 - output_rmse: 0.5236 - val_loss: 0.2676 - val_output_loss: 0.2676 - val_output_rmse: 0.5173\n",
      "Epoch 12/1000\n",
      "26/26 [==============================] - 6s 230ms/step - loss: 0.2689 - output_loss: 0.2689 - output_rmse: 0.5185 - val_loss: 0.2651 - val_output_loss: 0.2651 - val_output_rmse: 0.5149\n",
      "Epoch 13/1000\n",
      "26/26 [==============================] - 6s 230ms/step - loss: 0.2589 - output_loss: 0.2589 - output_rmse: 0.5088 - val_loss: 0.2551 - val_output_loss: 0.2551 - val_output_rmse: 0.5051\n",
      "Epoch 14/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.2547 - output_loss: 0.2547 - output_rmse: 0.5047 - val_loss: 0.2624 - val_output_loss: 0.2624 - val_output_rmse: 0.5123\n",
      "Epoch 15/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.2456 - output_loss: 0.2456 - output_rmse: 0.4956 - val_loss: 0.2478 - val_output_loss: 0.2478 - val_output_rmse: 0.4978\n",
      "Epoch 16/1000\n",
      "26/26 [==============================] - 6s 240ms/step - loss: 0.2466 - output_loss: 0.2466 - output_rmse: 0.4966 - val_loss: 0.2423 - val_output_loss: 0.2423 - val_output_rmse: 0.4923\n",
      "Epoch 17/1000\n",
      "26/26 [==============================] - 6s 228ms/step - loss: 0.2413 - output_loss: 0.2413 - output_rmse: 0.4912 - val_loss: 0.2591 - val_output_loss: 0.2591 - val_output_rmse: 0.5090\n",
      "Epoch 18/1000\n",
      "26/26 [==============================] - 6s 232ms/step - loss: 0.2378 - output_loss: 0.2378 - output_rmse: 0.4876 - val_loss: 0.2442 - val_output_loss: 0.2442 - val_output_rmse: 0.4942\n",
      "Epoch 19/1000\n",
      "26/26 [==============================] - 6s 238ms/step - loss: 0.2283 - output_loss: 0.2283 - output_rmse: 0.4778 - val_loss: 0.2331 - val_output_loss: 0.2331 - val_output_rmse: 0.4828\n",
      "Epoch 20/1000\n",
      "26/26 [==============================] - 6s 235ms/step - loss: 0.2221 - output_loss: 0.2221 - output_rmse: 0.4712 - val_loss: 0.2320 - val_output_loss: 0.2320 - val_output_rmse: 0.4816\n",
      "Epoch 21/1000\n",
      "26/26 [==============================] - 6s 230ms/step - loss: 0.2190 - output_loss: 0.2190 - output_rmse: 0.4680 - val_loss: 0.2368 - val_output_loss: 0.2368 - val_output_rmse: 0.4866\n",
      "Epoch 22/1000\n",
      "26/26 [==============================] - 6s 228ms/step - loss: 0.2206 - output_loss: 0.2206 - output_rmse: 0.4697 - val_loss: 0.2294 - val_output_loss: 0.2294 - val_output_rmse: 0.4789\n",
      "Epoch 23/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.2163 - output_loss: 0.2163 - output_rmse: 0.4651 - val_loss: 0.2380 - val_output_loss: 0.2380 - val_output_rmse: 0.4878\n",
      "Epoch 24/1000\n",
      "26/26 [==============================] - 6s 226ms/step - loss: 0.2129 - output_loss: 0.2129 - output_rmse: 0.4615 - val_loss: 0.2356 - val_output_loss: 0.2356 - val_output_rmse: 0.4854\n",
      "Epoch 25/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.2085 - output_loss: 0.2085 - output_rmse: 0.4566 - val_loss: 0.2350 - val_output_loss: 0.2350 - val_output_rmse: 0.4847\n",
      "Epoch 26/1000\n",
      "26/26 [==============================] - 6s 230ms/step - loss: 0.2054 - output_loss: 0.2054 - output_rmse: 0.4532 - val_loss: 0.2344 - val_output_loss: 0.2344 - val_output_rmse: 0.4841\n",
      "Epoch 27/1000\n",
      "26/26 [==============================] - 6s 232ms/step - loss: 0.2041 - output_loss: 0.2041 - output_rmse: 0.4518 - val_loss: 0.2299 - val_output_loss: 0.2299 - val_output_rmse: 0.4795\n",
      "Epoch 28/1000\n",
      "26/26 [==============================] - 6s 228ms/step - loss: 0.2053 - output_loss: 0.2053 - output_rmse: 0.4531 - val_loss: 0.2346 - val_output_loss: 0.2346 - val_output_rmse: 0.4843\n",
      "Epoch 29/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.1999 - output_loss: 0.1999 - output_rmse: 0.4471 - val_loss: 0.2312 - val_output_loss: 0.2312 - val_output_rmse: 0.4809\n",
      "Epoch 30/1000\n",
      "26/26 [==============================] - 6s 235ms/step - loss: 0.1979 - output_loss: 0.1979 - output_rmse: 0.4449 - val_loss: 0.2287 - val_output_loss: 0.2287 - val_output_rmse: 0.4782\n",
      "Epoch 31/1000\n",
      "26/26 [==============================] - 6s 240ms/step - loss: 0.1909 - output_loss: 0.1909 - output_rmse: 0.4369 - val_loss: 0.2397 - val_output_loss: 0.2397 - val_output_rmse: 0.4896\n",
      "Epoch 32/1000\n",
      "26/26 [==============================] - 6s 238ms/step - loss: 0.1952 - output_loss: 0.1952 - output_rmse: 0.4418 - val_loss: 0.2251 - val_output_loss: 0.2251 - val_output_rmse: 0.4745\n",
      "Epoch 33/1000\n",
      "26/26 [==============================] - 6s 246ms/step - loss: 0.1900 - output_loss: 0.1900 - output_rmse: 0.4359 - val_loss: 0.2297 - val_output_loss: 0.2297 - val_output_rmse: 0.4792\n",
      "Epoch 34/1000\n",
      "26/26 [==============================] - 6s 237ms/step - loss: 0.1891 - output_loss: 0.1891 - output_rmse: 0.4348 - val_loss: 0.2364 - val_output_loss: 0.2364 - val_output_rmse: 0.4863\n",
      "Epoch 35/1000\n",
      "26/26 [==============================] - 6s 231ms/step - loss: 0.1883 - output_loss: 0.1883 - output_rmse: 0.4339 - val_loss: 0.2250 - val_output_loss: 0.2250 - val_output_rmse: 0.4743\n",
      "Epoch 36/1000\n",
      "26/26 [==============================] - 6s 230ms/step - loss: 0.1850 - output_loss: 0.1850 - output_rmse: 0.4301 - val_loss: 0.2279 - val_output_loss: 0.2279 - val_output_rmse: 0.4774\n",
      "Epoch 37/1000\n",
      "26/26 [==============================] - 6s 228ms/step - loss: 0.1819 - output_loss: 0.1819 - output_rmse: 0.4264 - val_loss: 0.2227 - val_output_loss: 0.2227 - val_output_rmse: 0.4719\n",
      "Epoch 38/1000\n",
      "26/26 [==============================] - 6s 237ms/step - loss: 0.1808 - output_loss: 0.1808 - output_rmse: 0.4252 - val_loss: 0.2347 - val_output_loss: 0.2347 - val_output_rmse: 0.4844\n",
      "Epoch 39/1000\n",
      "26/26 [==============================] - 8s 320ms/step - loss: 0.1821 - output_loss: 0.1821 - output_rmse: 0.4267 - val_loss: 0.2348 - val_output_loss: 0.2348 - val_output_rmse: 0.4845\n",
      "Epoch 40/1000\n",
      "26/26 [==============================] - 6s 223ms/step - loss: 0.1742 - output_loss: 0.1742 - output_rmse: 0.4174 - val_loss: 0.2212 - val_output_loss: 0.2212 - val_output_rmse: 0.4704\n",
      "Epoch 41/1000\n",
      "26/26 [==============================] - 7s 252ms/step - loss: 0.1740 - output_loss: 0.1740 - output_rmse: 0.4172 - val_loss: 0.2316 - val_output_loss: 0.2316 - val_output_rmse: 0.4813\n",
      "Epoch 42/1000\n",
      "26/26 [==============================] - 6s 235ms/step - loss: 0.1730 - output_loss: 0.1730 - output_rmse: 0.4160 - val_loss: 0.2302 - val_output_loss: 0.2302 - val_output_rmse: 0.4798\n",
      "Epoch 43/1000\n",
      "26/26 [==============================] - 7s 251ms/step - loss: 0.1702 - output_loss: 0.1702 - output_rmse: 0.4126 - val_loss: 0.2322 - val_output_loss: 0.2322 - val_output_rmse: 0.4819\n",
      "Epoch 44/1000\n",
      "26/26 [==============================] - 7s 268ms/step - loss: 0.1712 - output_loss: 0.1712 - output_rmse: 0.4138 - val_loss: 0.2409 - val_output_loss: 0.2409 - val_output_rmse: 0.4908\n",
      "Epoch 45/1000\n",
      "26/26 [==============================] - 6s 246ms/step - loss: 0.1658 - output_loss: 0.1658 - output_rmse: 0.4071 - val_loss: 0.2285 - val_output_loss: 0.2285 - val_output_rmse: 0.4780\n",
      "Epoch 46/1000\n",
      "26/26 [==============================] - 6s 237ms/step - loss: 0.1639 - output_loss: 0.1639 - output_rmse: 0.4049 - val_loss: 0.2276 - val_output_loss: 0.2276 - val_output_rmse: 0.4770\n",
      "Epoch 47/1000\n",
      "26/26 [==============================] - 6s 222ms/step - loss: 0.1656 - output_loss: 0.1656 - output_rmse: 0.4069 - val_loss: 0.2269 - val_output_loss: 0.2269 - val_output_rmse: 0.4763\n",
      "Epoch 48/1000\n",
      "26/26 [==============================] - 6s 232ms/step - loss: 0.1671 - output_loss: 0.1671 - output_rmse: 0.4088 - val_loss: 0.2274 - val_output_loss: 0.2274 - val_output_rmse: 0.4769\n",
      "Epoch 49/1000\n",
      "26/26 [==============================] - 6s 228ms/step - loss: 0.1649 - output_loss: 0.1649 - output_rmse: 0.4060 - val_loss: 0.2293 - val_output_loss: 0.2293 - val_output_rmse: 0.4788\n",
      "Epoch 50/1000\n",
      "26/26 [==============================] - 6s 240ms/step - loss: 0.1631 - output_loss: 0.1631 - output_rmse: 0.4038 - val_loss: 0.2289 - val_output_loss: 0.2289 - val_output_rmse: 0.4784\n",
      "Epoch 51/1000\n",
      "26/26 [==============================] - 7s 252ms/step - loss: 0.1609 - output_loss: 0.1609 - output_rmse: 0.4011 - val_loss: 0.2238 - val_output_loss: 0.2238 - val_output_rmse: 0.4731\n",
      "Epoch 52/1000\n",
      "26/26 [==============================] - 7s 248ms/step - loss: 0.1575 - output_loss: 0.1575 - output_rmse: 0.3969 - val_loss: 0.2229 - val_output_loss: 0.2229 - val_output_rmse: 0.4721\n",
      "Epoch 53/1000\n",
      "26/26 [==============================] - 6s 243ms/step - loss: 0.1609 - output_loss: 0.1609 - output_rmse: 0.4012 - val_loss: 0.2400 - val_output_loss: 0.2400 - val_output_rmse: 0.4899\n",
      "Epoch 54/1000\n",
      "26/26 [==============================] - 7s 248ms/step - loss: 0.1548 - output_loss: 0.1548 - output_rmse: 0.3935 - val_loss: 0.2303 - val_output_loss: 0.2303 - val_output_rmse: 0.4799\n",
      "Epoch 55/1000\n",
      "26/26 [==============================] - 6s 242ms/step - loss: 0.1566 - output_loss: 0.1566 - output_rmse: 0.3957 - val_loss: 0.2180 - val_output_loss: 0.2180 - val_output_rmse: 0.4669\n",
      "Epoch 56/1000\n",
      "26/26 [==============================] - 7s 252ms/step - loss: 0.1513 - output_loss: 0.1513 - output_rmse: 0.3890 - val_loss: 0.2211 - val_output_loss: 0.2211 - val_output_rmse: 0.4703\n",
      "Epoch 57/1000\n",
      "26/26 [==============================] - 6s 238ms/step - loss: 0.1546 - output_loss: 0.1546 - output_rmse: 0.3932 - val_loss: 0.2226 - val_output_loss: 0.2226 - val_output_rmse: 0.4718\n",
      "Epoch 58/1000\n",
      "26/26 [==============================] - 6s 241ms/step - loss: 0.1513 - output_loss: 0.1513 - output_rmse: 0.3890 - val_loss: 0.2228 - val_output_loss: 0.2228 - val_output_rmse: 0.4720\n",
      "Epoch 59/1000\n",
      "26/26 [==============================] - 6s 246ms/step - loss: 0.1504 - output_loss: 0.1504 - output_rmse: 0.3879 - val_loss: 0.2208 - val_output_loss: 0.2208 - val_output_rmse: 0.4699\n",
      "Epoch 60/1000\n",
      "26/26 [==============================] - 6s 239ms/step - loss: 0.1465 - output_loss: 0.1465 - output_rmse: 0.3828 - val_loss: 0.2309 - val_output_loss: 0.2309 - val_output_rmse: 0.4805\n",
      "Epoch 61/1000\n",
      "26/26 [==============================] - 6s 240ms/step - loss: 0.1473 - output_loss: 0.1473 - output_rmse: 0.3838 - val_loss: 0.2387 - val_output_loss: 0.2387 - val_output_rmse: 0.4886\n",
      "Epoch 62/1000\n",
      "26/26 [==============================] - 7s 251ms/step - loss: 0.1471 - output_loss: 0.1471 - output_rmse: 0.3836 - val_loss: 0.2297 - val_output_loss: 0.2297 - val_output_rmse: 0.4793\n",
      "Epoch 63/1000\n",
      "26/26 [==============================] - 7s 249ms/step - loss: 0.1448 - output_loss: 0.1448 - output_rmse: 0.3805 - val_loss: 0.2314 - val_output_loss: 0.2314 - val_output_rmse: 0.4811\n",
      "Epoch 64/1000\n",
      "26/26 [==============================] - 6s 242ms/step - loss: 0.1428 - output_loss: 0.1428 - output_rmse: 0.3779 - val_loss: 0.2265 - val_output_loss: 0.2265 - val_output_rmse: 0.4759\n",
      "Epoch 65/1000\n",
      "26/26 [==============================] - 6s 233ms/step - loss: 0.1395 - output_loss: 0.1395 - output_rmse: 0.3735 - val_loss: 0.2355 - val_output_loss: 0.2355 - val_output_rmse: 0.4852\n",
      "Epoch 66/1000\n",
      "26/26 [==============================] - 6s 230ms/step - loss: 0.1425 - output_loss: 0.1425 - output_rmse: 0.3775 - val_loss: 0.2320 - val_output_loss: 0.2320 - val_output_rmse: 0.4817\n",
      "Epoch 67/1000\n",
      "26/26 [==============================] - 7s 252ms/step - loss: 0.1378 - output_loss: 0.1378 - output_rmse: 0.3712 - val_loss: 0.2306 - val_output_loss: 0.2306 - val_output_rmse: 0.4802\n",
      "Epoch 68/1000\n",
      "26/26 [==============================] - 7s 252ms/step - loss: 0.1375 - output_loss: 0.1375 - output_rmse: 0.3708 - val_loss: 0.2264 - val_output_loss: 0.2264 - val_output_rmse: 0.4758\n",
      "Epoch 69/1000\n",
      "26/26 [==============================] - 6s 242ms/step - loss: 0.1373 - output_loss: 0.1373 - output_rmse: 0.3705 - val_loss: 0.2245 - val_output_loss: 0.2245 - val_output_rmse: 0.4739\n",
      "Epoch 70/1000\n",
      "26/26 [==============================] - 6s 238ms/step - loss: 0.1385 - output_loss: 0.1385 - output_rmse: 0.3722 - val_loss: 0.2299 - val_output_loss: 0.2299 - val_output_rmse: 0.4795\n",
      "Epoch 71/1000\n",
      "26/26 [==============================] - 7s 249ms/step - loss: 0.1361 - output_loss: 0.1361 - output_rmse: 0.3689 - val_loss: 0.2340 - val_output_loss: 0.2340 - val_output_rmse: 0.4838\n"
     ]
    }
   ],
   "source": [
    "ft_periodic_encoder = FTTransformerEncoder(\n",
    "    numerical_features = NUMERIC_FEATURES,\n",
    "    categorical_features = [],\n",
    "    numerical_data = X_train[NUMERIC_FEATURES].values,\n",
    "    categorical_data = None,\n",
    "    y = None,\n",
    "    numerical_embedding_type='periodic',\n",
    "    numerical_bins=128,\n",
    "    embedding_dim=64,\n",
    "    depth=3,\n",
    "    heads=6,\n",
    "    attn_dropout=0.3,\n",
    "    ff_dropout=0.3,\n",
    "    explainable=True\n",
    ")\n",
    "\n",
    "# Pass th encoder to the model\n",
    "ft_periodic_transformer = FTTransformer(\n",
    "    encoder=ft_periodic_encoder,\n",
    "    out_dim=1,\n",
    "    out_activation=\"relu\",\n",
    ")\n",
    "\n",
    "LEARNING_RATE = 0.001\n",
    "WEIGHT_DECAY = 0.0001\n",
    "NUM_EPOCHS = 1000\n",
    "\n",
    "optimizer = tfa.optimizers.AdamW(\n",
    "        learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY\n",
    "    )\n",
    "\n",
    "ft_periodic_transformer.compile(\n",
    "    optimizer = optimizer,\n",
    "    loss = {\"output\": tf.keras.losses.MeanSquaredError(name='mse'), \"importances\": None},\n",
    "    metrics= {\"output\": [tf.keras.metrics.RootMeanSquaredError(name='rmse')], \"importances\": None},\n",
    ")\n",
    "\n",
    "early = EarlyStopping(monitor=\"val_output_loss\", mode=\"min\", patience=16, restore_best_weights=True)\n",
    "callback_list = [early]\n",
    "\n",
    "ft_periodic_history = ft_periodic_transformer.fit(\n",
    "    train_dataset, \n",
    "    epochs=NUM_EPOCHS, \n",
    "    validation_data=val_dataset,\n",
    "    callbacks=callback_list\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d17ead-5286-430b-bdb7-bb1ac5607ea8",
   "metadata": {},
   "source": [
    "## FT Transformer - PLE Quantile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "167eaf14-79c9-428f-965f-d4133d073462",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "26/26 [==============================] - 191s 3s/step - loss: 1.2836 - output_loss: 1.2836 - output_rmse: 1.1329 - val_loss: 0.4857 - val_output_loss: 0.4857 - val_output_rmse: 0.6969\n",
      "Epoch 2/1000\n",
      "26/26 [==============================] - 8s 289ms/step - loss: 0.4944 - output_loss: 0.4944 - output_rmse: 0.7032 - val_loss: 0.3911 - val_output_loss: 0.3911 - val_output_rmse: 0.6254\n",
      "Epoch 3/1000\n",
      "26/26 [==============================] - 7s 284ms/step - loss: 0.3846 - output_loss: 0.3846 - output_rmse: 0.6202 - val_loss: 0.3391 - val_output_loss: 0.3391 - val_output_rmse: 0.5823\n",
      "Epoch 4/1000\n",
      "26/26 [==============================] - 7s 272ms/step - loss: 0.3544 - output_loss: 0.3544 - output_rmse: 0.5953 - val_loss: 0.3212 - val_output_loss: 0.3212 - val_output_rmse: 0.5668\n",
      "Epoch 5/1000\n",
      "26/26 [==============================] - 7s 272ms/step - loss: 0.3272 - output_loss: 0.3272 - output_rmse: 0.5720 - val_loss: 0.2962 - val_output_loss: 0.2962 - val_output_rmse: 0.5443\n",
      "Epoch 6/1000\n",
      "26/26 [==============================] - 7s 274ms/step - loss: 0.3298 - output_loss: 0.3298 - output_rmse: 0.5743 - val_loss: 0.2850 - val_output_loss: 0.2850 - val_output_rmse: 0.5339\n",
      "Epoch 7/1000\n",
      "26/26 [==============================] - 7s 269ms/step - loss: 0.3015 - output_loss: 0.3015 - output_rmse: 0.5491 - val_loss: 0.2822 - val_output_loss: 0.2822 - val_output_rmse: 0.5312\n",
      "Epoch 8/1000\n",
      "26/26 [==============================] - 7s 283ms/step - loss: 0.2922 - output_loss: 0.2922 - output_rmse: 0.5405 - val_loss: 0.2719 - val_output_loss: 0.2719 - val_output_rmse: 0.5214\n",
      "Epoch 9/1000\n",
      "26/26 [==============================] - 8s 290ms/step - loss: 0.2806 - output_loss: 0.2806 - output_rmse: 0.5297 - val_loss: 0.2698 - val_output_loss: 0.2698 - val_output_rmse: 0.5194\n",
      "Epoch 10/1000\n",
      "26/26 [==============================] - 7s 278ms/step - loss: 0.2699 - output_loss: 0.2699 - output_rmse: 0.5196 - val_loss: 0.2548 - val_output_loss: 0.2548 - val_output_rmse: 0.5048\n",
      "Epoch 11/1000\n",
      "26/26 [==============================] - 7s 274ms/step - loss: 0.2626 - output_loss: 0.2626 - output_rmse: 0.5124 - val_loss: 0.2889 - val_output_loss: 0.2889 - val_output_rmse: 0.5375\n",
      "Epoch 12/1000\n",
      "26/26 [==============================] - 7s 276ms/step - loss: 0.2554 - output_loss: 0.2554 - output_rmse: 0.5053 - val_loss: 0.2409 - val_output_loss: 0.2409 - val_output_rmse: 0.4908\n",
      "Epoch 13/1000\n",
      "26/26 [==============================] - 7s 270ms/step - loss: 0.2481 - output_loss: 0.2481 - output_rmse: 0.4981 - val_loss: 0.2448 - val_output_loss: 0.2448 - val_output_rmse: 0.4948\n",
      "Epoch 14/1000\n",
      "26/26 [==============================] - 7s 283ms/step - loss: 0.2408 - output_loss: 0.2408 - output_rmse: 0.4907 - val_loss: 0.2598 - val_output_loss: 0.2598 - val_output_rmse: 0.5097\n",
      "Epoch 15/1000\n",
      "26/26 [==============================] - 7s 280ms/step - loss: 0.2330 - output_loss: 0.2330 - output_rmse: 0.4827 - val_loss: 0.2340 - val_output_loss: 0.2340 - val_output_rmse: 0.4838\n",
      "Epoch 16/1000\n",
      "26/26 [==============================] - 7s 274ms/step - loss: 0.2277 - output_loss: 0.2277 - output_rmse: 0.4772 - val_loss: 0.2311 - val_output_loss: 0.2311 - val_output_rmse: 0.4807\n",
      "Epoch 17/1000\n",
      "26/26 [==============================] - 8s 291ms/step - loss: 0.2265 - output_loss: 0.2265 - output_rmse: 0.4760 - val_loss: 0.2266 - val_output_loss: 0.2266 - val_output_rmse: 0.4760\n",
      "Epoch 18/1000\n",
      "26/26 [==============================] - 7s 274ms/step - loss: 0.2233 - output_loss: 0.2233 - output_rmse: 0.4726 - val_loss: 0.2424 - val_output_loss: 0.2424 - val_output_rmse: 0.4923\n",
      "Epoch 19/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.2210 - output_loss: 0.2210 - output_rmse: 0.4701 - val_loss: 0.2247 - val_output_loss: 0.2247 - val_output_rmse: 0.4740\n",
      "Epoch 20/1000\n",
      "26/26 [==============================] - 7s 279ms/step - loss: 0.2152 - output_loss: 0.2152 - output_rmse: 0.4639 - val_loss: 0.2561 - val_output_loss: 0.2561 - val_output_rmse: 0.5061\n",
      "Epoch 21/1000\n",
      "26/26 [==============================] - 7s 275ms/step - loss: 0.2238 - output_loss: 0.2238 - output_rmse: 0.4730 - val_loss: 0.2261 - val_output_loss: 0.2261 - val_output_rmse: 0.4755\n",
      "Epoch 22/1000\n",
      "26/26 [==============================] - 7s 269ms/step - loss: 0.2136 - output_loss: 0.2136 - output_rmse: 0.4621 - val_loss: 0.2294 - val_output_loss: 0.2294 - val_output_rmse: 0.4790\n",
      "Epoch 23/1000\n",
      "26/26 [==============================] - 7s 278ms/step - loss: 0.2091 - output_loss: 0.2091 - output_rmse: 0.4572 - val_loss: 0.2231 - val_output_loss: 0.2231 - val_output_rmse: 0.4724\n",
      "Epoch 24/1000\n",
      "26/26 [==============================] - 8s 289ms/step - loss: 0.2010 - output_loss: 0.2010 - output_rmse: 0.4484 - val_loss: 0.2244 - val_output_loss: 0.2244 - val_output_rmse: 0.4737\n",
      "Epoch 25/1000\n",
      "26/26 [==============================] - 8s 320ms/step - loss: 0.1986 - output_loss: 0.1986 - output_rmse: 0.4457 - val_loss: 0.2174 - val_output_loss: 0.2174 - val_output_rmse: 0.4662\n",
      "Epoch 26/1000\n",
      "26/26 [==============================] - 7s 275ms/step - loss: 0.2007 - output_loss: 0.2007 - output_rmse: 0.4480 - val_loss: 0.2135 - val_output_loss: 0.2135 - val_output_rmse: 0.4621\n",
      "Epoch 27/1000\n",
      "26/26 [==============================] - 7s 283ms/step - loss: 0.2003 - output_loss: 0.2003 - output_rmse: 0.4476 - val_loss: 0.2185 - val_output_loss: 0.2185 - val_output_rmse: 0.4674\n",
      "Epoch 28/1000\n",
      "26/26 [==============================] - 8s 294ms/step - loss: 0.1987 - output_loss: 0.1987 - output_rmse: 0.4458 - val_loss: 0.2312 - val_output_loss: 0.2312 - val_output_rmse: 0.4808\n",
      "Epoch 29/1000\n",
      "26/26 [==============================] - 7s 266ms/step - loss: 0.1977 - output_loss: 0.1977 - output_rmse: 0.4446 - val_loss: 0.2130 - val_output_loss: 0.2130 - val_output_rmse: 0.4615\n",
      "Epoch 30/1000\n",
      "26/26 [==============================] - 7s 274ms/step - loss: 0.1872 - output_loss: 0.1872 - output_rmse: 0.4327 - val_loss: 0.2211 - val_output_loss: 0.2211 - val_output_rmse: 0.4702\n",
      "Epoch 31/1000\n",
      "26/26 [==============================] - 7s 279ms/step - loss: 0.1867 - output_loss: 0.1867 - output_rmse: 0.4321 - val_loss: 0.2102 - val_output_loss: 0.2102 - val_output_rmse: 0.4585\n",
      "Epoch 32/1000\n",
      "26/26 [==============================] - 7s 266ms/step - loss: 0.1870 - output_loss: 0.1870 - output_rmse: 0.4324 - val_loss: 0.2166 - val_output_loss: 0.2166 - val_output_rmse: 0.4654\n",
      "Epoch 33/1000\n",
      "26/26 [==============================] - 7s 274ms/step - loss: 0.1826 - output_loss: 0.1826 - output_rmse: 0.4273 - val_loss: 0.2183 - val_output_loss: 0.2183 - val_output_rmse: 0.4672\n",
      "Epoch 34/1000\n",
      "26/26 [==============================] - 7s 275ms/step - loss: 0.1793 - output_loss: 0.1793 - output_rmse: 0.4234 - val_loss: 0.2127 - val_output_loss: 0.2127 - val_output_rmse: 0.4612\n",
      "Epoch 35/1000\n",
      "26/26 [==============================] - 7s 278ms/step - loss: 0.1828 - output_loss: 0.1828 - output_rmse: 0.4276 - val_loss: 0.2250 - val_output_loss: 0.2250 - val_output_rmse: 0.4744\n",
      "Epoch 36/1000\n",
      "26/26 [==============================] - 7s 280ms/step - loss: 0.1798 - output_loss: 0.1798 - output_rmse: 0.4240 - val_loss: 0.2110 - val_output_loss: 0.2110 - val_output_rmse: 0.4594\n",
      "Epoch 37/1000\n",
      "26/26 [==============================] - 7s 284ms/step - loss: 0.1797 - output_loss: 0.1797 - output_rmse: 0.4239 - val_loss: 0.2227 - val_output_loss: 0.2227 - val_output_rmse: 0.4719\n",
      "Epoch 38/1000\n",
      "26/26 [==============================] - 8s 292ms/step - loss: 0.1803 - output_loss: 0.1803 - output_rmse: 0.4247 - val_loss: 0.2155 - val_output_loss: 0.2155 - val_output_rmse: 0.4642\n",
      "Epoch 39/1000\n",
      "26/26 [==============================] - 8s 317ms/step - loss: 0.1804 - output_loss: 0.1804 - output_rmse: 0.4248 - val_loss: 0.2079 - val_output_loss: 0.2079 - val_output_rmse: 0.4559\n",
      "Epoch 40/1000\n",
      "26/26 [==============================] - 8s 288ms/step - loss: 0.1812 - output_loss: 0.1812 - output_rmse: 0.4257 - val_loss: 0.2095 - val_output_loss: 0.2095 - val_output_rmse: 0.4577\n",
      "Epoch 41/1000\n",
      "26/26 [==============================] - 7s 279ms/step - loss: 0.1753 - output_loss: 0.1753 - output_rmse: 0.4187 - val_loss: 0.2144 - val_output_loss: 0.2144 - val_output_rmse: 0.4630\n",
      "Epoch 42/1000\n",
      "26/26 [==============================] - 7s 269ms/step - loss: 0.1701 - output_loss: 0.1701 - output_rmse: 0.4124 - val_loss: 0.2085 - val_output_loss: 0.2085 - val_output_rmse: 0.4566\n",
      "Epoch 43/1000\n",
      "26/26 [==============================] - 7s 253ms/step - loss: 0.1683 - output_loss: 0.1683 - output_rmse: 0.4103 - val_loss: 0.2118 - val_output_loss: 0.2118 - val_output_rmse: 0.4602\n",
      "Epoch 44/1000\n",
      "26/26 [==============================] - 7s 250ms/step - loss: 0.1701 - output_loss: 0.1701 - output_rmse: 0.4124 - val_loss: 0.2156 - val_output_loss: 0.2156 - val_output_rmse: 0.4643\n",
      "Epoch 45/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.1692 - output_loss: 0.1692 - output_rmse: 0.4113 - val_loss: 0.2110 - val_output_loss: 0.2110 - val_output_rmse: 0.4593\n",
      "Epoch 46/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.1734 - output_loss: 0.1734 - output_rmse: 0.4164 - val_loss: 0.2169 - val_output_loss: 0.2169 - val_output_rmse: 0.4657\n",
      "Epoch 47/1000\n",
      "26/26 [==============================] - 7s 266ms/step - loss: 0.1678 - output_loss: 0.1678 - output_rmse: 0.4096 - val_loss: 0.2120 - val_output_loss: 0.2120 - val_output_rmse: 0.4604\n",
      "Epoch 48/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.1641 - output_loss: 0.1641 - output_rmse: 0.4050 - val_loss: 0.2170 - val_output_loss: 0.2170 - val_output_rmse: 0.4659\n",
      "Epoch 49/1000\n",
      "26/26 [==============================] - 7s 255ms/step - loss: 0.1667 - output_loss: 0.1667 - output_rmse: 0.4083 - val_loss: 0.2077 - val_output_loss: 0.2077 - val_output_rmse: 0.4558\n",
      "Epoch 50/1000\n",
      "26/26 [==============================] - 7s 271ms/step - loss: 0.1673 - output_loss: 0.1673 - output_rmse: 0.4090 - val_loss: 0.2251 - val_output_loss: 0.2251 - val_output_rmse: 0.4744\n",
      "Epoch 51/1000\n",
      "26/26 [==============================] - 7s 273ms/step - loss: 0.1673 - output_loss: 0.1673 - output_rmse: 0.4091 - val_loss: 0.2204 - val_output_loss: 0.2204 - val_output_rmse: 0.4695\n",
      "Epoch 52/1000\n",
      "26/26 [==============================] - 7s 273ms/step - loss: 0.1624 - output_loss: 0.1624 - output_rmse: 0.4030 - val_loss: 0.2065 - val_output_loss: 0.2065 - val_output_rmse: 0.4544\n",
      "Epoch 53/1000\n",
      "26/26 [==============================] - 7s 264ms/step - loss: 0.1587 - output_loss: 0.1587 - output_rmse: 0.3983 - val_loss: 0.2187 - val_output_loss: 0.2187 - val_output_rmse: 0.4677\n",
      "Epoch 54/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.1652 - output_loss: 0.1652 - output_rmse: 0.4065 - val_loss: 0.2165 - val_output_loss: 0.2165 - val_output_rmse: 0.4653\n",
      "Epoch 55/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.1585 - output_loss: 0.1585 - output_rmse: 0.3981 - val_loss: 0.2273 - val_output_loss: 0.2273 - val_output_rmse: 0.4768\n",
      "Epoch 56/1000\n",
      "26/26 [==============================] - 7s 263ms/step - loss: 0.1571 - output_loss: 0.1571 - output_rmse: 0.3964 - val_loss: 0.2124 - val_output_loss: 0.2124 - val_output_rmse: 0.4608\n",
      "Epoch 57/1000\n",
      "26/26 [==============================] - 7s 264ms/step - loss: 0.1505 - output_loss: 0.1505 - output_rmse: 0.3879 - val_loss: 0.2165 - val_output_loss: 0.2165 - val_output_rmse: 0.4653\n",
      "Epoch 58/1000\n",
      "26/26 [==============================] - 7s 263ms/step - loss: 0.1573 - output_loss: 0.1573 - output_rmse: 0.3966 - val_loss: 0.2274 - val_output_loss: 0.2274 - val_output_rmse: 0.4768\n",
      "Epoch 59/1000\n",
      "26/26 [==============================] - 7s 258ms/step - loss: 0.1541 - output_loss: 0.1541 - output_rmse: 0.3926 - val_loss: 0.2194 - val_output_loss: 0.2194 - val_output_rmse: 0.4684\n",
      "Epoch 60/1000\n",
      "26/26 [==============================] - 7s 262ms/step - loss: 0.1521 - output_loss: 0.1521 - output_rmse: 0.3900 - val_loss: 0.2198 - val_output_loss: 0.2198 - val_output_rmse: 0.4688\n",
      "Epoch 61/1000\n",
      "26/26 [==============================] - 7s 258ms/step - loss: 0.1537 - output_loss: 0.1537 - output_rmse: 0.3921 - val_loss: 0.2287 - val_output_loss: 0.2287 - val_output_rmse: 0.4782\n",
      "Epoch 62/1000\n",
      "26/26 [==============================] - 7s 255ms/step - loss: 0.1570 - output_loss: 0.1570 - output_rmse: 0.3963 - val_loss: 0.2150 - val_output_loss: 0.2150 - val_output_rmse: 0.4637\n",
      "Epoch 63/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.1483 - output_loss: 0.1483 - output_rmse: 0.3851 - val_loss: 0.2140 - val_output_loss: 0.2140 - val_output_rmse: 0.4626\n",
      "Epoch 64/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.1461 - output_loss: 0.1461 - output_rmse: 0.3822 - val_loss: 0.2185 - val_output_loss: 0.2185 - val_output_rmse: 0.4674\n",
      "Epoch 65/1000\n",
      "26/26 [==============================] - 7s 255ms/step - loss: 0.1440 - output_loss: 0.1440 - output_rmse: 0.3795 - val_loss: 0.2200 - val_output_loss: 0.2200 - val_output_rmse: 0.4691\n",
      "Epoch 66/1000\n",
      "26/26 [==============================] - 7s 261ms/step - loss: 0.1454 - output_loss: 0.1454 - output_rmse: 0.3813 - val_loss: 0.2211 - val_output_loss: 0.2211 - val_output_rmse: 0.4702\n",
      "Epoch 67/1000\n",
      "26/26 [==============================] - 7s 266ms/step - loss: 0.1461 - output_loss: 0.1461 - output_rmse: 0.3822 - val_loss: 0.2148 - val_output_loss: 0.2148 - val_output_rmse: 0.4634\n",
      "Epoch 68/1000\n",
      "26/26 [==============================] - 7s 259ms/step - loss: 0.1436 - output_loss: 0.1436 - output_rmse: 0.3790 - val_loss: 0.2195 - val_output_loss: 0.2195 - val_output_rmse: 0.4685\n",
      "Epoch 69/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.1390 - output_loss: 0.1390 - output_rmse: 0.3728 - val_loss: 0.2192 - val_output_loss: 0.2192 - val_output_rmse: 0.4682\n",
      "Epoch 70/1000\n",
      "26/26 [==============================] - 7s 258ms/step - loss: 0.1427 - output_loss: 0.1427 - output_rmse: 0.3777 - val_loss: 0.2263 - val_output_loss: 0.2263 - val_output_rmse: 0.4757\n",
      "Epoch 71/1000\n",
      "26/26 [==============================] - 7s 253ms/step - loss: 0.1414 - output_loss: 0.1414 - output_rmse: 0.3761 - val_loss: 0.2194 - val_output_loss: 0.2194 - val_output_rmse: 0.4684\n",
      "Epoch 72/1000\n",
      "26/26 [==============================] - 7s 255ms/step - loss: 0.1393 - output_loss: 0.1393 - output_rmse: 0.3733 - val_loss: 0.2117 - val_output_loss: 0.2117 - val_output_rmse: 0.4601\n"
     ]
    }
   ],
   "source": [
    "ft_pleq_encoder = FTTransformerEncoder(\n",
    "    numerical_features = NUMERIC_FEATURES,\n",
    "    categorical_features = [],\n",
    "    numerical_data = X_train[NUMERIC_FEATURES].values,\n",
    "    categorical_data = None,\n",
    "    y = None,\n",
    "    numerical_embedding_type='ple',\n",
    "    numerical_bins=128,\n",
    "    embedding_dim=64,\n",
    "    depth=3,\n",
    "    heads=6,\n",
    "    attn_dropout=0.3,\n",
    "    ff_dropout=0.3,\n",
    "    explainable=True\n",
    ")\n",
    "\n",
    "# Pass the encoder to the model\n",
    "ft_pleq_transformer = FTTransformer(\n",
    "    encoder=ft_pleq_encoder,\n",
    "    out_dim=1,\n",
    "    out_activation=\"relu\",\n",
    ")\n",
    "\n",
    "LEARNING_RATE = 0.001\n",
    "WEIGHT_DECAY = 0.0001\n",
    "NUM_EPOCHS = 1000\n",
    "\n",
    "optimizer = tfa.optimizers.AdamW(\n",
    "        learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY\n",
    "    )\n",
    "\n",
    "ft_pleq_transformer.compile(\n",
    "    optimizer = optimizer,\n",
    "    loss = {\"output\": tf.keras.losses.MeanSquaredError(name='mse'), \"importances\": None},\n",
    "    metrics= {\"output\": [tf.keras.metrics.RootMeanSquaredError(name='rmse')], \"importances\": None},\n",
    ")\n",
    "\n",
    "early = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=20, restore_best_weights=True)\n",
    "callback_list = [early]\n",
    "\n",
    "ft_pleq_history = ft_pleq_transformer.fit(\n",
    "    train_dataset, \n",
    "    epochs=NUM_EPOCHS, \n",
    "    validation_data=val_dataset,\n",
    "    callbacks=callback_list\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0ada4d-9c65-4edc-ad35-8bd50a848a19",
   "metadata": {},
   "source": [
    "## FT Transformer - PLE Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dcdd7125-82cd-4328-84fa-8658c5cf4cd3",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "26/26 [==============================] - 298s 4s/step - loss: 1.3294 - output_loss: 1.3294 - output_rmse: 1.1530 - val_loss: 0.6608 - val_output_loss: 0.6608 - val_output_rmse: 0.8129\n",
      "Epoch 2/1000\n",
      "26/26 [==============================] - 9s 303ms/step - loss: 0.5900 - output_loss: 0.5900 - output_rmse: 0.7681 - val_loss: 0.4128 - val_output_loss: 0.4128 - val_output_rmse: 0.6425\n",
      "Epoch 3/1000\n",
      "26/26 [==============================] - 8s 288ms/step - loss: 0.4200 - output_loss: 0.4200 - output_rmse: 0.6481 - val_loss: 0.3458 - val_output_loss: 0.3458 - val_output_rmse: 0.5881\n",
      "Epoch 4/1000\n",
      "26/26 [==============================] - 8s 297ms/step - loss: 0.3787 - output_loss: 0.3787 - output_rmse: 0.6154 - val_loss: 0.3258 - val_output_loss: 0.3258 - val_output_rmse: 0.5708\n",
      "Epoch 5/1000\n",
      "26/26 [==============================] - 7s 283ms/step - loss: 0.3394 - output_loss: 0.3394 - output_rmse: 0.5826 - val_loss: 0.3331 - val_output_loss: 0.3331 - val_output_rmse: 0.5772\n",
      "Epoch 6/1000\n",
      "26/26 [==============================] - 7s 271ms/step - loss: 0.3221 - output_loss: 0.3221 - output_rmse: 0.5675 - val_loss: 0.3090 - val_output_loss: 0.3090 - val_output_rmse: 0.5559\n",
      "Epoch 7/1000\n",
      "26/26 [==============================] - 7s 280ms/step - loss: 0.3047 - output_loss: 0.3047 - output_rmse: 0.5520 - val_loss: 0.2959 - val_output_loss: 0.2959 - val_output_rmse: 0.5440\n",
      "Epoch 8/1000\n",
      "26/26 [==============================] - 8s 295ms/step - loss: 0.2948 - output_loss: 0.2948 - output_rmse: 0.5429 - val_loss: 0.2946 - val_output_loss: 0.2946 - val_output_rmse: 0.5428\n",
      "Epoch 9/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.2807 - output_loss: 0.2807 - output_rmse: 0.5298 - val_loss: 0.2755 - val_output_loss: 0.2755 - val_output_rmse: 0.5249\n",
      "Epoch 10/1000\n",
      "26/26 [==============================] - 7s 277ms/step - loss: 0.2758 - output_loss: 0.2758 - output_rmse: 0.5252 - val_loss: 0.2732 - val_output_loss: 0.2732 - val_output_rmse: 0.5227\n",
      "Epoch 11/1000\n",
      "26/26 [==============================] - 8s 290ms/step - loss: 0.2667 - output_loss: 0.2667 - output_rmse: 0.5164 - val_loss: 0.2720 - val_output_loss: 0.2720 - val_output_rmse: 0.5216\n",
      "Epoch 12/1000\n",
      "26/26 [==============================] - 7s 262ms/step - loss: 0.2606 - output_loss: 0.2606 - output_rmse: 0.5105 - val_loss: 0.2546 - val_output_loss: 0.2546 - val_output_rmse: 0.5046\n",
      "Epoch 13/1000\n",
      "26/26 [==============================] - 7s 279ms/step - loss: 0.2732 - output_loss: 0.2732 - output_rmse: 0.5227 - val_loss: 0.3019 - val_output_loss: 0.3019 - val_output_rmse: 0.5495\n",
      "Epoch 14/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.2659 - output_loss: 0.2659 - output_rmse: 0.5156 - val_loss: 0.2603 - val_output_loss: 0.2603 - val_output_rmse: 0.5102\n",
      "Epoch 15/1000\n",
      "26/26 [==============================] - 7s 261ms/step - loss: 0.2458 - output_loss: 0.2458 - output_rmse: 0.4958 - val_loss: 0.2555 - val_output_loss: 0.2555 - val_output_rmse: 0.5054\n",
      "Epoch 16/1000\n",
      "26/26 [==============================] - 7s 260ms/step - loss: 0.2438 - output_loss: 0.2438 - output_rmse: 0.4938 - val_loss: 0.2479 - val_output_loss: 0.2479 - val_output_rmse: 0.4978\n",
      "Epoch 17/1000\n",
      "26/26 [==============================] - 7s 252ms/step - loss: 0.2377 - output_loss: 0.2377 - output_rmse: 0.4876 - val_loss: 0.2439 - val_output_loss: 0.2439 - val_output_rmse: 0.4938\n",
      "Epoch 18/1000\n",
      "26/26 [==============================] - 7s 255ms/step - loss: 0.2422 - output_loss: 0.2422 - output_rmse: 0.4922 - val_loss: 0.2465 - val_output_loss: 0.2465 - val_output_rmse: 0.4965\n",
      "Epoch 19/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.2293 - output_loss: 0.2293 - output_rmse: 0.4788 - val_loss: 0.2381 - val_output_loss: 0.2381 - val_output_rmse: 0.4879\n",
      "Epoch 20/1000\n",
      "26/26 [==============================] - 7s 256ms/step - loss: 0.2210 - output_loss: 0.2210 - output_rmse: 0.4701 - val_loss: 0.2447 - val_output_loss: 0.2447 - val_output_rmse: 0.4946\n",
      "Epoch 21/1000\n",
      "26/26 [==============================] - 7s 260ms/step - loss: 0.2219 - output_loss: 0.2219 - output_rmse: 0.4711 - val_loss: 0.2287 - val_output_loss: 0.2287 - val_output_rmse: 0.4782\n",
      "Epoch 22/1000\n",
      "26/26 [==============================] - 7s 261ms/step - loss: 0.2160 - output_loss: 0.2160 - output_rmse: 0.4648 - val_loss: 0.2365 - val_output_loss: 0.2365 - val_output_rmse: 0.4863\n",
      "Epoch 23/1000\n",
      "26/26 [==============================] - 7s 263ms/step - loss: 0.2157 - output_loss: 0.2157 - output_rmse: 0.4644 - val_loss: 0.2325 - val_output_loss: 0.2325 - val_output_rmse: 0.4822\n",
      "Epoch 24/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.2124 - output_loss: 0.2124 - output_rmse: 0.4609 - val_loss: 0.2319 - val_output_loss: 0.2319 - val_output_rmse: 0.4816\n",
      "Epoch 25/1000\n",
      "26/26 [==============================] - 8s 295ms/step - loss: 0.2086 - output_loss: 0.2086 - output_rmse: 0.4567 - val_loss: 0.2334 - val_output_loss: 0.2334 - val_output_rmse: 0.4832\n",
      "Epoch 26/1000\n",
      "26/26 [==============================] - 7s 258ms/step - loss: 0.2034 - output_loss: 0.2034 - output_rmse: 0.4510 - val_loss: 0.2241 - val_output_loss: 0.2241 - val_output_rmse: 0.4733\n",
      "Epoch 27/1000\n",
      "26/26 [==============================] - 7s 259ms/step - loss: 0.2000 - output_loss: 0.2000 - output_rmse: 0.4472 - val_loss: 0.2416 - val_output_loss: 0.2416 - val_output_rmse: 0.4915\n",
      "Epoch 28/1000\n",
      "26/26 [==============================] - 7s 255ms/step - loss: 0.2068 - output_loss: 0.2068 - output_rmse: 0.4548 - val_loss: 0.2282 - val_output_loss: 0.2282 - val_output_rmse: 0.4777\n",
      "Epoch 29/1000\n",
      "26/26 [==============================] - 7s 271ms/step - loss: 0.1995 - output_loss: 0.1995 - output_rmse: 0.4467 - val_loss: 0.2275 - val_output_loss: 0.2275 - val_output_rmse: 0.4770\n",
      "Epoch 30/1000\n",
      "26/26 [==============================] - 7s 255ms/step - loss: 0.1986 - output_loss: 0.1986 - output_rmse: 0.4457 - val_loss: 0.2212 - val_output_loss: 0.2212 - val_output_rmse: 0.4703\n",
      "Epoch 31/1000\n",
      "26/26 [==============================] - 7s 260ms/step - loss: 0.1973 - output_loss: 0.1973 - output_rmse: 0.4442 - val_loss: 0.2206 - val_output_loss: 0.2206 - val_output_rmse: 0.4697\n",
      "Epoch 32/1000\n",
      "26/26 [==============================] - 7s 280ms/step - loss: 0.2030 - output_loss: 0.2030 - output_rmse: 0.4506 - val_loss: 0.2353 - val_output_loss: 0.2353 - val_output_rmse: 0.4851\n",
      "Epoch 33/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.1977 - output_loss: 0.1977 - output_rmse: 0.4447 - val_loss: 0.2182 - val_output_loss: 0.2182 - val_output_rmse: 0.4671\n",
      "Epoch 34/1000\n",
      "26/26 [==============================] - 7s 254ms/step - loss: 0.1892 - output_loss: 0.1892 - output_rmse: 0.4350 - val_loss: 0.2196 - val_output_loss: 0.2196 - val_output_rmse: 0.4686\n",
      "Epoch 35/1000\n",
      "26/26 [==============================] - 7s 260ms/step - loss: 0.1848 - output_loss: 0.1848 - output_rmse: 0.4299 - val_loss: 0.2259 - val_output_loss: 0.2259 - val_output_rmse: 0.4753\n",
      "Epoch 36/1000\n",
      "26/26 [==============================] - 7s 270ms/step - loss: 0.1835 - output_loss: 0.1835 - output_rmse: 0.4284 - val_loss: 0.2150 - val_output_loss: 0.2150 - val_output_rmse: 0.4636\n",
      "Epoch 37/1000\n",
      "26/26 [==============================] - 8s 287ms/step - loss: 0.1852 - output_loss: 0.1852 - output_rmse: 0.4304 - val_loss: 0.2164 - val_output_loss: 0.2164 - val_output_rmse: 0.4652\n",
      "Epoch 38/1000\n",
      "26/26 [==============================] - 7s 267ms/step - loss: 0.1817 - output_loss: 0.1817 - output_rmse: 0.4263 - val_loss: 0.2225 - val_output_loss: 0.2225 - val_output_rmse: 0.4717\n",
      "Epoch 39/1000\n",
      "26/26 [==============================] - 7s 269ms/step - loss: 0.1801 - output_loss: 0.1801 - output_rmse: 0.4244 - val_loss: 0.2191 - val_output_loss: 0.2191 - val_output_rmse: 0.4681\n",
      "Epoch 40/1000\n",
      "26/26 [==============================] - 7s 280ms/step - loss: 0.1835 - output_loss: 0.1835 - output_rmse: 0.4284 - val_loss: 0.2173 - val_output_loss: 0.2173 - val_output_rmse: 0.4661\n",
      "Epoch 41/1000\n",
      "26/26 [==============================] - 8s 297ms/step - loss: 0.1800 - output_loss: 0.1800 - output_rmse: 0.4243 - val_loss: 0.2167 - val_output_loss: 0.2167 - val_output_rmse: 0.4655\n",
      "Epoch 42/1000\n",
      "26/26 [==============================] - 7s 261ms/step - loss: 0.1807 - output_loss: 0.1807 - output_rmse: 0.4251 - val_loss: 0.2086 - val_output_loss: 0.2086 - val_output_rmse: 0.4567\n",
      "Epoch 43/1000\n",
      "26/26 [==============================] - 7s 256ms/step - loss: 0.1754 - output_loss: 0.1754 - output_rmse: 0.4188 - val_loss: 0.2227 - val_output_loss: 0.2227 - val_output_rmse: 0.4719\n",
      "Epoch 44/1000\n",
      "26/26 [==============================] - 7s 259ms/step - loss: 0.1782 - output_loss: 0.1782 - output_rmse: 0.4221 - val_loss: 0.2148 - val_output_loss: 0.2148 - val_output_rmse: 0.4634\n",
      "Epoch 45/1000\n",
      "26/26 [==============================] - 7s 274ms/step - loss: 0.1760 - output_loss: 0.1760 - output_rmse: 0.4195 - val_loss: 0.2068 - val_output_loss: 0.2068 - val_output_rmse: 0.4547\n",
      "Epoch 46/1000\n",
      "26/26 [==============================] - 7s 263ms/step - loss: 0.1717 - output_loss: 0.1717 - output_rmse: 0.4144 - val_loss: 0.2104 - val_output_loss: 0.2104 - val_output_rmse: 0.4587\n",
      "Epoch 47/1000\n",
      "26/26 [==============================] - 7s 256ms/step - loss: 0.1696 - output_loss: 0.1696 - output_rmse: 0.4118 - val_loss: 0.2188 - val_output_loss: 0.2188 - val_output_rmse: 0.4678\n",
      "Epoch 48/1000\n",
      "26/26 [==============================] - 7s 256ms/step - loss: 0.1709 - output_loss: 0.1709 - output_rmse: 0.4134 - val_loss: 0.2140 - val_output_loss: 0.2140 - val_output_rmse: 0.4626\n",
      "Epoch 49/1000\n",
      "26/26 [==============================] - 7s 256ms/step - loss: 0.1757 - output_loss: 0.1757 - output_rmse: 0.4192 - val_loss: 0.2465 - val_output_loss: 0.2465 - val_output_rmse: 0.4965\n",
      "Epoch 50/1000\n",
      "26/26 [==============================] - 7s 258ms/step - loss: 0.1725 - output_loss: 0.1725 - output_rmse: 0.4153 - val_loss: 0.2124 - val_output_loss: 0.2124 - val_output_rmse: 0.4608\n",
      "Epoch 51/1000\n",
      "26/26 [==============================] - 7s 264ms/step - loss: 0.1633 - output_loss: 0.1633 - output_rmse: 0.4041 - val_loss: 0.2126 - val_output_loss: 0.2126 - val_output_rmse: 0.4611\n",
      "Epoch 52/1000\n",
      "26/26 [==============================] - 7s 257ms/step - loss: 0.1647 - output_loss: 0.1647 - output_rmse: 0.4058 - val_loss: 0.2205 - val_output_loss: 0.2205 - val_output_rmse: 0.4696\n",
      "Epoch 53/1000\n",
      "26/26 [==============================] - 7s 280ms/step - loss: 0.1607 - output_loss: 0.1607 - output_rmse: 0.4009 - val_loss: 0.2252 - val_output_loss: 0.2252 - val_output_rmse: 0.4745\n",
      "Epoch 54/1000\n",
      "26/26 [==============================] - 7s 281ms/step - loss: 0.1646 - output_loss: 0.1646 - output_rmse: 0.4058 - val_loss: 0.2145 - val_output_loss: 0.2145 - val_output_rmse: 0.4631\n",
      "Epoch 55/1000\n",
      "26/26 [==============================] - 8s 288ms/step - loss: 0.1640 - output_loss: 0.1640 - output_rmse: 0.4050 - val_loss: 0.2168 - val_output_loss: 0.2168 - val_output_rmse: 0.4656\n",
      "Epoch 56/1000\n",
      "26/26 [==============================] - 7s 272ms/step - loss: 0.1641 - output_loss: 0.1641 - output_rmse: 0.4051 - val_loss: 0.2337 - val_output_loss: 0.2337 - val_output_rmse: 0.4834\n",
      "Epoch 57/1000\n",
      "26/26 [==============================] - 7s 281ms/step - loss: 0.1663 - output_loss: 0.1663 - output_rmse: 0.4077 - val_loss: 0.2169 - val_output_loss: 0.2169 - val_output_rmse: 0.4657\n",
      "Epoch 58/1000\n",
      "26/26 [==============================] - 7s 265ms/step - loss: 0.1533 - output_loss: 0.1533 - output_rmse: 0.3916 - val_loss: 0.2260 - val_output_loss: 0.2260 - val_output_rmse: 0.4754\n",
      "Epoch 59/1000\n",
      "26/26 [==============================] - 7s 271ms/step - loss: 0.1541 - output_loss: 0.1541 - output_rmse: 0.3925 - val_loss: 0.2295 - val_output_loss: 0.2295 - val_output_rmse: 0.4790\n",
      "Epoch 60/1000\n",
      "26/26 [==============================] - 7s 277ms/step - loss: 0.1604 - output_loss: 0.1604 - output_rmse: 0.4005 - val_loss: 0.2173 - val_output_loss: 0.2173 - val_output_rmse: 0.4662\n",
      "Epoch 61/1000\n",
      "26/26 [==============================] - 7s 275ms/step - loss: 0.1848 - output_loss: 0.1848 - output_rmse: 0.4299 - val_loss: 0.2305 - val_output_loss: 0.2305 - val_output_rmse: 0.4801\n",
      "Epoch 62/1000\n",
      "26/26 [==============================] - 7s 279ms/step - loss: 0.1650 - output_loss: 0.1650 - output_rmse: 0.4062 - val_loss: 0.2169 - val_output_loss: 0.2169 - val_output_rmse: 0.4657\n",
      "Epoch 63/1000\n",
      "26/26 [==============================] - 7s 279ms/step - loss: 0.1541 - output_loss: 0.1541 - output_rmse: 0.3926 - val_loss: 0.2126 - val_output_loss: 0.2126 - val_output_rmse: 0.4611\n",
      "Epoch 64/1000\n",
      "26/26 [==============================] - 7s 284ms/step - loss: 0.1532 - output_loss: 0.1532 - output_rmse: 0.3914 - val_loss: 0.2187 - val_output_loss: 0.2187 - val_output_rmse: 0.4677\n",
      "Epoch 65/1000\n",
      "26/26 [==============================] - 7s 265ms/step - loss: 0.1502 - output_loss: 0.1502 - output_rmse: 0.3876 - val_loss: 0.2281 - val_output_loss: 0.2281 - val_output_rmse: 0.4776\n"
     ]
    }
   ],
   "source": [
    "ft_plet_encoder = FTTransformerEncoder(\n",
    "   numerical_features = NUMERIC_FEATURES,\n",
    "    categorical_features = [],\n",
    "    numerical_data = X_train[NUMERIC_FEATURES].values,\n",
    "    categorical_data = None,\n",
    "    y = X_train[LABEL].values,\n",
    "    task='regression',\n",
    "    numerical_embedding_type='ple',\n",
    "    numerical_bins=128,\n",
    "    embedding_dim=64,\n",
    "    depth=3,\n",
    "    heads=6,\n",
    "    attn_dropout=0.3,\n",
    "    ff_dropout=0.3,\n",
    "    ple_tree_params = {\n",
    "        \"min_samples_leaf\": 20,\n",
    "    },\n",
    "    explainable=True\n",
    ")\n",
    "\n",
    "\n",
    "# Pass th encoder to the model\n",
    "ft_plet_transformer = FTTransformer(\n",
    "    encoder=ft_plet_encoder,\n",
    "    out_dim=1,\n",
    "    out_activation=None,\n",
    ")\n",
    "\n",
    "LEARNING_RATE = 0.001\n",
    "WEIGHT_DECAY = 0.0001\n",
    "NUM_EPOCHS = 1000\n",
    "\n",
    "optimizer = tfa.optimizers.AdamW(\n",
    "        learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY\n",
    "    )\n",
    "\n",
    "ft_plet_transformer.compile(\n",
    "    optimizer = optimizer,\n",
    "    loss = {\"output\": tf.keras.losses.MeanSquaredError(name='mse'), \"importances\": None},\n",
    "    metrics= {\"output\": [tf.keras.metrics.RootMeanSquaredError(name='rmse')], \"importances\": None},\n",
    ")\n",
    "\n",
    "early = EarlyStopping(monitor=\"val_output_loss\", mode=\"min\", patience=20, restore_best_weights=True)\n",
    "callback_list = [early]\n",
    "\n",
    "ft_plet_history = ft_plet_transformer.fit(\n",
    "    train_dataset, \n",
    "    epochs=NUM_EPOCHS, \n",
    "    validation_data=val_dataset,\n",
    "    callbacks=callback_list\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d808c6c9-a188-4131-aead-71bda9f1019f",
   "metadata": {},
   "source": [
    "## Compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2d3f4865-aa39-426d-b0f9-bd41872730e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ft_linear_history.history['val_loss'][:72], label='Linear Val Loss')\n",
    "plt.plot(ft_periodic_history.history['val_loss'][:100], label='Periodic Val Loss')\n",
    "plt.plot(ft_pleq_history.history['val_loss'][:100], label='PLE Quantile Val Loss')\n",
    "plt.plot(ft_plet_history.history['val_loss'][:100], label='PLE Target Val Loss')\n",
    "\n",
    "plt.title('Model Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "220a564a-fe8a-44b0-88de-0a3f349355c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_test_preds = ft_linear_transformer.predict(test_dataset)\n",
    "linear_rms = mean_squared_error(test_data[LABEL], linear_test_preds['output'].ravel(), squared=False)\n",
    "\n",
    "periodic_test_preds = ft_periodic_transformer.predict(test_dataset)\n",
    "periodic_rms = mean_squared_error(test_data[LABEL], periodic_test_preds['output'].ravel()., squared=False)\n",
    "\n",
    "pleq_test_preds = ft_pleq_transformer.predict(test_dataset)\n",
    "pleq_rms = mean_squared_error(test_data[LABEL], pleq_test_preds['output'].ravel(), squared=False)\n",
    "\n",
    "plet_test_preds = ft_plet_transformer.predict(test_dataset)\n",
    "plet_rms = mean_squared_error(test_data[LABEL], plet_test_preds['output'].ravel(), squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cd904ea5-be28-4908-a06d-d0ff4ff64f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------- FT Transformer ---------------------------\n",
      "Linear Encoding RMSE: 0.5153\n",
      "Periodic Encoding RMSE: 0.4848\n",
      "PLE Encoding with Qantile Binning RMSE: 0.4553\n",
      "PLE Encoding with Target Binning RMSE: 0.4578\n",
      "\n",
      "------------------------------ Baselines ------------------------------\n",
      "Random Forest RMSE: 0.5111\n",
      "Catboost RMSE: 0.4487\n"
     ]
    }
   ],
   "source": [
    "print(\"-\" * 28 + \" FT Transformer \" + \"-\" * 27)\n",
    "print(\"Linear Encoding RMSE:\", linear_rms.round(4))\n",
    "print(\"Periodic Encoding RMSE:\", periodic_rms.round(4))\n",
    "print(\"PLE Encoding with Qantile Binning RMSE:\", pleq_rms.round(4))\n",
    "print(\"PLE Encoding with Target Binning RMSE:\", plet_rms.round(4))\n",
    "print(\"\")\n",
    "print(\"-\" * 30 + \" Baselines \" + \"-\" * 30)\n",
    "print(\"Random Forest RMSE:\", rf_rms.round(4))\n",
    "print(\"Catboost RMSE:\", catb_rms.round(4))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c47f96-2c17-42e6-9583-39a82bab3591",
   "metadata": {},
   "source": [
    "## Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6f604736-e0e5-41c3-ba49-59b7e16ed45d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import optuna\n",
    "# import gc\n",
    "\n",
    "# def objective(trial):\n",
    "#     ft_encoder = FTTransformerEncoder(\n",
    "#         numerical_features = NUMERIC_FEATURES,\n",
    "#         categorical_features = [],\n",
    "#         numerical_data = X_train[NUMERIC_FEATURES].values,\n",
    "#         categorical_data = None,\n",
    "#         y = X_train[LABEL].values,\n",
    "#         task='regression',\n",
    "#         numerical_embedding_type= 'ple',\n",
    "#         numerical_bins=trial.suggest_int('numerical_bins', 20, 200),\n",
    "#         embedding_dim=trial.suggest_int('embedding_dim', 8, 100),\n",
    "#         depth=trial.suggest_int('depth', 1, 6),\n",
    "#         heads=trial.suggest_int('heads', 2, 8),\n",
    "#         attn_dropout=trial.suggest_float('attn_dropout', 0., 0.5),\n",
    "#         ff_dropout=trial.suggest_float('ff_dropout', 0., 0.5),\n",
    "#         explainable=True\n",
    "#     )\n",
    "\n",
    "\n",
    "#     # Pass th encoder to the model\n",
    "#     ft_transformer = FTTransformer(\n",
    "#         encoder=ft_encoder,\n",
    "#         out_dim=1,\n",
    "#         out_activation=housing_act\n",
    "#     )\n",
    "\n",
    "#     LEARNING_RATE = 0.001\n",
    "#     WEIGHT_DECAY = 0.00001\n",
    "#     NUM_EPOCHS = 1000\n",
    "\n",
    "#     optimizer = tfa.optimizers.AdamW(\n",
    "#             learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY\n",
    "#         )\n",
    "\n",
    "#     ft_transformer.compile(\n",
    "#         optimizer = optimizer,\n",
    "#         loss = {\"output\": tf.keras.losses.MeanSquaredError(name='mse'), \"importances\": None},\n",
    "#         metrics= {\"output\": [tf.keras.metrics.RootMeanSquaredError(name='rmse')], \"importances\": None},\n",
    "#     )\n",
    "\n",
    "#     early = EarlyStopping(monitor=\"val_output_loss\", mode=\"min\", patience=20, restore_best_weights=True)\n",
    "#     callback_list = [early]\n",
    "\n",
    "#     ft_history = ft_transformer.fit(\n",
    "#         train_dataset, \n",
    "#         epochs=NUM_EPOCHS, \n",
    "#         validation_data=val_dataset,\n",
    "#         callbacks=callback_list\n",
    "#     ) \n",
    "    \n",
    "#     preds = ft_transformer.predict(test_dataset)\n",
    "    \n",
    "#     rmse = mean_squared_error(test_data[LABEL], preds['output'].ravel().clip(0, 5),squared=False)\n",
    "#     gc.collect()\n",
    "    \n",
    "#     return rmse\n",
    "\n",
    "# study = optuna.create_study(direction='minimize')\n",
    "# study.optimize(objective, n_trials=50)\n",
    "\n",
    "# print('Number of finished trials:', len(study.trials))\n",
    "# print('Best trial:', study.best_trial.params)"
   ]
  }
 ],
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