pc_demo_03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e32eca0a-aed8-47d7-9816-4d4c57bf5871",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Objectives:\n",
    "# 1. Set up a basic example using TensorFlow Ranking using our dataset.\n",
    "# 2. Run it successfully end to end even if the results are not good.\n",
    "# 3. Clean up the data loading part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cdf3f845-0819-46fa-bf2a-59b8b966583b",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 10\n",
    "BATCH_SIZE = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a84d6ac-a687-445a-b6e5-d4dd5dfff7d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# Make numpy values easier to read.\n",
    "np.set_printoptions(precision=3, suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78a64afc-4da8-43f8-af18-2e78fd194737",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "596bdc50-6dea-495e-ae17-1d808d66541a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\chris\\AppData\\Local\\anaconda3\\envs\\rankenv\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "2.15.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff2edc2a-5710-4eff-b9ac-6966cb95d846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'keras.api._v2.keras.layers' from 'C:\\\\Users\\\\chris\\\\AppData\\\\Local\\\\anaconda3\\\\envs\\\\rankenv\\\\lib\\\\site-packages\\\\keras\\\\api\\\\_v2\\\\keras\\\\layers\\\\__init__.py'>\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import layers\n",
    "print(layers)\n",
    "# Our dataset is definitely heterogenous therefore we need to follow this guide and build the preprocessing into the model\n",
    "# https://www.tensorflow.org/tutorials/load_data/pandas_dataframe#full_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f361a590-36cc-4bdb-b7a5-253a460efa06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from typing import Dict, Tuple\n",
    "# import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5740371f-9076-4f42-ac63-4f8a37372db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import our custom Dataloader\n",
    "from data_loader import DataLoader\n",
    "\n",
    "# Create an instance of DataLoader and pass the name of the dir that holds the dataset\n",
    "data_loader = DataLoader(data_dir=\"data\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc14b081-0296-40ff-8c75-62a3d41c8335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanger-dose-response.csv read successfully.\n",
      "        DRUG_ID  ARXSPAN_ID  IC50_PUBLISHED\n",
      "268718     1003  ACH-000958        0.025129\n",
      "268719     1003  ACH-000651        0.049577\n",
      "268720     1003  ACH-000856        0.028549\n",
      "268721     1003  ACH-000360        0.039996\n",
      "268722     1003  ACH-001199        1.986678\n",
      "...         ...         ...             ...\n",
      "387621     2172  ACH-000288       25.410793\n",
      "387622     2172  ACH-001065        0.339325\n",
      "387623     2172  ACH-000930        7.780877\n",
      "387624     2172  ACH-000859      534.688321\n",
      "387625     2172  ACH-000536      120.177282\n",
      "\n",
      "[118908 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Read cell line to drug id to ic50\n",
    "# https://depmap.org/portal/data_page/?tab=allData&releasename=Sanger+GDSC1+and+GDSC2&filename=sanger-dose-response.csv\n",
    "# IC50 score of drugs (DRUG_ID) per cell line (COSMIC_ID) for GDSC1 and GDSC2 \n",
    "\n",
    "# Load the \"resopnse\" data\n",
    "df_response = data_loader.load_data('sanger-dose-response.csv')\n",
    "\n",
    "df_response = df_response.query('DATASET == \"GDSC2\"')\n",
    "df_response = df_response.filter(items=[\"DRUG_ID\", \"ARXSPAN_ID\", \"IC50_PUBLISHED\"])\n",
    "print(df_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b54653bb-0679-4108-92a5-3a544859a51a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model.csv read successfully.\n",
      "         ModelID OncotreeCode AgeCategory     Sex\n",
      "0     ACH-000001        HGSOC       Adult  Female\n",
      "1     ACH-000002          AML       Adult  Female\n",
      "2     ACH-000003         COAD       Adult    Male\n",
      "3     ACH-000004          AML       Adult    Male\n",
      "4     ACH-000005          AML       Adult    Male\n",
      "...          ...          ...         ...     ...\n",
      "1954  ACH-003161          NaN     Unknown     NaN\n",
      "1955  ACH-003181          LMS       Adult  Female\n",
      "1956  ACH-003183          MFS       Adult    Male\n",
      "1957  ACH-003184          LMS       Adult  Female\n",
      "1958  ACH-003191         GCTB       Adult    Male\n",
      "\n",
      "[1959 rows x 4 columns]\n",
      "ModelID           0\n",
      "OncotreeCode    141\n",
      "AgeCategory       0\n",
      "Sex              25\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Read the Model.csv\n",
    "# This dataset contains demographics and cancer type\n",
    "# https://depmap.org/portal/data_page/?tab=allData&releasename=DepMap+Public+24Q2&filename=Model.csv\n",
    "# Mapping between 'ModelID', 'PatientID', 'SangerModelID', 'COSMICID', etc\n",
    "# ModelID here is the cell line id.\n",
    "# OncotreeCode is the type of cancer.\n",
    "\n",
    "df_model = data_loader.load_data('Model.csv')\n",
    "df_model = df_model.filter(items=['ModelID', \n",
    "                                  'OncotreeCode', \n",
    "                                  'AgeCategory', \n",
    "                                  'Sex'])\n",
    "                                  # 'PatientRace', \n",
    "                                  # 'PrimaryOrMetastasis'])\n",
    "print(df_model)\n",
    "\n",
    "# Check for any remaining missing values\n",
    "print(df_model.isnull().sum())\n",
    "# [1959 rows x 4 columns]\n",
    "# ModelID           0\n",
    "# OncotreeCode    141\n",
    "# AgeCategory       0\n",
    "# Sex              25\n",
    "# dtype: int64\n",
    "df_model['OncotreeCode'] = df_model['OncotreeCode'].fillna('Unknown')\n",
    "df_model['OncotreeCode'] = df_model['OncotreeCode'].fillna('NON_CANCEROUS')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cc0bd4ee-e4ad-45f8-81f1-6820a091e60b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OmicsExpressionProteinCodingGenesTPMLogp1BatchCorrected.csv read successfully.\n",
      "      ARXSPAN_ID  ZMIZ1 (57178)  ENG (2022)  FGFR1 (2260)  PAWR (5074)\n",
      "0     ACH-000058       5.625773    4.795912      4.550313     5.691600\n",
      "1     ACH-000082       5.744045    6.685257      7.885196     1.309678\n",
      "2     ACH-000867       4.740270    3.052492      2.024326     4.669322\n",
      "3     ACH-000437       6.113573    6.810016      6.047486     4.245211\n",
      "4     ACH-000544       3.368808    0.071018      0.312412     3.782876\n",
      "...          ...            ...         ...           ...          ...\n",
      "1512  ACH-001743       4.140309    1.357624      5.281085     4.031491\n",
      "1513  ACH-001578       5.544128    0.617270      8.321176     4.445862\n",
      "1514  ACH-002669       2.527378    0.700254      0.629052     4.718483\n",
      "1515  ACH-001858       5.641719    6.415130      5.081939     5.225997\n",
      "1516  ACH-001997       4.608237    3.047769      0.302899     5.484335\n",
      "\n",
      "[1517 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Load the gene expression data from \"OmicsExpressionProteinCodingGenesTPMLogp1BatchCorrected\"\n",
    "df_expression = data_loader.load_data(\"OmicsExpressionProteinCodingGenesTPMLogp1BatchCorrected.csv\")\n",
    "\n",
    "# The first column header which is the cell line id happens to be empty in the dataset, so we set it manually\n",
    "df_expression = df_expression.rename(columns={'': 'ARXSPAN_ID'})\n",
    "\n",
    "# Select specific columns\n",
    "df_expression = df_expression[['ARXSPAN_ID',\n",
    "                               'ZMIZ1 (57178)', \n",
    "                               'ENG (2022)',\n",
    "                               'FGFR1 (2260)',\n",
    "                               'PAWR (5074)']] \n",
    "                               # 'KRT17 (3872)', \n",
    "                               # 'MPO (4353)',\n",
    "                               # 'LAT2 (7462)']]\n",
    "print(df_expression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "875e08ef-98b2-4642-9d8a-999b248a9547",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of df_response_model = (115502, 6)\n",
      "   DRUG_ID  ARXSPAN_ID  IC50_PUBLISHED OncotreeCode AgeCategory     Sex\n",
      "0     1003  ACH-000958        0.025129         COAD       Adult  Female\n",
      "1     1003  ACH-000651        0.049577         COAD       Adult    Male\n",
      "2     1003  ACH-000856        0.028549         BRCA       Adult  Female\n",
      "3     1003  ACH-000360        0.039996         COAD       Adult    Male\n",
      "4     1003  ACH-001199        1.986678         COAD       Adult  Female\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Join response and model\n",
    "\n",
    "try:\n",
    "    assert df_response[\"ARXSPAN_ID\"].dtype == df_model[\"ModelID\"].dtype\n",
    "    df_response_model = pd.merge(df_response, df_model, left_on='ARXSPAN_ID', right_on='ModelID')\n",
    "    df_response_model = df_response_model.drop('ModelID', axis=1) # we drop ModelID since it's the same info as ARXSPAN_ID \n",
    "    print(\"Shape of df_response_model = {}\".format(df_response_model.shape))\n",
    "    print(df_response_model.head())\n",
    "except Exception as e:\n",
    "    print(f\"Error: {e}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "78f7bb19-f852-4b07-bf41-ca3450fba1ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of df_response_model_expression = (88976, 10)\n",
      "   DRUG_ID  ARXSPAN_ID  IC50_PUBLISHED OncotreeCode AgeCategory     Sex  \\\n",
      "0     1003  ACH-000958        0.025129         COAD       Adult  Female   \n",
      "1     1003  ACH-000651        0.049577         COAD       Adult    Male   \n",
      "2     1003  ACH-000856        0.028549         BRCA       Adult  Female   \n",
      "3     1003  ACH-000360        0.039996         COAD       Adult    Male   \n",
      "4     1003  ACH-000400        0.004820         COAD       Adult    Male   \n",
      "\n",
      "   ZMIZ1 (57178)  ENG (2022)  FGFR1 (2260)  PAWR (5074)  \n",
      "0       2.882901    3.896383      1.022058     4.631481  \n",
      "1       3.227695    1.078813      5.141713     4.124477  \n",
      "2       3.945238    0.059106      5.912215     4.875870  \n",
      "3       3.358643    2.312424      0.553884     3.706209  \n",
      "4       3.129009    3.147827      0.109936     5.092328  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Join response_model and expression\n",
    "\n",
    "try:\n",
    "    assert df_response_model['ARXSPAN_ID'].dtype == df_expression['ARXSPAN_ID'].dtype\n",
    "    df_response_model_expression = pd.merge(df_response_model, df_expression, left_on='ARXSPAN_ID', right_on='ARXSPAN_ID')\n",
    "    print(\"Shape of df_response_model_expression = {}\".format(df_response_model_expression.shape))\n",
    "    print(df_response_model_expression.head())\n",
    "except Exception as e:\n",
    "    print(f\"Error: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f98bc857-948f-4660-b179-2cb1332669d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Persist csv right after join and before split for classification\n",
    "df_response_model_expression.to_csv('data/df_response_model_expression.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6e4c9128-a278-4d18-a2c5-b8898d0c97ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DRUG_ID             int64\n",
       "ARXSPAN_ID         object\n",
       "IC50_PUBLISHED    float64\n",
       "OncotreeCode       object\n",
       "AgeCategory        object\n",
       "Sex                object\n",
       "ZMIZ1 (57178)     float64\n",
       "ENG (2022)        float64\n",
       "FGFR1 (2260)      float64\n",
       "PAWR (5074)       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_response_model_expression.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "17a5d55a-ca43-47c7-be2c-e8deae99a090",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df_response_model_expression\n",
    "target = features.pop('IC50_PUBLISHED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "791aa76f-7e91-40d9-879e-f12edcb3c907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "WARNING:tensorflow:From C:\\Users\\chris\\AppData\\Local\\anaconda3\\envs\\rankenv\\lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\chris\\AppData\\Local\\anaconda3\\envs\\rankenv\\lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Necessary setup to convert the problem to classification just for testing.\n",
    "bins = [0, 1, 10, 100, float('inf')]\n",
    "labels = ['HIGH', 'MODERATE', 'LOW', 'INACTIVE']\n",
    "target_binned = pd.cut(target, bins=bins, labels=labels, right=False)\n",
    "num_classes = len(np.unique(target_binned))\n",
    "print(num_classes)\n",
    "\n",
    "\n",
    "# Convert CategoricalDtype to string\n",
    "target_binned = target_binned.astype(str)\n",
    "\n",
    "# Convert the categorical labels to numeric indices using StringLookup\n",
    "lookup = tf.keras.layers.StringLookup(vocabulary=labels, mask_token=None, num_oov_indices=0, output_mode='int')\n",
    "target_indices = lookup(target_binned)\n",
    "\n",
    "# Ensure that the indices are in the range [0, len(labels)-1]\n",
    "num_classes = len(labels)  # Should be 4 in this case\n",
    "\n",
    "# Now use CategoryEncoding to one-hot encode these indices\n",
    "target_one_hot = tf.keras.layers.CategoryEncoding(num_tokens=num_classes, output_mode='one_hot')(target_indices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ecfaf01-cb32-41ef-bf36-c0c9ccf41e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.tensorflow.org/tutorials/load_data/pandas_dataframe#build_the_preprocessing_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4e46f28-9214-49ce-9e1d-18b4ff54801b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DRUG_ID': <KerasTensor: shape=(None, 1) dtype=int64 (created by layer 'DRUG_ID')>,\n",
       " 'ARXSPAN_ID': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'ARXSPAN_ID')>,\n",
       " 'OncotreeCode': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'OncotreeCode')>,\n",
       " 'AgeCategory': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'AgeCategory')>,\n",
       " 'Sex': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'Sex')>,\n",
       " 'ZMIZ1 (57178)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'ZMIZ1 (57178)')>,\n",
       " 'ENG (2022)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'ENG (2022)')>,\n",
       " 'FGFR1 (2260)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'FGFR1 (2260)')>,\n",
       " 'PAWR (5074)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'PAWR (5074)')>}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the preprocessing head\n",
    "binary_feature_names = []\n",
    "categorical_feature_names = ['DRUG_ID', 'ARXSPAN_ID', 'OncotreeCode', 'AgeCategory', 'Sex']\n",
    "\n",
    "inputs = {}\n",
    "for name, column in features.items():\n",
    "  if type(column[0]) == str:\n",
    "    dtype = tf.string\n",
    "  elif (name in categorical_feature_names or\n",
    "        name in binary_feature_names):\n",
    "    dtype = tf.int64\n",
    "  else:\n",
    "    dtype = tf.float32\n",
    "\n",
    "  inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)\n",
    "\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c73fb53-b58d-47e2-a1fc-504dc06b6953",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessed = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9614765b-471b-46f9-89f8-a24ac328f056",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Binary inputs\n",
    "\n",
    "# for name in binary_feature_names:\n",
    "#   inp = inputs[name]\n",
    "#   preprocessed.append(inp)\n",
    "\n",
    "# preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "74557420-4cd9-47a0-93fd-f05e944d9213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<KerasTensor: shape=(None, 2) dtype=float32 (created by layer 'normalization')>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numeric inputs\n",
    "numeric_feature_names = ['ZMIZ1 (57178)', 'ENG (2022)']\n",
    "numeric_features = features[numeric_feature_names]\n",
    "\n",
    "numeric_features_dict = {key: value.to_numpy()[:, tf.newaxis] for key, value in dict(numeric_features).items()}\n",
    "\n",
    "normalizer = tf.keras.layers.Normalization(axis=-1)\n",
    "normalizer.adapt(np.concatenate([value for key, value in sorted(numeric_features_dict.items())], axis=1))\n",
    "\n",
    "numeric_inputs = []\n",
    "for name in numeric_feature_names:\n",
    "  numeric_inputs.append(inputs[name])\n",
    "\n",
    "numeric_inputs = tf.keras.layers.Concatenate(axis=-1)(numeric_inputs)\n",
    "numeric_normalized = normalizer(numeric_inputs)\n",
    "\n",
    "preprocessed.append(numeric_normalized)\n",
    "\n",
    "preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "409947b9-dfb2-4ef6-b72b-c48b6b6239b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DRUG_ID\n",
      "name: DRUG_ID\n",
      "vocab: [1003, 1004, 1005, 1006, 1007, 1010, 1011, 1012, 1013, 1017, 1021, 1022, 1025, 1030, 1032, 1036, 1038, 1042, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1057, 1058, 1059, 1060, 1068, 1073, 1079, 1080, 1083, 1084, 1085, 1086, 1088, 1089, 1091, 1093, 1096, 1129, 1131, 1168, 1177, 1179, 1180, 1190, 1191, 1192, 1194, 1199, 1200, 1237, 1239, 1248, 1249, 1250, 1259, 1268, 1372, 1373, 1375, 1507, 1510, 1511, 1512, 1529, 1549, 1553, 1557, 1558, 1559, 1560, 1561, 1563, 1564, 1576, 1578, 1593, 1594, 1598, 1613, 1614, 1615, 1617, 1618, 1620, 1621, 1622, 1624, 1625, 1626, 1627, 1629, 1630, 1631, 1632, 1634, 1635, 1786, 1799, 1802, 1804, 1806, 1807, 1808, 1809, 1810, 1811, 1813, 1814, 1816, 1818, 1819, 1825, 1827, 1830, 1835, 1838, 1849, 1852, 1853, 1854, 1855, 1866, 1873, 1908, 1909, 1910, 1911, 1912, 1913, 1915, 1916, 1917, 1918, 1919, 1922, 1924, 1925, 1926, 1927, 1928, 1930, 1931, 1932, 1933, 1936, 1939, 1940, 1941, 1996, 1997, 1998, 2040, 2043, 2044, 2045, 2046, 2047, 2048, 2096, 2106, 2107, 2109, 2110, 2111, 2169, 2170, 2171, 2172]\n",
      "\n",
      "ARXSPAN_ID\n",
      "name: ARXSPAN_ID\n",
      "vocab: ['ACH-000001', 'ACH-000002', 'ACH-000004', 'ACH-000006', 'ACH-000007', 'ACH-000008', 'ACH-000009', 'ACH-000012', 'ACH-000015', 'ACH-000019', 'ACH-000020', 'ACH-000021', 'ACH-000023', 'ACH-000024', 'ACH-000030', 'ACH-000035', 'ACH-000040', 'ACH-000042', 'ACH-000045', 'ACH-000046', 'ACH-000048', 'ACH-000050', 'ACH-000052', 'ACH-000053', 'ACH-000054', 'ACH-000055', 'ACH-000056', 'ACH-000059', 'ACH-000060', 'ACH-000061', 'ACH-000065', 'ACH-000070', 'ACH-000072', 'ACH-000073', 'ACH-000074', 'ACH-000075', 'ACH-000081', 'ACH-000082', 'ACH-000087', 'ACH-000090', 'ACH-000091', 'ACH-000092', 'ACH-000094', 'ACH-000096', 'ACH-000098', 'ACH-000099', 'ACH-000100', 'ACH-000101', 'ACH-000102', 'ACH-000103', 'ACH-000104', 'ACH-000105', 'ACH-000106', 'ACH-000107', 'ACH-000108', 'ACH-000111', 'ACH-000113', 'ACH-000114', 'ACH-000115', 'ACH-000116', 'ACH-000117', 'ACH-000118', 'ACH-000120', 'ACH-000121', 'ACH-000129', 'ACH-000137', 'ACH-000138', 'ACH-000139', 'ACH-000142', 'ACH-000144', 'ACH-000145', 'ACH-000147', 'ACH-000148', 'ACH-000149', 'ACH-000153', 'ACH-000155', 'ACH-000157', 'ACH-000159', 'ACH-000161', 'ACH-000162', 'ACH-000168', 'ACH-000169', 'ACH-000171', 'ACH-000174', 'ACH-000176', 'ACH-000178', 'ACH-000181', 'ACH-000183', 'ACH-000187', 'ACH-000189', 'ACH-000190', 'ACH-000191', 'ACH-000192', 'ACH-000193', 'ACH-000196', 'ACH-000198', 'ACH-000201', 'ACH-000204', 'ACH-000207', 'ACH-000210', 'ACH-000211', 'ACH-000212', 'ACH-000213', 'ACH-000217', 'ACH-000219', 'ACH-000221', 'ACH-000222', 'ACH-000223', 'ACH-000225', 'ACH-000226', 'ACH-000227', 'ACH-000231', 'ACH-000232', 'ACH-000233', 'ACH-000235', 'ACH-000236', 'ACH-000238', 'ACH-000242', 'ACH-000243', 'ACH-000244', 'ACH-000245', 'ACH-000247', 'ACH-000248', 'ACH-000249', 'ACH-000250', 'ACH-000252', 'ACH-000254', 'ACH-000257', 'ACH-000258', 'ACH-000259', 'ACH-000260', 'ACH-000263', 'ACH-000264', 'ACH-000265', 'ACH-000267', 'ACH-000269', 'ACH-000270', 'ACH-000271', 'ACH-000273', 'ACH-000276', 'ACH-000277', 'ACH-000281', 'ACH-000282', 'ACH-000288', 'ACH-000290', 'ACH-000291', 'ACH-000292', 'ACH-000293', 'ACH-000295', 'ACH-000298', 'ACH-000301', 'ACH-000303', 'ACH-000304', 'ACH-000305', 'ACH-000308', 'ACH-000311', 'ACH-000315', 'ACH-000319', 'ACH-000320', 'ACH-000322', 'ACH-000323', 'ACH-000326', 'ACH-000327', 'ACH-000330', 'ACH-000332', 'ACH-000334', 'ACH-000335', 'ACH-000336', 'ACH-000337', 'ACH-000338', 'ACH-000341', 'ACH-000343', 'ACH-000346', 'ACH-000347', 'ACH-000348', 'ACH-000349', 'ACH-000350', 'ACH-000351', 'ACH-000352', 'ACH-000353', 'ACH-000354', 'ACH-000355', 'ACH-000356', 'ACH-000358', 'ACH-000359', 'ACH-000360', 'ACH-000361', 'ACH-000362', 'ACH-000363', 'ACH-000364', 'ACH-000366', 'ACH-000367', 'ACH-000372', 'ACH-000373', 'ACH-000374', 'ACH-000375', 'ACH-000379', 'ACH-000380', 'ACH-000381', 'ACH-000383', 'ACH-000384', 'ACH-000386', 'ACH-000389', 'ACH-000391', 'ACH-000392', 'ACH-000395', 'ACH-000396', 'ACH-000400', 'ACH-000403', 'ACH-000408', 'ACH-000410', 'ACH-000411', 'ACH-000414', 'ACH-000417', 'ACH-000420', 'ACH-000421', 'ACH-000424', 'ACH-000425', 'ACH-000427', 'ACH-000429', 'ACH-000430', 'ACH-000431', 'ACH-000432', 'ACH-000434', 'ACH-000437', 'ACH-000438', 'ACH-000439', 'ACH-000440', 'ACH-000441', 'ACH-000442', 'ACH-000443', 'ACH-000447', 'ACH-000448', 'ACH-000449', 'ACH-000451', 'ACH-000452', 'ACH-000456', 'ACH-000457', 'ACH-000464', 'ACH-000465', 'ACH-000469', 'ACH-000470', 'ACH-000472', 'ACH-000473', 'ACH-000475', 'ACH-000476', 'ACH-000478', 'ACH-000480', 'ACH-000481', 'ACH-000482', 'ACH-000483', 'ACH-000486', 'ACH-000489', 'ACH-000491', 'ACH-000493', 'ACH-000496', 'ACH-000501', 'ACH-000505', 'ACH-000508', 'ACH-000510', 'ACH-000514', 'ACH-000516', 'ACH-000518', 'ACH-000522', 'ACH-000524', 'ACH-000527', 'ACH-000528', 'ACH-000530', 'ACH-000532', 'ACH-000534', 'ACH-000535', 'ACH-000536', 'ACH-000538', 'ACH-000544', 'ACH-000545', 'ACH-000546', 'ACH-000547', 'ACH-000551', 'ACH-000552', 'ACH-000554', 'ACH-000555', 'ACH-000556', 'ACH-000558', 'ACH-000560', 'ACH-000561', 'ACH-000562', 'ACH-000563', 'ACH-000565', 'ACH-000566', 'ACH-000567', 'ACH-000568', 'ACH-000569', 'ACH-000571', 'ACH-000572', 'ACH-000573', 'ACH-000574', 'ACH-000577', 'ACH-000579', 'ACH-000580', 'ACH-000581', 'ACH-000585', 'ACH-000586', 'ACH-000587', 'ACH-000595', 'ACH-000596', 'ACH-000599', 'ACH-000601', 'ACH-000605', 'ACH-000607', 'ACH-000610', 'ACH-000611', 'ACH-000613', 'ACH-000616', 'ACH-000617', 'ACH-000620', 'ACH-000621', 'ACH-000624', 'ACH-000625', 'ACH-000626', 'ACH-000627', 'ACH-000628', 'ACH-000631', 'ACH-000633', 'ACH-000636', 'ACH-000637', 'ACH-000638', 'ACH-000639', 'ACH-000643', 'ACH-000644', 'ACH-000647', 'ACH-000648', 'ACH-000649', 'ACH-000650', 'ACH-000651', 'ACH-000652', 'ACH-000653', 'ACH-000654', 'ACH-000655', 'ACH-000656', 'ACH-000657', 'ACH-000660', 'ACH-000662', 'ACH-000663', 'ACH-000665', 'ACH-000666', 'ACH-000667', 'ACH-000668', 'ACH-000669', 'ACH-000670', 'ACH-000672', 'ACH-000674', 'ACH-000677', 'ACH-000678', 'ACH-000679', 'ACH-000680', 'ACH-000681', 'ACH-000684', 'ACH-000691', 'ACH-000693', 'ACH-000694', 'ACH-000696', 'ACH-000697', 'ACH-000698', 'ACH-000699', 'ACH-000702', 'ACH-000703', 'ACH-000704', 'ACH-000706', 'ACH-000711', 'ACH-000716', 'ACH-000717', 'ACH-000719', 'ACH-000720', 'ACH-000722', 'ACH-000724', 'ACH-000725', 'ACH-000729', 'ACH-000730', 'ACH-000733', 'ACH-000738', 'ACH-000740', 'ACH-000743', 'ACH-000744', 'ACH-000745', 'ACH-000747', 'ACH-000748', 'ACH-000749', 'ACH-000750', 'ACH-000751', 'ACH-000752', 'ACH-000754', 'ACH-000755', 'ACH-000756', 'ACH-000757', 'ACH-000759', 'ACH-000763', 'ACH-000766', 'ACH-000767', 'ACH-000768', 'ACH-000769', 'ACH-000770', 'ACH-000775', 'ACH-000776', 'ACH-000778', 'ACH-000781', 'ACH-000783', 'ACH-000784', 'ACH-000786', 'ACH-000787', 'ACH-000788', 'ACH-000789', 'ACH-000790', 'ACH-000792', 'ACH-000793', 'ACH-000794', 'ACH-000798', 'ACH-000800', 'ACH-000802', 'ACH-000803', 'ACH-000804', 'ACH-000805', 'ACH-000806', 'ACH-000809', 'ACH-000811', 'ACH-000815', 'ACH-000816', 'ACH-000817', 'ACH-000818', 'ACH-000819', 'ACH-000821', 'ACH-000822', 'ACH-000823', 'ACH-000824', 'ACH-000825', 'ACH-000827', 'ACH-000828', 'ACH-000832', 'ACH-000835', 'ACH-000838', 'ACH-000841', 'ACH-000843', 'ACH-000846', 'ACH-000847', 'ACH-000848', 'ACH-000849', 'ACH-000852', 'ACH-000853', 'ACH-000855', 'ACH-000856', 'ACH-000857', 'ACH-000858', 'ACH-000859', 'ACH-000860', 'ACH-000861', 'ACH-000863', 'ACH-000864', 'ACH-000865', 'ACH-000866', 'ACH-000869', 'ACH-000873', 'ACH-000874', 'ACH-000875', 'ACH-000876', 'ACH-000878', 'ACH-000879', 'ACH-000880', 'ACH-000881', 'ACH-000882', 'ACH-000883', 'ACH-000885', 'ACH-000886', 'ACH-000888', 'ACH-000890', 'ACH-000892', 'ACH-000893', 'ACH-000894', 'ACH-000895', 'ACH-000896', 'ACH-000900', 'ACH-000902', 'ACH-000903', 'ACH-000905', 'ACH-000906', 'ACH-000910', 'ACH-000911', 'ACH-000913', 'ACH-000916', 'ACH-000917', 'ACH-000918', 'ACH-000919', 'ACH-000920', 'ACH-000926', 'ACH-000927', 'ACH-000930', 'ACH-000932', 'ACH-000934', 'ACH-000935', 'ACH-000936', 'ACH-000937', 'ACH-000938', 'ACH-000939', 'ACH-000940', 'ACH-000943', 'ACH-000944', 'ACH-000945', 'ACH-000947', 'ACH-000948', 'ACH-000949', 'ACH-000950', 'ACH-000951', 'ACH-000953', 'ACH-000955', 'ACH-000956', 'ACH-000957', 'ACH-000958', 'ACH-000960', 'ACH-000962', 'ACH-000963', 'ACH-000965', 'ACH-000966', 'ACH-000968', 'ACH-000969', 'ACH-000970', 'ACH-000971', 'ACH-000973', 'ACH-000974', 'ACH-000976', 'ACH-000977', 'ACH-000978', 'ACH-000980', 'ACH-000981', 'ACH-000983', 'ACH-000985', 'ACH-000986', 'ACH-000987', 'ACH-000988', 'ACH-000989', 'ACH-000991', 'ACH-000995', 'ACH-000997', 'ACH-000998', 'ACH-000999', 'ACH-001016', 'ACH-001064', 'ACH-001086', 'ACH-001106', 'ACH-001151', 'ACH-001190', 'ACH-001193', 'ACH-001274', 'ACH-001306', 'ACH-001307', 'ACH-001332', 'ACH-001333', 'ACH-001334', 'ACH-001336', 'ACH-001338', 'ACH-001341', 'ACH-001344', 'ACH-001345', 'ACH-001350', 'ACH-001360', 'ACH-001368', 'ACH-001374', 'ACH-001384', 'ACH-001399', 'ACH-001401', 'ACH-001402', 'ACH-001443', 'ACH-001453', 'ACH-001494', 'ACH-001496', 'ACH-001497', 'ACH-001498', 'ACH-001500', 'ACH-001517', 'ACH-001519', 'ACH-001525', 'ACH-001526', 'ACH-001528', 'ACH-001549', 'ACH-001578', 'ACH-001603', 'ACH-001626', 'ACH-001630', 'ACH-001638', 'ACH-001641', 'ACH-001642', 'ACH-001650', 'ACH-001653', 'ACH-001654', 'ACH-001655', 'ACH-001674', 'ACH-001680', 'ACH-001702', 'ACH-001703', 'ACH-001709', 'ACH-001711', 'ACH-001715', 'ACH-001716', 'ACH-002170', 'ACH-002291']\n",
      "\n",
      "OncotreeCode\n",
      "name: OncotreeCode\n",
      "vocab: ['AASTR', 'ACC', 'ALCLALKP', 'AML', 'AMOL', 'ARMS', 'ASTR', 'ATLL', 'BL', 'BLCA', 'BLL', 'BLLBCRABL1', 'BPLL', 'BRCA', 'BRCNOS', 'CCOV', 'CCRCC', 'CEAD', 'CELNOS', 'CESC', 'CMLBCRABL1', 'COAD', 'DA', 'DCIS', 'DDCHS', 'DLBCLNOS', 'DSTAD', 'ECAD', 'EHCH', 'EMBCA', 'EMCHS', 'EOV', 'EPIS', 'ERMS', 'ES', 'ESCA', 'ESCC', 'ESS', 'FIBS', 'GB', 'GCB', 'GCLC', 'GEJ', 'GSARC', 'HCC', 'HGSOC', 'HL', 'HNSC', 'HPHSC', 'IDC', 'IHCH', 'ILC', 'LCLC', 'LMS', 'LUAD', 'LUAS', 'LUCA', 'LUSC', 'MACR', 'MBL', 'MBN', 'MCCE', 'MCL', 'MEL', 'MFH', 'MOV', 'MRT', 'MTNN', 'NBL', 'NSCLC', 'OCSC', 'OMGCT', 'OPHSC', 'OS', 'PAAD', 'PAASC', 'PANET', 'PCM', 'PLBMESO', 'PLEMESO', 'PLSMESO', 'PNET', 'PRAD', 'PRCC', 'RCC', 'READ', 'SCLC', 'SKCM', 'SOC', 'SSRCC', 'STAD', 'STAS', 'STSC', 'SYNS', 'THAP', 'THFO', 'THPA', 'THPD', 'TLL', 'TSTAD', 'UASC', 'UCEC', 'UCU', 'ULMS', 'USARC', 'Unknown']\n",
      "\n",
      "AgeCategory\n",
      "name: AgeCategory\n",
      "vocab: ['Adult', 'Pediatric', 'Unknown']\n",
      "\n",
      "Sex\n",
      "name: Sex\n",
      "vocab: ['Female', 'Male', 'Unknown']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Categorical features\n",
    "for name in categorical_feature_names:\n",
    "    print(name)\n",
    "    vocab = sorted(set(features[name]))\n",
    "    print(f'name: {name}')\n",
    "    print(f'vocab: {vocab}\\n')\n",
    "    \n",
    "    if type(vocab[0]) is str:\n",
    "        lookup = tf.keras.layers.StringLookup(vocabulary=vocab, output_mode='one_hot')\n",
    "    else:\n",
    "        lookup = tf.keras.layers.IntegerLookup(vocabulary=vocab, output_mode='one_hot')\n",
    "    x = inputs[name]\n",
    "    x = lookup(x)\n",
    "    preprocessed.append(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e7a48cd5-0c97-4a96-8835-00f9d082dc63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 896) dtype=float32 (created by layer 'concatenate_1')>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_result = tf.keras.layers.Concatenate(axis=1)(preprocessed)\n",
    "preprocessed_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d5167612-c3e7-48a2-90c9-28ec5e018cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = tf.keras.Model(inputs, preprocessed_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3911f7a-fff4-4c53-baf9-1286eaa3bb0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(preprocessor, rankdir=\"LR\", show_shapes=True,  show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "619b534e-f7fd-4d36-8583-44ba0af91f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessor(dict(df_response_model_expression.iloc[:1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19d4fc6c-8fd3-46be-b7be-ccf2781ff147",
   "metadata": {},
   "outputs": [],
   "source": [
    "body = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(num_classes, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "886bddf2-78d4-4d3b-9bc1-364c3e9c2073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DRUG_ID': <KerasTensor: shape=(None, 1) dtype=int64 (created by layer 'DRUG_ID')>,\n",
       " 'ARXSPAN_ID': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'ARXSPAN_ID')>,\n",
       " 'OncotreeCode': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'OncotreeCode')>,\n",
       " 'AgeCategory': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'AgeCategory')>,\n",
       " 'Sex': <KerasTensor: shape=(None, 1) dtype=string (created by layer 'Sex')>,\n",
       " 'ZMIZ1 (57178)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'ZMIZ1 (57178)')>,\n",
       " 'ENG (2022)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'ENG (2022)')>,\n",
       " 'FGFR1 (2260)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'FGFR1 (2260)')>,\n",
       " 'PAWR (5074)': <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'PAWR (5074)')>}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "79b889fc-a8c7-4111-b96a-004708d1f74b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 896) dtype=float32 (created by layer 'model')>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = preprocessor(inputs)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2418725c-368d-40e4-9872-f809b2360e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 4) dtype=float32 (created by layer 'sequential')>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = body(x)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "34cbea20-f00a-4541-840c-89024f25aa4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\chris\\AppData\\Local\\anaconda3\\envs\\rankenv\\lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Model(inputs, result)\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "                # loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "                loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "                metrics=['accuracy'\n",
    "                    # tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),\n",
    "                    # tf.keras.metrics.Precision(name='precision'),\n",
    "                    # tf.keras.metrics.Recall(name='recall'),\n",
    "                    # tf.keras.metrics.AUC(name='auc'),\n",
    "                    # tf.keras.metrics.TopKCategoricalAccuracy(k=5, name='top_5_accuracy')\n",
    "                ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0dfe58f8-8b7b-4b63-a1c8-e3fda299f5b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(model, show_shapes=True,  show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ea4c76b3-87b4-4c0d-b46f-066280a94cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "WARNING:tensorflow:From C:\\Users\\chris\\AppData\\Local\\anaconda3\\envs\\rankenv\\lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "5561/5561 [==============================] - 47s 8ms/step - loss: 0.7696 - accuracy: 0.6554\n",
      "Epoch 2/10\n",
      "5561/5561 [==============================] - 43s 8ms/step - loss: 0.6715 - accuracy: 0.7009\n",
      "Epoch 3/10\n",
      "5561/5561 [==============================] - 45s 8ms/step - loss: 0.6366 - accuracy: 0.7170\n",
      "Epoch 4/10\n",
      "5561/5561 [==============================] - 43s 8ms/step - loss: 0.6072 - accuracy: 0.7304\n",
      "Epoch 5/10\n",
      "5561/5561 [==============================] - 43s 8ms/step - loss: 0.5801 - accuracy: 0.7445\n",
      "Epoch 6/10\n",
      "5561/5561 [==============================] - 44s 8ms/step - loss: 0.5565 - accuracy: 0.7557\n",
      "Epoch 7/10\n",
      "5561/5561 [==============================] - 44s 8ms/step - loss: 0.5379 - accuracy: 0.7665\n",
      "Epoch 8/10\n",
      "5561/5561 [==============================] - 41s 7ms/step - loss: 0.5192 - accuracy: 0.7745\n",
      "Epoch 9/10\n",
      "5561/5561 [==============================] - 43s 8ms/step - loss: 0.5050 - accuracy: 0.7802\n",
      "Epoch 10/10\n",
      "5561/5561 [==============================] - 44s 8ms/step - loss: 0.4939 - accuracy: 0.7845\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(dict(df_response_model_expression), target_one_hot, epochs=EPOCHS, batch_size=BATCH_SIZE)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}