18_Feature_Tokenizer_Transformer.ipynb

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    "---\n",
    "title: 19 Feature Tokenizer Transformer\n",
    "description: An implementation of Feature Tokenizer Transformer on a classification task\n",
    "---"
   ]
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    "<a href=\"https://colab.research.google.com/drive/1tdPifaZCTVpjzCh1FOyGPywMEfrbBZLh?usp=sharing\" target=\"_blank\"><img align=\"left\" alt=\"Colab\" title=\"Open in Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a>"
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    "<a href=\"https://www.kaggle.com/code/ritvik1909/feature-tokenizer-transformer/\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>"
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   "source": [
    "# Feature Tokenizer Transformer\n",
    "Featured in the paper [Revisiting Deep Learning Models for Tabular Data (2021, June)](https://arxiv.org/abs/2106.11959) Feature Tokenizer Transformer is a simple adaptation of the Transformer architecture for the tabular domain. In a nutshell, Feature Tokenizer Transformer transforms all features (categorical and numerical) to embeddings and applies a stack of Transformer layers to the embeddings. Thus, every Transformer layer operates on the feature level of one object.\n",
    "\n",
    "In this notebook we will be implementing Feature Tokenizer Transformer using TensorFlow 2 from scratch."
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   "source": [
    "%%capture\n",
    "!pip install tensorflow-addons"
   ]
  },
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers as L\n",
    "from tensorflow_addons.activations import sparsemax\n",
    "from tensorflow.data import Dataset\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import make_column_selector, ColumnTransformer\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import joblib\n",
    "from tensorflow.keras import utils"
   ]
  },
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   "source": [
    "# Data\n",
    "Loading the train and test csv files into `pandas.DataFrame` and splitting the columns as features and target.\n",
    "\n",
    "We will be using Stratified K folds as our local cross validation."
   ]
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       "   longitude  latitude  housing_median_age total_rooms total_bedrooms  \\\n",
       "0    -114.31     34.19                15.0           2              3   \n",
       "1    -114.47     34.40                19.0           0              1   \n",
       "2    -114.56     33.69                17.0           0              4   \n",
       "3    -114.57     33.64                14.0           1              7   \n",
       "4    -114.57     33.57                20.0           4              6   \n",
       "\n",
       "   population  households  median_income  median_house_value  \n",
       "0      1015.0       472.0         1.4936                 0.0  \n",
       "1      1129.0       463.0         1.8200                 0.0  \n",
       "2       333.0       117.0         1.6509                 0.0  \n",
       "3       515.0       226.0         3.1917                 0.0  \n",
       "4       624.0       262.0         1.9250                 0.0  "
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   "source": [
    "data = pd.read_csv('/content/sample_data/california_housing_train.csv')\n",
    "data['total_rooms'] = (data['total_rooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
    "data['total_bedrooms'] = (data['total_bedrooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
    "data['median_house_value'] = data['median_house_value'] // 100_000 # converting the problem for a rgression to a classification problem\n",
    "print(data.shape)\n",
    "data.head()"
   ]
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      "(3000, 9)\n"
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   "source": [
    "test = pd.read_csv('/content/sample_data/california_housing_test.csv')\n",
    "test['total_rooms'] = (test['total_rooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
    "test['total_bedrooms'] = (test['total_bedrooms'] % 10).astype(int).astype(str) # for demonstration purpose we will treat these columns as categorical\n",
    "test['median_house_value'] = test['median_house_value'] // 100_000 # converting the problem for a rgression to a classification problem\n",
    "print(test.shape)\n",
    "X_test = test.drop(['median_house_value'], axis=1)\n",
    "y_test = pd.get_dummies(test['median_house_value'])"
   ]
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   "source": [
    "X = data.drop(['median_house_value'], axis=1)\n",
    "y = pd.get_dummies(data['median_house_value'])"
   ]
  },
  {
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   "source": [
    "# Model\n",
    "**Creating some data utility classes**:\n",
    "\n",
    "`DataConfig` helps to segregate the features into numeric features and categorical features and maintain a vocabulary for the categorical ones.\n",
    "\n",
    "`DataLoader` class creates `tf.data.Dataset` objects from `pandas.DataFrame` to ensure efficiency in the input pipeline to the model."
   ]
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   "source": [
    "class DataConfig:\n",
    "    def __init__(self, numeric_feature_names, categorical_features_with_vocabulary):\n",
    "        self.NUMERIC_FEATURE_NAMES = numeric_feature_names\n",
    "        self.CATEGORICAL_FEATURES_WITH_VOCABULARY = categorical_features_with_vocabulary\n",
    "        self.CATEGORICAL_FEATURE_NAMES = list(self.CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())\n",
    "        self.FEATURE_NAMES = self.NUMERIC_FEATURE_NAMES + self.CATEGORICAL_FEATURE_NAMES\n",
    "        \n",
    "class DataLoader:\n",
    "    @classmethod\n",
    "    def from_df(cls, X, y=None, batch_size=1024):\n",
    "        return (\n",
    "            Dataset.from_tensor_slices(({col: X[col].values.tolist() for col in X.columns}, y.values.tolist())).batch(\n",
    "                batch_size\n",
    "            )\n",
    "            if y is not None\n",
    "            else Dataset.from_tensor_slices({col: X[col].values.tolist() for col in X.columns}).batch(batch_size)\n",
    "        )        "
   ]
  },
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   "source": [
    "**Creating Input Layers and Feature Encoding Layers**\n",
    "\n",
    "`get_inputs` returns a dictionary of Input Layers based on the data types of the feature columns mentioned in the `DataConfig` object.\n",
    "\n",
    "`encode_inputs` applies StringLookup and Embedding Layer to the categorical features and Reshapes the Numeric Features in order to encode the inputs."
   ]
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   "source": [
    "def get_inputs(config):\n",
    "    return {\n",
    "        feature_name: L.Input(\n",
    "            name=feature_name,\n",
    "            shape=(),\n",
    "            dtype=(tf.float32 if feature_name in config.NUMERIC_FEATURE_NAMES else tf.string),\n",
    "        )\n",
    "        for feature_name in config.FEATURE_NAMES\n",
    "    }\n",
    "\n",
    "def encode_inputs(inputs, config, use_embeddings=False, embedding_dim=32, prefix=\"\", concat_features=False):\n",
    "    cat_features = []\n",
    "    num_features = []\n",
    "    for feature_name in inputs:\n",
    "        if feature_name in config.CATEGORICAL_FEATURE_NAMES:\n",
    "            vocabulary = config.CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n",
    "            lookup = L.StringLookup(\n",
    "                vocabulary=vocabulary,\n",
    "                mask_token=None,\n",
    "                num_oov_indices=0,\n",
    "                output_mode=\"int\" if use_embeddings else \"binary\",\n",
    "                name=f\"{prefix}{feature_name}_lookup\",\n",
    "            )\n",
    "            if use_embeddings:\n",
    "                encoded_feature = lookup(inputs[feature_name])\n",
    "                embedding = L.Embedding(\n",
    "                    input_dim=len(vocabulary),\n",
    "                    output_dim=embedding_dim,\n",
    "                    name=f\"{prefix}{feature_name}_embeddings\",\n",
    "                )\n",
    "                encoded_feature = embedding(encoded_feature)\n",
    "            else:\n",
    "                encoded_feature = lookup(\n",
    "                    L.Reshape((1,), name=f\"{prefix}{feature_name}_reshape\")(inputs[feature_name])\n",
    "                )\n",
    "            cat_features.append(encoded_feature)\n",
    "        else:\n",
    "            encoded_feature = L.Reshape((1,), name=f\"{prefix}{feature_name}_reshape\")(inputs[feature_name])\n",
    "            num_features.append(encoded_feature)\n",
    "\n",
    "    features = (\n",
    "        L.Concatenate(name=f\"{prefix}inputs_concatenate\")(cat_features + num_features)\n",
    "        if concat_features\n",
    "        else (cat_features, num_features)\n",
    "    )\n",
    "\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
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   },
   "source": [
    "**Defining Model Configurations**\n",
    "* Number of Outputs\n",
    "* Activation of the Output Layer\n",
    "* Number of Transformer Blocks\n",
    "* Number of heads in the Transformer Blocks\n",
    "* Embedding Dimension for the features\n",
    "* Dimesion of the Dense Projections in the transfomer blocks"
   ]
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   "source": [
    "class FeatureTokenizerTransformerConfig:\n",
    "    def __init__(\n",
    "        self,\n",
    "        num_outputs,\n",
    "        out_activation,\n",
    "        num_transformer_blocks=2,\n",
    "        num_heads=8,\n",
    "        embedding_dim=32,\n",
    "        dense_dim=16,\n",
    "    ):\n",
    "        self.NUM_OUT = num_outputs\n",
    "        self.OUT_ACTIVATION = out_activation\n",
    "        self.NUM_TRANSFORMER_BLOCKS = num_transformer_blocks\n",
    "        self.NUM_HEADS = num_heads\n",
    "        self.EMBEDDING_DIM = embedding_dim\n",
    "        self.DENSE_DIM = dense_dim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956b6ba0",
   "metadata": {
    "id": "956b6ba0",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Defining a standard Transformer Block**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c5ca0fb",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class TransformerBlock(L.Layer):\n",
    "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.embed_dim = embed_dim\n",
    "        self.dense_dim = dense_dim\n",
    "        self.num_heads = num_heads\n",
    "        self.attention = L.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n",
    "        self.dense_proj = tf.keras.Sequential([L.Dense(dense_dim, activation=\"relu\"), L.Dense(embed_dim)])\n",
    "        self.layernorm1 = L.LayerNormalization()\n",
    "        self.layernorm2 = L.LayerNormalization()\n",
    "\n",
    "    def call(self, inputs, mask=None):\n",
    "        if mask is not None:\n",
    "            mask = mask[: tf.newaxis, :]\n",
    "        attention_output = self.attention(inputs, inputs, attention_mask=mask)\n",
    "        proj_input = self.layernorm1(inputs + attention_output)\n",
    "        proj_output = self.dense_proj(proj_input)\n",
    "        return self.layernorm2(proj_input + proj_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d148c442",
   "metadata": {
    "id": "d148c442",
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     "duration": 0.023095,
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     "status": "completed"
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   },
   "source": [
    "**Defining the Model**\n",
    "The model takes Inputs Layers and then encodes the features from the functions defined above, the numerical features are then passed through a Dense layer of the same dimensions as the embeddings of the categorical features.\n",
    "\n",
    "All the feature embeddings are then stacked and then passed through a series of Transformer Blocks followed by the Global Average Pooling and Final Output Layer"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "class FeatureTokenizerTransformer:\n",
    "    @classmethod\n",
    "    def from_config(cls, data_config, model_config, name):\n",
    "        inputs = get_inputs(data_config)\n",
    "        cat_features, num_features = encode_inputs(\n",
    "            inputs,\n",
    "            data_config,\n",
    "            use_embeddings=True,\n",
    "            embedding_dim=model_config.EMBEDDING_DIM,\n",
    "            prefix=\"\",\n",
    "            concat_features=False,\n",
    "        )\n",
    "        num_feat_emb = [\n",
    "            L.Dense(model_config.EMBEDDING_DIM, name=f\"{feature_name}_embeddings\")\n",
    "            for _, feature_name in zip(range(len(num_features)), data_config.NUMERIC_FEATURE_NAMES)\n",
    "        ]\n",
    "        num_features = [emb(feat) for emb, feat in zip(num_feat_emb, num_features)]\n",
    "\n",
    "        features = L.Concatenate(axis=1, name=\"feature_embeddings_stack\")(\n",
    "            [\n",
    "                L.Reshape((1, 32), name=f\"{feat_name}_reshape_2\")(feat)\n",
    "                for feat, feat_name in zip((num_features + cat_features), data_config.FEATURE_NAMES)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        for _ in range(model_config.NUM_TRANSFORMER_BLOCKS):\n",
    "            features = TransformerBlock(\n",
    "                embed_dim=model_config.EMBEDDING_DIM,\n",
    "                dense_dim=model_config.DENSE_DIM,\n",
    "                num_heads=model_config.NUM_HEADS,\n",
    "            )(features)\n",
    "        features = L.GlobalMaxPooling1D()(features)\n",
    "        outputs = L.Dense(\n",
    "            units=model_config.NUM_OUT,\n",
    "            activation=model_config.OUT_ACTIVATION,\n",
    "            name=\"outputs\",\n",
    "        )(features)\n",
    "        model = keras.Model(inputs=inputs, outputs=outputs, name=name)\n",
    "        return model"
   ]
  },
  {
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   "id": "e7b3a0ee",
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    "id": "e7b3a0ee",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Creating instances of the various classes defined so far**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d324e5a",
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    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [],
   "source": [
    "data_config = DataConfig(\n",
    "    numeric_feature_names=['longitude', 'latitude', 'housing_median_age', 'population', 'households', 'median_income'], \n",
    "    categorical_features_with_vocabulary={\n",
    "        'total_rooms': [str(i) for i in range(10)],\n",
    "        'total_bedrooms': [str(i) for i in range(10)],\n",
    "    }\n",
    ")\n",
    "model_config = FeatureTokenizerTransformerConfig(num_outputs=y.shape[1], out_activation='softmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf11fc2c",
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
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    "scrolled": true,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/numeric.py:2446: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return bool(asarray(a1 == a2).all())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"ftt\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " longitude (InputLayer)         [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " latitude (InputLayer)          [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " housing_median_age (InputLayer  [(None,)]           0           []                               \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " population (InputLayer)        [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " households (InputLayer)        [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " median_income (InputLayer)     [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " total_rooms (InputLayer)       [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " total_bedrooms (InputLayer)    [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " longitude_reshape (Reshape)    (None, 1)            0           ['longitude[0][0]']              \n",
      "                                                                                                  \n",
      " latitude_reshape (Reshape)     (None, 1)            0           ['latitude[0][0]']               \n",
      "                                                                                                  \n",
      " housing_median_age_reshape (Re  (None, 1)           0           ['housing_median_age[0][0]']     \n",
      " shape)                                                                                           \n",
      "                                                                                                  \n",
      " population_reshape (Reshape)   (None, 1)            0           ['population[0][0]']             \n",
      "                                                                                                  \n",
      " households_reshape (Reshape)   (None, 1)            0           ['households[0][0]']             \n",
      "                                                                                                  \n",
      " median_income_reshape (Reshape  (None, 1)           0           ['median_income[0][0]']          \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " total_rooms_lookup (StringLook  (None,)             0           ['total_rooms[0][0]']            \n",
      " up)                                                                                              \n",
      "                                                                                                  \n",
      " total_bedrooms_lookup (StringL  (None,)             0           ['total_bedrooms[0][0]']         \n",
      " ookup)                                                                                           \n",
      "                                                                                                  \n",
      " longitude_embeddings (Dense)   (None, 32)           64          ['longitude_reshape[0][0]']      \n",
      "                                                                                                  \n",
      " latitude_embeddings (Dense)    (None, 32)           64          ['latitude_reshape[0][0]']       \n",
      "                                                                                                  \n",
      " housing_median_age_embeddings   (None, 32)          64          ['housing_median_age_reshape[0][0\n",
      " (Dense)                                                         ]']                              \n",
      "                                                                                                  \n",
      " population_embeddings (Dense)  (None, 32)           64          ['population_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " households_embeddings (Dense)  (None, 32)           64          ['households_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " median_income_embeddings (Dens  (None, 32)          64          ['median_income_reshape[0][0]']  \n",
      " e)                                                                                               \n",
      "                                                                                                  \n",
      " total_rooms_embeddings (Embedd  (None, 32)          320         ['total_rooms_lookup[0][0]']     \n",
      " ing)                                                                                             \n",
      "                                                                                                  \n",
      " total_bedrooms_embeddings (Emb  (None, 32)          320         ['total_bedrooms_lookup[0][0]']  \n",
      " edding)                                                                                          \n",
      "                                                                                                  \n",
      " longitude_reshape_2 (Reshape)  (None, 1, 32)        0           ['longitude_embeddings[0][0]']   \n",
      "                                                                                                  \n",
      " latitude_reshape_2 (Reshape)   (None, 1, 32)        0           ['latitude_embeddings[0][0]']    \n",
      "                                                                                                  \n",
      " housing_median_age_reshape_2 (  (None, 1, 32)       0           ['housing_median_age_embeddings[0\n",
      " Reshape)                                                        ][0]']                           \n",
      "                                                                                                  \n",
      " population_reshape_2 (Reshape)  (None, 1, 32)       0           ['population_embeddings[0][0]']  \n",
      "                                                                                                  \n",
      " households_reshape_2 (Reshape)  (None, 1, 32)       0           ['households_embeddings[0][0]']  \n",
      "                                                                                                  \n",
      " median_income_reshape_2 (Resha  (None, 1, 32)       0           ['median_income_embeddings[0][0]'\n",
      " pe)                                                             ]                                \n",
      "                                                                                                  \n",
      " total_rooms_reshape_2 (Reshape  (None, 1, 32)       0           ['total_rooms_embeddings[0][0]'] \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " total_bedrooms_reshape_2 (Resh  (None, 1, 32)       0           ['total_bedrooms_embeddings[0][0]\n",
      " ape)                                                            ']                               \n",
      "                                                                                                  \n",
      " feature_embeddings_stack (Conc  (None, 8, 32)       0           ['longitude_reshape_2[0][0]',    \n",
      " atenate)                                                         'latitude_reshape_2[0][0]',     \n",
      "                                                                  'housing_median_age_reshape_2[0]\n",
      "                                                                 [0]',                            \n",
      "                                                                  'population_reshape_2[0][0]',   \n",
      "                                                                  'households_reshape_2[0][0]',   \n",
      "                                                                  'median_income_reshape_2[0][0]',\n",
      "                                                                  'total_rooms_reshape_2[0][0]',  \n",
      "                                                                  'total_bedrooms_reshape_2[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " transformer_block (Transformer  (None, 8, 32)       34768       ['feature_embeddings_stack[0][0]'\n",
      " Block)                                                          ]                                \n",
      "                                                                                                  \n",
      " transformer_block_1 (Transform  (None, 8, 32)       34768       ['transformer_block[0][0]']      \n",
      " erBlock)                                                                                         \n",
      "                                                                                                  \n",
      " global_max_pooling1d (GlobalMa  (None, 32)          0           ['transformer_block_1[0][0]']    \n",
      " xPooling1D)                                                                                      \n",
      "                                                                                                  \n",
      " outputs (Dense)                (None, 6)            198         ['global_max_pooling1d[0][0]']   \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 70,758\n",
      "Trainable params: 70,758\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = FeatureTokenizerTransformer.from_config(data_config, model_config, name='ftt')\n",
    "model.summary()\n",
    "utils.plot_model(model, show_shapes=True, expand_nested=True, rankdir='LR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "668dd940",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-31T14:05:39.761148Z",
     "iopub.status.busy": "2022-03-31T14:05:39.760110Z",
     "iopub.status.idle": "2022-03-31T14:05:39.762782Z",
     "shell.execute_reply": "2022-03-31T14:05:39.763361Z",
     "shell.execute_reply.started": "2022-02-07T16:59:04.408088Z"
    },
    "id": "668dd940",
    "papermill": {
     "duration": 0.042795,
     "end_time": "2022-03-31T14:05:39.763510",
     "exception": false,
     "start_time": "2022-03-31T14:05:39.720715",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "MAX_EPOCHS  = 10_000\n",
    "\n",
    "get_callbacks = lambda : [\n",
    "    keras.callbacks.EarlyStopping(min_delta=1e-4, patience=10, verbose=1, restore_best_weights=True),\n",
    "    keras.callbacks.ReduceLROnPlateau(patience=3, verbose=1),\n",
    "    keras.callbacks.CSVLogger(\"training_log.csv\"),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "D7i50Z7R_LAr",
   "metadata": {
    "id": "D7i50Z7R_LAr"
   },
   "outputs": [],
   "source": [
    "get_preprocessor = lambda : ColumnTransformer(\n",
    "    transformers=[(\"num\", StandardScaler(), data_config.NUMERIC_FEATURE_NAMES)],\n",
    "    remainder='passthrough'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54ce176",
   "metadata": {
    "id": "a54ce176",
    "papermill": {
     "duration": 0.029339,
     "end_time": "2022-03-31T14:05:39.822871",
     "exception": false,
     "start_time": "2022-03-31T14:05:39.793532",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26c8d63d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-31T14:05:39.896069Z",
     "iopub.status.busy": "2022-03-31T14:05:39.894947Z",
     "iopub.status.idle": "2022-03-31T22:43:20.878373Z",
     "shell.execute_reply": "2022-03-31T22:43:20.879492Z",
     "shell.execute_reply.started": "2022-02-07T16:59:04.417412Z"
    },
    "id": "26c8d63d",
    "papermill": {
     "duration": 31061.028116,
     "end_time": "2022-03-31T22:43:20.879702",
     "exception": false,
     "start_time": "2022-03-31T14:05:39.851586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "preprocessor = get_preprocessor().fit(X)\n",
    "X = pd.DataFrame(preprocessor.transform(X), columns=data_config.NUMERIC_FEATURE_NAMES + data_config.CATEGORICAL_FEATURE_NAMES)\n",
    "X_test = pd.DataFrame(preprocessor.transform(X_test), columns=data_config.NUMERIC_FEATURE_NAMES + data_config.CATEGORICAL_FEATURE_NAMES)\n",
    "\n",
    "for col in data_config.NUMERIC_FEATURE_NAMES:\n",
    "  X[col] = X[col].astype(float)\n",
    "  X_test[col] = X_test[col].astype(float)\n",
    "\n",
    "for col in data_config.CATEGORICAL_FEATURE_NAMES:\n",
    "  X[col] = X[col].astype(str)\n",
    "  X_test[col] = X_test[col].astype(str)\n",
    "    \n",
    "data_train = DataLoader.from_df(X, y, batch_size=512)\n",
    "data_test = DataLoader.from_df(X_test, y_test, batch_size=512)\n",
    "    \n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics='accuracy')\n",
    "model.fit(\n",
    "    data_train, validation_data=data_test, callbacks=get_callbacks(), \n",
    "    epochs=MAX_EPOCHS\n",
    ")  \n",
    "preds = model.predict(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec702ad",
   "metadata": {
    "id": "4ec702ad",
    "papermill": {
     "duration": 15.733372,
     "end_time": "2022-03-31T22:43:52.092011",
     "exception": false,
     "start_time": "2022-03-31T22:43:36.358639",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5eacd805",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-03-31T22:44:23.944481Z",
     "iopub.status.busy": "2022-03-31T22:44:23.943887Z",
     "iopub.status.idle": "2022-03-31T22:44:24.411346Z",
     "shell.execute_reply": "2022-03-31T22:44:24.410395Z",
     "shell.execute_reply.started": "2022-02-07T17:05:49.950752Z"
    },
    "id": "5eacd805",
    "outputId": "4a7f1209-d4b1-40ab-e394-c57116574aac",
    "papermill": {
     "duration": 16.863791,
     "end_time": "2022-03-31T22:44:24.411495",
     "exception": false,
     "start_time": "2022-03-31T22:44:07.547704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[357, 144,  11,   0,   0,   0],\n",
       "       [101, 960, 160,   3,   0,   1],\n",
       "       [  3, 213, 442,  50,   2,   4],\n",
       "       [  2,  28, 132, 120,   6,  16],\n",
       "       [  1,   7,  30,  42,  11,  25],\n",
       "       [  2,   4,  12,  22,   3,  86]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test['median_house_value'], preds.argmax(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6Z3YRbCey9nG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6Z3YRbCey9nG",
    "outputId": "303edea8-9a17-4b9d-e1bd-39033117f5e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.77      0.70      0.73       512\n",
      "         1.0       0.71      0.78      0.74      1225\n",
      "         2.0       0.56      0.62      0.59       714\n",
      "         3.0       0.51      0.39      0.44       304\n",
      "         4.0       0.50      0.09      0.16       116\n",
      "         5.0       0.65      0.67      0.66       129\n",
      "\n",
      "    accuracy                           0.66      3000\n",
      "   macro avg       0.62      0.54      0.55      3000\n",
      "weighted avg       0.65      0.66      0.65      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test['median_house_value'], preds.argmax(axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Guet_Des7W13",
   "metadata": {
    "id": "Guet_Des7W13"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Feature-Tokenizer-Transformer.ipynb",
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   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
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   "start_time": "2022-03-31T14:04:24.854687",
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