regression_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_tabnet.tab_model import TabNetRegressor\n",
    "\n",
    "import torch\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "\n",
    "\n",
    "import os\n",
    "import wget\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download census-income dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\n",
    "dataset_name = 'census-income'\n",
    "out = Path(os.getcwd()+'/data/'+dataset_name+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out.parent.mkdir(parents=True, exist_ok=True)\n",
    "if out.exists():\n",
    "    print(\"File already exists.\")\n",
    "else:\n",
    "    print(\"Downloading file...\")\n",
    "    wget.download(url, out.as_posix())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data and split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(out)\n",
    "target = ' <=50K'\n",
    "if \"Set\" not in train.columns:\n",
    "    train[\"Set\"] = np.random.choice([\"train\", \"valid\", \"test\"], p =[.8, .1, .1], size=(train.shape[0],))\n",
    "\n",
    "train_indices = train[train.Set==\"train\"].index\n",
    "valid_indices = train[train.Set==\"valid\"].index\n",
    "test_indices = train[train.Set==\"test\"].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple preprocessing\n",
    "\n",
    "Label encode categorical features and fill empty cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = []\n",
    "categorical_dims =  {}\n",
    "for col in train.columns[train.dtypes == object]:\n",
    "    print(col, train[col].nunique())\n",
    "    l_enc = LabelEncoder()\n",
    "    train[col] = train[col].fillna(\"VV_likely\")\n",
    "    train[col] = l_enc.fit_transform(train[col].values)\n",
    "    categorical_columns.append(col)\n",
    "    categorical_dims[col] = len(l_enc.classes_)\n",
    "\n",
    "for col in train.columns[train.dtypes == 'float64']:\n",
    "    train.fillna(train.loc[train_indices, col].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define categorical features for categorical embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unused_feat = ['Set']\n",
    "\n",
    "features = [ col for col in train.columns if col not in unused_feat+[target]] \n",
    "\n",
    "cat_idxs = [ i for i, f in enumerate(features) if f in categorical_columns]\n",
    "\n",
    "cat_dims = [ categorical_dims[f] for i, f in enumerate(features) if f in categorical_columns]\n",
    "\n",
    "# define your embedding sizes : here just a random choice\n",
    "cat_emb_dim = [5, 4, 3, 6, 2, 2, 1, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = TabNetRegressor(cat_dims=cat_dims, cat_emb_dim=cat_emb_dim, cat_idxs=cat_idxs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[features].values[train_indices]\n",
    "y_train = train[target].values[train_indices].reshape(-1, 1)\n",
    "\n",
    "X_valid = train[features].values[valid_indices]\n",
    "y_valid = train[target].values[valid_indices].reshape(-1, 1)\n",
    "\n",
    "X_test = train[features].values[test_indices]\n",
    "y_test = train[target].values[test_indices].reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_epochs = 100 if not os.getenv(\"CI\", False) else 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_tabnet.augmentations import RegressionSMOTE\n",
    "aug = RegressionSMOTE(p=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clf.fit(\n",
    "    X_train=X_train, y_train=y_train,\n",
    "    eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
    "    eval_name=['train', 'valid'],\n",
    "    eval_metric=['rmsle', 'mae', 'rmse', 'mse'],\n",
    "    max_epochs=max_epochs,\n",
    "    patience=50,\n",
    "    batch_size=1024, virtual_batch_size=128,\n",
    "    num_workers=0,\n",
    "    drop_last=False,\n",
    "    augmentations=aug, #aug\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deprecated : best model is automatically loaded at end of fit\n",
    "# clf.load_best_model()\n",
    "\n",
    "preds = clf.predict(X_test)\n",
    "\n",
    "y_true = y_test\n",
    "\n",
    "test_score = mean_squared_error(y_pred=preds, y_true=y_true)\n",
    "\n",
    "print(f\"BEST VALID SCORE FOR {dataset_name} : {clf.best_cost}\")\n",
    "print(f\"FINAL TEST SCORE FOR {dataset_name} : {test_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save model and load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save tabnet model\n",
    "saving_path_name = \"./tabnet_model_test_1\"\n",
    "saved_filepath = clf.save_model(saving_path_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define new model with basic parameters and load state dict weights\n",
    "loaded_clf = TabNetRegressor()\n",
    "loaded_clf.load_model(saved_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_preds = loaded_clf.predict(X_test)\n",
    "loaded_test_mse = mean_squared_error(loaded_preds, y_test)\n",
    "\n",
    "print(f\"FINAL TEST SCORE FOR {dataset_name} : {loaded_test_mse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert(test_score == loaded_test_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global explainability : feat importance summing to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Local explainability and masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explain_matrix, masks = clf.explain(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(20,20))\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].imshow(masks[i][:50])\n",
    "    axs[i].set_title(f\"mask {i}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "clf_xgb = XGBRegressor(max_depth=8,\n",
    "    learning_rate=0.1,\n",
    "    n_estimators=1000,\n",
    "    verbosity=0,\n",
    "    silent=None,\n",
    "    objective='reg:linear',\n",
    "    booster='gbtree',\n",
    "    n_jobs=-1,\n",
    "    nthread=None,\n",
    "    gamma=0,\n",
    "    min_child_weight=1,\n",
    "    max_delta_step=0,\n",
    "    subsample=0.7,\n",
    "    colsample_bytree=1,\n",
    "    colsample_bylevel=1,\n",
    "    colsample_bynode=1,\n",
    "    reg_alpha=0,\n",
    "    reg_lambda=1,\n",
    "    scale_pos_weight=1,\n",
    "    base_score=0.5,\n",
    "    random_state=0,\n",
    "    seed=None,)\n",
    "\n",
    "clf_xgb.fit(X_train, y_train,\n",
    "        eval_set=[(X_valid, y_valid)],\n",
    "        early_stopping_rounds=40,\n",
    "        verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = np.array(clf_xgb.predict(X_valid))\n",
    "valid_auc = mean_squared_error(y_pred=preds, y_true=y_valid)\n",
    "print(valid_auc)\n",
    "\n",
    "preds = np.array(clf_xgb.predict(X_test))\n",
    "test_auc = mean_squared_error(y_pred=preds, y_true=y_test)\n",
    "print(test_auc)"
   ]
  }
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