{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d021c80-8290-4533-9f09-71b0edba468d",
   "metadata": {},
   "source": [
    "# Example: Accelerating pipelines on GPU\n",
    "----------------------------------------\n",
    "\n",
    "This example shows how to accelerate a pipeline on GPU using cuML.\n",
    "\n",
    "The data used is a synthetic dataset created using sklearn's [make_classification](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b57d1dff-114e-4f92-b2a5-536aaf5ce2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from atom import ATOMClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Create a dummy dataset\n",
    "X, y = make_classification(n_samples=100000, n_features=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da7bcaba-27ed-4d19-92c8-826ed808fe5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<< ================== ATOM ================== >>\n",
      "Algorithm task: binary classification.\n",
      "GPU training enabled.\n",
      "Backend engine: cuml.\n",
      "\n",
      "Dataset stats ==================== >>\n",
      "Shape: (100000, 41)\n",
      "Memory: 32.80 MB\n",
      "Scaled: True\n",
      "Outlier values: 8127 (0.2%)\n",
      "-------------------------------------\n",
      "Train set size: 80000\n",
      "Test set size: 20000\n",
      "-------------------------------------\n",
      "|   |       dataset |         train |          test |\n",
      "| - | ------------- | ------------- | ------------- |\n",
      "| 0 |   50006 (1.0) |   40005 (1.0) |   10001 (1.0) |\n",
      "| 1 |   49994 (1.0) |   39995 (1.0) |    9999 (1.0) |\n",
      "\n"
     ]
    }
   ],
   "source": [
    "atom = ATOMClassifier(X, y, device=\"gpu\", engine=\"cuml\", verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f986f25-d52f-45e9-b3e2-e645204363d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting Scaler...\n",
      "Scaling features...\n"
     ]
    }
   ],
   "source": [
    "atom.scale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72caeba1-d3a0-4ea1-8eea-2d2cdb4ba352",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.489864</td>\n",
       "      <td>1.861048</td>\n",
       "      <td>-0.353861</td>\n",
       "      <td>0.720823</td>\n",
       "      <td>-1.522117</td>\n",
       "      <td>-0.737707</td>\n",
       "      <td>-1.573936</td>\n",
       "      <td>-0.832174</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.019885</td>\n",
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       "      <td>-0.364059</td>\n",
       "      <td>-1.091604</td>\n",
       "      <td>-1.336692</td>\n",
       "      <td>0.186689</td>\n",
       "      <td>-0.274142</td>\n",
       "      <td>0.020563</td>\n",
       "      <td>0.693235</td>\n",
       "      <td>-1.908658</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.610058</td>\n",
       "      <td>-0.365231</td>\n",
       "      <td>0.284908</td>\n",
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       "      <td>-0.236553</td>\n",
       "      <td>-0.573761</td>\n",
       "      <td>-0.107317</td>\n",
       "      <td>-2.480178</td>\n",
       "      <td>0.420341</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.516618</td>\n",
       "      <td>-0.013420</td>\n",
       "      <td>-0.753879</td>\n",
       "      <td>-0.488243</td>\n",
       "      <td>0.560051</td>\n",
       "      <td>0.395817</td>\n",
       "      <td>-0.522523</td>\n",
       "      <td>-1.083503</td>\n",
       "      <td>-0.073398</td>\n",
       "      <td>0.383061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966283</td>\n",
       "      <td>1.405546</td>\n",
       "      <td>-0.658654</td>\n",
       "      <td>0.339090</td>\n",
       "      <td>-1.615997</td>\n",
       "      <td>-1.312444</td>\n",
       "      <td>0.984578</td>\n",
       "      <td>0.602858</td>\n",
       "      <td>-1.110684</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.111861</td>\n",
       "      <td>-0.966334</td>\n",
       "      <td>0.208509</td>\n",
       "      <td>0.494328</td>\n",
       "      <td>-0.766835</td>\n",
       "      <td>-0.003399</td>\n",
       "      <td>-0.500449</td>\n",
       "      <td>-0.530622</td>\n",
       "      <td>-0.481663</td>\n",
       "      <td>-1.146132</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.304896</td>\n",
       "      <td>2.030211</td>\n",
       "      <td>-1.189488</td>\n",
       "      <td>-1.238600</td>\n",
       "      <td>1.658765</td>\n",
       "      <td>-0.255644</td>\n",
       "      <td>0.572194</td>\n",
       "      <td>0.195496</td>\n",
       "      <td>0.617734</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.160135</td>\n",
       "      <td>-0.873517</td>\n",
       "      <td>0.719142</td>\n",
       "      <td>-2.020767</td>\n",
       "      <td>0.421435</td>\n",
       "      <td>-1.941230</td>\n",
       "      <td>0.835615</td>\n",
       "      <td>-1.178845</td>\n",
       "      <td>0.235273</td>\n",
       "      <td>-0.328574</td>\n",
       "      <td>...</td>\n",
       "      <td>1.633662</td>\n",
       "      <td>-0.631118</td>\n",
       "      <td>1.814046</td>\n",
       "      <td>1.031754</td>\n",
       "      <td>0.328665</td>\n",
       "      <td>1.704483</td>\n",
       "      <td>2.153710</td>\n",
       "      <td>-1.430552</td>\n",
       "      <td>-0.543915</td>\n",
       "      <td>1</td>\n",
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       "      <th>99995</th>\n",
       "      <td>1.100240</td>\n",
       "      <td>0.092581</td>\n",
       "      <td>-0.346265</td>\n",
       "      <td>0.234024</td>\n",
       "      <td>0.590199</td>\n",
       "      <td>0.755019</td>\n",
       "      <td>-1.688456</td>\n",
       "      <td>-1.031070</td>\n",
       "      <td>-0.620193</td>\n",
       "      <td>-0.283336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.356480</td>\n",
       "      <td>1.346821</td>\n",
       "      <td>-0.299087</td>\n",
       "      <td>2.343587</td>\n",
       "      <td>-2.003646</td>\n",
       "      <td>-0.933179</td>\n",
       "      <td>0.764255</td>\n",
       "      <td>-0.233526</td>\n",
       "      <td>-1.462311</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>-1.142596</td>\n",
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       "      <td>-0.974006</td>\n",
       "      <td>0.390418</td>\n",
       "      <td>0.404722</td>\n",
       "      <td>-0.324256</td>\n",
       "      <td>-0.288176</td>\n",
       "      <td>1.009458</td>\n",
       "      <td>0.860912</td>\n",
       "      <td>-0.191313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044618</td>\n",
       "      <td>-2.030135</td>\n",
       "      <td>1.448640</td>\n",
       "      <td>-0.854798</td>\n",
       "      <td>1.441451</td>\n",
       "      <td>1.347461</td>\n",
       "      <td>-0.937607</td>\n",
       "      <td>0.572504</td>\n",
       "      <td>-0.787673</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1.658252</td>\n",
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       "      <td>-0.020324</td>\n",
       "      <td>0.225917</td>\n",
       "      <td>0.154092</td>\n",
       "      <td>-1.208507</td>\n",
       "      <td>-0.199919</td>\n",
       "      <td>1.063016</td>\n",
       "      <td>-0.395696</td>\n",
       "      <td>-0.060886</td>\n",
       "      <td>...</td>\n",
       "      <td>1.563345</td>\n",
       "      <td>-1.261853</td>\n",
       "      <td>-0.810122</td>\n",
       "      <td>-0.503823</td>\n",
       "      <td>1.565602</td>\n",
       "      <td>-1.264792</td>\n",
       "      <td>-0.591644</td>\n",
       "      <td>1.588397</td>\n",
       "      <td>0.601721</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>-0.288042</td>\n",
       "      <td>-1.139792</td>\n",
       "      <td>1.548338</td>\n",
       "      <td>0.501413</td>\n",
       "      <td>0.361604</td>\n",
       "      <td>-0.315720</td>\n",
       "      <td>-0.564607</td>\n",
       "      <td>1.500870</td>\n",
       "      <td>0.501768</td>\n",
       "      <td>0.649079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344663</td>\n",
       "      <td>1.734476</td>\n",
       "      <td>0.660177</td>\n",
       "      <td>0.767554</td>\n",
       "      <td>1.461940</td>\n",
       "      <td>0.310189</td>\n",
       "      <td>-1.469978</td>\n",
       "      <td>0.900132</td>\n",
       "      <td>1.114330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>-3.093351</td>\n",
       "      <td>-0.636463</td>\n",
       "      <td>-0.449575</td>\n",
       "      <td>1.169980</td>\n",
       "      <td>-1.041870</td>\n",
       "      <td>-0.257173</td>\n",
       "      <td>2.072777</td>\n",
       "      <td>-0.101111</td>\n",
       "      <td>-0.956916</td>\n",
       "      <td>-0.251162</td>\n",
       "      <td>...</td>\n",
       "      <td>2.250647</td>\n",
       "      <td>0.746250</td>\n",
       "      <td>-0.610311</td>\n",
       "      <td>0.445467</td>\n",
       "      <td>-0.636288</td>\n",
       "      <td>-0.187444</td>\n",
       "      <td>0.226108</td>\n",
       "      <td>-0.186927</td>\n",
       "      <td>-1.024960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             x0        x1        x2        x3        x4        x5        x6  \\\n",
       "0      2.021646 -0.634557 -0.867811  1.103642  1.559011  0.122284 -0.864821   \n",
       "1     -0.019885  0.846568 -0.364059 -1.091604 -1.336692  0.186689 -0.274142   \n",
       "2      0.516618 -0.013420 -0.753879 -0.488243  0.560051  0.395817 -0.522523   \n",
       "3      0.111861 -0.966334  0.208509  0.494328 -0.766835 -0.003399 -0.500449   \n",
       "4      0.160135 -0.873517  0.719142 -2.020767  0.421435 -1.941230  0.835615   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "99995  1.100240  0.092581 -0.346265  0.234024  0.590199  0.755019 -1.688456   \n",
       "99996 -1.142596  0.321843 -0.974006  0.390418  0.404722 -0.324256 -0.288176   \n",
       "99997  1.658252  0.303637 -0.020324  0.225917  0.154092 -1.208507 -0.199919   \n",
       "99998 -0.288042 -1.139792  1.548338  0.501413  0.361604 -0.315720 -0.564607   \n",
       "99999 -3.093351 -0.636463 -0.449575  1.169980 -1.041870 -0.257173  2.072777   \n",
       "\n",
       "             x7        x8        x9  ...       x31       x32       x33  \\\n",
       "0      1.411657  0.147997 -2.269082  ... -0.489864  1.861048 -0.353861   \n",
       "1      0.020563  0.693235 -1.908658  ... -1.610058 -0.365231  0.284908   \n",
       "2     -1.083503 -0.073398  0.383061  ...  0.966283  1.405546 -0.658654   \n",
       "3     -0.530622 -0.481663 -1.146132  ... -0.304896  2.030211 -1.189488   \n",
       "4     -1.178845  0.235273 -0.328574  ...  1.633662 -0.631118  1.814046   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "99995 -1.031070 -0.620193 -0.283336  ...  0.356480  1.346821 -0.299087   \n",
       "99996  1.009458  0.860912 -0.191313  ...  0.044618 -2.030135  1.448640   \n",
       "99997  1.063016 -0.395696 -0.060886  ...  1.563345 -1.261853 -0.810122   \n",
       "99998  1.500870  0.501768  0.649079  ...  0.344663  1.734476  0.660177   \n",
       "99999 -0.101111 -0.956916 -0.251162  ...  2.250647  0.746250 -0.610311   \n",
       "\n",
       "            x34       x35       x36       x37       x38       x39  target  \n",
       "0      0.720823 -1.522117 -0.737707 -1.573936 -0.832174  0.203154       0  \n",
       "1      0.170156 -0.236553 -0.573761 -0.107317 -2.480178  0.420341       0  \n",
       "2      0.339090 -1.615997 -1.312444  0.984578  0.602858 -1.110684       1  \n",
       "3     -1.238600  1.658765 -0.255644  0.572194  0.195496  0.617734       1  \n",
       "4      1.031754  0.328665  1.704483  2.153710 -1.430552 -0.543915       1  \n",
       "...         ...       ...       ...       ...       ...       ...     ...  \n",
       "99995  2.343587 -2.003646 -0.933179  0.764255 -0.233526 -1.462311       1  \n",
       "99996 -0.854798  1.441451  1.347461 -0.937607  0.572504 -0.787673       0  \n",
       "99997 -0.503823  1.565602 -1.264792 -0.591644  1.588397  0.601721       0  \n",
       "99998  0.767554  1.461940  0.310189 -1.469978  0.900132  1.114330       0  \n",
       "99999  0.445467 -0.636288 -0.187444  0.226108 -0.186927 -1.024960       1  \n",
       "\n",
       "[100000 rows x 41 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96f5ee24-5f1a-4293-98d1-a1896c7ad1e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaler used: StandardScaler()\n",
      "Scaler's module: cuml._thirdparty.sklearn.preprocessing._data\n"
     ]
    }
   ],
   "source": [
    "print(f\"Scaler used: {atom.standard}\")\n",
    "print(f\"Scaler's module: {atom.standard.__class__.__module__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7c74dcf-a570-488a-bdcc-2d5bb234e887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training ========================= >>\n",
      "Models: RF, SGD, XGB\n",
      "Metric: f1\n",
      "\n",
      "\n",
      "Results for RandomForest:\n",
      "Fit ---------------------------------------------\n",
      "Train evaluation --> f1: 0.9726\n",
      "Test evaluation --> f1: 0.9431\n",
      "Time elapsed: 1.935s\n",
      "-------------------------------------------------\n",
      "Total time: 1.935s\n",
      "\n",
      "\n",
      "Results for StochasticGradientDescent:\n",
      "Fit ---------------------------------------------\n",
      "Train evaluation --> f1: 0.9236\n",
      "Test evaluation --> f1: 0.9219\n",
      "Time elapsed: 02m:16s\n",
      "-------------------------------------------------\n",
      "Total time: 02m:16s\n",
      "\n",
      "\n",
      "Results for XGBoost:\n",
      "Fit ---------------------------------------------\n",
      "Train evaluation --> f1: 0.9749\n",
      "Test evaluation --> f1: 0.9437\n",
      "Time elapsed: 6.394s\n",
      "-------------------------------------------------\n",
      "Total time: 6.394s\n",
      "\n",
      "\n",
      "Final results ==================== >>\n",
      "Total time: 02m:24s\n",
      "-------------------------------------\n",
      "RandomForest              --> f1: 0.9431\n",
      "StochasticGradientDescent --> f1: 0.9219\n",
      "XGBoost                   --> f1: 0.9437 !\n"
     ]
    }
   ],
   "source": [
    "atom.run(models=[\"RF\", \"SGD\", \"XGB\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a86a67e-9067-454f-bc92-dafb955e42bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score_train</th>\n",
       "      <th>score_test</th>\n",
       "      <th>time_fit</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RF</th>\n",
       "      <td>0.9726</td>\n",
       "      <td>0.9431</td>\n",
       "      <td>1.934512</td>\n",
       "      <td>1.934512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGD</th>\n",
       "      <td>0.9236</td>\n",
       "      <td>0.9219</td>\n",
       "      <td>135.871493</td>\n",
       "      <td>135.871493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.9749</td>\n",
       "      <td>0.9437</td>\n",
       "      <td>6.394416</td>\n",
       "      <td>6.394416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     score_train  score_test    time_fit        time\n",
       "RF        0.9726      0.9431    1.934512    1.934512\n",
       "SGD       0.9236      0.9219  135.871493  135.871493\n",
       "XGB       0.9749      0.9437    6.394416    6.394416"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "582646d5-49f9-451f-a85e-ea67c849e83d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF's module: cuml.ensemble.randomforestclassifier\n",
      "SGD's module: sklearn.linear_model._stochastic_gradient\n",
      "XGB's module: xgboost.sklearn\n"
     ]
    }
   ],
   "source": [
    "for m in atom.models:\n",
    "    print(f\"{m}'s module: {atom[m].estimator.__class__.__module__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f893e9e-9702-45f4-b845-a60f777d3015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>average_precision</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>matthews_corrcoef</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RF</th>\n",
       "      <td>0.9429</td>\n",
       "      <td>0.9741</td>\n",
       "      <td>0.9429</td>\n",
       "      <td>0.9431</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>0.8858</td>\n",
       "      <td>0.9391</td>\n",
       "      <td>0.9472</td>\n",
       "      <td>0.9792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGD</th>\n",
       "      <td>0.9217</td>\n",
       "      <td>0.9635</td>\n",
       "      <td>0.9218</td>\n",
       "      <td>0.9219</td>\n",
       "      <td>0.8551</td>\n",
       "      <td>0.8435</td>\n",
       "      <td>0.9203</td>\n",
       "      <td>0.9235</td>\n",
       "      <td>0.9676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.9434</td>\n",
       "      <td>0.9753</td>\n",
       "      <td>0.9434</td>\n",
       "      <td>0.9437</td>\n",
       "      <td>0.8933</td>\n",
       "      <td>0.8868</td>\n",
       "      <td>0.9385</td>\n",
       "      <td>0.9489</td>\n",
       "      <td>0.9798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     accuracy  average_precision  balanced_accuracy      f1  jaccard  \\\n",
       "RF     0.9429             0.9741             0.9429  0.9431   0.8924   \n",
       "SGD    0.9217             0.9635             0.9218  0.9219   0.8551   \n",
       "XGB    0.9434             0.9753             0.9434  0.9437   0.8933   \n",
       "\n",
       "     matthews_corrcoef  precision  recall  roc_auc  \n",
       "RF              0.8858     0.9391  0.9472   0.9792  \n",
       "SGD             0.8435     0.9203  0.9235   0.9676  \n",
       "XGB             0.8868     0.9385  0.9489   0.9798  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom.evaluate()"
   ]
  }
 ],
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