tutorials/cuML_Regression_Example.ipynb

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   "source": [
    "This notebook walks through a basic example of using the GPU-accelerated estimators from [RAPIDS](https://rapids.ai/) cuML and [DMLC/XGBoost](https://github.com/dmlc/xgboost) with TPOT for classification tasks. You must have access to an NVIDIA GPU and have cuML installed in your environment. Running this notebook without cuML will cause TPOT to raise a `ValueError`, indicating you should install cuML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tpot import TPOTRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSAMPLES = 50000\n",
    "NFEATURES = 50\n",
    "SEED = 12\n",
    "\n",
    "# For cuML with TPOT, you must use CPU data (such as NumPy arrays)\n",
    "X, y = make_regression(\n",
    "    n_samples=NSAMPLES,\n",
    "    n_features=NFEATURES,\n",
    "    n_informative=NFEATURES,\n",
    "    random_state=SEED,\n",
    "    noise=200,\n",
    ")\n",
    "\n",
    "X = X.astype(\"float32\")\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for cuML to work correctly, you must set `n_jobs=1` (the default setting)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "text": [
      "\n",
      "Generation 1 - Current best internal CV score: -40245.878012401336\n",
      "Generation 2 - Current best internal CV score: -40245.878012401336\n",
      "Generation 3 - Current best internal CV score: -40245.878012401336\n",
      "Generation 4 - Current best internal CV score: -40245.87130877891\n",
      "Generation 5 - Current best internal CV score: -40245.87130877891\n",
      "Best pipeline: Ridge(RobustScaler(input_matrix))\n",
      "0.8281615479382644\n"
     ]
    }
   ],
   "source": [
    "# TPOT setup\n",
    "GENERATIONS = 5\n",
    "POP_SIZE = 100\n",
    "CV = 5\n",
    "\n",
    "tpot = TPOTRegressor(\n",
    "    generations=GENERATIONS,\n",
    "    population_size=POP_SIZE,\n",
    "    random_state=SEED,\n",
    "    config_dict=\"TPOT cuML\",\n",
    "    n_jobs=1, # cuML requires n_jobs=1\n",
    "    cv=CV,\n",
    "    verbosity=2,\n",
    ")\n",
    "\n",
    "tpot.fit(X_train, y_train)\n",
    "\n",
    "preds = tpot.predict(X_test)\n",
    "print(r2_score(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from cuml.linear_model import Ridge\n",
      "from sklearn.model_selection import train_test_split\n",
      "from sklearn.pipeline import make_pipeline\n",
      "from sklearn.preprocessing import RobustScaler\n",
      "from tpot.export_utils import set_param_recursive\n",
      "\n",
      "# NOTE: Make sure that the outcome column is labeled 'target' in the data file\n",
      "tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)\n",
      "features = tpot_data.drop('target', axis=1)\n",
      "training_features, testing_features, training_target, testing_target = \\\n",
      "            train_test_split(features, tpot_data['target'], random_state=12)\n",
      "\n",
      "# Average CV score on the training set was: -40245.87130877891\n",
      "exported_pipeline = make_pipeline(\n",
      "    RobustScaler(),\n",
      "    Ridge()\n",
      ")\n",
      "# Fix random state for all the steps in exported pipeline\n",
      "set_param_recursive(exported_pipeline.steps, 'random_state', 12)\n",
      "\n",
      "exported_pipeline.fit(training_features, training_target)\n",
      "results = exported_pipeline.predict(testing_features)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tpot.export('tpot_regression_cuml_pipeline.py')\n",
    "print(tpot.export())"
   ]
  }
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