cuml/linear_regression_demo.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression\n",
    "LinearRegression is a simple machine learning model where the response y is modelled by a linear combination of the predictors in X."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For information on converting your dataset to cuDF format, refer to the [cuDF documentation](https://rapidsai.github.io/projects/cudf/en/latest/)\n",
    "\n",
    "For additional information cuML's linear regression: https://rapidsai.github.io/projects/cuml/en/latest/api.html#linear-regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import cudf as gd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from cuml.linear_model import LinearRegression as cuLR\n",
    "from sklearn.linear_model import LinearRegression as skLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 2**20\n",
    "n_features = 399"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Data\n",
    "\n",
    "### Host"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "X,y = make_regression(n_samples=n_samples, n_features=n_features, random_state=0)\n",
    "\n",
    "X = pd.DataFrame(X)\n",
    "y = pd.Series(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "X_cudf = gd.DataFrame.from_pandas(X_train)\n",
    "X_cudf_test = gd.DataFrame.from_pandas(X_test)\n",
    "\n",
    "y_cudf = gd.Series(y_train.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn Model\n",
    "\n",
    "### Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "ols_sk = skLR(fit_intercept=True,\n",
    "              normalize=True,\n",
    "              n_jobs=-1)\n",
    "\n",
    "ols_sk.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "predict_sk = ols_sk.predict(X_test)\n",
    "\n",
    "error_sk = mean_squared_error(y_test, predict_sk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cuML Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "ols_cuml = cuLR(fit_intercept=True,\n",
    "                normalize=True,\n",
    "                algorithm='eig')\n",
    "\n",
    "ols_cuml.fit(X_cudf, y_cudf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "predict_cuml = ols_cuml.predict(X_cudf_test).to_array()\n",
    "\n",
    "error_cuml = mean_squared_error(y_test, predict_cuml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"SKL MSE(y): %s\" % error_sk)\n",
    "print(\"CUML MSE(y): %s\" % error_cuml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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