A sample of Ridge regression using scikit-learn.

1d_RidgeRegression.ipynb

2d_RidgeRegression.ipynb

SAlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.76100994, -2.53413626,  0.11658254],\n",
       "       [ 1.11060209, -2.53413626,  0.11658254],\n",
       "       [-1.76100994, -1.38058271,  0.11658254],\n",
       "       ..., \n",
       "       [-2.64765084,  1.89906822, -2.29790322],\n",
       "       [-2.64765084,  0.7025632 ,  1.48182544],\n",
       "       [-0.05215535,  1.89906822,  1.48182544]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from SALib.sample import saltelli\n",
    "\n",
    "problem = {\n",
    "  'bounds': [[-3.14159265359, 3.14159265359],\n",
    "                     [-3.14159265359, 3.14159265359],\n",
    "                     [-3.14159265359, 3.14159265359]],\n",
    " 'dists': None,\n",
    " 'groups': None,\n",
    " 'names': ['x1', 'x2', 'x3'],\n",
    " 'num_vars': 3 }\n",
    "\n",
    "param_values = saltelli.sample(problem, 1000, calc_second_order=False)\n",
    "param_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.29855261,  3.17651742,  5.76778927, ...,  4.47637742,\n",
       "        2.22011328,  6.19510567])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from SALib.test_functions import Ishigami\n",
    "Y = Ishigami.evaluate(param_values)\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter S1 S1_conf ST ST_conf\n",
      "x1 0.306443 0.081622 0.560137 0.083834\n",
      "x2 0.447767 0.070281 0.438722 0.038621\n",
      "x3 -0.001049 0.081337 0.242845 0.023344\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'S1': array([ 0.30644324,  0.44776661, -0.00104936]),\n",
       " 'S1_conf': array([ 0.08162168,  0.0702807 ,  0.0813368 ]),\n",
       " 'ST': array([ 0.56013728,  0.4387225 ,  0.24284474]),\n",
       " 'ST_conf': array([ 0.08383399,  0.03862135,  0.02334374])}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from SALib.analyze import sobol\n",
    "\n",
    "Si = sobol.analyze(problem, Y, calc_second_order=False, conf_level=0.95, print_to_console=True)\n",
    "Si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAFkCAYAAABMyWOlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBkmSlMnAIEmSMhkYJElSpv8PxAaRYKnW/iUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e02acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_Si( Si, x_names ):\n",
    "  x_vals = np.arange( len(Si['S1']) )\n",
    "  bar_width = 0.4\n",
    "  y_vals = Si['S1']\n",
    "  plt.bar(x_vals,                       Si['S1'], yerr=Si['S1_conf'], width = bar_width, label='S1', align='center', ecolor='black', color='skyblue')\n",
    "  plt.bar(x_vals + bar_width, Si['ST'], yerr=Si['ST_conf'], width = bar_width, label='ST', align='center', ecolor='black', color='plum')\n",
    "  plt.xticks(x_vals + bar_width / 2, x_names )\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "plot_Si(Si, problem['names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:ml]",
   "language": "python",
   "name": "conda-env-ml-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}