Learning Sine curve.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "N = 500 # number of data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.linspace(0, 4*np.pi, N)\n",
    "# t = t/(max(t))\n",
    "data = 15.0*np.sin(t+0.001) + 0.05 + np.random.randn(N) # create artificial data with noise\n",
    "# data = 3.0*np.sin(t+100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df= pd.DataFrame({'X1':t, 'Y':data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xgHtRURF27dpFWTbjQGgGU1VVFZxOJ9RqNdRqNQoLC5NaWXs8Hng8HpawIsJx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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8b93f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_data(data =df):\n",
    "    plt.plot(data['X1'],data['Y'],'k.')\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "\n",
    "plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def polynomial_data(data, power =1):\n",
    "    tmp_df = pd.DataFrame()\n",
    "    col_degree = ['X'+str(i) for i in range(1,power+1)]\n",
    "    \n",
    "    for i in range(1,power+1):\n",
    "        tmp_df[col_degree[i-1]]=df['X1']**i\n",
    "        \n",
    "    return tmp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#5 degree polynomial\n",
    "# df_poly = pd.concat([polynomial_data(df,5), df['Y']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#n degree polynomial\n",
    "df_poly = pd.concat([polynomial_data(df,50), df['Y']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X42</th>\n",
       "      <th>X43</th>\n",
       "      <th>X44</th>\n",
       "      <th>X45</th>\n",
       "      <th>X46</th>\n",
       "      <th>X47</th>\n",
       "      <th>X48</th>\n",
       "      <th>X49</th>\n",
       "      <th>X50</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.684765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.025183</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>4.021956e-07</td>\n",
       "      <td>1.012853e-08</td>\n",
       "      <td>2.550680e-10</td>\n",
       "      <td>6.423404e-12</td>\n",
       "      <td>1.617613e-13</td>\n",
       "      <td>4.073651e-15</td>\n",
       "      <td>1.025872e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>7.024111e-68</td>\n",
       "      <td>1.768889e-69</td>\n",
       "      <td>4.454613e-71</td>\n",
       "      <td>1.121810e-72</td>\n",
       "      <td>2.825066e-74</td>\n",
       "      <td>7.114395e-76</td>\n",
       "      <td>1.791626e-77</td>\n",
       "      <td>4.511870e-79</td>\n",
       "      <td>1.136229e-80</td>\n",
       "      <td>0.785382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.050366</td>\n",
       "      <td>0.002537</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>6.435129e-06</td>\n",
       "      <td>3.241131e-07</td>\n",
       "      <td>1.632435e-08</td>\n",
       "      <td>8.221957e-10</td>\n",
       "      <td>4.141089e-11</td>\n",
       "      <td>2.085710e-12</td>\n",
       "      <td>1.050493e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>3.089237e-55</td>\n",
       "      <td>1.555932e-56</td>\n",
       "      <td>7.836639e-58</td>\n",
       "      <td>3.947018e-59</td>\n",
       "      <td>1.987964e-60</td>\n",
       "      <td>1.001262e-61</td>\n",
       "      <td>5.042978e-63</td>\n",
       "      <td>2.539957e-64</td>\n",
       "      <td>1.279280e-65</td>\n",
       "      <td>1.973190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.075549</td>\n",
       "      <td>0.005708</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>3.257784e-05</td>\n",
       "      <td>2.461234e-06</td>\n",
       "      <td>1.859445e-07</td>\n",
       "      <td>1.404798e-08</td>\n",
       "      <td>1.061316e-09</td>\n",
       "      <td>8.018168e-11</td>\n",
       "      <td>6.057672e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>7.685711e-48</td>\n",
       "      <td>5.806503e-49</td>\n",
       "      <td>4.386774e-50</td>\n",
       "      <td>3.314178e-51</td>\n",
       "      <td>2.503839e-52</td>\n",
       "      <td>1.891633e-53</td>\n",
       "      <td>1.429116e-54</td>\n",
       "      <td>1.079688e-55</td>\n",
       "      <td>8.156966e-57</td>\n",
       "      <td>0.944683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.100732</td>\n",
       "      <td>0.010147</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>1.029621e-04</td>\n",
       "      <td>1.037162e-05</td>\n",
       "      <td>1.044758e-06</td>\n",
       "      <td>1.052410e-07</td>\n",
       "      <td>1.060119e-08</td>\n",
       "      <td>1.067883e-09</td>\n",
       "      <td>1.075705e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>1.358661e-42</td>\n",
       "      <td>1.368612e-43</td>\n",
       "      <td>1.378636e-44</td>\n",
       "      <td>1.388734e-45</td>\n",
       "      <td>1.398905e-46</td>\n",
       "      <td>1.409151e-47</td>\n",
       "      <td>1.419472e-48</td>\n",
       "      <td>1.429869e-49</td>\n",
       "      <td>1.440342e-50</td>\n",
       "      <td>2.554676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2        X3            X4            X5            X6  \\\n",
       "0  0.000000  0.000000  0.000000  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "1  0.025183  0.000634  0.000016  4.021956e-07  1.012853e-08  2.550680e-10   \n",
       "2  0.050366  0.002537  0.000128  6.435129e-06  3.241131e-07  1.632435e-08   \n",
       "3  0.075549  0.005708  0.000431  3.257784e-05  2.461234e-06  1.859445e-07   \n",
       "4  0.100732  0.010147  0.001022  1.029621e-04  1.037162e-05  1.044758e-06   \n",
       "\n",
       "             X7            X8            X9           X10    ...     \\\n",
       "0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00    ...      \n",
       "1  6.423404e-12  1.617613e-13  4.073651e-15  1.025872e-16    ...      \n",
       "2  8.221957e-10  4.141089e-11  2.085710e-12  1.050493e-13    ...      \n",
       "3  1.404798e-08  1.061316e-09  8.018168e-11  6.057672e-12    ...      \n",
       "4  1.052410e-07  1.060119e-08  1.067883e-09  1.075705e-10    ...      \n",
       "\n",
       "            X42           X43           X44           X45           X46  \\\n",
       "0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "1  7.024111e-68  1.768889e-69  4.454613e-71  1.121810e-72  2.825066e-74   \n",
       "2  3.089237e-55  1.555932e-56  7.836639e-58  3.947018e-59  1.987964e-60   \n",
       "3  7.685711e-48  5.806503e-49  4.386774e-50  3.314178e-51  2.503839e-52   \n",
       "4  1.358661e-42  1.368612e-43  1.378636e-44  1.388734e-45  1.398905e-46   \n",
       "\n",
       "            X47           X48           X49           X50         Y  \n",
       "0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00 -1.684765  \n",
       "1  7.114395e-76  1.791626e-77  4.511870e-79  1.136229e-80  0.785382  \n",
       "2  1.001262e-61  5.042978e-63  2.539957e-64  1.279280e-65  1.973190  \n",
       "3  1.891633e-53  1.429116e-54  1.079688e-55  8.156966e-57  0.944683  \n",
       "4  1.409151e-47  1.419472e-48  1.429869e-49  1.440342e-50  2.554676  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_poly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n",
      "Warning: Your H2O cluster version is too old (3 months and 26 days)! Please download and install the latest version from http://h2o.ai/download/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>1 hour 18 mins</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.10.0.10</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>3 months and 26 days !!!</td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_user_fawld0</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>1.851 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.11 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------\n",
       "H2O cluster uptime:         1 hour 18 mins\n",
       "H2O cluster version:        3.10.0.10\n",
       "H2O cluster version age:    3 months and 26 days !!!\n",
       "H2O cluster name:           H2O_from_python_user_fawld0\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    1.851 Gb\n",
       "H2O cluster total cores:    2\n",
       "H2O cluster allowed cores:  2\n",
       "H2O cluster status:         locked, healthy\n",
       "H2O connection url:         http://localhost:54321\n",
       "H2O connection proxy:\n",
       "Python version:             2.7.11 final\n",
       "--------------------------  ---------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "h2o.init(min_mem_size='2G')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "hdf= h2o.H2OFrame(df_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate = hdf.split_frame(ratios=[0.70], seed=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'X1',\n",
       " u'X2',\n",
       " u'X3',\n",
       " u'X4',\n",
       " u'X5',\n",
       " u'X6',\n",
       " u'X7',\n",
       " u'X8',\n",
       " u'X9',\n",
       " u'X10',\n",
       " u'X11',\n",
       " u'X12',\n",
       " u'X13',\n",
       " u'X14',\n",
       " u'X15',\n",
       " u'X16',\n",
       " u'X17',\n",
       " u'X18',\n",
       " u'X19',\n",
       " u'X20',\n",
       " u'X21',\n",
       " u'X22',\n",
       " u'X23',\n",
       " u'X24',\n",
       " u'X25',\n",
       " u'X26',\n",
       " u'X27',\n",
       " u'X28',\n",
       " u'X29',\n",
       " u'X30',\n",
       " u'X31',\n",
       " u'X32',\n",
       " u'X33',\n",
       " u'X34',\n",
       " u'X35',\n",
       " u'X36',\n",
       " u'X37',\n",
       " u'X38',\n",
       " u'X39',\n",
       " u'X40',\n",
       " u'X41',\n",
       " u'X42',\n",
       " u'X43',\n",
       " u'X44',\n",
       " u'X45',\n",
       " u'X46',\n",
       " u'X47',\n",
       " u'X48',\n",
       " u'X49',\n",
       " u'X50']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_name= hdf.col_names\n",
    "input_name.remove('Y')\n",
    "input_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl0 = H2ODeepLearningEstimator(seed =10,hidden =[500, 500, 599], epochs =5000,l2=1e-55, model_id='dl10')\n",
    "dl0.train(x = input_name, y = 'Y', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IFdGx4YLXgcViCYl78p9JKOcP5ezQdPLLBXH1t2LFCi688EKsViuzZ8/GbDZzwQUXBGXe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\nEEIIETApDSGEEAH7/3BlDN7cXIPeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa365f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl0.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train mse:  1.63089437004\n",
      "valid mse:  1.47970865993\n"
     ]
    }
   ],
   "source": [
    "print 'train mse: ', dl0.mse(train=True)\n",
    "print 'valid mse: ', dl0.mse(valid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# model = h2o.estimators.deeplearning(x=input_name, y='Y', training_frame=hdf_train, validation_frame=hdf_validate,\n",
    "#                                     distribution=\"multinomial\", activation=\"RectifierWithDropout\", \n",
    "#                                     hidden=[10,10,10], input_dropout_ratio=0.2, l1=1e-5, epochs=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc6c0908>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hOfGuQwx2cTWiD4YUHCEZc1Zk94I9WUa9GodD9LuJd9U2HzMLTPz1izrKXCqK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa700d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction= dl0.predict(hdf_validate)\n",
    "\n",
    "prediction['target']=hdf_validate['Y']\n",
    "output= prediction.as_data_frame()\n",
    "output.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}