Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Read in train and test as Pandas DataFrames\n",
    "\"\"\"\n",
    "df_train = pd.read_csv(\"train.csv\")\n",
    "df_test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles</th>\n",
       "      <th>feat_001</th>\n",
       "      <th>feat_002</th>\n",
       "      <th>feat_003</th>\n",
       "      <th>feat_004</th>\n",
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       "      <th>feat_251</th>\n",
       "      <th>feat_252</th>\n",
       "      <th>feat_253</th>\n",
       "      <th>feat_254</th>\n",
       "      <th>feat_255</th>\n",
       "      <th>feat_256</th>\n",
       "      <th>gap</th>\n",
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       "      <th>1</th>\n",
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       "      <td>[nH]1c2-c3occc3Cc2c2c1cc(-c1cccc3=C[SiH2]C=c13...</td>\n",
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       "<p>5 rows × 258 columns</p>\n",
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       "                                              smiles  feat_001  feat_002  \\\n",
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       "2  [nH]1c-2c([SiH2]c3cc(-c4scc5C=CCc45)c4nsnc4c-2...       1.0       0.0   \n",
       "3  [nH]1c2-c3occc3Cc2c2c1cc(-c1cccc3=C[SiH2]C=c13...       1.0       0.0   \n",
       "4     c1cnc2c3oc4cc(-c5ncncn5)c5nsnc5c4c3c3cocc3c2c1       0.0       0.0   \n",
       "\n",
       "   feat_003  feat_004  feat_005  feat_006  feat_007  feat_008  feat_009  ...   \\\n",
       "0       0.0       0.0       1.0       0.0       1.0       0.0       0.0  ...    \n",
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       "2       0.0       0.0       1.0       1.0       1.0       0.0       0.0  ...    \n",
       "3       0.0       0.0       1.0       1.0       1.0       0.0       0.0  ...    \n",
       "4       0.0       0.0       1.0       0.0       1.0       0.0       0.0  ...    \n",
       "\n",
       "   feat_248  feat_249  feat_250  feat_251  feat_252  feat_253  feat_254  \\\n",
       "0       1.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "1       1.0       0.0       0.0       1.0       0.0       0.0       0.0   \n",
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       "4       1.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "   feat_255  feat_256   gap  \n",
       "0       0.0       0.0  1.19  \n",
       "1       0.0       0.0  1.60  \n",
       "2       0.0       0.0  1.49  \n",
       "3       0.0       0.0  1.36  \n",
       "4       0.0       0.0  1.98  \n",
       "\n",
       "[5 rows x 258 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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       "   Id                                             smiles  feat_001  feat_002  \\\n",
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       "1   2         [nH]1cccc1-c1cc2c3nsnc3c3c4sccc4[nH]c3c2s1       0.0       0.0   \n",
       "2   3  [nH]1c2cc(-c3ccc[se]3)c3nsnc3c2c2c3cscc3c3ccc4...       1.0       0.0   \n",
       "3   4  [nH]1c(cc2cnc3c(c12)c1=C[SiH2]C=c1c1ccc2=CCC=c...       1.0       0.0   \n",
       "4   5  c1sc(-c2sc(-c3sc(-c4scc5[se]ccc45)c4ccoc34)c3c...       0.0       0.0   \n",
       "\n",
       "   feat_003  feat_004  feat_005  feat_006  feat_007  feat_008    ...     \\\n",
       "0       0.0       0.0       1.0       1.0       1.0       0.0    ...      \n",
       "1       0.0       0.0       1.0       1.0       1.0       0.0    ...      \n",
       "2       0.0       0.0       1.0       1.0       1.0       0.0    ...      \n",
       "3       0.0       0.0       1.0       1.0       1.0       0.0    ...      \n",
       "4       0.0       0.0       1.0       0.0       1.0       0.0    ...      \n",
       "\n",
       "   feat_247  feat_248  feat_249  feat_250  feat_251  feat_252  feat_253  \\\n",
       "0       0.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "1       0.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "2       0.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "3       0.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "4       0.0       1.0       0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "   feat_254  feat_255  feat_256  \n",
       "0       0.0       0.0       0.0  \n",
       "1       0.0       0.0       0.0  \n",
       "2       0.0       0.0       0.0  \n",
       "3       0.0       0.0       0.0  \n",
       "4       0.0       0.0       0.0  \n",
       "\n",
       "[5 rows x 258 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#store gap values\n",
    "Y_train = df_train.gap.values\n",
    "#row where testing examples start\n",
    "test_idx = df_train.shape[0]\n",
    "#delete 'Id' column\n",
    "df_test = df_test.drop(['Id'], axis=1)\n",
    "#delete 'gap' column\n",
    "df_train = df_train.drop(['gap'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C1=CC=C(C1)c1cc2ncc3c4[SiH2]C=Cc4ncc3c2c2=C[Si...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[nH]1c-2c([SiH2]c3cc(-c4scc5C=CCc45)c4nsnc4c-2...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[nH]1c2-c3occc3Cc2c2c1cc(-c1cccc3=C[SiH2]C=c13...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c1cnc2c3oc4cc(-c5ncncn5)c5nsnc5c4c3c3cocc3c2c1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 257 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles  feat_001  feat_002  \\\n",
       "0  c1ccc(o1)-c1ccc(s1)-c1cnc(-c2scc3[se]ccc23)c2n...       0.0       0.0   \n",
       "1  C1=CC=C(C1)c1cc2ncc3c4[SiH2]C=Cc4ncc3c2c2=C[Si...       1.0       0.0   \n",
       "2  [nH]1c-2c([SiH2]c3cc(-c4scc5C=CCc45)c4nsnc4c-2...       1.0       0.0   \n",
       "3  [nH]1c2-c3occc3Cc2c2c1cc(-c1cccc3=C[SiH2]C=c13...       1.0       0.0   \n",
       "4     c1cnc2c3oc4cc(-c5ncncn5)c5nsnc5c4c3c3cocc3c2c1       0.0       0.0   \n",
       "\n",
       "   feat_003  feat_004  feat_005  feat_006  feat_007  feat_008  feat_009  \\\n",
       "0       0.0       0.0       1.0       0.0       1.0       0.0       0.0   \n",
       "1       0.0       0.0       1.0       0.0       1.0       0.0       0.0   \n",
       "2       0.0       0.0       1.0       1.0       1.0       0.0       0.0   \n",
       "3       0.0       0.0       1.0       1.0       1.0       0.0       0.0   \n",
       "4       0.0       0.0       1.0       0.0       1.0       0.0       0.0   \n",
       "\n",
       "     ...     feat_247  feat_248  feat_249  feat_250  feat_251  feat_252  \\\n",
       "0    ...          0.0       1.0       0.0       0.0       0.0       0.0   \n",
       "1    ...          0.0       1.0       0.0       0.0       1.0       0.0   \n",
       "2    ...          0.0       1.0       0.0       0.0       0.0       1.0   \n",
       "3    ...          0.0       1.0       0.0       0.0       0.0       1.0   \n",
       "4    ...          0.0       1.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "   feat_253  feat_254  feat_255  feat_256  \n",
       "0       0.0       0.0       0.0       0.0  \n",
       "1       0.0       0.0       0.0       0.0  \n",
       "2       0.0       0.0       0.0       0.0  \n",
       "3       0.0       0.0       0.0       0.0  \n",
       "4       0.0       0.0       0.0       0.0  \n",
       "\n",
       "[5 rows x 257 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame with all train and test examples so we can more easily apply feature engineering on\n",
    "df_all = pd.concat((df_train, df_test), axis=0)\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train features: (1000000, 256)\n",
      "Train gap: (1000000,)\n",
      "Test features: (824230, 256)\n"
     ]
    }
   ],
   "source": [
    "#Drop the 'smiles' column\n",
    "df_all = df_all.drop(['smiles'], axis=1)\n",
    "vals = df_all.values\n",
    "X_train = vals[:test_idx]\n",
    "X_test = vals[test_idx:]\n",
    "print \"Train features:\", X_train.shape\n",
    "print \"Train gap:\", Y_train.shape\n",
    "print \"Test features:\", X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LR = LinearRegression()\n",
    "LR.fit(X_train, Y_train)\n",
    "LR_pred = LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RF = RandomForestRegressor()\n",
    "RF.fit(X_train, Y_train)\n",
    "RF_pred = RF.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reg = RidgeCV(alphas=[0.1,1.0,10.0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg.fit(X_train, Y_train)\n",
    "reg_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_to_file(filename, predictions):\n",
    "    with open(filename, \"w\") as f:\n",
    "        f.write(\"Id,Prediction\\n\")\n",
    "        for i,p in enumerate(predictions):\n",
    "            f.write(str(i+1) + \",\" + str(p) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "write_to_file(\"basic_ridge.csv\", reg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.linear_model import lasso_path, enet_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'eps' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-f0c362fe02c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0malphas_lasso\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoefs_lasso\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlasso_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_intercept\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'eps' is not defined"
     ]
    }
   ],
   "source": [
    "alphas_lasso, coefs_lasso, _ = lasso_path(X_train, Y_train, eps, fit_intercept=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eps = 5e-3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alphas_lasso, coefs_lasso, _ = lasso_path(X_train, Y_train, eps, fit_intercept=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNetCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "elasticnet = ElasticNetCV(l1_ratio= [0.0,0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,1.0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "elasticnet.fit(X_train, Y_train)\n",
    "elasticnet_predict=elasticnet.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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