blend_length_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pylab as pl\n",
    "from sklearn.linear_model import BayesianRidge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_len(row):\n",
    "    from collections import Counter\n",
    "    return Counter(list(row))['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/components-blends-knight.csv',sep='\\t',index_col=0)\n",
    "df[\"slen\"]=df.source.apply(len)\n",
    "df[\"tlen\"]=df.target.apply(get_len)\n",
    "df[\"ratio\"]=df[\"slen\"]/df[\"tlen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = BayesianRidge(verbose=True, compute_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[\"slen\"].values.reshape(-1,1)\n",
    "y=df[\"tlen\"].values\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test=train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convergence after  2  iterations\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=True, copy_X=True,\n",
       "       fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\n",
       "       normalize=False, tol=0.001, verbose=True)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.562570846424661"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,m.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7747074064529744"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.predict([[16]], return_std=True)[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RTHrje/a8TCSy8mzs9aicVz1ds8hDF6/pRESKUAnrBkEaHv4Rhw5dlmnWF4/3LdnR1cuA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80cfcfc950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df.slen.values, df.tlen.values, color='y', x_bins=20)\n",
    "pl.xlabel('$|Components|$', fontsize=14)\n",
    "pl.ylabel('$|Blend|$', fontsize=14)\n",
    "pl.savefig('./figs/components_blend_regplot.pdf', bbox_inches='tight')"
   ]
  },
  {
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}