memory.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sklearn.datasets\n",
    "import sklearn.ensemble\n",
    "import sklearn.preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_features = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_samples = 1500000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 481.08 MiB, increment: 417.99 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit X, y = sklearn.datasets.make_classification(n_features=n_features, n_samples=n_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 481.10 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit standard_scaler = sklearn.preprocessing.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 481.17 MiB, increment: 0.07 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit X = standard_scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 481.18 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit classifier = sklearn.ensemble.RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 585.13 MiB, increment: 103.93 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 617.26 MiB, increment: 251.26 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit predictions = classifier.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 536.41 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "%memit probabilities = classifier.predict_proba(X)"
   ]
  }
 ],
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