creme mini-batch performance

linear-models.ipynb

standard-scaling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard scaling in mini-batches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.95"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.random.randint(0, 10, size=20)\n",
    "x.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating with one example at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.95"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = 0\n",
    "n = 0\n",
    "\n",
    "for xi in x:\n",
    "    n += 1\n",
    "    m += (xi - m) / n\n",
    "    \n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating in mini-batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.949999999999999"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = 0\n",
    "n = 0\n",
    "\n",
    "for chunk in np.array_split(x, 3):\n",
    "    chunk_mean = chunk.mean()\n",
    "    \n",
    "    n += len(chunk)\n",
    "    m += len(chunk) * (chunk_mean - m) / n\n",
    "    \n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.1475"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.var(ddof=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.471052631578948"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.var(ddof=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating with one example at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.471052631578946"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = 0\n",
    "v = 0\n",
    "n = 0\n",
    "ddof = 1\n",
    "\n",
    "for xi in x:\n",
    "    \n",
    "    prev_m = m\n",
    "    \n",
    "    n += 1\n",
    "    m += (xi - m) / n\n",
    "    \n",
    "    if n > ddof:\n",
    "        v += ((xi - m) * (xi - prev_m) - v) / (n - ddof)\n",
    "    \n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating in mini-batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.1275"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = 0\n",
    "var = 0\n",
    "m = 0\n",
    "\n",
    "for chunk in np.array_split(x, 3):\n",
    "    \n",
    "    new_mean = chunk.mean()\n",
    "    new_var = chunk.var()\n",
    "    \n",
    "    n = len(chunk)\n",
    "    \n",
    "    old_mean = mean\n",
    "    \n",
    "    a = m / (m + n)\n",
    "    b = n / (m + n)\n",
    "    \n",
    "    mean = a * old_mean + b * new_mean\n",
    "    var  = (\n",
    "        a * var +\n",
    "        b * new_var +\n",
    "        a * b * (old_mean - new_mean) ** 2\n",
    "    )\n",
    "    \n",
    "    m += n\n",
    "    \n",
    "var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.78 s ± 259 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "233 µs ± 10.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from creme import preprocessing\n",
    "\n",
    "scaler = preprocessing.StandardScaler()\n",
    "\n",
    "chunksize = 1000\n",
    "\n",
    "%timeit for batch in pd.read_csv('/Users/mhalford/creme_data/CreditCard/creditcard.csv', chunksize=chunksize): scaler.fit_many(batch)\n",
    "%timeit scaler.transform_many(batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.23 s ± 186 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "659 µs ± 24.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "sk_scaler = preprocessing.StandardScaler()\n",
    "\n",
    "%timeit for batch in pd.read_csv('/Users/mhalford/creme_data/CreditCard/creditcard.csv', chunksize=chunksize): sk_scaler.partial_fit(batch)\n",
    "%timeit pd.DataFrame(sk_scaler.transform(batch), columns=batch.columns)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}