amueller/constant_values.ipynb

## constant_values.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import quantile_transform, QuantileTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.gamma(10, size=10000) - 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zYAgsD/N6nMZKV1Xnuukl4K8ZHCZr1cUkawG66aUJ17PkqupiVV2pqueAP6OBz0OSVzII\ngI9W1Se75rF+FgyB5aH5x2kkuTHJdz8/D/wM8NTLv2pFOwzs7OZ3Ag9PsJaJeP6Lr/MLrPDPQ5IA\nHwKOV9X7h1aN9bPgzWLLRHf52wd44XEa+yZc0pJK8kYG//3D4E72j7WyD5J8HLiDwdMiLwL3A38D\nHAK+DzgN3FNVK/bE6Rz74A4Gh4IKOAX86tCx8RUnyVuBfwaeBJ7rmt/H4LzA2D4LhoAkNczDQZLU\nMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSG/R82Fd1Fz2zhYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281b909e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x, bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kzkkuTvJgkv1J/m2haxy1Af62fz7JPyR5qJvzOxejzlFJcluSY0kePcX28eZXVb3oH8x8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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281be1ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qt = QuantileTransformer()\n",
    "qx = qt.fit_transform(x.reshape(-1, 1)).ravel()\n",
    "plt.hist(qx, bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f281baaf588>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33Ad6o3ILs3MA+EDuA70RZucA8IFCBzqzcwD4QK4DvdEF0emDAyn2BADyL9eBvnn73rrH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281c56f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(qt.quantiles_, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.hstack([x, np.zeros(10000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rRzuqkfqFqrp5Ht3CuBNYP6W2FdhbVauAve311Wwn/TkA+Gh7L9zcrldezc4CH6yq1cBa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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281c1eabe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y, bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d9xyv98PATa09AXwTOGvctY+47rcCbwQeOsH2Of39dTofOQzzlRwbgDtqyh7g1UkumO9C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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281c24ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qt = QuantileTransformer()\n",
    "qy = qt.fit_transform(y.reshape(-1, 1)).ravel()\n",
    "plt.hist(qy, bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f281bc79fd0>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281c80f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(qt.quantiles_, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}