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Randomized two sided
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "source": [ | |
| "We're going to compare unrandomized to randomized thresholding at 2, specifically for power." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(-3.7578729420553452, 3.7578729420553452)" | |
| ] | |
| }, | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import numpy as np\n", | |
| "from scipy.stats import norm as ndist\n", | |
| "from selection.truncated.gaussian import truncated_gaussian_old as TG\n", | |
| "from selection.distributions.discrete_family import discrete_family\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "cutoff = 3\n", | |
| "\n", | |
| "def weight_fn(Z, variance, Itrunc):\n", | |
| " Ltrunc, Utrunc = Itrunc # interval that is complement of truncation region\n", | |
| " if variance == 0:\n", | |
| " return (Z >= Utrunc) + (Z <= Ltrunc)\n", | |
| " else:\n", | |
| " sd = np.sqrt(variance)\n", | |
| " C = np.sqrt(1 - variance)\n", | |
| " upper = ndist.sf((Utrunc - Z * C) / sd)\n", | |
| " lower = ndist.cdf((Ltrunc - Z * C) / sd)\n", | |
| " return upper + lower\n", | |
| " \n", | |
| "Z = np.random.standard_normal(5000000)\n", | |
| "Z = np.hstack([Z, -Z]) # for symmetry\n", | |
| "F = discrete_family(Z, weight_fn(Z, 0.25, (-3, 3)))\n", | |
| "L, U = F.equal_tailed_acceptance(0, alpha=0.05)\n", | |
| "L, U" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "[0.14417830127231085, 0.32680833972642032, 0.81386199266826698]" | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "def power(F, L, U, mu, variance, cutoff=cutoff, Z=None):\n", | |
| " if Z is None:\n", | |
| " Z = np.random.standard_normal(1000000) \n", | |
| " Z = Z + mu\n", | |
| " F = discrete_family(Z, weight_fn(Z, variance, (-cutoff, cutoff)))\n", | |
| " return F.ccdf(0, U) + F.cdf(0, L)\n", | |
| "\n", | |
| "[power(F, L, U, m, 0.25) for m in [1, 2, 4]]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/Users/jonathantaylor/Desktop/git-repos/selection/selection/distributions/discrete_family.py:86: RuntimeWarning: divide by zero encountered in log\n", | |
| " self._lw = np.log(xw[:,1])\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(-3.810311563895783, 3.810311563895783)" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "F0 = discrete_family(Z, weight_fn(Z, 0., (-cutoff, cutoff)))\n", | |
| "L0, U0 = F0.equal_tailed_acceptance(0, alpha=0.05)\n", | |
| "L0, U0" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "[0.10856914397405718, 0.22226001511715787, 0.68375246378327847]" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "[power(F0, L0, U0, m, 0) for m in [1, 2, 4]]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "((3, 3.810311563895783), (-3.810311563895783, 3.810311563895783))" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "F1 = discrete_family(Z, weight_fn(Z, 0., (-cutoff, cutoff)) * (Z > 0))\n", | |
| "U1 = F1.one_sided_acceptance(0, alpha=0.05)[1]\n", | |
| "(cutoff, U1), (L0, U0) # one-sided truncation acceptance, two-sided truncation acceptance" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": true, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def power_oneside(F, U, mu, cutoff=cutoff, Z=None):\n", | |
| " if Z is None:\n", | |
| " Z = np.random.standard_normal(1000000)\n", | |
| " Z = Z + mu\n", | |
| " F = discrete_family(Z, weight_fn(Z, 0, (-cutoff, cutoff)) * (Z > 0))\n", | |
| " return F.ccdf(0, U)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "source": [ | |
| "# Power plot" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x112cc0a90>" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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CCKdXUL+3HBUUFKTXr1+vw8PD9X/+8x+ttdbffPON7tChg05KStI+Pj56+vTpOisrS8+c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\njiaKIO+S3rSp3oYVB1cYz77zTjh1Cg4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aXpek4K9p9UND32LHA3eTKk1a98ElaKmQS8Dx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\nRIKYCrmIiEgQ+39AYoGJBTtHtAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x13cb804d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(8,8))\n", | |
| "Z = np.random.standard_normal(500000)\n", | |
| "mu = np.linspace(0, 6, 13)\n", | |
| "nonrandom = [power(F0, L0, U0, m, 0, Z=Z) for m in mu]\n", | |
| "onesided = [power_oneside(F1, U1, m, Z=Z) for m in mu]\n", | |
| "random = [power(F, L, U, m, 0.25, Z=Z) for m in mu]\n", | |
| "plt.plot(np.hstack([-mu[::-1], mu]), np.hstack([random[::-1], random]), label='Randomized')\n", | |
| "plt.plot(np.hstack([-mu[::-1], mu]), np.hstack([nonrandom[::-1], nonrandom]), label='Nonrandomized')\n", | |
| "plt.plot(np.hstack([mu]), np.hstack([onesided]), label='Onesided')\n", | |
| "plt.gca().set_xlabel(r'True mean $\\mu$')\n", | |
| "plt.gca().set_ylabel('Power')\n", | |
| "plt.legend()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "source": [ | |
| "# Smaller cutoff -- less exaggerated" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": true, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "F = discrete_family(Z, weight_fn(Z, 0.25, (-2, 2)))\n", | |
| "L, U = F.equal_tailed_acceptance(0, alpha=0.05)\n", | |
| "F0 = discrete_family(Z, weight_fn(Z, 0., (-2, 2)))\n", | |
| "L0, U0 = F0.equal_tailed_acceptance(0, alpha=0.05)\n", | |
| "F1 = discrete_family(Z, weight_fn(Z, 0., (-2, 2)) * (Z > 0))\n", | |
| "U1 = F1.one_sided_acceptance(0, alpha=0.05)[1]\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x12382af10>" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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eC59UfHO7EEJUuwkT4JFHKj62/aUH2dWntTTxaiJn5LXI9N3TeXPdm2y4f8Ml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\nRERCmBq5iIhICPs/Q6CUDB9AUFEAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x11fbe4ed0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(8,8))\n", | |
| "mu = np.linspace(0, 6, 13)\n", | |
| "Z = np.random.standard_normal(500000)\n", | |
| "nonrandom = [power(F0, L0, U0, m, 0, cutoff=2, Z=Z) for m in mu]\n", | |
| "random = [power(F, L, U, m, 0.25, cutoff=2, Z=Z) for m in mu]\n", | |
| "onesided = [power_oneside(F1, U1, m, cutoff=2, Z=Z) for m in mu]\n", | |
| "plt.plot(np.hstack([-mu[::-1], mu]), np.hstack([random[::-1], random]), label='Randomized')\n", | |
| "plt.plot(np.hstack([-mu[::-1], mu]), np.hstack([nonrandom[::-1], nonrandom]), label='Nonrandomized')\n", | |
| "plt.plot(np.hstack([mu]), np.hstack([onesided]), label='Onesided')\n", | |
| "plt.gca().set_xlabel(r'True mean $\\mu$')\n", | |
| "plt.gca().set_ylabel('Power')\n", | |
| "plt.legend()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true, | |
| "deletable": true, | |
| "editable": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "anaconda-cloud": {}, | |
| "kernelspec": { | |
| "display_name": "Python [default]", | |
| "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 | |
| } |
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