{"nbformat_minor": 1, "metadata": {"language_info": {"name": "python", "file_extension": ".py", "pygments_lexer": "ipython3", "codemirror_mode": {"name": "ipython", "version": 3}, "nbconvert_exporter": "python", "version": "3.6.3", "mimetype": "text/x-python"}, "kernelspec": {"name": "python3", "display_name": "Python 3", "language": "python"}}, "cells": [{"metadata": {}, "cell_type": "markdown", "source": ["How are you handling weights?  This is one way..."]}, {"outputs": [], "metadata": {"_cell_guid": "cf9b1bed-edf6-43e4-94cd-a42beb32319c", "_uuid": "61a734cfe0c3d18a174cb7970336d6a3ffaaf249", "collapsed": true}, "cell_type": "code", "source": ["import numpy as np # linear algebra\n", "import pandas as pd"], "execution_count": 1}, {"outputs": [], "metadata": {"collapsed": true}, "cell_type": "code", "source": ["def transformtargets(originaltargets, weights):\n", "    return np.log(np.power(originaltargets + 1.,np.sqrt(weights)))\n", "\n", "def inversetransformtargets(transformedtargets, weights):\n", "    return np.exp(transformedtargets/np.sqrt(weights)) - 1."], "execution_count": 2}, {"outputs": [], "metadata": {"collapsed": true}, "cell_type": "code", "source": ["originaltargets =np.array([0,1,2,3])\n", "weights = np.array([1,1.25,1,1])"], "execution_count": 3}, {"outputs": [], "metadata": {}, "cell_type": "code", "source": ["transformtargets(originaltargets,weights)"], "execution_count": 4}, {"outputs": [], "metadata": {}, "cell_type": "code", "source": ["inversetransformtargets(transformtargets(originaltargets,weights),weights)"], "execution_count": 5}], "nbformat": 4}