{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
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Enter Password:
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\n", "\n", "show code\n" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%run ../initscript.py\n", "HTML(\"\"\"\n", "
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Enter Password:
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\n", "\n", "show code\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " show code\n", " " ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%run loadoptfuncs.py\n", "toggle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Secretary Problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Imagine you're interviewing a set of applicants for a position as a secretary, and your goal is to maximize the chance of hiring the single best applicant in the pool. While you have no idea how to assign scores to individual applicants, you can easily judge which one you prefer. You interview the applicants one at a time. You can decide to offer the job to an applicant at any point and they are guaranteed to accept, terminating the search. But if you pass over an applicant, deciding not to hire them, they are gone forever.\n", "\n", "In your search for a secretary, there are two ways you can fail: stopping early and stopping late. When you stop too early, you leave the best applicant undiscovered. When you stop too late, you hold out for a better applicant who doesn't exist.\n", "\n", "The optimal solution takes the form of the **Look-Then-Leap** Rule: \n", "\n", "- You set a predetermined amount of time for \"looking\", that is, exploring your options, gathering data — in which you categorically don't choose anyone, no matter how impressive. \n", "\n", "- After that point, you enter the \"leap\" phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.\n", "\n", "We can see how the Look-Then-Leap Rule emerges by considering how the secretary problem plays out in a small applicant pool." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " show answer\n", " " ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hide_answer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- One applicant the problem is easy to solve — hire her!\n", "\n", "- With two applicants, you have a 50% chance of success no matter what you do. You can hire the first applicant (who'll turn out to be the best half the time), or dismiss the first and by default hire the second (who is also best half the time).\n", "\n", "- Add a third applicant, and all of a sudden things get interesting. Can we do better than chance which is 33%? \n", "\n", " - When we see the first applicant, we have no information — she'll always appear to be the best yet. \n", " \n", " - When we see the third applicant, we have no agency — we have to make an offer to the final applicant.\n", " \n", " - For the second applicant, we have a little bit of both: we know whether she is better or worse than the first, and we have the freedom to either hire or dismiss her.\n", " \n", "- What happens when we just hire her if she's better than the first applicant, and dismiss her if she's not? This turns out to be the best possible strategy with 50% of chance getting the best candidate.\n", "\n", "- Suppose there are three candidates: Best, Good, Bad. There are 6 possible orders:\n", "\n", " 1. *Best, Good, Bad*: hire Bad (the last one) \n", " \n", " 2. *Best, Bad, Good*: hire Good (the last one)\n", " \n", " 3. *Good, Best, Bad*: hire Best\n", " \n", " 4. *Good, Bad, Best*: hire Best\n", " \n", " 5. *Bad, Best, Good*: hire Best\n", " \n", " 6. *Bad, Good, Best*: hire Good" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The best candidate is hired 2171 times in 5000 trials with 2 rejection\n" ] }, { "data": { "text/html": [ "
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person1person2person3person4person5best_scorebestbest_at_stophired_scorehired
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" ], "text/plain": [ " person1 person2 person3 person4 person5 best_score best \\\n", "0 40 35 81 61 98 98 person5 \n", "1 14 51 45 13 52 52 person5 \n", "2 75 65 72 19 44 75 person1 \n", "3 96 20 29 83 86 96 person1 \n", "4 61 79 59 52 18 79 person2 \n", "5 73 63 85 83 4 85 person3 \n", "6 86 70 61 94 15 94 person4 \n", "7 97 23 86 76 44 97 person1 \n", "8 14 86 61 65 40 86 person2 \n", "9 20 2 66 28 10 66 person3 \n", "\n", " best_at_stop hired_score hired \n", "0 40 81 person3 \n", "1 51 52 person5 \n", "2 75 44 person5 \n", "3 96 86 person5 \n", "4 79 18 person5 \n", "5 73 85 person3 \n", "6 86 94 person4 \n", "7 97 44 person5 \n", "8 86 40 person5 \n", "9 20 66 person3 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{2: 43.419999999999995}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "successful_rate(5, [2], printtable=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dc436a2455d74a80ac0fe7ed460d7fbc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(BoundedIntText(value=20, description='number of candidates:', min=5, style=DescriptionSt…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "style = {'description_width': '150px'}\n", "widgets.interact_manual.opts['manual_name'] = 'Run Simulation'\n", "interact_manual(secretary,\n", " n=widgets.BoundedIntText(value=20, min=5, max=100,\n", " description='number of candidates:', disabled=False, style=style));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optimal rule is the 37% Rule: look at the first 37% of the applicants, choosing none, then be ready to leap for anyone better than all those you've seen so far. To be precise, the mathematically optimal proportion of applicants to look at is $1/e$. So, anything between 35% and 40% provides a success rate extremely close to the maximum.\n", "\n", "In the secretary problem we know nothing about the applicants other than how they compare to one another. Some variations of the problem are studied:\n", "\n", "- If every secretary, for instance, had a GRE scored, which is the only thing that matters about our applicants. Then, we should use the Threshold Rule, where we immediately accept an applicant if she is above a certain percentile.\n", "\n", "- If waiting has a cost measured in dollars, a good candidate today beats a slightly better one several months from now.\n", "\n", "- If we have ability to recall a past candidate." ] } ], "metadata": { "hide_input": false, "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": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "widgets": { 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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 250\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 251\u001b[1;33m 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\u001b[0;36msecretary\u001b[1;34m(n)\u001b[0m\n\u001b[0;32m 300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 301\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msecretary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m \u001b[0mrate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuccessful_rate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 303\u001b[0m \u001b[0mlists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 304\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mlists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mD:\\Dropbox\\Work\\Web\\business-analytics\\docs\\optimization\\loadoptfuncs.py\u001b[0m in \u001b[0;36msuccessful_rate\u001b[1;34m(num_candidates, num_reject, num_sim, printtable)\u001b[0m\n\u001b[0;32m 273\u001b[0m printtable = False):\n\u001b[0;32m 274\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1234\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 275\u001b[1;33m \u001b[0mcandidates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_candidates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_sim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 276\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'person'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 277\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'best_score'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnum_candidates\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mD:\\Dropbox\\Work\\Web\\business-analytics\\docs\\optimization\\loadoptfuncs.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 273\u001b[0m printtable = False):\n\u001b[0;32m 274\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1234\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 275\u001b[1;33m \u001b[0mcandidates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_candidates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_sim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 276\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'person'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 277\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'best_score'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnum_candidates\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.choice\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'" ] } ] } }, "5fcfa17810044bdbb3c5cbf1088bc1e0": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_7d0fb6e110d3400c82ec8ba9c98236fa", "outputs": [ { "data": { "image/png": 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5aezNL2Li3M00qhvDree19TqSBICwK/R73l7JjgNHeO2Wn1Cvdi2v44hUiZlxz8Wd2H+4iIc+XEPDuGiu0LvNsBdWhT5z0Q7eWryTMQPak9GqgddxRE5JRITxyOXdOHCkiHEzl1E/LpqBnZK9jiUeCpsPRbfsPcyf/7WCXq0acHu/NK/jiFSL6KgInr22J12bJ3L7q4uYv3m/15HEQ2FR6EUlZdw5YzGREcaEq9KJ1Ff7JYTExUTx0vVn0Kx+bUZNWcDqLO3QGK7CotAfm72OpTtyeehnp9MssbbXcUSqXYO4aKbe2Iu46ChGTprP9v1HvI4kHgj5Qp+7fi/PfraRq3ulMrhritdxRPymef06TB3Vi8KSMkZM/Ia9+YVeR5IaFtKFvi+/kDGvLSGtcV3uubiT13FE/K59cjyTrs9g96ECbnhpAfmF2iIgnIRsoTvn+N0by8g9WsyTV3endrS+2i/hoWfLBjzz8x6syjrELS9nUlhS6nUkqSEhW+iTv9zCJ2v28KfBHeiYkuB1HJEa1a9DMg//7HTmbdjH2H8upVRbBISFkDwPfdWuQ/zt/TX079CYkWe38jqOiCd+1rM5+w8Xcf/7q2kQF819wzrrm9EhLuQK/UhRCXdMX0RinVr8v8u76RdYwtpNfduwN7+Q5z7fRMO60Ywe0N7rSOJHFRa6mcUCnwMxvse/4Zwbb2atgRlAA2ARMMI55/nO+//37io27T3MK6POpEFctNdxRDw3bnAH9h0u4u8fradh3RhGnNXS60jiJ5VZQy8E+jnnugHpwCAzOwt4CJjgnGsHHABG+S9m5by/PIvp87fzy3Pb0jutkddxRAKCmfHgpV3p36Ex97y9gveWZXkdSfykwkJ35fJ9N2v5Lg7oB7zhu38KcIlfElbSzoNHGffmMrq1SGTsQL2tFDleVGQET13Tg56p9RnzzyV8uWGv15HEDyp1louZRZrZEmAPMBvYCBx0zh07yXUH0OwEz73ZzDLNLDMnJ6c6Mn9PSWkZo2cspszBE1elUysyZE/eEamy2tGRTBx5Bq0bxXHT1EyW78j1OpJUs0o1n3Ou1DmXDjQHegEdf+hhJ3ju8865DOdcRlJSUtWT/oin5mxgwZYD/PWSLrRsGOeX1xAJBfXq1GLKjb1IrBPN9S/NZ/Pew15Hkmp0UoeyzrmDwKfAWUCimR37ULU5sKt6o1XO/M37eeLj9VzavRmXdP/BNwkicpwm9WJ5eVQvHDBi4jdkHyrwOpJUkwoL3cySzCzRd702MABYDcwBLvM9bCTwtr9CnkjukWJGz1hMiwZ1uO+SLjX98iJBq01SXV66/gz2Hy5i5KT55B4t9jqSVIPKHKGnAHPMbBmwAJjtnHsX+AMw1sw2AA2Bif6L+X3OOcbNXMaevEKeuKo7dWNC7pR6Eb/q1iKR50b0ZGNOPjdNyaSgWFsEBLvKnOWyzDnX3Tl3unOui3PuPt/9m5xzvZxzac65y51zNbq124wF2/lgxW5+e+FpdGuRWJMvLRIyzmmXxGNXpLNg635uf3UxJaVlXkeSUxCUp4Ns2JPHX/69kj5pjbj5nDZexxEJakO7NeXeoZ35aHU2f3prOc5p35dgFXTrFAXFpdwxfQl1oqN47IpuRGj6kMgpG3l2K/blF/LEJxtoVDeG3w/q4HUkqYKgK/QHP1jD6qxDTLo+g8YJsV7HEQkZYwa2Z+/hIp75dCMN68Ywqk9rryPJSQqqQv9kTTaTv9zC9We3ol8HTTcXqU5mxv8N68KBw0X837uraBgXrVOBg0zQrKHvOVTAb19fRseUBMYN1ttBEX+IjDAmXJnOWW0a8NvXlzJn7R6vI8lJCIpCLytzjH1tKUeKSnjy6nRia2n6kIi/xNaK5IXrMjitSTy/emURi7Yd8DqSVFJQFPrzX2xi7oa9jB/ambTG8V7HEQl58bG1mHxDLxonxHDj5AVs2JPndSSphKAo9JR6sVzeszlXndHC6ygiYSMpPoaXbzyTWpERjJg4n10Hj3odSSoQFIU+LL2Zpg+JeCC1YR2m3NCL/IISrps0nwOHPZ9hIz8iKApdRLzTqWkCL4zMYNv+I9wweQFHikoqfpJ4QoUuIhU6q01DnriqO8t2HOTWVxZRrC0CApIKXUQqZVCXJjwwvCufrcvhd68vpaxMWwQEmqD6YpGIeOuqXqnszS/kkVnraNUojtEDNO4xkKjQReSk3HZ+Gpv3HuHvH62nQ5MEBnVp4nUk8dGSi4icFDPj/uFdygeyv7aENbsPeR1JfFToInLSYmtF8vyIntSNieKmqZk6nTFAqNBFpEqSE2J5dkRPsnMLuX36Ig3HCAAqdBGpsh6p9fnr8C7M27CPB95f43WcsKcPRUXklFyR0aJ8RsG8zXRMiefyDG3R4RUdoYvIKbtrSEfObtuQu95awWLtzugZFbqInLKoyAievqYHyfViuOXlhWQfKvA6UlhSoYtItagfF80L12WQX1jCLS8vpKC41OtIYUeFLiLVpkOTBB67Ip0l2w9y11srcE7bA9SkCgvdzFqY2RwzW21mK83sTt/995rZTjNb4rsM8X9cEQl0g7o0YfSAdry5aAeT5m3xOk5YqcxZLiXAb5xzi8wsHlhoZrN9P5vgnHvEf/FEJBj9ul87Vmcd4v73VnFacjx92jXyOlJYqPAI3TmX5Zxb5LueB6wGNApcRE4oIsJ49Ip02jWO57ZXF7F132GvI4WFk1pDN7NWQHfgG99dt5vZMjObZGb1qzmbiASxujFRvHBdBmZw09RM8gs1GMPfKl3oZlYXeBMY7Zw7BPwDaAukA1nAoyd43s1mlmlmmTk5OdUQWUSCRWrDOjx9TQ825hxm7D+XaA91P6tUoZtZLcrLfJpzbiaAcy7bOVfqnCsDXgB6/dBznXPPO+cynHMZSUlJ1ZVbRIJE77RG3DWkI7NWZfP4x+u9jhPSKnOWiwETgdXOuceOuz/luIcNB1ZUfzwRCQU39G7FZT2b8/jH6/lwRZbXcUJWZc5y6Q2MAJab2RLffX8CrjazdMABW4Bb/JJQRIKemfHXS7qwYU8+Y19bSqtGcXRokuB1rJBjNXnif0ZGhsvMzKyx1xORwJJ9qIChT84lplYE79zWh/px0V5HCgpmttA5l1HR4/RNURGpMckJsTzn20P9tle1h3p1U6GLSI3qnlqfBy7typcb93H/+6u9jhNStB+6iNS4y3o2Z9WuY3uoJ3CF9lCvFjpCFxFP/GlIB/qkNeLut1awcKv2UK8OKnQR8URUZARPXdOdJvVi+eUrC9mdqz3UT5UKXUQ8k1gnmhdHZnCksIRbXs7UHuqnSIUuIp5qnxzPY1ems3RHLn+auVx7qJ8CFbqIeO7Czk0YM6A9MxfvZOLczV7HCVoqdBEJCHf0S2NQ5yY88P5qvlivjfyqQoUuIgGhfA/1brRrHM/try5my17toX6yVOgiEjDitIf6KVGhi0hAObaH+qa9hxmjPdRPigpdRAJO77RG3H1RR2avyubv2kO90vTVfxEJSNef3YpVuw7xxMfr6dgknsFdUyp+UpjTEbqIBCQz46/Du9A9NZHfvL6U1VmHvI4U8FToIhKwYqIiee7ansTHRnHT1Ez2Hy7yOlJAU6GLSEBrnBDLcyMy2JNXyG3TFlGsPdRPSIUuIgEvvUUifxvela827eP+97SH+onoQ1ERCQo/69mc1VmHeHHuZjqmxHPlGaleRwo4OkIXkaAxbnAHzmnXiLv/tYKFW/d7HSfgqNBFJGhERUbw5NXdaZpYm1teXkRW7lGvIwUUFbqIBJXEOtG8cF0GR4tKuOXlhdpD/TgqdBEJOu2T45lwZTrLduTyR+2h/i0VuogEpQs6N2HswPa8pT3Uv1VhoZtZCzObY2arzWylmd3pu7+Bmc02s/W+P+v7P66IyH/dfn4ag7uU76H++TrtoV6ZI/QS4DfOuY7AWcBtZtYJGAd87JxrB3zsuy0iUmMiIoxHLu9G++R4bn91UdjvoV5hoTvnspxzi3zX84DVQDNgGDDF97ApwCX+CikiciLH9lCPiDB+MTWTw2G8h/pJraGbWSugO/ANkOycy4Ly0gcan+A5N5tZppll5uToLZGIVL8WDcr3UN+wJ5+n5mzwOo5nKl3oZlYXeBMY7Zyr9LZnzrnnnXMZzrmMpKSkqmQUEalQ77RGXNqjGRO/2MzWfeG59FKpQjezWpSX+TTn3Ezf3dlmluL7eQqwxz8RRUQq5w+DOhAVaWG730tlznIxYCKw2jn32HE/egcY6bs+Eni7+uOJiFReckIst52fxqxV2cxdv9frODWuMkfovYERQD8zW+K7DAEeBAaa2XpgoO+2iIinRvVpTWqDOtz37kpKwmyr3Qp3W3TOzQXsBD/uX71xREROTWytSO66qCO3vLyQad9sY+TZrbyOVGP0TVERCTkXdEqmd1pDHpu9jgNhNOVIhS4iIcfMuOfizuQVFDPho3Vex6kxKnQRCUmnNYnn2rNa8srXW1mzOzwGTKvQRSRkjR3YnoTatbjv36vCYkdGFbqIhKzEOtGMHdieLzfu4z8rs72O43cqdBEJadf0SqV9cl3uf39VyA/DUKGLSEiLioxg/NDObN9/NOT3TVehi0jI653WiAs6JfP0nA1kHyrwOo7fqNBFJCzcfVEnSkodD32wxusofqNCF5GwkNqwDr84pzUzF+9k0bYDXsfxCxW6iISNX52fRuP4GP7y71WUlYXeaYwqdBEJG3VjovjDoA4s3X6Qtxbv9DpOtVOhi0hYGd69Gd1aJPLQh2vID7FxdSp0EQkrERHGvUM7sSevkKdDbFydCl1Ewk731PohOa5OhS4iYSkUx9Wp0EUkLIXiuDoVuoiErVAbV6dCF5GwdWxc3brsfKZ9s83rOKdMhS4iYS2UxtWp0EUkrIXSuDoVuoiEvVAZV6dCFxEBxgxoT3xscI+rq7DQzWySme0xsxXH3Xevme00syW+yxD/xhQR8a/6cdH85oLgHldXmSP0ycCgH7h/gnMu3Xd5v3pjiYjUvGAfV1dhoTvnPgf210AWERFPBfu4ulNZQ7/dzJb5lmTqV1siEREPBfO4uqoW+j+AtkA6kAU8eqIHmtnNZpZpZpk5OTlVfDkRkZpz10Udg3JcXZUK3TmX7Zwrdc6VAS8AvX7ksc875zKccxlJSUlVzSkiUmNaNowLynF1VSp0M0s57uZwYMWJHisiEoyCcVxdZU5bnA58BZxmZjvMbBTwsJktN7NlwPnAGD/nFBGpUcE4ri6qogc4567+gbsn+iGLiEhAGd69GVO/3spDH67hwi5NqBtTYWV6St8UFRE5gWAbV6dCFxH5EcE0rk6FLiJSgWAZV6dCFxGpQLCMq1Ohi4hUwqg+rWnRoHZAj6tToYuIVEJsrUjuGtIpoMfVqdBFRCrpws6BPa5OhS4iUkmBPq5OhS4ichICeVydCl1E5CQF6rg6FbqIyEmqHxfN2IGBN65OhS4iUgU/PzPwxtWp0EVEqiAQx9Wp0EVEqijQxtWp0EVETsG34+o+9H5cnQpdROQUtGwYx6hzWjNz0U4WezyuToUuInKKbvONq7vX43F1KnQRkVMUKOPqVOgiItVgePdmdGuRyEMfriG/sMSTDCp0EZFqEBFhjPeNq3vGo3F1KnQRkWrSI7U+l3ZvxosejatToYuIVKM/DPZuXJ0KXUSkGnk5rk6FLiJSzbwaV1dhoZvZJDPbY2YrjruvgZnNNrP1vj/r+zemiEjw8GpcXWWO0CcDg75z3zjgY+dcO+Bj320REfG5sHMyZ7et2XF1FRa6c+5zYP937h4GTPFdnwJcUs25RESCmpkxfmjNjqur6hp6snMuC8D3Z+PqiyQiEhpqelxdlL9fwMxuBm4GSE1N9ffLiYgElDED2rN572FKa2CPl6oeoWebWQqA7889J3qgc+5551yGcy4jKSmpii8nIhKc6sdF8/KoM+nctJ7fX6uqhf4OMNJ3fSTwdvXEERGRqqrMaYvTga+A08xsh5mNAh4EBprZemCg77aIiHiowjV059zVJ/hR/2rOIiIip0DfFBURCREqdBGREKFCFxEJESp0EZEQoUIXEQkR5lzNTag2sxxgaxWf3gio2c2FK0e5To68dej9AAAELklEQVRynRzlOjmBmgtOLVtL51yF38ys0UI/FWaW6ZzL8DrHdynXyVGuk6NcJydQc0HNZNOSi4hIiFChi4iEiGAq9Oe9DnACynVylOvkKNfJCdRcUAPZgmYNXUREflwwHaGLiMiPCPhC/6Eh1YHAzFqY2RwzW21mK83sTq8zAZhZrJnNN7Olvlx/8TrT8cws0swWm9m7Xmc5xsy2mNlyM1tiZple5znGzBLN7A0zW+P7PftJAGQ6zff3dOxyyMxGe50LwMzG+H7nV5jZdDOL9ToTgJnd6cu00t9/VwG/5GJmfYF8YKpzrovXeY7xDfZIcc4tMrN4YCFwiXNulce5DIhzzuWbWS1gLnCnc+5rL3MdY2ZjgQwgwTl3sdd5oLzQgQznXECdv2xmU4AvnHMvmlk0UMc5d9DrXMeYWSSwEzjTOVfV75dUV5ZmlP+ud3LOHTWz14D3nXOTPc7VBZgB9AKKgA+BW51z6/3xegF/hH6CIdWec85lOecW+a7nAauBZt6mAlcu33ezlu8SEP/VNrPmwEXAi15nCXRmlgD0BSYCOOeKAqnMffoDG70u8+NEAbXNLAqoA+zyOA9AR+Br59wR51wJ8Bkw3F8vFvCFHgzMrBXQHfjG2yTlfMsaSygfDTjbORcQuYC/A78HyrwO8h0OmGVmC30zcANBGyAHeMm3RPWimcV5Heo7rgKmex0CwDm3E3gE2AZkAbnOuVnepgJgBdDXzBqaWR1gCNDCXy+mQj9FZlYXeBMY7Zzz/1jvSnDOlTrn0oHmQC/f2z5PmdnFwB7n3EKvs/yA3s65HsBg4DbfMp/XooAewD+cc92Bw8A4byP9l28J6KfA615nATCz+sAwoDXQFIgzs2u9TQXOudXAQ8BsypdblgIl/no9Ffop8K1RvwlMc87N9DrPd/neon8KDPI4CkBv4Ke+9eoZQD8ze8XbSOWcc7t8f+4B3qJ8vdNrO4Adx727eoPygg8Ug4FFzrlsr4P4DAA2O+dynHPFwEzgbI8zAeCcm+ic6+Gc60v58rFf1s9BhV5lvg8fJwKrnXOPeZ3nGDNLMrNE3/XalP+ir/E2FTjn/uica+6ca0X5W/VPnHOeH0GZWZzvQ218SxoXUP422VPOud3AdjM7zXdXf8DTD9y/42oCZLnFZxtwlpnV8f272Z/yz7U8Z2aNfX+mApfix7+3CmeKes03pPo8oJGZ7QDGO+cmepsKKD/iHAEs961XA/zJOfe+h5kAUoApvjMQIoDXnHMBc4pgAEoG3irvAKKAV51zH3ob6Vt3ANN8yxubgBs8zgOAby14IHCL11mOcc59Y2ZvAIsoX9JYTOB8a/RNM2sIFAO3OecO+OuFAv60RRERqRwtuYiIhAgVuohIiFChi4iECBW6iEiIUKGLiIQIFbqISIhQoYuIhAgVuohIiPj//txq4Rcx6qQAAAAASUVORK5CYII=\n", "text/plain": "
" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": "optimal rejection is 4 with 40.0% chance to hire the best candidate\n" } ] } }, "6075945dab854108b7c5aca8cfeef7c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "647bf998ff11463eaeb64db866a9d445": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "ButtonModel", "state": { "description": "Run Simulation", "layout": "IPY_MODEL_b35a29d73686434291c2b89452b3f390", "style": "IPY_MODEL_cedeb7ff813d473dbbe2c692dadedd68" } }, "683a68bbc5514389a0d5daeed2baf1a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "6c396eff290d4cd29ba02350a680a823": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "VBoxModel", "state": { "_dom_classes": [ "widget-interact" ], "children": [ "IPY_MODEL_c946d5b686a04dfc82586328feb5696f", "IPY_MODEL_647bf998ff11463eaeb64db866a9d445", "IPY_MODEL_cd918347ed6b4ee2b8f899cfcda4a675" ], "layout": "IPY_MODEL_f674baea28564b87b6d3367dea3dd4c4" } }, "72b713a4240a40d7a32b6d4ead17a3bc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "ButtonStyleModel", "state": {} }, "74461a384ee04bd7a2677a08f0f3357a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "150px" } }, "781a9d80422f4ae3a3b6082213ca80ae": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_d73e4192d50740deb7d3c03a9289545c", "outputs": [ { "name": "stderr", "output_type": "stream", "text": "ERROR:root:Internal Python error in the inspect module.\nBelow is the traceback from this internal error.\n\n" }, { "name": "stdout", "output_type": "stream", "text": "Traceback (most recent call last):\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\", line 251, in update\n self.result = self.f(**self.kwargs)\n File \"D:\\Dropbox\\Work\\Web\\business-analytics\\docs\\optimization\\loadoptfuncs.py\", line 302, in secretary\n rate = successful_rate(n, range(1,n))\n File \"D:\\Dropbox\\Work\\Web\\business-analytics\\docs\\optimization\\loadoptfuncs.py\", line 275, in successful_rate\n candidates = [np.random.choice(range(100), num_candidates, replace=False) for i in range(num_sim)]\n File \"D:\\Dropbox\\Work\\Web\\business-analytics\\docs\\optimization\\loadoptfuncs.py\", line 275, in \n candidates = [np.random.choice(range(100), num_candidates, replace=False) for i in range(num_sim)]\n File \"mtrand.pyx\", line 1168, in mtrand.RandomState.choice\nValueError: Cannot take a larger sample than population when 'replace=False'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2033, in showtraceback\n stb = value._render_traceback_()\nAttributeError: 'ValueError' object has no attribute '_render_traceback_'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1095, in get_records\n return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 313, in wrapped\n return f(*args, **kwargs)\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 347, in _fixed_getinnerframes\n records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\inspect.py\", line 1502, in getinnerframes\n frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\inspect.py\", line 1460, in getframeinfo\n filename = getsourcefile(frame) or getfile(frame)\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\inspect.py\", line 696, in getsourcefile\n if getattr(getmodule(object, filename), '__loader__', None) is not None:\n File \"C:\\Users\\mzhao\\AppData\\Local\\Continuum\\anaconda3\\envs\\bzan\\lib\\inspect.py\", line 742, in getmodule\n os.path.realpath(f)] = module.__name__\nAttributeError: module has no attribute '__name__'\n" }, { "ename": "ValueError", "evalue": "Cannot take a larger sample than population when 'replace=False'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m" ] } ] } }, "78605095177043c58df732baa680b566": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "7a2d48dc74f348fba7e5be3398f2c403": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "ButtonStyleModel", "state": {} }, "7d0fb6e110d3400c82ec8ba9c98236fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "9d8539db338f489d865b889ddeae936e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "a056cb2d1ee345fd994fa5e8e153c1b2": { "model_module": "@jupyter-widgets/base", 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\n", 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\n", 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\n", "text/plain": "
" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": "optimal rejection is 7 with 40.0% chance to hire the best candidate\n" } ] } }, "f2ee28954d4e4277884759c53328fc44": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "BoundedIntTextModel", "state": { "description": "number of candidates:", "layout": "IPY_MODEL_4cae5e1f1ca64d8a8b759db5a71b0bcd", "max": 1000, "min": 10, "style": "IPY_MODEL_cebdbbe650074b039c7c3215b72b2875", "value": 100 } }, "f3623bdb597f46ac8edc0aa1997168ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "f674baea28564b87b6d3367dea3dd4c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.1.0", "model_name": "LayoutModel", "state": {} }, "f7f1000779da4181b58eeec4c4d0c6fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.4.0", "model_name": "BoundedIntTextModel", "state": { "description": "number of candidates:", "layout": "IPY_MODEL_25915e87d6774739aa28104efd268c8f", "max": 1000, "min": 5, "style": "IPY_MODEL_ab502b32fb97404696d58f2b126eaca0", "value": 200 } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 2 }