├── README.md
├── index.html
├── .gitignore
├── LICENSE
└── notebooks
└── Multi-Armed Bandit.ipynb
/README.md:
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1 | # BlogExperiment
2 |
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/index.html:
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1 |
2 |
3 | DataBozo
4 |
5 |
6 | The Simplest Blog Ever
7 | This is a list of all of the notebooks that I have published:
8 |
11 |
12 |
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 |
5 | # C extensions
6 | *.so
7 |
8 | # Distribution / packaging
9 | .Python
10 | env/
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | *.egg-info/
23 | .installed.cfg
24 | *.egg
25 |
26 | # PyInstaller
27 | # Usually these files are written by a python script from a template
28 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
29 | *.manifest
30 | *.spec
31 |
32 | # Installer logs
33 | pip-log.txt
34 | pip-delete-this-directory.txt
35 |
36 | # Unit test / coverage reports
37 | htmlcov/
38 | .tox/
39 | .coverage
40 | .coverage.*
41 | .cache
42 | nosetests.xml
43 | coverage.xml
44 | *,cover
45 |
46 | # Translations
47 | *.mo
48 | *.pot
49 |
50 | # Django stuff:
51 | *.log
52 |
53 | # Sphinx documentation
54 | docs/_build/
55 |
56 | # PyBuilder
57 | target/
58 |
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/LICENSE:
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1 | The MIT License (MIT)
2 |
3 | Copyright (c) 2015 Justin Bozonier
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
23 |
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/notebooks/Multi-Armed Bandit.ipynb:
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1 | {
2 | "metadata": {
3 | "name": "",
4 | "signature": "sha256:d4d29c98043f2f7f932439c44c9f71bc3cb9a16d16ac3d8d5d38da75eb58536d"
5 | },
6 | "nbformat": 3,
7 | "nbformat_minor": 0,
8 | "worksheets": [
9 | {
10 | "cells": [
11 | {
12 | "cell_type": "code",
13 | "collapsed": false,
14 | "input": [
15 | "def sample_a_mean(payoff_mean, payoff_stdev, cost_mean, cost_stdev, cost_probability, sample_size):\n",
16 | " payoffs = np.random.normal(loc=payoff_mean, scale=payoff_stdev, size=sample_size)\n",
17 | " costs = np.random.normal(loc=cost_mean, scale=cost_stdev, size=sample_size) * np.random.binomial(1,cost_probability, size=sample_size)\n",
18 | " return (payoffs - costs).mean()"
19 | ],
20 | "language": "python",
21 | "metadata": {},
22 | "outputs": [],
23 | "prompt_number": 72
24 | },
25 | {
26 | "cell_type": "code",
27 | "collapsed": false,
28 | "input": [
29 | "(np.random.normal(loc=23, scale=1, size=1000) - (np.random.normal(loc=10, scale=1, size=1000) * np.random.binomial(1, 1, size=1000))).mean()"
30 | ],
31 | "language": "python",
32 | "metadata": {},
33 | "outputs": [
34 | {
35 | "metadata": {},
36 | "output_type": "pyout",
37 | "prompt_number": 71,
38 | "text": [
39 | "12.979089497402763"
40 | ]
41 | }
42 | ],
43 | "prompt_number": 71
44 | },
45 | {
46 | "cell_type": "code",
47 | "collapsed": false,
48 | "input": [
49 | "np.random.binomial(1, 1, size=100)"
50 | ],
51 | "language": "python",
52 | "metadata": {},
53 | "outputs": [
54 | {
55 | "metadata": {},
56 | "output_type": "pyout",
57 | "prompt_number": 66,
58 | "text": [
59 | "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
60 | " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
61 | " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
62 | " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
63 | " 1, 1, 1, 1, 1, 1, 1, 1])"
64 | ]
65 | }
66 | ],
67 | "prompt_number": 66
68 | },
69 | {
70 | "cell_type": "code",
71 | "collapsed": false,
72 | "input": [
73 | "sample_a_mean(23.25, 2.00, 10.00, 1.00, .5, 10000)"
74 | ],
75 | "language": "python",
76 | "metadata": {},
77 | "outputs": [
78 | {
79 | "metadata": {},
80 | "output_type": "pyout",
81 | "prompt_number": 78,
82 | "text": [
83 | "18.285669604250632"
84 | ]
85 | }
86 | ],
87 | "prompt_number": 78
88 | },
89 | {
90 | "cell_type": "code",
91 | "collapsed": false,
92 | "input": [
93 | "sample_a_mean(23.25, 1.00, 10.00, 1.00, .01, 100000)"
94 | ],
95 | "language": "python",
96 | "metadata": {},
97 | "outputs": [
98 | {
99 | "metadata": {},
100 | "output_type": "pyout",
101 | "prompt_number": 48,
102 | "text": [
103 | "23.009061829881642"
104 | ]
105 | }
106 | ],
107 | "prompt_number": 48
108 | },
109 | {
110 | "cell_type": "code",
111 | "collapsed": false,
112 | "input": [
113 | "np.random.binomial(1,.15, size=10)"
114 | ],
115 | "language": "python",
116 | "metadata": {},
117 | "outputs": [
118 | {
119 | "metadata": {},
120 | "output_type": "pyout",
121 | "prompt_number": 11,
122 | "text": [
123 | "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0])"
124 | ]
125 | }
126 | ],
127 | "prompt_number": 11
128 | },
129 | {
130 | "cell_type": "code",
131 | "collapsed": false,
132 | "input": [
133 | "np.array([1,2,3,4])*np.array([0,.15,.5,1])"
134 | ],
135 | "language": "python",
136 | "metadata": {},
137 | "outputs": [
138 | {
139 | "metadata": {},
140 | "output_type": "pyout",
141 | "prompt_number": 5,
142 | "text": [
143 | "array([ 0. , 0.3, 1.5, 4. ])"
144 | ]
145 | }
146 | ],
147 | "prompt_number": 5
148 | },
149 | {
150 | "cell_type": "code",
151 | "collapsed": false,
152 | "input": [
153 | "payoffs = np.random.normal(loc=payoff_mean, scale=payoff_stdev, size=sample_size)\n",
154 | "costs = np.random.normal(loc=payoff_mean, scale=payoff_stdev, size=sample_size) * np.random.binomial(1,cost_probability, size=sample_size)\n"
155 | ],
156 | "language": "python",
157 | "metadata": {},
158 | "outputs": []
159 | },
160 | {
161 | "cell_type": "code",
162 | "collapsed": false,
163 | "input": [
164 | "class Bandit:\n",
165 | " def __init__(self, control_payoff_mean, control_cost_mean, variant_payoff_mean, variant_cost_mean):\n",
166 | " self._control_payoff_mean = control_payoff_mean\n",
167 | " self._control_cost_mean = control_cost_mean\n",
168 | " self._variant_payoff_mean = variant_payoff_mean\n",
169 | " self._variant_cost_mean = variant_cost_mean\n",
170 | " self._control_cost_probability = .1\n",
171 | " self._variant_cost_probability = .1\n",
172 | " self._control_rewards = []\n",
173 | " self._variant_rewards = []\n",
174 | " self._chosen_rewards = []\n",
175 | " \n",
176 | " def _pull(self):\n",
177 | " control_payoff = self._pull_control()\n",
178 | " variant_payoff = self._pull_variant()\n",
179 | " self._control_rewards.append(control_payoff)\n",
180 | " self._variant_rewards.append(variant_payoff)\n",
181 | " return {'control': control_payoff, 'variant': variant_payoff}\n",
182 | "\n",
183 | " def pull_control(self):\n",
184 | " chosen_reward = self._pull()['control']\n",
185 | " self._chosen_rewards.append(chosen_reward)\n",
186 | " return chosen_reward\n",
187 | "\n",
188 | " def pull_variant(self):\n",
189 | " chosen_reward = self._pull()['variant']\n",
190 | " self._chosen_rewards.append(chosen_reward)\n",
191 | " return chosen_reward\n",
192 | "\n",
193 | " def _pull_control(self):\n",
194 | " return np.random.normal(loc=self._control_payoff_mean, scale=5.00) - np.random.binomial(1, self._control_cost_probability)*np.random.normal(loc=self._control_cost_mean, scale=1.00)\n",
195 | " \n",
196 | " def _pull_variant(self):\n",
197 | " return np.random.normal(loc=self._variant_payoff_mean, scale=5.00) - np.random.binomial(1, self._variant_cost_probability)*np.random.normal(loc=self._variant_cost_mean, scale=1.00)\n",
198 | "\n",
199 | "\n"
200 | ],
201 | "language": "python",
202 | "metadata": {},
203 | "outputs": [],
204 | "prompt_number": 86
205 | },
206 | {
207 | "cell_type": "code",
208 | "collapsed": false,
209 | "input": [
210 | "class RPMStrategy:\n",
211 | " def __init__(self):\n",
212 | " self._control_observations = []\n",
213 | " self._variant_observations = []\n",
214 | " \n",
215 | " def log_control_observation(self, observation):\n",
216 | " self._control_observations.append(observation)\n",
217 | " \n",
218 | " def log_variant_observation(self, observation):\n",
219 | " self._variant_observations.append(observation)\n",
220 | "\n",
221 | " def _better_bootstrap(self, data, max=200.):\n",
222 | " p_unobserved = 1./(len(data) + 1.)\n",
223 | " number_of_randoms = np.random.binomial(len(data)+1, p_unobserved)\n",
224 | " random_values = [numpy.random.uniform(0., max) for i in range(number_of_randoms)]\n",
225 | " new_data = data + random_values\n",
226 | " sample_mean = np.random.choice(new_data, size=len(new_data)).mean()\n",
227 | " return sample_mean\n",
228 | "\n",
229 | " def choose_arm(self, control_lambda, variant_lambda):\n",
230 | " control_mean = self._better_bootstrap(self._control_observations)\n",
231 | " variant_mean = self._better_bootstrap(self._variant_observations)\n",
232 | " if control_mean > variant_mean:\n",
233 | " self._control_observations.append(control_lambda())\n",
234 | " else:\n",
235 | " self._variant_observations.append(variant_lambda())"
236 | ],
237 | "language": "python",
238 | "metadata": {},
239 | "outputs": [],
240 | "prompt_number": 129
241 | },
242 | {
243 | "cell_type": "code",
244 | "collapsed": false,
245 | "input": [
246 | "experiment = Bandit(22.00, 9.00, 24.00, 10.00)\n",
247 | "bandit = RPMStrategy()\n",
248 | "for i in range(2000):\n",
249 | " bandit.choose_arm(experiment.pull_control, experiment.pull_variant)"
250 | ],
251 | "language": "python",
252 | "metadata": {},
253 | "outputs": [],
254 | "prompt_number": 176
255 | },
256 | {
257 | "cell_type": "code",
258 | "collapsed": false,
259 | "input": [
260 | "plt.plot(np.array(experiment._chosen_rewards).cumsum())\n",
261 | "plt.plot(np.array(experiment._control_rewards).cumsum())\n",
262 | "plt.plot(np.array(experiment._variant_rewards).cumsum())"
263 | ],
264 | "language": "python",
265 | "metadata": {},
266 | "outputs": [
267 | {
268 | "metadata": {},
269 | "output_type": "pyout",
270 | "prompt_number": 177,
271 | "text": [
272 | "[]"
273 | ]
274 | },
275 | {
276 | "metadata": {},
277 | "output_type": "display_data",
278 | "png": 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tHVpLUIgkQ3oWQsTEhQvQuzf68WM2ddxMz6VVGT4c+veHNNH8Kzp+4zh9dvQh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\nT+Olm0BeINxYfumWUY7x+wa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279 | "text": [
280 | ""
281 | ]
282 | }
283 | ],
284 | "prompt_number": 177
285 | },
286 | {
287 | "cell_type": "code",
288 | "collapsed": false,
289 | "input": [
290 | "plt.plot(np.array(experiment._variant_rewards).cumsum() - np.array(experiment._chosen_rewards).cumsum())"
291 | ],
292 | "language": "python",
293 | "metadata": {},
294 | "outputs": [
295 | {
296 | "metadata": {},
297 | "output_type": "pyout",
298 | "prompt_number": 178,
299 | "text": [
300 | "[]"
301 | ]
302 | },
303 | {
304 | "metadata": {},
305 | "output_type": "display_data",
306 | "png": 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C4GQB0dNPL25eREQ6sy4RGF59NdZc2Gmn2N9+++LmR0SkM+v0gWH6dDjooFjT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\n",
307 | "text": [
308 | ""
309 | ]
310 | }
311 | ],
312 | "prompt_number": 178
313 | },
314 | {
315 | "cell_type": "code",
316 | "collapsed": false,
317 | "input": [
318 | "plt.plot(np.array(experiment._variant_rewards).cumsum() - np.array(experiment._control_rewards).cumsum())"
319 | ],
320 | "language": "python",
321 | "metadata": {},
322 | "outputs": [
323 | {
324 | "metadata": {},
325 | "output_type": "pyout",
326 | "prompt_number": 179,
327 | "text": [
328 | "[]"
329 | ]
330 | },
331 | {
332 | "metadata": {},
333 | "output_type": "display_data",
334 | "png": 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IaqNGmS6NiEigpqQM+fBDeP31MPJIQUFEGiIFhmr47W/h6KPh0kvDKCQRkYao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\n",
335 | "text": [
336 | ""
337 | ]
338 | }
339 | ],
340 | "prompt_number": 179
341 | },
342 | {
343 | "cell_type": "code",
344 | "collapsed": false,
345 | "input": [
346 | "plt.plot(np.array(experiment._chosen_rewards).cumsum() - np.array(experiment._control_rewards).cumsum())"
347 | ],
348 | "language": "python",
349 | "metadata": {},
350 | "outputs": [
351 | {
352 | "metadata": {},
353 | "output_type": "pyout",
354 | "prompt_number": 180,
355 | "text": [
356 | "[]"
357 | ]
358 | },
359 | {
360 | "metadata": {},
361 | "output_type": "display_data",
362 | "png": 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363 | "text": [
364 | ""
365 | ]
366 | }
367 | ],
368 | "prompt_number": 180
369 | },
370 | {
371 | "cell_type": "code",
372 | "collapsed": false,
373 | "input": [
374 | "experiment._control_rewards"
375 | ],
376 | "language": "python",
377 | "metadata": {},
378 | "outputs": [
379 | {
380 | "metadata": {},
381 | "output_type": "pyout",
382 | "prompt_number": 126,
383 | "text": [
384 | "[27.187810323006076,\n",
385 | " 10.826196058597532,\n",
386 | " 20.019452872108214,\n",
387 | " 32.86248311672894,\n",
388 | " 18.310223130844633]"
389 | ]
390 | }
391 | ],
392 | "prompt_number": 126
393 | },
394 | {
395 | "cell_type": "code",
396 | "collapsed": false,
397 | "input": [
398 | "np.random.binomial(1000, .001)"
399 | ],
400 | "language": "python",
401 | "metadata": {},
402 | "outputs": [
403 | {
404 | "metadata": {},
405 | "output_type": "pyout",
406 | "prompt_number": 107,
407 | "text": [
408 | "1"
409 | ]
410 | }
411 | ],
412 | "prompt_number": 107
413 | },
414 | {
415 | "cell_type": "code",
416 | "collapsed": false,
417 | "input": [
418 | "def better_bootstrap(data, max=200.):\n",
419 | " p_unobserved = 1./(len(data) + 1.)\n",
420 | " number_of_randoms = np.random.binomial(len(data)+1, p_unobserved)\n",
421 | " random_values = [numpy.random.uniform(0., max) for i in range(number_of_randoms)]\n",
422 | " new_data = data + random_values\n",
423 | " sample_mean = np.random.choice(new_data, size=len(new_data)).mean()\n",
424 | " return sample_mean"
425 | ],
426 | "language": "python",
427 | "metadata": {},
428 | "outputs": [],
429 | "prompt_number": 181
430 | },
431 | {
432 | "cell_type": "code",
433 | "collapsed": false,
434 | "input": [
435 | "le_data = list(np.random.normal(loc=25, scale=5, size=5))\n",
436 | "plt.hist([better_bootstrap(le_data) for i in range(5000)], bins=100)\n",
437 | "''"
438 | ],
439 | "language": "python",
440 | "metadata": {},
441 | "outputs": [
442 | {
443 | "metadata": {},
444 | "output_type": "pyout",
445 | "prompt_number": 200,
446 | "text": [
447 | "''"
448 | ]
449 | },
450 | {
451 | "metadata": {},
452 | "output_type": "display_data",
453 | "png": 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454 | "text": [
455 | ""
456 | ]
457 | }
458 | ],
459 | "prompt_number": 200
460 | },
461 | {
462 | "cell_type": "code",
463 | "collapsed": false,
464 | "input": [
465 | "def random_interval_bootstrap(data, sample_count=1000, max=200.):\n",
466 | " \"\"\"A bootstrap that works on an interval like 0 to 200. It assumes that \n",
467 | " the likelihood of seeing a value we haven't seen is 1/N.\"\"\"\n",
468 | " sampled_means = []\n",
469 | " for x in range(sample_count):\n",
470 | " p_unobserved = 1./(len(data) + 1.)\n",
471 | " number_of_randoms = np.random.binomial(len(data)+1, p_unobserved)\n",
472 | " random_values = [numpy.random.uniform(0., max) for i in range(number_of_randoms)]\n",
473 | " new_data = data + random_values\n",
474 | " sample_mean = np.random.choice(new_data, size=len(new_data)).mean()\n",
475 | " sampled_means.append(sample_mean)\n",
476 | " return sampled_means\n",
477 | "\n",
478 | "le_data = list(np.random.normal(loc=25, scale=5, size=2))\n",
479 | "plt.hist(random_interval_bootstrap(le_data, 5000), bins=100)\n",
480 | "''"
481 | ],
482 | "language": "python",
483 | "metadata": {},
484 | "outputs": [
485 | {
486 | "metadata": {},
487 | "output_type": "pyout",
488 | "prompt_number": 215,
489 | "text": [
490 | "''"
491 | ]
492 | },
493 | {
494 | "metadata": {},
495 | "output_type": "display_data",
496 | "png": 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497 | "text": [
498 | ""
499 | ]
500 | }
501 | ],
502 | "prompt_number": 215
503 | },
504 | {
505 | "cell_type": "code",
506 | "collapsed": false,
507 | "input": [
508 | "import random, numpy\n",
509 | "\n",
510 | "class BanditScenario:\n",
511 | " def __init__(self, scenario):\n",
512 | " self._scenario = scenario\n",
513 | " self._scenario_payoffs = {treatment_name:[] for treatment_name in self._scenario.keys()}\n",
514 | " def next_visitor(self, show_treatment):\n",
515 | " for key, value in self._scenario.items():\n",
516 | " ordered = np.random.binomial(1, value['conversion_rate'])\n",
517 | " order_average = numpy.random.normal(loc=value['order_average'], scale=5.00)\n",
518 | " self._scenario_payoffs[key].append(ordered*order_average)\n",
519 | " return self._scenario_payoffs[show_treatment][-1]\n",
520 | "\n",
521 | "class BanditPlayer:\n",
522 | " def __init__(self, treatments):\n",
523 | " self._payoffs = []\n",
524 | " self._treatments = treatments\n",
525 | " self._results = {treatment_name:[] for treatment_name in treatments}\n",
526 | " def next_treatment_to_show(self):\n",
527 | " return random.choice(self._treatments)\n",
528 | " def visitor_results(self, treatment_shown, money_made):\n",
529 | " treatment_results = self._results[treatment_shown]\n",
530 | " treatment_results.append(money_made)\n",
531 | " self._payoffs.append(money_made)\n",
532 | " \n",
533 | "simulated_experiment = BanditScenario({\n",
534 | " 'A': {\n",
535 | " 'conversion_rate': .05,\n",
536 | " 'order_average': 35.00\n",
537 | " }, \n",
538 | " 'B':{\n",
539 | " 'conversion_rate': .06,\n",
540 | " 'order_average': 36.00\n",
541 | " }, \n",
542 | " 'C':{\n",
543 | " 'conversion_rate': .07,\n",
544 | " 'order_average': 31.00\n",
545 | " }\n",
546 | "})\n",
547 | "\n",
548 | "bandit = BanditPlayer(['A', 'B', 'C'])\n",
549 | "\n",
550 | "visitor_count = 500\n",
551 | "\n",
552 | "for i in range(visitor_count):\n",
553 | " treatment_name = bandit.next_treatment_to_show()\n",
554 | " money_made = simulated_experiment.next_visitor(show_treatment=treatment_name)\n",
555 | " bandit.visitor_results(treatment_name, money_made)\n"
556 | ],
557 | "language": "python",
558 | "metadata": {},
559 | "outputs": [],
560 | "prompt_number": 276
561 | },
562 | {
563 | "cell_type": "code",
564 | "collapsed": false,
565 | "input": [
566 | "plt.plot(np.array(simulated_experiment._scenario_payoffs['A']).cumsum())\n",
567 | "plt.plot(np.array(simulated_experiment._scenario_payoffs['B']).cumsum())\n",
568 | "plt.plot(np.array(simulated_experiment._scenario_payoffs['C']).cumsum())\n"
569 | ],
570 | "language": "python",
571 | "metadata": {},
572 | "outputs": [
573 | {
574 | "metadata": {},
575 | "output_type": "pyout",
576 | "prompt_number": 277,
577 | "text": [
578 | "[]"
579 | ]
580 | },
581 | {
582 | "metadata": {},
583 | "output_type": "display_data",
584 | "png": 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ZeJm+S0j9H/rnZjVwUdDxR/lcXIh3z3eN//5WA5en4vkAzgBWAWuB\n14Ab/PKUOxd1zstkjo4OSrlzAaT5/3+sAd4CbormudDDYiIiKSwRbgeJiEhAlARERFKYkoCISApT\nEhARSWFKAiIiKUxJQEQkhSkJiIikMCUBEZEU9v8BA6n3LuQrs1YAAAAASUVORK5CYII=\n",
585 | "text": [
586 | ""
587 | ]
588 | }
589 | ],
590 | "prompt_number": 277
591 | },
592 | {
593 | "cell_type": "code",
594 | "collapsed": false,
595 | "input": [
596 | "plt.plot(np.array(bandit._payoffs).cumsum())"
597 | ],
598 | "language": "python",
599 | "metadata": {},
600 | "outputs": [
601 | {
602 | "metadata": {},
603 | "output_type": "pyout",
604 | "prompt_number": 278,
605 | "text": [
606 | "[]"
607 | ]
608 | },
609 | {
610 | "metadata": {},
611 | "output_type": "display_data",
612 | "png": 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613 | "text": [
614 | ""
615 | ]
616 | }
617 | ],
618 | "prompt_number": 278
619 | },
620 | {
621 | "cell_type": "code",
622 | "collapsed": false,
623 | "input": [
624 | "random.choice([1,2,3,4,5])"
625 | ],
626 | "language": "python",
627 | "metadata": {},
628 | "outputs": [
629 | {
630 | "metadata": {},
631 | "output_type": "pyout",
632 | "prompt_number": 219,
633 | "text": [
634 | "2"
635 | ]
636 | }
637 | ],
638 | "prompt_number": 219
639 | },
640 | {
641 | "cell_type": "code",
642 | "collapsed": false,
643 | "input": [
644 | "random.uniform(0,1)"
645 | ],
646 | "language": "python",
647 | "metadata": {},
648 | "outputs": [
649 | {
650 | "metadata": {},
651 | "output_type": "pyout",
652 | "prompt_number": 223,
653 | "text": [
654 | "0.7424947747669501"
655 | ]
656 | }
657 | ],
658 | "prompt_number": 223
659 | },
660 | {
661 | "cell_type": "code",
662 | "collapsed": false,
663 | "input": [
664 | "np.random.binomial(1, .9)"
665 | ],
666 | "language": "python",
667 | "metadata": {},
668 | "outputs": [
669 | {
670 | "metadata": {},
671 | "output_type": "pyout",
672 | "prompt_number": 257,
673 | "text": [
674 | "1"
675 | ]
676 | }
677 | ],
678 | "prompt_number": 257
679 | },
680 | {
681 | "cell_type": "code",
682 | "collapsed": false,
683 | "input": [
684 | "{}.items()"
685 | ],
686 | "language": "python",
687 | "metadata": {},
688 | "outputs": [
689 | {
690 | "metadata": {},
691 | "output_type": "pyout",
692 | "prompt_number": 258,
693 | "text": [
694 | "[]"
695 | ]
696 | }
697 | ],
698 | "prompt_number": 258
699 | },
700 | {
701 | "cell_type": "code",
702 | "collapsed": false,
703 | "input": [
704 | "class BanditScenario:\n",
705 | " def __init__(self, scenario):\n",
706 | " self._scenario = scenario\n",
707 | " self._scenario_payoffs = {treatment_name:[] for treatment_name in self._scenario.keys()}\n",
708 | " self._bandit_payoffs = []\n",
709 | " def next_visitor(self, show_treatment):\n",
710 | " for key, value in self._scenario.items():\n",
711 | " ordered = np.random.binomial(1, value['conversion_rate'])\n",
712 | " order_average = numpy.random.normal(loc=value['order_average'], scale=5.00)\n",
713 | " self._scenario_payoffs[key].append(ordered*order_average)\n",
714 | " if key == show_treatment:\n",
715 | " self._bandit_payoffs.append(ordered*order_average)\n",
716 | " return self._scenario_payoffs[show_treatment][-1]\n",
717 | "\n",
718 | "class SimpleBandit:\n",
719 | " def __init__(self, treatments):\n",
720 | " \tself._treatments = treatments\n",
721 | " \tself._selection_count = 0\n",
722 | " \tself._exploitation_count = 0\n",
723 | " \tself._payouts = {treatment: 0 for treatment in treatments}\n",
724 | " def choose_treatment(self):\n",
725 | " \tself._selection_count += 1\n",
726 | " \tif self._selection_count <= 5*len(self._treatments):\n",
727 | " \t\treturn self._treatments[(self._selection_count-1) / 5]\n",
728 | " \telse:\n",
729 | " \t\tself._exploitation_count += 1\n",
730 | " \t\tif self._exploitation_count == 5:\n",
731 | " \t\t\tself._exploitation_count = 0\n",
732 | " \t\t\tself._selection_count = 0\n",
733 | " \t\treturn sorted(self._payouts.items(), key=lambda x: x[1], reverse=True)[0][0]\n",
734 | " def log_payout(self, treatment, amount):\n",
735 | " self._payouts[treatment] += amount\n",
736 | " \n",
737 | "class RPMBandit:\n",
738 | " def __init__(self, treatments):\n",
739 | " self._treatments = treatments\n",
740 | " self._payoffs = {treatment: [] for treatment in treatments}\n",
741 | " def choose_treatment(self):\n",
742 | " max_treatment = self._treatments[0]\n",
743 | " max_value = float('-inf')\n",
744 | " for key, value in self._payoffs.items():\n",
745 | " random_numbers_from_range = np.random.binomial(len(value)+1, 1.0/(len(value)+1))\n",
746 | " generated_data = value + [random.uniform(0,200) for i in range(random_numbers_from_range)]\n",
747 | " sampled_mean = np.random.choice(generated_data, size=len(generated_data)).mean()\n",
748 | " if sampled_mean > max_value:\n",
749 | " max_treatment = key\n",
750 | " max_value = sampled_mean\n",
751 | " return max_treatment\n",
752 | " def log_payout(self, treatment, payout):\n",
753 | " self._payoffs[treatment].append(payout)"
754 | ],
755 | "language": "python",
756 | "metadata": {},
757 | "outputs": [],
758 | "prompt_number": 1
759 | },
760 | {
761 | "cell_type": "code",
762 | "collapsed": false,
763 | "input": [
764 | "simulated_experiment = BanditScenario({\n",
765 | " 'A': {\n",
766 | " 'conversion_rate': .05,\n",
767 | " 'order_average': 35.00\n",
768 | " }, \n",
769 | " 'B':{\n",
770 | " 'conversion_rate': .06,\n",
771 | " 'order_average': 36.00\n",
772 | " }\n",
773 | "})\n",
774 | "\n",
775 | "simple_bandit = SimpleBandit(['A', 'B'])\n",
776 | "\n",
777 | "for visitor_i in range(1500):\n",
778 | " treatment = simple_bandit.choose_treatment()\n",
779 | " payout = simulated_experiment.next_visitor(treatment)\n",
780 | " simple_bandit.log_payout(treatment, payout)\n",
781 | "\n",
782 | "plt.title('Money made by different strategies')\n",
783 | "plt.xlabel('Visitor #')\n",
784 | "plt.ylabel('Total $ made')\n",
785 | " \n",
786 | "plt.plot(np.array(simulated_experiment._scenario_payoffs['B']).cumsum(), label='Treatment B')\n",
787 | "plt.plot(np.array(simulated_experiment._bandit_payoffs).cumsum(),label='Bandit')\n",
788 | "plt.plot(np.array(simulated_experiment._scenario_payoffs['A']).cumsum(), label='Treatment A')\n",
789 | "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
790 | ],
791 | "language": "python",
792 | "metadata": {},
793 | "outputs": [
794 | {
795 | "metadata": {},
796 | "output_type": "pyout",
797 | "prompt_number": 342,
798 | "text": [
799 | ""
800 | ]
801 | },
802 | {
803 | "metadata": {},
804 | "output_type": "display_data",
805 | "png": 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V7efnR1RUFCdPnkzLHxwcnHYcEBCQbr5GTtavX090dDT9+/cH4NFHH2X37t3s\n3LnT6TJSadCglCpyfoz+kd3P73b7DpRvvWXt79DRvbGJKoIyTnysW7cuM2bM4Pz582mPK1euEBgY\nyNy5cwkPD2flypVcuHCBn3/+Gbj5Ld/eSjrHOrPKU7duXUJCQtLVnZCQwJgxY3IsM/XukOzqnz17\nNsYYWrVqRWBgYFrQM3v27BzLz0iDBqVUgTPGcOT8EQ6dPXTLY++pvSQmJ9KkahO31b94Mbz7Lixd\nCtOnw2efua0qVUwMHz6cyZMn89NPP5GcnMzp06dZtGgRYN26WalSJby8vIiOjmb8+PHprvX392fX\nrl0kJSVlVnSOhgwZwsaNGwkPD+f8+fNcu3aNiIiItJ4IyDooqFGjBh4eHmzZsiXT89euXWP+/PnM\nmDGDnTt3pj0++ugj5s6dS3Jycq7aqkGDUqrAbY3bSst/t+TBuQ/e8nj4q4fp0bBH2jcod/i//4OD\nB6F7d+jWzW3VqCIs4+/Xiy++yJAhQ3jmmWfw9fWlY8eOREZGAhAaGsq9995LcHAwDz30EIMGDUp3\n/bBhwzh+/DjVqlVLGwLIqU4RSXseFBTE999/z8yZM2nUqBF169ZlypQp6QKFrK718vLitddeo0+f\nPvj5+aW1OdW3335LpUqVePLJJ6lZs2ba46mnniIpKYnvvvsud++bs5MoijoRMSXltShV0n174Ftm\nbp/JosGLCqV+X1/49VfwK3o7Yxc4u2s9XxGa/v0tWbL7nSjeq1kopYqNWdtn8a+f/wXA2atn6dWo\nl1vre/11WLYs83PXrkGVorkztlJFmgYNSqkC8UP0D/Rt2pcHmjwAQCO/Rm6tb8UKaxiiRSZzKStX\nhhxW3FVKZUKDBqVUgYhLiGNo66G0q9XOrfVcvw6zZ1u3TnbuDPXqubU6pUoVjbWVUgUi7lIcgd6B\nbq9n2zaYMAHCwqB2bbdXp1Spoj0NSim3izoXxb7T+wj0cX/QcPo0tGtn3VKplHItt/U0iEiQiKwR\nkb0iEiEiYXb6RBGJEZHt9uN+h2tGi8guEdkmIr9zSG8uIpvtc391V5uVUu6x7JdltKrZimoVq7mt\njv/+1woWXnwR/P3dVo1SpZo7expuAC8ZY3aISHVgj4hsBgww1Rgz1TGziLQAngbuBGoDq0SkiX0f\nz2xgpDEmUkSWiUhvY8wKN7ZdKeVC566e4+FmD7t17YW1a629Ix57DBo2dFs1Kgvu/NmqosNtQYMx\n5iRw0j7BrJZ5AAAgAElEQVQ+IyI/YwUDAJn9doUC84wxN4BoEYkCOojIUcDHGJO6YkU40A/QoEGp\nImDNr2tY9ksW9zam5olewxOtn8hzHZs3wxdfZJ9n5UqYONHqbVAFK7/rPKjio0DmNIhIY6AlsBHo\nBIwSkWH28z8ZY34DagGbHC6LwQoybtjHqWK5GXwopQrZJ1s/oaxnWVrXbJ1lnkEtB9G/edYr5eXk\n66/hxAm4996s8zRvDr1757kKpZQT3B40iIg38CXWUMVlEfkYeAuoDPwDmAIMc0VdEydOTDsOCQkh\nJCTEFcUqpTLx5Z4vOXbhGFtObOHTvp8SUj/EpeVfuQKff27tRLluHYwcCUOHurSKUikiIoKIiIjC\nboYqpty6jLSIlAWWAMuNMe9ncr4NMMcY00pExgEYYybZ51YAE4CjwBpjTHM7fTDQ1RgzPENZuoyp\nUgWo8juVGdZ2GBXLVmTc78ZRuXxll5a/ahUMGwahoSACL78MDjsZlxz//jcscnI57Zdegl6uXUnT\nFctIq9LDbT0NYs2K+QzY6xgwiEigMSZORMoAjwO77VOLgLkiMhVr+KEJEGmMMSJyUUQ6AJHAUOBD\nd7VbKZW1s1fOcu7qORKTE0lMTmRqr6kunQD37LNw6pR1HBNjbSb1YUn/3/7113D//dA66+GdNC1b\nur89SmXDncMTnYAngF0ist1OexUYLCLBQCKwFngJwBizT0RmAVuBJCDMoevgKWAW4AUs0TsnlCoc\nIbNDSLieQFnPsi7fiTIhAebMgblzb6a1beuy4ouOF16A3btvPt+2DT75BG67rfDapJSTdJdLpZTT\nqk2uxsGRB6nuVT1f5Zw8CSNGWPMVUl25AkePwqFD+WxkYdmyxdoJKye9esG330KFCtbz8uWhfXtr\nDKYQ6PCEyg1dEVIp5ZQbyTe4eP0iVStWzXdZu3bBkSPWLZKO6tbNd9GF49gxa6OLO+/MOe+DD0LP\nnu5vk1JuoEGDUsopZ66coWrFqnhI3heSvXTJ2q76p5+sWyRDQ13YwMJ04gS0amW9MKVKMA0alFI5\nSk5JZsPxDdSsVDNf5axYYd0F0bEjDBrkosblVVyctRJUYmL+y0pMhPvuy385ShVxGjQopXK0PGo5\nwxYNY1jb/C2pcuoUPPAATJvmooblx6+/Qq1aVteHK1R27S2nShVFGjQopXIUlxDHI80fYUqvKXku\n47PP4O234bnnXNiwnJw6ZW13mZJy67kjR6y9s2vUKMAGKVW8adCglMrRrB2z6Fy3c77KWLPGutvw\npZdc1ChnrFsH332X+VKSAQHQqVMBNkap4k+DBqVUtlJMChtjNjIzdGa+yomLgyefhEqVXNSwVJMn\nW7c7ZubwYejSxZpIoZTKNw0alFJZSkpJ4uu9X+NbwZdm1Zvlq6y4OAgMdFHDHM2YAaNGWT0HmenY\n0Q2VKlU6adCglMrStrhtjFo+ihHtR+T62s2b4b//vfn86FEXBA3nzlmbUjhK7cLw9c1n4UqpnGjQ\noJRKc+HaBX797de05xuPb+SeoHt4+963c13W7NkQHw8dOljPp0yBatXy2cA5c+Bf/4I2bW6mDRkC\nVarks2CllDM0aFBKpXl19assPrQ43aqPYcFh2V4TGQk//HBr+rp18Oab0L9/HhsTFwcHD95aWVgY\nvPJKHgtVSuWHBg1KqTTHLx7nw/s/pF+zfk5f8957cPUqNMsw5eGhh6yVlfPs//7P2tipeoZ9LoYP\nz0ehSqn80KBBKZXmRMIJavnUcirvvHlw/Lh148L06dY21i6zbBls3QozZ8LvfufCgpVS+eHUIvIi\n0kFExtnHdUXkLvc2SylVEG4k3+Ba0rW0R26Chuees0YQBgxwbp+mXAkLg5AQaN3axQUrpfIjx62x\nReRjoArQ1hjTXESqAt8bY9oVRAOdpVtjK5U7xhhq/KMGlxIvpaX5VvDl+EvHKetZNotrIDra2sb6\nzjutYQmX7+ickmJtF33lCpTNvB3KdXRrbJUbzgxP9ACaAlsBjDHnRKSiW1ullHK7s1fPkpSSxLXX\nrzl9zbZt1mhBnTrQo0cuA4Y9e2D8eCvyyE5SEnh7a8CgVBHkTNBwHUj73ysizQFPt7VIKVUgXln1\nChXL5i7+P37cChYWL85DhevXW70Hzmw+8eqreahAKeVuzgQNk4CVQE0RmQXcBxTk6vFKKRe7kXyD\nVb+uYmbf3C0NPWECBAfnpcIbsH+/Nabx8MN5KEApVRTkGDQYY+aIyFagu5002Riz373NUkq50/rj\n64n+LZo7Au/I1XVHj6Zf5dFp4eHWY2b+9q9QShWuLIMGe8JjqnhgLiCAEZGqxphz7m6cUsq1ridd\n51LiJX45+wsDWw7E39vf6WtTUuDyZahbNw8Vx8fDs89CP+fXf1BKFT3Z9TRsAwxWoFAXa24DQHng\nKNDAvU1TSrla//n9WXd0HWU9y/Lne/6cq2vPn7fmJ5Yrl+HEhQtWF0R2Dh2CFi1y11ilVJGTZdBg\njKkPICIfAjuB/9inBgO569NUShUJ0b9Fs/7p9bTyb5Xra0+dgpo1Mznx8svw3Xc5bxj1xBO5rlMp\nVbQ4MxGyD/BHY0wKgIjMBl4HRrmzYUqp3Pnf/v/xz5//mW2eqHNRBHhnsYV0Jo4etW6xvJFo8E08\nReuWKRCXIdPhw/DJJ3D//XlotVKqOHEmaDgMvC8in2MNVQy107IlIkFAOFATOA18boz5XER8gC+A\nhnY5Q40xl+xrRgPPAEnAaGPMT3Z6c+BzoCKw2BjzWi5eo1KlwupfV9OyRktCm4ZmmadSuUrUqFTD\n6TIPHYIGDeDb57/H76lQiPK7tZ+xTBlo2jSPrVZKFSfOBA2PAWFYt14CLAfecuK6G8BLxpgdIlId\n2CMim4GngA3GmH4iMhar12KciLQAngbuBGoDq0Skib3M42xgpDEmUkSWiUhvY8yKXLxOpUq8s1fP\n0ve2vnRv2D3nzJmIiIDXMoTjZ85YW1tXvRIDjz+udz8oVco5c8vlOWCq/XCaMeYkcNI+PiMiP2MF\nA32Brna22UAEMA4IBeYZY24A0SISBXQQkaOAjzEm0r4mHOgHaNCglG3JoSV8uedLRrYfmecy1q2D\nJk3gD39In96kCTDkS2jbNn+NVEoVezkGDSJSExgDtMAaHgAwxph7na1ERBoDLYFNgL8xJt4+FQ+k\n3vNVyz6fKgYryLhhH6eKtdOVUrZDZw8xsv1IOtXt5PQ1c+daO1Wm2rvXmtPYKWMRxsCqVfDOO65p\nrFKq2HJmeGIiEA08iNUj8Htgh7MViIg38CXWUMUlcVis3hhjRMRlu0xNnDgx7TgkJISQkBBXFa1U\nkfXlni9ZeHAhvRr1ytV18+dDmzbW8EOqkJanYd2B9BkvX4bKlaFdkdqjTuVRREQEERERhd0MVUw5\ns8vldmNMWxHZDbQBKgDrjDE5boYrImWBJcByY8z7dtoBIMQYc1JEAoE1xphmqVtvG2Mm2flWABOw\n1oRYY4xpbqcPBroaY4ZnqEt3uVSlUtB7QQxqOYg/3PEHmlZ3bkLirl1WwLBnD7Rs6XDi+edh9Wrw\nz7DoU4sW1h0SqsTRXS5Vbji7YRVYQwdhQBTWXRTZEqtL4TNgb2rAYFuE1Vvxd/vfbx3S54rIVKzh\nhyZApN0bcVFEOgCRWHdvfOhEu5Uq8Q6fO0zMxRgm9ZhEGQ9n/jvb1x2Gvn0zBAwAMTHw7rvWSaWU\nysCZvzJ/FRFfYDLwGtbiTn9y4rpOwBPALhHZbqe9AvwF+EJEdmHfcglgjNlnb4i1FeuWyzCHroOn\ngFmAF7BE75xQyrIrfhedgjo5HTC8/TasXAknT8K9mc1KOnECAgNd20ilVImR4/BEcaHDE6o0emzB\nY1QuX5npD013Kv8dd8ALL1h3RLRoATUqJMC1azcztGoFW7dCbZ1rXFro8ITKDWfunqgDDAI6Yu07\nAdYcRu2/VKqQLdi3gKWPL830XHKytazC9es306KjoVcvCArCuivCt076zSR8fW+dz6CUUjZn+jRn\nYM1n+ATr9kewNrJSShWiH6N/JNkk07NRTyshJcXaPMp2cB9MGgv9+9+8ZvRQqFUROA+cPWut5nj6\ndIG2WylVfDlz98QW4K7UvSeKKh2eUKVN51mdCfQOZP6A+Vy+DCdH/IX68/5GSrkKgNXTcOMG+Hhn\nU8jdd8Py5QXTYFUk6fCEyg2nJkIC74nIQuC31ERjzDa3tUoplaML1y7wz/utDarmz4dyXx9nccP3\nWVrnubQ8oaEwMu+LRCqlVDrOBA1NgSeBdkCiQ3o3t7RIKZWlH379gTd/fBOwVoGsUakG7NiBz5pj\ntPPdS/23e/HHRwq5kUqpEsuZoOEZICh1J0qlVOFZd3QdDXwb8FTwU1QsW5GaFWtx4p7O1EhuRFKT\nGro/hFLKrZwJGnZi7Q+hQYNSBWxF1AouXr+Y9nxT7CYeaPwAXetbe77F7Yin1tUjxK7dS607K1gr\nmSillJs4EzT4AvtEJJKbcxr0lkul3Oy3a78R+mUooU1D09J8yvmkBQxbtkDUe5HcVb4Z7TtXKKxm\nKqVKEWeChr9kkqa3KSiVDxeuXSAhMSHbPIfPHaa+b33mD5if6fmxY2HkkTUk3tnRHU1USqlb5Bg0\nGGMiCqAdSpUq7We0JyExAU/xzDZf2hoMmThzBkJP/BuPCdNc3TyllMqU8zvcKKVcwhhDzMUYTv/5\nNJXKVco+8549sGlT2tPt2+Hzz61jvwMGMSnwxBPua6xSSjnQoEGpArbk0BKuJl3NOWBISLA2i7jj\njrSkGrEw6ipUrwEejUFuf9ha1VEppQqA/rVRqoCtOrKKZ9o+k22eq1dh09w47qpWl9Wv3uxpmDED\n7rsPRo1ydyuVUupW2QYNInIv8Isx5riI1AbeASoDb+mKkErl3l/X/pXvDn/HxJCJt54cPRr+aa3w\nWAHoZgxba/Tm009vZhGB3/2uQJqqlFK3yHbvCfs2y+7GmAQRmQqcBiKBPxljHiigNjpF955QRV1S\nShIV3q7A+73fZ0irIfhV9Euf4b774KWXMPf14t//ht27YdonYkUKSrmJ7j2hciPLngYRmQgEAS+J\niABPAJ8CvwNaisgEAGPMmwXQTqWKLWMMBsOZK2fwreDLyLtGwmuvwf796TP+/DMEBvLW2x68+SZM\nnQron3KlVBGSU0/Dt1hbY9cEuhhjnrLT1xtjOhVME52jPQ2qqGr+r+YcOHMAQbi7zt1sGLYBqlWD\n994Db4ctKMuWhQce4Infe9KrFwwdWnhtVqWH9jSo3MhpIuRbwAQgCRgDICItgR1ubpdSJcLVG1c5\ncOYAia8nUtazrJUYHw/nzlm3Snp43HLNyZMQEFDADVVKKSdkGzTYkx1DM6TtBUa4s1FKlRRv/fgW\n1SpWuxkwAHzwATRtmmnAABAXB4GBBdRApZTKBb3lUik3Mcaw69Quptw3Jf2JX36BP/85XdIXX8DK\nldbxkSPa06CUKpoy/6qjlMq3r/Z+xbJflnF7zdvTn1iwAG67LV3SZ59BUBD06AFz50L16gXYUKWU\nclK2EyGLE50IqQpackoyicmJWZ6fsnEKF69fZHLPyTcTb9wALy+4fh08PFi82AoSli+HzZutUQul\nCpJOhFS5kd0tl3eSzW6WuriTKu0e/fpRlh5aiqdH1ptOTeuTYTOp/v3B3z9tPsP8+VC1KsyceUvn\ng1JKFTnZzWmYQvZbYHfLqXARmQn0AU4ZY1rZaROBZ7AWigJ41Riz3D432j6XBIw2xvxkpzcHPgcq\nAouNMa/lVLdS7hZ1Loqf//AzbQLaZJtv7FhrGkOZ5OvMXbqCUT0OEN/fOrdhA8yZYw1LKKVUUZdl\n0GCMCXFB+bOAj4Bwx6KBqcaYqY4ZRaQF8DRwJ1AbWCUiTewxh9nASGNMpIgsE5HexpgVLmifUnny\n4eYP2XNqD3Uq18k2n/ntApve388bb4DX1bMkra1Oz+GN0s7//vfQpYu7W6uUUq7h1N0TInI70AJr\nSXwAjDHhWV+RlmediNTPrMhM0kKBecaYG0C0iEQBHUTkKOBjjIm084UD/QANGlSh2XB8A1Pum0I1\nr2rp0h97DA4fvvn8heOT+CJpHnWX2LdDPNaP/v0LsKFKKeVCOQYNIvI88AegDrAe6A4sJn3vQW6N\nEpFhwEasfSx+A2oBmxzyxGD1ONywj1PF2ulKucQLS19g2S/LcnVN/OV4Rt2VfqtJY+CbbyAiwlrc\nUW4k0uy5CC6//Bd4WZd3VEoVf870NAwF7ga2G2MeFpHbgH/mo86PsVaarAz8A2vuxLB8lJdm4sSJ\nacchISGEhIS4olhVwq07to5pD06jWfVmTl/jKZ7phiauXIF166B8ebjnHjvxi69gzya87/8080KU\nKgQRERFEREQUdjNUMeVM0FDWGJMoItH29tiHsTayyhNjzCn78IKI/AuYYz+PzVBuHawehlj72DE9\nNrOyHYMGpXJijOHl71/m8LnDtA1oi7+3f57LmjsXxo+3hicAK4rYtQvGjYOWLV3TYKVcIOMXqjff\n1D0HlfOcWdxpi4j4YU1GXAfsA/6X1wpFJND+twzwOLDbPrUIeExEyolIA6AJEGmMOQlcFJEO9m6b\nQ4Fv81q/UqkiYyOZuWMmM/vOpmr5miQlkedHTAw8/TTMmGEX/sc/wuzZ0L59ob5GpZRypVwt7iQi\nPoCfMeaYk/nnAV2B6kA81uZXIUAwkAisBf5ujIm3879I+lsu19npLbDuxPAClhhjXsmkLl3cSTnt\nt2u/4f+uP2x9huTF/8pTGZ4miQpcS3s+fToMGmQ/6dcPXn4Zevd2QWuVch9d3EnlRo5Bg4isNsZ0\nzymtsGnQoJwRezGWGyk3OHg6isfCR1F17v50dzvkSmgofP89eGayuFOZMrBlCzRunK/2KuVuGjSo\n3MhuRciKWN/sa4hIVYdTNQEfdzdMKVfbc2oPd06/k0DvQJKT4cr+XiyYno8CDx+21n5u3dplbVRK\nqaIsu4mQzwEvYt0KudUhPQH40J2NUiq/Tp2CFRlW8th1JYbbyoXw56rfcfw4LDgM3fPaX/bBB7B3\nL9TWu3+VUqVHditCvg+8LyKjjTEaJKhiZc4ca+fI8t3/zsmKPwBwzfMkfoltWLXHyvPcc/moYONG\nK3CoVi3nvEopVUI4M6ehHPA80AVrCegfgWn2yo1Fhs5pUI5eeQW8veF/NdoRFhxGk6pNAGhZs2WO\nSz8DMHIkfJvNTTpnzsAPPzgsyqBU8aRzGlRuOLNOw8dANeA/WMs/DwbaYN3loFSh6d/futUxM79G\nG157+yynLp+iT5M+NPBrkLvC16+37p9s1Srz856eEBCQuzKVUqqYy7KnQUTKGGOSRORXoIUx5qqd\nXhHYZ4zJ5V9h99KehtIlMREqVYKffgLJ5DvSwuOfMvXAKPwr+bN/xH4qlq2Yc6EffQRff20dR0ZC\ndLQGBqrE054GlRvZ9TREAncA14DWwGY7vRVw1c3tUgqAs2et5Zkz+u03qF4dOnTI/Lqll48z5p4x\nvNktF6vdLVli3UbZvj14eWnAoJRSGWQXNKRGniOAf9lzGwCu22lKud2sWdaiSS1a3HpuWBY7lhhj\n+Hrf17zQ/gXnKvnmG2tNhV274O9/h+DgvDdYKaVKsOyGJ2KAqdwMHrzs48uAMcZMLZAWOkmHJ0qm\nsWPBz8/awsFZcQlx1Jpai+gXo6nnWy/nC9q3t7os6teH0aOhXLkcL1GqpNDhCZUb2fU0eJL5Ik7e\nbmqLKqXWroUFCzI/t2aN9TmeG5tjN9OserOcA4aNGyEhAY4dsxpQz4kAQymlSrHsgoaTxhjd/ky5\n3X/+AxcuZH73YpMm0Ldv7srbFreNNv5tss907hx06wZdukCnThAYmLtKlFKqFHLmlkul3MIYuHoV\nTp6EoUPh0Udzd/3Okzs5ePbgLekbYzbyaPNMCjMGdu+GGzfgyBFo1MjaO0IppZRTsgsaehRYK1Sp\n9NlnMHw4VKwIEyfm/voRy0ZQoUwFqlasmi69asWqdKnX5dYLdu60ehWaNbOe9+mT+0qVUqoUy24Z\n6bMF2RBV+vz6K0yYAG+8kbfr4y/Hs/TxpdxW7TbnLjhxwhqOWL48bxUqpVQpp8MTqlD89hvMnWvd\nHZHRP9b/g6mbcr455+yVswR457CWwquv3hyCOHcOQkJy31illFKABg2qkKxZYy3cdN99t577+cTP\nTOg6gdCmodmWUaFMBSqXr5x9Rd99By+9dHNIokGRWshUKaWKFQ0aVKHYsAGGDIGGDWHBvgXM2DYj\n7dyWE1sY0X4EgT75uKPh+HG4csWaZdm5s95OqZRSLqBBgyoU27bdnIe4ImoFLaq3oHfj3gB4enjS\nqW6nvBd+8aJ1Z0SDBtZS0P7+LmixUkopDRpUoTh95TQ1W8ewPQ4Onz/M6LtG06txL9cUHh0NderA\nwVtvx1RKKZV3GjSoAvPRR/D559bxnmbP8NquPVQ9XBlP8aRlzZauq+ibb6BWLdeVp5RSCtCgQRWg\nH3+EQYOge3d4av1JPnl4Dh2DOrq+orNnYcAA15erlFKlnAYNqsCcOwcpDb7nkxMLOHblIDUq1XB9\nJY0bW6s9Llni+rKVUqqU06BBud3Vq9YX/y1bwOviHPzLleXD+z+koV/D/BX8yy9w+PDN54mJ1gJO\niYlQRn+1lVLK1dz6l1VEZgJ9gFPGmFZ2mg/wBdAQOAwMNcZcss+NBp4BkoDRxpif7PTmwOdARWCx\nMeY1d7Zb5V1ySjIfRX7EtaRraWnx8bBeYPC/4McLkYxq+YFrJj0OG2YFCL6+N9OefVYDBqWUchMx\nxrivcJHOwCUg3CFomAycMcZMFpGxgJ8xZpyItADmAu2B2sAqoIkxxohIJDDSGBMpIsuAD40xKzLU\nZdz5WpRzIo8c4J4Znal0aFhaWlKSdefjgAFQxqMMf+r4J/wq+uW/sqZNYeHCmws3KaVyTUQwxkhh\nt0MVD279SmaMWSci9TMk9wW62sezgQhgHBAKzDPG3ACiRSQK6CAiRwEfY0ykfU040A9IFzSoouGv\nEf+gfEIzdk6ZlC69WjXw8clHwSNGwKpV6dN+/dWKRpRSShWIwujH9TfGxNvH8UDqyju1gE0O+WKw\nehxu2MepYu10VcQkpSSx6PhM7jq/gvr1XVz4Dz/Ae+9Zizalqlgx/dCEUkoptyrUwV976MFlYwoT\nHfZXDgkJIUQ3J8qTlBTrkRsfRL7P5mM/Q0IA62blc77CypXw3/+mT4uOhg4drC4LpVSeRUREEBER\nUdjNUMWUW+c0ANjDE4sd5jQcAEKMMSdFJBBYY4xpJiLjAIwxk+x8K4AJwFE7T3M7fTDQ1RgzPEM9\nOqfBRbp2hZ9+AsnFKGfyqPrIlhe4zedODizrnr8GPPkkeHpaQUKqypVh8ODcNUoplSOd06ByozB6\nGhYBvwf+bv/7rUP6XBGZijX80ASItHsjLopIByASGAp8WPDNLtmOH4f1663jnTutfZ5q5GIZBe+/\nnSFu1fP4lM9h4sLevdYqT9nZuhWmTIHevZ1vgFJKKbdz9y2X87AmPVYTkePAeOAvwBcisgv7lksA\nY8w+EZkFbMW65TLMoevgKWAW4AUsyXjnhMq/qVNh7Vpo0gQefxyqV888X8t/t2Tf6X23pNfwqoF3\nOW/nKoqOtu58yMq990K7ds41XCmlVIFx+/BEQdHhibz5+ms4cAAWLIDXXoOBA7POm5ySTIW/VuDy\nq5cp61E2/ck9e5DY2JwrnDAB3ngDHnwwfw1XSrmEDk+o3NBVcEq5sWOtz+9+/aBbt5vppy+fpu0n\nbbmefD0tzRhDoHcg5TzL3VrQwIHWeIaXV/YVVq8OwcEuar1SSqmCpEFDKbRuHZw5Yx3HxcE770Cl\nSunzHDl/hJqVavLdE9+lS/cqm0VQEB9vFZzVuIZSSqliT4OGUiYpCXr2hPvvt54PHXprwPDSipdY\nHrWc5jWa37qp1KuvQlTUrQUnJICfC1Z5VEopVWTpnIZS4ocf4IMP4Pp12L8fjh615ijEXYq7JW+7\n6e34tO+ntKvVjgBvhxUXjbEijBkzoGyGOQ3Vqll7XiulihWd06ByQ3saSomVK6FKFXjkEQgKstL+\nGflPXl/zOlXKV0mXt2almtxfpxuer71uRRmpkpKszaCGDCnAliullCoqNGgoBU6csHoX+vSB0NCb\n6ccuHGN8l/H8udOfb71o507rlopXXkmfrnc9KKVUqaVBQynw5JPWYk133mk9f2jeQ8QlxGEOH2Z6\n0gOwacqtF/3yCzRvDi+8ULCNVUopVWRp0FAKnDgB8+fD7bfDpcRLrDqyinVPrSPwvc8I3LAD7slk\np8hKlWDUqIJvrFJKqSJLg4YSbs0aa2iiVi3r+UebP6Lu1fK0u+IL8Zet5R81OFBKKeUEj8JugHKv\nnTut4YmqVa3nO05uZ/fUq9a+Dhs3Qps2hdtApZRSxYb2NJRQX30Fy5fDjh0wYADEXIxh1ZFVnD24\nA88UMl9rQSmllMqGrtNQQvXqZXUitGgB990HnxycwOJDi/lgwWU6xnpQZt/+wm6iUqoI0HUaVG5o\nT0Mx9NNP8N572eeJjLR2l779drh64yqxW2J55o5n6LxiJbw9tGAaqpRSqkTRoKEYWr4cKlSA/v2z\nzhMWZvUyfHvgWx6d/yj3R5dhUNRO2HMYXn65wNqqlFKq5NDhiWLm2jWoXBmmT7cCg5xM2TCF2IRY\npv5Q1tqlavBgCAmxVnZUSpV6OjyhckM/OYqZmBjw8IBBgzI/v3jZ++z6+p9pz89ePUvPhj1h40kr\nyujRo2AaqpRSqsTRoKGY+Oor2LXLWqjpzjuhYsXM83n9ewZDj1ylwu3BaWl+seWgcWPo0qWAWquU\nUqok0qChmJgwAR54wPrsz6yXIerMIRaumUbbo8c49YfhtHvpHwXfSKWUUiWazmkoJnx94ciRm4s0\nZSKMXXUAAA+gSURBVPTdn/vT+aOF3KjsDUuWUOWuzgXbQKVUsaRzGlRuaE9DEWSMITE5Me351atw\nJRG8fOB6UubXSEws24b24HczviugViqllCptNGgoDAcOwDvvQBY9I1tObOHAmQMIN4P/TxvDgvZZ\nF9k+NoVrI4a7uqVKKaVUGg0aCsPq1daMxqGZL7K0duNO7rzrRcInt00bjritKTz7hxzK7dPHte1U\nSimlHGjQUNA++4yr7/yFD9pc5Z9nMl/K+VStU0xv9wUHf2zN+vUF3D6llFIqC4UWNIhINHARSAZu\nGGPuEhEf4AugIXAYGGqMuWTnHw08AyQBo40xPxVKw/MoOSUZAI/F/9/evUdZVd5nHP8+MKiAaGVJ\nBgUMIFCx3vCC9T5ajYDxUjWLQKIQXMY2y2qiNirR5aVNolEUk9bYoiiNEYlxYcBYxQqj8QaCCCJg\nHQUtIKiggIqg8Osfe4+eueFBmbP3mXk+a501+7zv3ptnNpzhN+++vFOZM+Qglp9QyfNDft7oujOe\nqODesV3p0qWUCc3MzLYuy5GGAKoiYk1B29XAsxFxhqTLgauAKyTtC4wCDgG6Af8jqV9EbCl56q9g\n5oO/5q8uuBgBPdbCj88T/9DvP+m+S/dG1586EXr1ggsuKG1OMzOzrcn69ET923xOA45LlycA1cAV\nwOnAxIj4FFgqqQYYCDxfopxfyydzX+DDv+7FIXc/ChUVzO7VC9TwDqfp02HmTJgzB+6+GwYMyCCs\nmZlZE7IeaZguaQtwe0SMAyojYlXavwqoTJf3pG6BsIxkxCEzb69/m4f/9+FG+zquXMPOK1d//n6H\nWc9Aj0ro12+r+7z+eujRA4YPd8FgZmb5k2XRcFREvC2pP/CIpMWFnRERkrb2tKYGfddee+3ny1VV\nVVRVVW2nqA1NXDCRCfMmMHDPgQ36Lr3qz3Rcv5FP2rf7vG3z8LOa3NfDD8Mf/gAvvQTjxkHfvs0S\n2cyM6upqqqurs45hZSoXT4SUdAuwHDif5DqHlZL2AGZExD6SrgCIiBvS9R8FromImQX7KOkTIW/7\nj1FUTZnPgV0PbNj54IPw4ovQu3dR+xo2LHnSY1UVnH12o2cuzMyahZ8Iadsik5EGSR2AthGxXlIX\nYAhwETAFGAHcmH59KN1kCnBfWlx0A/oCs0oevMCez86n89qNcOaRDTtPPDG5krGeSy+FBQsarj57\nNkye7PmkzMws37I6PVEJTFbyK/Vq4NaImCbpOeB3kuaT3nIJEBELJd0NzCG55XJk1hNNtH13DeuO\nrILzzit6m/Hj4c47Yeed67ZXVMCRjdQeZmZmeZJJ0RARS4CDGmlfD5zRxDa3Abc1c7Si7bRmLTt1\n26vRvtGj4a236rZFJHNInHmmTz+YmVl5yvqWy7LV8YOP2blbw1MQW7bAzTcnIwpt2tTtGznSBYOZ\nmZUvFw1FWLdxHes2rgOSEYNJk+CkDzby+At9WLuq7robNkCnTnDuuRkENTMza0YuGoowcNxA1m5c\nS1u1ZfNmWLUKVnxUwaSVPVn7YcP1r7669BnNzMyam4uGrVm2jA1PTWf/p17l6n1/T4XasXw5zJgB\nXTd+j1+M6wI7ZB3SzMysNFw0bM2YMWx67E8M3dye956YjNokB+ycrsAPfwg7uGIwM7PWw0XD1qxY\nwbNnn8R35m9i0wN3067dl29iZmbWUrlo2Ip3auZxR6c1dN5xlAsGMzNr9Vw01PfRR/Dxx8nyihVU\nnjicIYzONpOZmVkOuGiob8AAWL0a2rQh+IxXlpzAIV13yTqVmZlZ5tp8+SqtSAS8+SasWAHvvsvg\nf92H9et7cUajz6g0MzNrXVrvSMPTT8NZZyWFQq0I6NyZxeuW8u/P38lrq2uoXLUHXbpkF9PMzCwv\nWm/RsGABDBoEN91Ut71DB6656w4emPcCO71+Ax++3Y1u3bKJaGZmlietr2h46y0444zkFMSFF8I3\nvtFgleUfvEdVj5OZPv5HGQQ0MzPLp9ZXNCxcCO3bw7Rp0KdPna4PPoAjbz+ZVzc/zbBOv80ooJmZ\nWT61vqLh5puhVy844IAGXdOmBYs3PMX31r7C6GENZ7A0MzNrzRSFFwKWMUlR1PfSsWNyEeSAATxa\n8yhjnhvDp58ms1OuXLWFlZrNxuvWNn9gM7MckEREKOscVh5ax0jDYYfBypXJsgQHHgjAtNensfdu\ne/PWY2exeA502gWuH+ZbJczMzBrT8kcaPv4YOneG115L3nfsCJ07U7Omhv1u3487Tx3PL4YNZ8wY\nGDy4tJnNzLLmkQbbFi13pGHzZpg3Lxlh2H136NGjTvdTbz5F791602XNqSxaBPvvn1FOMzOzMtEi\ni4ZNmzdx75gRDLt+Mu/tuSvLDqjkgccuqbPO7BW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806 | "text": [
807 | ""
808 | ]
809 | }
810 | ],
811 | "prompt_number": 342
812 | },
813 | {
814 | "cell_type": "code",
815 | "collapsed": false,
816 | "input": [
817 | "len(simulated_experiment._bandit_payoffs)"
818 | ],
819 | "language": "python",
820 | "metadata": {},
821 | "outputs": [
822 | {
823 | "metadata": {},
824 | "output_type": "pyout",
825 | "prompt_number": 323,
826 | "text": [
827 | "500"
828 | ]
829 | }
830 | ],
831 | "prompt_number": 323
832 | },
833 | {
834 | "cell_type": "code",
835 | "collapsed": false,
836 | "input": [
837 | "plt.title('Distribution of means for N(35,5) distribution (sampling 100 vs 500 data points)')\n",
838 | "plt.xlabel('')\n",
839 | "plt.ylabel('Counts')\n",
840 | "\n",
841 | "plt.hist([np.random.normal(loc=35, scale=5, size=100).mean() for i in range(2500)], label='100 sample mean')\n",
842 | "plt.hist([np.random.normal(loc=35, scale=5, size=500).mean() for i in range(2500)], label='500 sample mean')\n",
843 | "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
844 | "''"
845 | ],
846 | "language": "python",
847 | "metadata": {},
848 | "outputs": [
849 | {
850 | "metadata": {},
851 | "output_type": "pyout",
852 | "prompt_number": 354,
853 | "text": [
854 | "''"
855 | ]
856 | },
857 | {
858 | "metadata": {},
859 | "output_type": "display_data",
860 | "png": 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Bg3n//fez1msmBZc4Ab8Ennf3rwIDgXeA64C57j4QmAdcC2BmA4AJwNHAWcC9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\nREREElLiJCIiIpLQ/wd3zxeZDDXtVwAAAABJRU5ErkJggg==\n",
861 | "text": [
862 | ""
863 | ]
864 | }
865 | ],
866 | "prompt_number": 354
867 | },
868 | {
869 | "cell_type": "code",
870 | "collapsed": false,
871 | "input": [
872 | "plt.hist([np.random.normal(loc=35, scale=5, size=100).mean() - \n",
873 | " np.random.normal(loc=34, scale=5, size=100).mean() \n",
874 | " for i in range(2500)], bins=30, label='100 sample mean')\n",
875 | "''"
876 | ],
877 | "language": "python",
878 | "metadata": {},
879 | "outputs": [
880 | {
881 | "metadata": {},
882 | "output_type": "pyout",
883 | "prompt_number": 360,
884 | "text": [
885 | "''"
886 | ]
887 | },
888 | {
889 | "metadata": {},
890 | "output_type": "display_data",
891 | "png": 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892 | "text": [
893 | ""
894 | ]
895 | }
896 | ],
897 | "prompt_number": 360
898 | },
899 | {
900 | "cell_type": "code",
901 | "collapsed": false,
902 | "input": [
903 | "plt.title('Distributions of a mean of 34 and 35 with 50 samples')\n",
904 | "plt.xlabel('')\n",
905 | "plt.ylabel('Counts')\n",
906 | "\n",
907 | "plt.hist([np.random.normal(loc=35, scale=5, size=50).mean()\n",
908 | " for i in range(2500)], \n",
909 | " bins=30, label='mean of 35', alpha=.8)\n",
910 | "plt.hist([np.random.normal(loc=34, scale=5, size=50).mean()\n",
911 | " for i in range(2500)], \n",
912 | " bins=30, label='mean of 34', alpha=.8)\n",
913 | "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
914 | "''"
915 | ],
916 | "language": "python",
917 | "metadata": {},
918 | "outputs": [
919 | {
920 | "metadata": {},
921 | "output_type": "pyout",
922 | "prompt_number": 370,
923 | "text": [
924 | "''"
925 | ]
926 | },
927 | {
928 | "metadata": {},
929 | "output_type": "display_data",
930 | "png": 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kzAUjWSttSYAFp/yPAkvd/d64oueAbwE/Cf8+Gzf+N2Z2D8FlgCHAW+mKT7Jf\ndW01BTdEHzE33Lthv5YbWxVjUPGghGUHkli09FwA0LMBRCS7pLMm4LPApcBiM1sQjvs+cDvwhJkt\nBpYD4wDcvcLMpgFvA7uBUndPdqlAZL94nkcmF/ubWIiItEXpvDvgdaJvQTw/Yp77gPvSFZOIiIjs\noUcJS5uVrFo/FotRQAYvvouItAFKAqTNSlatXzkpYZtSERGJox75REREcpRqAkTiJLvEANl3W6KI\nyIFQEiASJ9klBtDdAyLSvuhygIiISI5SEiAiIpKjlASIiIjkKCUBIiIiOUpJgIiISI5SEiAiIpKj\nlASIiIjkKCUBIiIiOUqdBYmkaOnS96hds5tBg0YkLI/F1lKgZxaJSBuiJEAkRfX10LHjERQUzE9Y\nXlnZr5UjEhE5MLocICIikqOUBIiIiOSofbocYGadgEJ3X5OmeESyWs2OGEuWJ37KYF2H9a0cjYjI\ngWkxCTCz14BzgV3Au8AuM3vS3e9Od3AiWaeTk1eauPVfzUOVrRyMiMiBSeVyQHd3/ydwMfAM8C/A\n+WmNSkRERNIulcsBW8zsKOBbwPXu7mbWNc1xiUiGxGIrIm+DBMjPh4qKxHdIiEjbkkoScDvwGPC6\nuy82s6OBD9IblohkivshkbdBAmzYEJ0giEjbkkoSsMPdSxoG3H25mf08fSFJLikeVkx1bXXCslgs\nRgHqfUdEJF1SSQIeAE5KYZzIPquurabghsQH+spJamgnIpJOkUmAmY0CTgMKzGwiYGFRAfBxK8Qm\nIiIiaZSsJqAz0A3oGP5tsB64Op1BiYiISPpFJgHu/hrwmplNd/ePWi8kERERaQ2ptAkwM/sRMAo4\nJBzn7n5W+sISERGRdEuls6AHgDpgCvC9uFeLzOwxM6sysyVx48rMbI2ZLQhf58SVXWdmi83sHTM7\nfd/+FREREdkXqdQEDHT3r+zn8qcRJBEz4sY5cI+73xM/oZkVA+OBk4F+wF/M7Fh3r9/PdYuIiEgS\nqdQE/MbMJpvZ0WbWs+GVysLdfQ6wOUGRJRh3HjDT3WvDNggfAqeksh4RERHZd6nUBFxNcPZ+WZPx\ngw9gvdea2RXAm8BN7v4JUATMjZtmDUGNgIiIiKRBi0mAux95kNc5laB9wWHAT4GfAVdErT7RyLKy\nssb3JSUllJSUHNQARUTauvLycsrLyzMdhmS5VB4l/C0SHIzdfUaCyVvk7g0PXd9iZr8AngyHK4EB\ncZP2D8fUwNIpAAAQFklEQVQ1E58EiIhIc01PkCZPnpy5YCRrpdIm4F/jXl8G7gn/7hcz6xv+zQO+\nCTTcOfAcMNbMOpvZYGAI8Nb+rkdERESSS+VywHfjh82sH8FTBVtkZjOBzwO9zWw1cBtQYmbDgRrg\nb8CN4XoqzGwa8DawGyh194SXA0REROTApdIwsKktpNhgz90vTjA6MoFw9/uA+/YjJhEREdlHqbQJ\neD5usAtQDPxn2iISaat217Fk+aCERR3q8jn+2IpWDkhEJLlUagJ+Fv51YCewyN13pi8kaW+KhxVT\nXVudsCwWi1FA4kcJtzmdIK808f+ye/qGVg5GRKRlqbQJKDezIwgaBnYjuLVPSYCkrLq2moIbEh8c\nKyclvAFERERaQYt3B5jZRcBigk6DvgO8a2ZfT3dgIiIikl6pXA64C/icuy8DMLNjgReBp9IZmIiI\niKRXKv0EdADWxQ1XpTifiIiIZLFUagL+AMwys6cJHvxzAfB0WqMSERGRtItMAsxsCFDk7t8zsxOB\nrxDcIfADINZK8YmIiEiaJKvWvxfYBeDui939Lnf/MbAD+HlrBCciIiLpkywJONLd5zYd6e5/58Ae\nIywiIiJZIFmbgNokZXUHOxARaRtisRUMGjQisjw/Hyoq5rdiRCKyv5IlAQvM7Cp3fyh+pJldCSxI\nb1girW/p0veo213HkiXvJSyvqUmWF+cO90MoKIg+yG/YEJ0giEh2SZYETAReNLNLCJ7sB3AywfMD\nRqc7MJHWVl8PWEfy8o5LWF5Ts7h1AxIRSbPIJMDdN5vZacCZwAkEdwb82d3/2lrBSduQ7NkA0M6e\nDyAi0o4k7SfA3R34a/gSSSjZswFAzwcQEclWqXQWJCIHqGZHjCXLB1HXpSrh44b1qGERyQQlASKt\noZOTV1pAzfa15HVtXmuiRw2LSCboGQAiIiI5SkmAiIhIjlISICIikqOUBIiIiOQoJQEiIiI5SkmA\niIhIjtItgpIzli59j/p6Ip8PoGcDiEiuURIgOaO+HvLyjqPGFiZ8PoCeDSAiuUaXA0RERHKUkgAR\nEZEcpSRAREQkR6U1CTCzx8ysysyWxI3rZmbPmtliM3vGzA6NK7suHP+OmZ2ezthERERyXbprAqYB\nX24y7ofAG+5+IjAXuBXAzIqB8cDJwIXAdDNTTYWIiEiapPUg6+5zgM1NRo8BHg/fPw6cH74/D5jp\n7rXu/hHwIXBKOuMTERHJZZk40y5096rwfRVQGL4vAtbETbcG6NeagYmIiOSSjPYT4O5uZp5skkQj\ny8rKGt+XlJRQUlJycAOTZoqHFVNdW52wLBaLUUBBK0ckIsmUl5dTXl6e6TAky2UiCagysyPcfZ2Z\n9QXWh+MrgQFx0/UPxzUTnwRI66iurabghsQH+spJCT8mEcmgpidIkydPzlwwkrUycTngOeBb4ftv\nAc/GjR9rZp3NbDAwBHgrA/GJiIjkhLTWBJjZTODzQC8zWw38CLgdeMLMFgPLgXEA7l5hZtOAt4Hd\nQKm7J7tUICIiIgcgrUmAu18cUXR+opHufh9wX/oiEslONTtiLFk+iLouVSxZPmivsg51+Rx/bEWG\nIhOR9kwPEBLJBp2cvNICaravJa/r3m0vdk/fkKGgRKS9U2c8IiIiOUo1ASJyUMViKxg0aERkeX4+\nVFTMb8WIRCSKkgAROajcD6GgIPogv2FDdIIgIq1LlwNERERylJIAERGRHKUkQEREJEcpCRAREclR\nSgJERERylJIAERGRHKUkQEREJEcpCRAREclRSgJERERylJIAERGRHKUkQEREJEcpCRAREclReoCQ\nSJar2RFjyfJBANR1qWp8D9ChLp/jj63IVGgi0sYpCRDJdp2cvNICAGq2ryWva0Fj0e7pGzIVlYi0\nA0oCpF1YuvQ96uv3DNftrmPJkvf2mqamppY8feNFRBpplyjtQn095OUd1zhcYwv3GgaoqVnc2mGJ\niGQ1NQwUERHJUaoJkEbFw4qprq1OWBaLxSigIGGZiIi0TUoCpFF1bTUFNyQ+0FdOqmzlaKS9isVW\nMGjQiKTT5OdDRcX8VopIJHcpCRCRVuV+CAUFyQ/wGzYkTxJE5OBQmwAREZEcpSRAREQkRykJEBER\nyVFKAkRERHJUxhoGmtlHwD+BOqDW3U8xs27AE8BRwHJgnLtvy1SMIiIi7VkmawIcKHH3k9z9lHDc\nD4E33P1EYC5wa8aiExERaecyfYugNRkeA3w+fP84UA7c0poBiUjmtdSXgPoREDk4MpkEOPBXM6sH\nfunuDwOF7l4VllcBhRmLTkQypqW+BNSPgMjBkckk4LPuvtbMjgNeNLN/xBe6u5uZZyg2kTahZkeM\nJcsHNQ7Xdanaa7hDXT7HH1uRidBEpA3IWBLg7mvDv++Z2TPAKUCVmR3h7uvMrC+wPtG8ZWVlje9L\nSkooKSlJf8Ai2aiTk1e6p6vnmu1ryeu6Z3j39A2ZiEqyQHl5OeXl5ZkOQ7JcRpIAM+sKdHT3rWZW\nAIwGrgOeA74F/CT8+2yi+eOTABERaa7pCdLkyZMzF4xkrUzVBBQCz5gZwMfAz939ZTN7E3jCzBYT\n3iKYofhERETavYwkAe6+AhieYPxW4PzWj0hERCT3qMdAERGRHJXpfgJEUrJ06XvU1+89rm53HUuW\nvAdATU0tefo2i4jsE+02pU2or4e8vOP2GldjCxvH1dQszkRYIiJtmpKAHFI8rJjq2urI8lgsRgEF\nkeUiItK+KAnIIdW11RTcEH2Qr5xU2YrRiIhIpikJEGnHkvUoqN4ERURJgGQFNfxLkyQ9Cqo3QRHR\nblWyghr+iYi0PiUBIjmq6aWCBnVdqlj6frEuFYjkACUBIrmqyaWCBjXb11L/++i7SESk/VAS0M4k\nuw1QtwCKiEg8JQHtTLLbAHULoIiIxNOzA0RERHKUkgAREZEcpSRAREQkRykJEBERyVFqGNgG6Q4A\nSbeoPgQgO/oRiMVWMGjQiMjydetWcMQRgyPL8/OhomJ+OkITaVOUBLRBugNA0i6iDwHIjn4E3A+h\noCD6IF5Z2S9p+YYN0QmESC7R5QAREZEcpSRAREQkR+lygLSKRE8JhD1PCtRTAkVEWp92u9IqEj0l\nEPY8KVBPCRQRaX1KArJQstb/oDsARA5US3cX6O4ByRVKArJQstb/oDsARA5US3cX6O4ByRVqGCgi\nIpKjVBOQIe2tw5+ohn8QNP6rU8O/diVpZ0Id1rdyNCKyv7RbzpD21uFPVMM/CBr/STuTrDOhh9re\n91ckV+lygIiISI7KupoAMzsDuJcgtofd/YEMh5Tzli59r/F+/kRU3S+pWvp+MXVdqhJeSuhQl5/R\n5xGI5KKs2m2bWUfgMeCLQCXwdzP7i7snPvq0QeXl5ZSUlGQ6jH1SXw9YR2xtfzoO6NasvM1U98fq\n4ZhMB7H/6lZvhV6ZjmL/bd1aTn3Hari4A3ldm19K2D19QwaiSl1b/O2KtCSrkgDgFOBDd/8IwMx+\nC5wHZG0SkKyB37rV6zhiwBF7jftkwyd0L+jeJhv/1a3ZmjAJaDNi3raTgDVtPwloKxL1I/DJJzG6\ndy9qHFZfAtIeZFsS0A9YHTe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931 | "text": [
932 | ""
933 | ]
934 | }
935 | ],
936 | "prompt_number": 370
937 | },
938 | {
939 | "cell_type": "code",
940 | "collapsed": false,
941 | "input": [
942 | "sum([np.random.normal(loc=34, scale=5, size=50).mean() > np.random.normal(loc=35, scale=5, size=50).mean() \n",
943 | " for i in range(1500)])/1500."
944 | ],
945 | "language": "python",
946 | "metadata": {},
947 | "outputs": [
948 | {
949 | "metadata": {},
950 | "output_type": "pyout",
951 | "prompt_number": 372,
952 | "text": [
953 | "0.154"
954 | ]
955 | }
956 | ],
957 | "prompt_number": 372
958 | },
959 | {
960 | "cell_type": "code",
961 | "collapsed": false,
962 | "input": [
963 | "np.random.choice([34,32,33], size=3).mean()"
964 | ],
965 | "language": "python",
966 | "metadata": {},
967 | "outputs": [
968 | {
969 | "metadata": {},
970 | "output_type": "pyout",
971 | "prompt_number": 378,
972 | "text": [
973 | "32.333333333333336"
974 | ]
975 | }
976 | ],
977 | "prompt_number": 378
978 | },
979 | {
980 | "cell_type": "code",
981 | "collapsed": false,
982 | "input": [
983 | "np.random.binomial(1, .8, 3)"
984 | ],
985 | "language": "python",
986 | "metadata": {},
987 | "outputs": [
988 | {
989 | "metadata": {},
990 | "output_type": "pyout",
991 | "prompt_number": 382,
992 | "text": [
993 | "array([1, 0, 1])"
994 | ]
995 | }
996 | ],
997 | "prompt_number": 382
998 | },
999 | {
1000 | "cell_type": "code",
1001 | "collapsed": false,
1002 | "input": [
1003 | "random\n"
1004 | ],
1005 | "language": "python",
1006 | "metadata": {},
1007 | "outputs": [
1008 | {
1009 | "metadata": {},
1010 | "output_type": "pyout",
1011 | "prompt_number": 383,
1012 | "text": [
1013 | ""
1014 | ]
1015 | }
1016 | ],
1017 | "prompt_number": 383
1018 | },
1019 | {
1020 | "cell_type": "code",
1021 | "collapsed": false,
1022 | "input": [
1023 | "[random.uniform(0,200) for i in range(10)]"
1024 | ],
1025 | "language": "python",
1026 | "metadata": {},
1027 | "outputs": [
1028 | {
1029 | "metadata": {},
1030 | "output_type": "pyout",
1031 | "prompt_number": 386,
1032 | "text": [
1033 | "[84.54080497801762,\n",
1034 | " 153.39546708119394,\n",
1035 | " 61.49010649403734,\n",
1036 | " 108.75419385506284,\n",
1037 | " 3.322612810857284,\n",
1038 | " 145.54794677188755,\n",
1039 | " 129.65442911078392,\n",
1040 | " 37.78473021807234,\n",
1041 | " 57.758856660216715,\n",
1042 | " 146.95638784747035]"
1043 | ]
1044 | }
1045 | ],
1046 | "prompt_number": 386
1047 | },
1048 | {
1049 | "cell_type": "code",
1050 | "collapsed": false,
1051 | "input": [
1052 | "np.random.binomial(0, 1/7., 2)"
1053 | ],
1054 | "language": "python",
1055 | "metadata": {},
1056 | "outputs": [
1057 | {
1058 | "metadata": {},
1059 | "output_type": "pyout",
1060 | "prompt_number": 392,
1061 | "text": [
1062 | "array([0, 0])"
1063 | ]
1064 | }
1065 | ],
1066 | "prompt_number": 392
1067 | },
1068 | {
1069 | "cell_type": "code",
1070 | "collapsed": false,
1071 | "input": [
1072 | "np.random.binomial?\n"
1073 | ],
1074 | "language": "python",
1075 | "metadata": {},
1076 | "outputs": [],
1077 | "prompt_number": 390
1078 | },
1079 | {
1080 | "cell_type": "code",
1081 | "collapsed": false,
1082 | "input": [
1083 | "plt.hist([better_bootstrap([1]) for i in range(5000)], bins=100)\n",
1084 | "''"
1085 | ],
1086 | "language": "python",
1087 | "metadata": {},
1088 | "outputs": [
1089 | {
1090 | "metadata": {},
1091 | "output_type": "pyout",
1092 | "prompt_number": 398,
1093 | "text": [
1094 | "''"
1095 | ]
1096 | },
1097 | {
1098 | "metadata": {},
1099 | "output_type": "display_data",
1100 | "png": 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1101 | "text": [
1102 | ""
1103 | ]
1104 | }
1105 | ],
1106 | "prompt_number": 398
1107 | },
1108 | {
1109 | "cell_type": "code",
1110 | "collapsed": false,
1111 | "input": [
1112 | "def run_bandit_sim(bandit_algorithm):\n",
1113 | " simulated_experiment = BanditScenario({\n",
1114 | " 'A': {\n",
1115 | " 'conversion_rate': 1,\n",
1116 | " 'order_average': 35.00\n",
1117 | " }, \n",
1118 | " 'B':{\n",
1119 | " 'conversion_rate': 1,\n",
1120 | " 'order_average': 50.00\n",
1121 | " }\n",
1122 | " })\n",
1123 | "\n",
1124 | " simple_bandit = bandit_algorithm\n",
1125 | "\n",
1126 | " for visitor_i in range(500):\n",
1127 | " treatment = simple_bandit.choose_treatment()\n",
1128 | " payout = simulated_experiment.next_visitor(treatment)\n",
1129 | " simple_bandit.log_payout(treatment, payout)\n",
1130 | "\n",
1131 | " return sum(simulated_experiment._bandit_payoffs)\n",
1132 | "\n",
1133 | "simple_bandit_results = np.array([run_bandit_sim(SimpleBandit(['A', 'B'])) for i in range(300)])\n",
1134 | "rpm_bandit_results = np.array([run_bandit_sim(RPMBandit(['A', 'B'])) for i in range(300)])\n",
1135 | "\n",
1136 | "print 'SimpleBandit: ' + str(mean(simple_bandit_results))\n",
1137 | "print 'RPMBandit: ' + str(mean(rpm_bandit_results))\n",
1138 | "\n",
1139 | "plt.title('Payoffs of SimpleBandit vs RPMBandit')\n",
1140 | "plt.xlabel('Total Payoff')\n",
1141 | "plt.ylabel('Observations')\n",
1142 | "plt.hist(simple_bandit_results, label='SimpleBandit', alpha=.8, bins=40)\n",
1143 | "plt.hist(rpm_bandit_results, label='RPMBandit', alpha=.8, bins=40)\n",
1144 | "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
1145 | "''"
1146 | ],
1147 | "language": "python",
1148 | "metadata": {},
1149 | "outputs": [
1150 | {
1151 | "output_type": "stream",
1152 | "stream": "stdout",
1153 | "text": [
1154 | "SimpleBandit: 22443.9230864\n",
1155 | "RPMBandit: 24567.9045708\n"
1156 | ]
1157 | },
1158 | {
1159 | "metadata": {},
1160 | "output_type": "pyout",
1161 | "prompt_number": 42,
1162 | "text": [
1163 | "''"
1164 | ]
1165 | },
1166 | {
1167 | "metadata": {},
1168 | "output_type": "display_data",
1169 | "png": 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EREQaXzaDD+cAg4EzgFUECcLWXAYlIiIi8cimx+AUdz/OzF5x95+b2XzgiVwH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\nkYgSAxEREYn8f74fc0vFwaSnAAAAAElFTkSuQmCC\n",
1170 | "text": [
1171 | ""
1172 | ]
1173 | }
1174 | ],
1175 | "prompt_number": 42
1176 | },
1177 | {
1178 | "cell_type": "code",
1179 | "collapsed": false,
1180 | "input": [
1181 | "sum(simple_bandit_results)"
1182 | ],
1183 | "language": "python",
1184 | "metadata": {},
1185 | "outputs": [
1186 | {
1187 | "metadata": {},
1188 | "output_type": "pyout",
1189 | "prompt_number": 32,
1190 | "text": [
1191 | "1143566.6613173415"
1192 | ]
1193 | }
1194 | ],
1195 | "prompt_number": 32
1196 | },
1197 | {
1198 | "cell_type": "code",
1199 | "collapsed": false,
1200 | "input": [
1201 | "sum(rpm_bandit_results)"
1202 | ],
1203 | "language": "python",
1204 | "metadata": {},
1205 | "outputs": [
1206 | {
1207 | "metadata": {},
1208 | "output_type": "pyout",
1209 | "prompt_number": 33,
1210 | "text": [
1211 | "1162335.767616957"
1212 | ]
1213 | }
1214 | ],
1215 | "prompt_number": 33
1216 | },
1217 | {
1218 | "cell_type": "code",
1219 | "collapsed": false,
1220 | "input": [
1221 | "sum(rpm_bandit_results)/sum(simple_bandit_results)"
1222 | ],
1223 | "language": "python",
1224 | "metadata": {},
1225 | "outputs": [
1226 | {
1227 | "metadata": {},
1228 | "output_type": "pyout",
1229 | "prompt_number": 34,
1230 | "text": [
1231 | "1.0164127784890076"
1232 | ]
1233 | }
1234 | ],
1235 | "prompt_number": 34
1236 | },
1237 | {
1238 | "cell_type": "code",
1239 | "collapsed": false,
1240 | "input": [],
1241 | "language": "python",
1242 | "metadata": {},
1243 | "outputs": []
1244 | }
1245 | ],
1246 | "metadata": {}
1247 | }
1248 | ]
1249 | }
--------------------------------------------------------------------------------