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Merge pull request #2 from Dallinger/stories/341-bot-data
Story 341: Script/Notebook to collect and replay bot-driven data
2 parents cf0d4b8 + cd8a045 commit 1c43061

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‎Dockerfile‎

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@@ -32,7 +32,7 @@ RUN sed /etc/postgresql/10/main/pg_hba.conf -e 's/md5/trust/g' --in-place
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RUN apt-get -yq install redis-server
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RUN sudo pip3 install matplotlib==2.1.0
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RUN sudo pip3 install -e git+https://github.com/Dallinger/Dallinger.git@stories/342-realtime-playback#egg=dallinger[data,jupyter]
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RUN sudo pip3 install -e git+https://github.com/Dallinger/Dallinger.git@stories/341-bot-data#egg=dallinger[data,jupyter]
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RUN sudo pip3 install -e git+https://github.com/Dallinger/Griduniverse.git@stories/342-realtime-playback#egg=dlgr-griduniverse
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# My binder specifics as per:

‎README.md‎

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# NGS2 Cycle 2 pilot data
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3-
[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Dallinger/ngs2-cycle2-pilot-data/stories%2F340-replay-widget-p3?filepath=analyses.ipynb)
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[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Dallinger/ngs2-cycle2-pilot-data/stories%2F341-bot-data?filepath=analyses.ipynb)
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# Bot Run Data
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[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Dallinger/ngs2-cycle2-pilot-data/stories%2F341-bot-data?filepath=bot_runs%2Fbot_collector.ipynb)

‎bot_runs/bot_collector.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import csv\n",
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"import dallinger\n",
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"from dallinger.experiments import Griduniverse\n",
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"\n",
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"\n",
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"sys.path.append('..')\n",
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"import binder_helpers\n",
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"binder_helpers.start_services()\n",
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"\n",
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"ROWS = 49\n",
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"COLS = 49\n",
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"ORIG_CSV_LIMIT = csv.field_size_limit(ROWS*COLS*1024)\n",
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"\n",
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"BASE_ID = \"{}01daa{}-f7ed-43fa-ad6b-9928aa51f8e1\"\n",
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"PARTICIPANTS = 9\n",
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"NUM_AS_EXPERIMENTS = 3\n",
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"NUM_RANDOM_EXPERIMENTS = 3\n",
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"\n",
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"EXP_CONFIG = {\n",
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" \"recruiter\": \"bots\",\n",
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" \"max_participants\": PARTICIPANTS,\n",
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"}\n",
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"\n",
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"exp = Griduniverse()\n",
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"data = {}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting 3 experiments with 9 AdvantageSeekingBot players\n",
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">>>> Retrieve: Data found for experiment ab01daa0-f7ed-43fa-ad6b-9928aa51f8e1, retrieving.\n",
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">>>> Retrieve: Data found for experiment ab01daa1-f7ed-43fa-ad6b-9928aa51f8e1, retrieving.\n",
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">>>> Retrieve: Data found for experiment ab01daa2-f7ed-43fa-ad6b-9928aa51f8e1, retrieving.\n",
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"Data retrieval complete\n"
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]
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}
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],
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"source": [
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"print('Collecting {} experiments with {} AdvantageSeekingBot players'.format(\n",
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" NUM_AS_EXPERIMENTS, PARTICIPANTS\n",
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"))\n",
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"\n",
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"for count in range(NUM_AS_EXPERIMENTS):\n",
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" exp_id = BASE_ID.format('ab', count)\n",
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" config = EXP_CONFIG.copy()\n",
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" config['bot_policy'] = 'AdvantageSeekingBot'\n",
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" data[exp_id] = {\n",
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" 'title': '{} Experiment #{} ({})'.format(config['bot_policy'], count + 1, exp_id),\n",
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" 'data': exp.collect(exp_id, exp_config=config)\n",
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" }\n",
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" \n",
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"print('Data retrieval complete.')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting 3 experiments with 9 RandomBot players\n",
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">>>> Retrieve: Data found for experiment ad01daa0-f7ed-43fa-ad6b-9928aa51f8e1, retrieving.\n",
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">>>> Retrieve: Data found for experiment ad01daa1-f7ed-43fa-ad6b-9928aa51f8e1, retrieving.\n",
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">>>> Retrieve: Data found for experiment ad01daa2-f7ed-43fa-ad6b-9928aa51f8e1, retrieving.\n",
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"Data retrieval complete.\n"
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]
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}
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],
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"source": [
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"print('Collecting {} experiments with {} RandomBot players'.format(\n",
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" NUM_RANDOM_EXPERIMENTS, PARTICIPANTS\n",
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"))\n",
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"\n",
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"for count in range(NUM_RANDOM_EXPERIMENTS):\n",
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" exp_id = BASE_ID.format('ad', count)\n",
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" config = EXP_CONFIG.copy()\n",
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" config['bot_policy'] = 'RandomBot'\n",
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" data[exp_id] = {\n",
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" 'title': '{} Experiment #{} ({})'.format(config['bot_policy'], count + 1, exp_id),\n",
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" 'data': exp.collect(exp_id, exp_config=config)\n",
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" }\n",
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"\n",
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"print('Data retrieval complete.') "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Successfully collected data from 6 bot experiments. Rendering replay widgets:\n",
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"Ingesting dataset from ab01daa0-f7ed-43fa-ad6b-9928aa51f8e1-data.zip...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "38f06c4e23f44271ac6f6875fdd9066a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(ExperimentWidget(children=(HTML(value='\\n<h2>AdvantageSeekingBot Experiment #1 (ab01daa0-f7ed-4…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ingesting dataset from ab01daa1-f7ed-43fa-ad6b-9928aa51f8e1-data.zip...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "db5a419157a64f2fbc53fb6d8b3cb8da",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(ExperimentWidget(children=(HTML(value='\\n<h2>AdvantageSeekingBot Experiment #2 (ab01daa1-f7ed-4…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ingesting dataset from ab01daa2-f7ed-43fa-ad6b-9928aa51f8e1-data.zip...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "23c4951777ab4e37af3d8deffb690e29",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(ExperimentWidget(children=(HTML(value='\\n<h2>AdvantageSeekingBot Experiment #3 (ab01daa2-f7ed-4…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ingesting dataset from ad01daa0-f7ed-43fa-ad6b-9928aa51f8e1-data.zip...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "058a6f9ff1144200989455a030dca0a8",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(ExperimentWidget(children=(HTML(value='\\n<h2>RandomBot Experiment #1 (ad01daa0-f7ed-43fa-ad6b-9…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ingesting dataset from ad01daa1-f7ed-43fa-ad6b-9928aa51f8e1-data.zip...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8f32522f6e264e3f8cea262aebdf14b5",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(ExperimentWidget(children=(HTML(value='\\n<h2>RandomBot Experiment #2 (ad01daa1-f7ed-43fa-ad6b-9…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ingesting dataset from ad01daa2-f7ed-43fa-ad6b-9928aa51f8e1-data.zip...\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "0dc3e866a56a4200bd6028ec6a73d27b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(ExperimentWidget(children=(HTML(value='\\n<h2>RandomBot Experiment #3 (ad01daa2-f7ed-43fa-ad6b-9…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"print('Successfully collected data from {} bot experiments. Rendering replay widgets:'.format(len(data)))\n",
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"\n",
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"for exp_id in data:\n",
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" replay_exp = Griduniverse()\n",
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" replay_exp.task = data[exp_id]['title']\n",
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" replay_exp.jupyter_replay(\n",
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" app_id=exp_id,\n",
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" session=dallinger.db.init_db(drop_all=True),\n",
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" rows=ROWS, columns=COLS\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎bot_runs/bot_runs.py‎

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import csv
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from dallinger.experiments import Griduniverse
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ROWS = 49
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COLS = 49
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ORIG_CSV_LIMIT = csv.field_size_limit(ROWS*COLS*1024)
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BASE_ID = "{}01daa{}-f7ed-43fa-ad6b-9928aa51f8e1"
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PARTICIPANTS = 9
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NUM_AS_EXPERIMENTS = 3
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NUM_RANDOM_EXPERIMENTS = 3
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EXP_CONFIG = {
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"recruiter": "bots",
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"max_participants": PARTICIPANTS,
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}
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exp = Griduniverse()
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data = {}
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print('Collecting {} experiments with {} AdvantageSeekingBot players'.format(
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NUM_AS_EXPERIMENTS, PARTICIPANTS
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))
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for count in range(NUM_AS_EXPERIMENTS):
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exp_id = BASE_ID.format('ab', count)
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config = EXP_CONFIG.copy()
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config['bot_policy'] = 'AdvantageSeekingBot'
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data[exp_id] = {
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'title': '{} Experiment #{} ({})'.format(config['bot_policy'],
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count + 1, exp_id),
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'data': exp.collect(exp_id, exp_config=config)
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}
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print('Collecting {} experiments with {} RandomBot players'.format(
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NUM_RANDOM_EXPERIMENTS, PARTICIPANTS
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))
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for count in range(NUM_RANDOM_EXPERIMENTS):
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exp_id = BASE_ID.format('ad', count)
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config = EXP_CONFIG.copy()
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config['bot_policy'] = 'RandomBot'
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data[exp_id] = {
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'title': '{} Experiment #{} ({})'.format(config['bot_policy'],
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count + 1, exp_id),
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'data': exp.collect(exp_id, exp_config=config)
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}
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print(
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'Successfully collected data from '
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'{} bot experiments. Rendering replay widgets:'.format(
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len(data))
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)

‎bot_runs/data/ab01daa0-f7ed-43fa-ad6b-9928aa51f8e1-data.zip‎

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‎bot_runs/data/ab01daa1-f7ed-43fa-ad6b-9928aa51f8e1-data.zip‎

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‎bot_runs/data/ab01daa2-f7ed-43fa-ad6b-9928aa51f8e1-data.zip‎

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‎bot_runs/data/ad01daa0-f7ed-43fa-ad6b-9928aa51f8e1-data.zip‎

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‎bot_runs/data/ad01daa1-f7ed-43fa-ad6b-9928aa51f8e1-data.zip‎

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‎bot_runs/data/ad01daa2-f7ed-43fa-ad6b-9928aa51f8e1-data.zip‎

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