robinvanemden/LiF_Decoy_Server_Test.py

## LiF_Decoy_Server_Test.py
# -*- coding: utf-8 -*-

#==============================================================================

# Reset IPython - may delete the following when not making use of Spyder IDE

from IPython import get_ipython
get_ipython().magic('reset -sf')

#==============================================================================

import sys,random
import json
import asyncio,aiohttp,pymongo,time
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
from scipy import misc

#==============================================================================

MONGO_IP = "78.46.212.194"
MONGO_PORT = 27017
SB_BASE_URL = "https://strm.mnds.org:8080"

#==============================================================================

print_progress = 1                # print progress to console?
exp_key = "36c067b1d3"            # streaminbandit key
exp_id = 1                        # experiment id
question_nr = 891441222           # question id

#==============================================================================

x_value_min = 0                   # Domain, min X
x_value_max = 100                 # Domain, max X, is of influence on
                                  # Start value and *Amplitude* !

rounded_to = 0                    # X shown to subject is rounded
                                  # to the given number of decimals.
                                  # Rounded_to has to round to at least
                                  # an order of magnitude divided by 2
                                  # smaller values than x_value_max.
                                  # If not, the displayed x
                                  # would end up being 0.0 all of the time.

y_line = 850                      # y of decoy line

x0 = 0.3 * x_value_max            # Start value
A = 0.07 * x_value_max            # Amlitude

#==============================================================================

parallel_processes = 10          # how many parallel requests
repeats_per_process = 400        # how many reapeats per process

                                 # total: 10 x 400 = 4000

max_time_set_reward = 0.1        # max delay before reward is choosen
choice_probability = 0.95        # probability that agent chooses a value

#==============================================================================
#
# In the current simulation we make use of a Decoy decision model from:
#
# Trueblood, Jennifer S., Scott D. Brown, and Andrew Heathcote.
# "The multiattribute linear ballistic accumulator model of context effects in
# multialternative choice."
# Psychological review 121.2 (2014): 179.
#
# The model of Trueblood et al is itself based on data from previous
# experiments. We will use it here to set the parameters of our simulation.
#
#==============================================================================

# import matrix graph from article

decoy_img = misc.imread("Decoy_Matrix.bmp")

# create brightness matrix

decoy_img_brightness = decoy_img.sum(axis=2) / decoy_img.shape[2]

# set decoy accroding to brightness along the chosen line
# approximation, brightness seems not to be 1:1 to p,
# but good enough for our purpose

decoy_y = decoy_img_brightness[y_line] / 255

# create xvalues, from x min to x max

decoy_x = np.arange(x_value_min, decoy_y.size)
decoy_x = (decoy_x / decoy_y.size) * x_value_max

# create a spline approximation of the decoy values
# based on brightness data

spline = UnivariateSpline (decoy_x, decoy_y-0.04, k=4, s=1)
spline_roots = spline.derivative().roots()

# relevant local max - hardcoded,
# might be neat to use second deriv pos check, ie
# spline.derivatives(spline.derivative().roots()[x])[1] == postive

spline_local_max = spline_roots[1]


#==============================================================================


start_at_t = 0

start = time.time()

def end():
     end = time.time()
     secs = end - start
     #msecs = secs * 1000  # millisecs
     print("elapsed time:" + str(secs-3.1))


def main():
    req_version = (3,5)
    cur_version = sys.version_info
    if cur_version >= req_version:

        # Necessary to run code more than once in same console in Win
        if sys.platform == 'win32':
            loop = asyncio.ProactorEventLoop()
            asyncio.set_event_loop(loop)
        else :
            loop = asyncio.SelectorEventLoop()
            asyncio.set_event_loop(loop)

        future = asyncio.Future()
        asyncio.ensure_future(do_async(future))
        loop.run_until_complete(future)

        loop.stop()
        loop.close()
        end()

        do_chart()

    else:
       print("This script makes use of asyncio, introduced in Python 3.5")
       print("Consider upgrading your Python interpreter to 3.5 or higher.")
       sys.exit(0)

async def do_async(future):

   for i in range(parallel_processes):
        asyncio.ensure_future(get_action(future))

async def get_action(future):

    for i in range(repeats_per_process):

        request =  SB_BASE_URL + "/" + str(exp_id)
        request += "/getAction.json?key="+exp_key
        request += "&context={\"question\":"+str(question_nr)
        request += ",\"x0\":"+str(x0)+",\"A\":"+str(A)+"}"

        conn = aiohttp.TCPConnector(verify_ssl=False)
        session = aiohttp.ClientSession(connector=conn)
        response = await session.get(request,encoding='utf-8')
        body = await response.read(decode=False)
        response.close()
        conn.close()
        obj = json.loads(str(body.decode('utf-8')))

        t = obj["action"]["t"]
        x = obj["action"]["x"]

        global start_at_t
        if (start_at_t==0):
            start_at_t = t

        if print_progress:
            print("#####"+str(t))

        asyncio.ensure_future(set_reward(t,x,future))

async def set_reward(t,x,future):

   # just a failsafe, makes sure value not out of bounds, mostly superfluous

    if(x > x_value_max): x = x_value_max
    if(x < x_value_min): x = x_value_min

    # in our experiment, displayed x will often be rounded to integers

    display_x = np.round(x,rounded_to)

    # randomly pick A's or B's - p changes according to position of decoy C
    # (funct. np.random.binomial would have been other option for same result)

    sampleChoice = np.random.choice(["B","A"],
                                    size=(1,),
                                    p=[spline(display_x),
                                    1-spline(display_x)])

    # did agent/subject choose A or B

    if sampleChoice == 'B':
       y = 1.0                                  # chooses B! Hurray!
    else:
       y = 0.0                                  # did not choose B

    if np.random.binomial(1, choice_probability, 1)==1:

       request =  SB_BASE_URL + "/" + str(exp_id) + "/setReward.json"
       request += "?key="+exp_key
       request += "&context={\"question\":"+str(question_nr)
       request += ",\"x0\":"+str(x0)+",\"A\":"+str(A)+"}"
       request += "&action={\"x\":" + str(float(x))
       request += ",\"t\":" + str(float(t)) + "}"
       request += "&reward=" + str(float(y))

       await asyncio.sleep(random.uniform(0.0, max_time_set_reward))

       conn = aiohttp.TCPConnector(verify_ssl=False)
       session = aiohttp.ClientSession(connector=conn)
       response = await session.get(request,encoding='utf-8')

       response.close()
       conn.close()

       if print_progress:
           print("$$$$$$$$$$"+str(t))

       if t>=repeats_per_process*parallel_processes+start_at_t-1:
           await asyncio.sleep(max_time_set_reward+3)
           future.set_result(future)

def do_chart():

    client = pymongo.MongoClient(MONGO_IP, MONGO_PORT)
    db = client.logs
    cursor = db.logs.find({"type": "setreward","q":question_nr}) \
                           .sort([("t", pymongo.ASCENDING)])

    result_list = list(cursor)
    client.close();

    t = [ts['t'] for ts in result_list]
    x0 = [xs['x0'] for xs in result_list]

    fig = plt.figure(figsize=(15,7))
    ax = fig.add_subplot(1,1,1)
    major_ticks = np.arange(0, len(t), 100)
    minor_ticks = np.arange(0, len(t), 50)
    ax.tick_params(which = 'both', direction = 'out')
    ax.set_xticks(major_ticks)
    ax.set_xticks(minor_ticks, minor=True)
    ax.grid(which='both')

    plt.plot(t,x0)
    plt.ylim([0,x_value_max/2])
    plt.axhline(y=spline_local_max,linewidth=1, color='r',linestyle='--')
    plt.show()


if __name__ == '__main__':
    main()
	# -- coding: utf-8 --

	#==============================================================================

	# Reset IPython - may delete the following when not making use of Spyder IDE

	from IPython import get_ipython
	get_ipython().magic('reset -sf')

	#==============================================================================

	import sys,random
	import json
	import asyncio,aiohttp,pymongo,time
	import numpy as np
	import matplotlib.pyplot as plt
	from scipy.interpolate import UnivariateSpline
	from scipy import misc

	#==============================================================================

	MONGO_IP = "78.46.212.194"
	MONGO_PORT = 27017
	SB_BASE_URL = "https://strm.mnds.org:8080"

	#==============================================================================

	print_progress = 1 # print progress to console?
	exp_key = "36c067b1d3" # streaminbandit key
	exp_id = 1 # experiment id
	question_nr = 891441222 # question id

	#==============================================================================

	x_value_min = 0 # Domain, min X
	x_value_max = 100 # Domain, max X, is of influence on
	# Start value and Amplitude !

	rounded_to = 0 # X shown to subject is rounded
	# to the given number of decimals.
	# Rounded_to has to round to at least
	# an order of magnitude divided by 2
	# smaller values than x_value_max.
	# If not, the displayed x
	# would end up being 0.0 all of the time.

	y_line = 850 # y of decoy line

	x0 = 0.3 * x_value_max # Start value
	A = 0.07 * x_value_max # Amlitude

	#==============================================================================

	parallel_processes = 10 # how many parallel requests
	repeats_per_process = 400 # how many reapeats per process

	# total: 10 x 400 = 4000

	max_time_set_reward = 0.1 # max delay before reward is choosen
	choice_probability = 0.95 # probability that agent chooses a value

	#==============================================================================
	#
	# In the current simulation we make use of a Decoy decision model from:
	#
	# Trueblood, Jennifer S., Scott D. Brown, and Andrew Heathcote.
	# "The multiattribute linear ballistic accumulator model of context effects in
	# multialternative choice."
	# Psychological review 121.2 (2014): 179.
	#
	# The model of Trueblood et al is itself based on data from previous
	# experiments. We will use it here to set the parameters of our simulation.
	#
	#==============================================================================

	# import matrix graph from article

	decoy_img = misc.imread("Decoy_Matrix.bmp")

	# create brightness matrix

	decoy_img_brightness = decoy_img.sum(axis=2) / decoy_img.shape[2]

	# set decoy accroding to brightness along the chosen line
	# approximation, brightness seems not to be 1:1 to p,
	# but good enough for our purpose

	decoy_y = decoy_img_brightness[y_line] / 255

	# create xvalues, from x min to x max

	decoy_x = np.arange(x_value_min, decoy_y.size)
	decoy_x = (decoy_x / decoy_y.size) * x_value_max

	# create a spline approximation of the decoy values
	# based on brightness data

	spline = UnivariateSpline (decoy_x, decoy_y-0.04, k=4, s=1)
	spline_roots = spline.derivative().roots()

	# relevant local max - hardcoded,
	# might be neat to use second deriv pos check, ie
	# spline.derivatives(spline.derivative().roots()[x])[1] == postive

	spline_local_max = spline_roots[1]


	#==============================================================================


	start_at_t = 0

	start = time.time()

	def end():
	end = time.time()
	secs = end - start
	#msecs = secs * 1000 # millisecs
	print("elapsed time:" + str(secs-3.1))


	def main():
	req_version = (3,5)
	cur_version = sys.version_info
	if cur_version >= req_version:

	# Necessary to run code more than once in same console in Win
	if sys.platform == 'win32':
	loop = asyncio.ProactorEventLoop()
	asyncio.set_event_loop(loop)
	else :
	loop = asyncio.SelectorEventLoop()
	asyncio.set_event_loop(loop)

	future = asyncio.Future()
	asyncio.ensure_future(do_async(future))
	loop.run_until_complete(future)

	loop.stop()
	loop.close()
	end()

	do_chart()

	else:
	print("This script makes use of asyncio, introduced in Python 3.5")
	print("Consider upgrading your Python interpreter to 3.5 or higher.")
	sys.exit(0)

	async def do_async(future):

	for i in range(parallel_processes):
	asyncio.ensure_future(get_action(future))

	async def get_action(future):

	for i in range(repeats_per_process):

	request = SB_BASE_URL + "/" + str(exp_id)
	request += "/getAction.json?key="+exp_key
	request += "&context={\"question\":"+str(question_nr)
	request += ",\"x0\":"+str(x0)+",\"A\":"+str(A)+"}"

	conn = aiohttp.TCPConnector(verify_ssl=False)
	session = aiohttp.ClientSession(connector=conn)
	response = await session.get(request,encoding='utf-8')
	body = await response.read(decode=False)
	response.close()
	conn.close()
	obj = json.loads(str(body.decode('utf-8')))

	t = obj["action"]["t"]
	x = obj["action"]["x"]

	global start_at_t
	if (start_at_t==0):
	start_at_t = t

	if print_progress:
	print("#####"+str(t))

	asyncio.ensure_future(set_reward(t,x,future))

	async def set_reward(t,x,future):

	# just a failsafe, makes sure value not out of bounds, mostly superfluous

	if(x > x_value_max): x = x_value_max
	if(x < x_value_min): x = x_value_min

	# in our experiment, displayed x will often be rounded to integers

	display_x = np.round(x,rounded_to)

	# randomly pick A's or B's - p changes according to position of decoy C
	# (funct. np.random.binomial would have been other option for same result)

	sampleChoice = np.random.choice(["B","A"],
	size=(1,),
	p=[spline(display_x),
	1-spline(display_x)])

	# did agent/subject choose A or B

	if sampleChoice == 'B':
	y = 1.0 # chooses B! Hurray!
	else:
	y = 0.0 # did not choose B

	if np.random.binomial(1, choice_probability, 1)==1:

	request = SB_BASE_URL + "/" + str(exp_id) + "/setReward.json"
	request += "?key="+exp_key
	request += "&context={\"question\":"+str(question_nr)
	request += ",\"x0\":"+str(x0)+",\"A\":"+str(A)+"}"
	request += "&action={\"x\":" + str(float(x))
	request += ",\"t\":" + str(float(t)) + "}"
	request += "&reward=" + str(float(y))

	await asyncio.sleep(random.uniform(0.0, max_time_set_reward))

	conn = aiohttp.TCPConnector(verify_ssl=False)
	session = aiohttp.ClientSession(connector=conn)
	response = await session.get(request,encoding='utf-8')

	response.close()
	conn.close()

	if print_progress:
	print("$$$$$$$$$$"+str(t))

	if t>=repeats_per_process*parallel_processes+start_at_t-1:
	await asyncio.sleep(max_time_set_reward+3)
	future.set_result(future)

	def do_chart():

	client = pymongo.MongoClient(MONGO_IP, MONGO_PORT)
	db = client.logs
	cursor = db.logs.find({"type": "setreward","q":question_nr}) \
	.sort([("t", pymongo.ASCENDING)])

	result_list = list(cursor)
	client.close();

	t = [ts['t'] for ts in result_list]
	x0 = [xs['x0'] for xs in result_list]

	fig = plt.figure(figsize=(15,7))
	ax = fig.add_subplot(1,1,1)
	major_ticks = np.arange(0, len(t), 100)
	minor_ticks = np.arange(0, len(t), 50)
	ax.tick_params(which = 'both', direction = 'out')
	ax.set_xticks(major_ticks)
	ax.set_xticks(minor_ticks, minor=True)
	ax.grid(which='both')

	plt.plot(t,x0)
	plt.ylim([0,x_value_max/2])
	plt.axhline(y=spline_local_max,linewidth=1, color='r',linestyle='--')
	plt.show()


	if __name__ == '__main__':
	main()