def run_job(self, my_task_id, num_tasks, num_clicks=1500, trials=10):
for period_num in self.period_li:
param_dict = dict()
param_dict["time_rotate"] = period_num
param_dict["N_pred"] = 3
param_dict["num_words"] = 17
for click_dist, dp_dist in zip(self.click_dists, self.dp_dists):
param_dict["click_dist"] = click_dist
param_dict["dp_dist"] = dp_dist
self.parameters_list += [param_dict.copy()]
print(len(self.parameters_list))
num_jobs = len(self.parameters_list)
job_indicies = np.array_split(np.arange(1, num_jobs + 1),
num_tasks)[my_task_id - 1]
print(job_indicies)
for job_index in job_indicies:
parameters = self.parameters_list[job_index - 1]
user_num = int((job_index * 0.999) / num_jobs)
print(user_num)
sim = SimulatedUser(parentdir, job_num=user_num)
sim.parameter_metrics(parameters,
num_clicks=num_clicks,
trials=trials)
def run_job(self, my_task_id, num_tasks, num_clicks=500, trials=20):
for fp_rate in self.false_positives:
param_dict = dict()
self.parameters_dict["false_positive"] = fp_rate
self. parameters_dict["order"] = "sorted"
self.parameters_dict["words_first"] = True
self.parameters_dict["num_words"] = 7
for dist in self.click_dists:
self.parameters_dict["click_dist"] = dist
self.parameters_list += [self.parameters_dict.copy()]
print(len(self.parameters_list))
num_jobs = len(self.parameters_list)
job_indicies = np.array_split(np.arange(1, num_jobs+1), num_tasks)[my_task_id-1]
print(job_indicies)
for job_index in job_indicies:
parameters = self.parameters_list[job_index-1]
user_num = int((job_index*0.999)/num_jobs)
print(user_num)
sim = SimulatedUser(parentdir, job_num=user_num)
sim.parameter_metrics(parameters, num_clicks=num_clicks, trials=trials)
def run_job(self, my_task_id, num_tasks, num_clicks=1500, trials=30):
for click_dist, dp_dist in zip(self.click_dists, self.dp_dists):
# click_dist = click_dist / np.sum(click_dist)
param_dict = {}
param_dict["click_dist"] = click_dist
param_dict["dp_dist"] = dp_dist
param_dict["time_rotate"] = 4
for n_pred in self.n_pred_range:
param_dict["N_pred"] = n_pred
for num_words in range(0, n_pred * 26, 3):
param_dict["num_words"] = num_words
self.parameters_list += [param_dict.copy()]
print(len(self.parameters_list))
num_jobs = len(self.parameters_list)
job_indicies = np.array_split(np.arange(1, num_jobs + 1),
num_tasks)[my_task_id - 1]
print(job_indicies)
for job_index in job_indicies:
parameters = self.parameters_list[job_index - 1]
user_num = int((job_index * 0.999) / num_jobs)
print(user_num)
sim = SimulatedUser(parentdir, job_num=user_num)
sim.parameter_metrics(parameters,
num_clicks=num_clicks,
trials=trials)
def run_job(self, my_task_id, num_tasks, num_clicks=500, trials=1):
for fp_rate in self.false_positives:
param_dict = dict()
self.parameters_dict["false_positive"] = fp_rate
param_dict["N_pred"] = 3
param_dict["num_words"] = 17
for dist in self.click_dists:
hist = dist(np.arange(80))
hist = hist / np.sum(hist)
param_dict["click_dist"] = hist.tolist()
self.parameters_list += [self.parameters_dict.copy()]
print(len(self.parameters_list))
num_jobs = len(self.parameters_list)
job_indicies = np.array_split(np.arange(1, num_jobs + 1),
num_tasks)[my_task_id - 1]
print(job_indicies)
for job_index in job_indicies:
parameters = self.parameters_list[job_index - 1]
user_num = int((job_index * 0.999) / num_jobs)
print(user_num)
sim = SimulatedUser(parentdir, job_num=user_num)
sim.parameter_metrics(parameters,
num_clicks=num_clicks,
trials=trials)
my_task_id = 24
num_tasks = 24
words_first_range = [True, False]
order_range = ["sorted", "default"]
# order_range = ["sorted"]
num_words_range = np.arange(0, 20, 1).tolist()
print(num_words_range)
parameters_list = []
parameters_dict = dict()
for wf in words_first_range:
for kc in order_range:
for nw in num_words_range:
parameters_dict["order"] = kc
parameters_dict["words_first"] = wf
parameters_dict["num_words"] = nw
parameters_list += [parameters_dict.copy()]
print(len(parameters_list))
num_jobs = len(parameters_list)
job_indicies = np.array_split(np.arange(1, num_jobs+1), num_tasks)[my_task_id-1]
print(job_indicies)
for job_index in job_indicies:
parameters = parameters_list[job_index-1]
user_num = int((job_index*0.999)/num_jobs)
print(user_num)
sim = SimulatedUser(parentdir, job_num=user_num)
sim.parameter_metrics(parameters, num_clicks=500, trials=30)
from simulated_user import SimulatedUser, normal_hist
from matplotlib import pyplot as plt
try:
my_task_id = int(sys.argv[1])
num_tasks = int(sys.argv[2])
except IndexError:
my_task_id = 1
num_tasks = 10
jobs = np.arange(1, 21)
first_half, second_half = np.array_split(jobs, 2)
weaved_jobs = np.empty((first_half.size + second_half.size, ),
dtype=first_half.dtype)
weaved_jobs[0::2] = first_half
weaved_jobs[1::2] = second_half[::-1]
job_indicies = np.array_split(weaved_jobs, num_tasks)[my_task_id - 1]
parameters_list = [{
"click_dist": (normal_hist(0, 2) + normal_hist(0 + i * 2, 2)) / 2
} for i in job_indicies]
attributes = [i * 2 for i in job_indicies]
for parameters, attribute in zip(parameters_list, attributes):
sim = SimulatedUser(currentdir)
sim.parameter_metrics(parameters,
num_clicks=500,
trials=20,
attribute=attribute)
parameters_dict = dict()
for dist in click_dists:
delta = dist[1]
scan_delays = list({0.5, max(0.5, delta + 6 * 0.05)})
print(scan_delays)
for scan_delay in scan_delays:
parameters_dict["order"] = "default"
parameters_dict["words_first"] = True
parameters_dict["num_words"] = 0
parameters_dict["click_dist"] = dist[0]
parameters_dict["attribute"] = dist[1]
parameters_dict["scan_delay"] = scan_delay
parameters_list += [parameters_dict.copy()]
print(len(parameters_list))
num_jobs = len(parameters_list)
job_indicies = np.array_split(np.arange(1, num_jobs + 1),
num_tasks)[my_task_id - 1]
print(job_indicies)
for job_index in job_indicies:
parameters = parameters_list[job_index - 1]
user_num = int((job_index * 0.999) / num_jobs)
print(user_num)
sim = SimulatedUser(parentdir, job_num=user_num)
sim.parameter_metrics(parameters,
num_clicks=500,
trials=1,
attribute=parameters["attribute"])