class Experiment03(object):
"""
Experiment03 class
"""
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.data_exporter = DataExporter(self.config)
self.logger = logging.getLogger("cuda_logger")
self.expt_name = "expt_03"
self.config['RL_parameters']['experiment'] = self.expt_name
@staticmethod
def run_rl_training(config):
rl_trainer = RunRLTrainingJob(config)
data = rl_trainer.run()
return data
def run(self):
"""
Run experiment
"""
num_drivers = np.arange(1000, 6500, 500)
objectives = ['pickups', 'revenue']
combinations = list(itertools.product(num_drivers, objectives))
# Create a pool of processes
num_processes = mp.cpu_count()
pool = ProcessPool(nodes=num_processes)
configs = []
count = 0
for comb in combinations:
self.config['RL_parameters'][
'experiment'] = self.expt_name + "_" + str(count)
self.config['RL_parameters'][
'city_states_filename'] = "city_states.dill"
self.config['RL_parameters']['num_drivers'] = comb[0]
self.config['RL_parameters']['num_strategic_drivers'] = comb[0]
self.config['RL_parameters']['objective'] = comb[1]
configs.append(deepcopy(self.config))
count += 1
self.logger.info("Starting expt_03")
results = pool.amap(self.run_rl_training, configs).get()
pool.close()
pool.join()
pool.clear()
self.logger.info("Finished expt_03")
# Export best episode
self.data_exporter.export_episode(results, self.expt_name + ".dill")
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.logger = logging.getLogger("cuda_logger")
self.data_exporter = DataExporter(self.config)
class Experiment05(object):
"""
Experiment05 class
"""
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.data_exporter = DataExporter(self.config)
self.logger = logging.getLogger("cuda_logger")
self.expt_name = "expt_05"
self.config['RL_parameters']['experiment'] = self.expt_name
@staticmethod
def run_rl_training(config):
rl_trainer = RunRLTrainingJob(config)
data = rl_trainer.run()
return data
def run(self):
"""
Run experiment
"""
num_drivers = self.config['RL_parameters']['num_drivers']
percent_strategic_drivers = np.arange(0, 1.1, 0.1)
num_strategic_drivers = [int(x * num_drivers) for x in percent_strategic_drivers]
# Create a pool of processes
num_processes = mp.cpu_count()
pool = ProcessPool(nodes=num_processes)
configs = []
count = 0
for drivers in num_strategic_drivers:
self.config['RL_parameters']['experiment'] = self.expt_name + "_" + str(count)
self.config['RL_parameters']['num_strategic_drivers'] = drivers
configs.append(deepcopy(self.config))
count += 1
self.logger.info("Starting expt_05")
results = pool.amap(self.run_rl_training, configs).get()
pool.close()
pool.join()
pool.clear()
self.logger.info("Finished expt_05")
# Export best episode
self.data_exporter.export_episode(results, self.expt_name + ".dill")
def __init__(self, config):
"""
Constructor
:param config:
:return:
"""
self.config = config
self.logger = logging.getLogger("so_logger")
self.data_provider = DataProvider(self.config)
self.data_exporter = DataExporter(self.config)
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.data_exporter = DataExporter(self.config)
self.logger = logging.getLogger("cuda_logger")
self.expt_name = "expt_05"
self.config['RL_parameters']['experiment'] = self.expt_name
def run(self):
"""
This method executes the job
:param:
:return:
"""
self.logger.info("Starting job: CreateCityStateJob\n")
city_state_creator = CityStateCreator(self.config)
city_state = city_state_creator.get_city_states()
self.logger.info("Exporting city states\n")
filename = self.config['city_state_creator'].get('filename', 'city_states.dill')
data_exporter = DataExporter(self.config)
data_exporter.export_city_state(city_state, filename)
self.logger.info("Finished job: CreateCityStateJob")
def run(self):
"""
This method executes the job
:param:
:return:
"""
self.logger.info("Starting job: SparseMatrixFillerJob\n")
sparse_matrix_filler = SparseMatrixFiller(self.config)
city_state = sparse_matrix_filler.fill_matrices()
self.logger.info("Exporting city states\n")
filename = self.config['city_state_creator'].get(
'filename', 'city_states.dill')
data_exporter = DataExporter(self.config)
data_exporter.export_city_state(city_state, filename)
self.logger.info("Finished job: SparseMatrixFillerJob")
def run(self):
"""
Execute the job
:param:
:return:
"""
self.logger.info("Starting job: GuruDataProcessor\n")
data_provider = DataProvider(self.config)
data_exporter = DataExporter(self.config)
# Read guru data
df = data_provider.read_guru_user_data()
df = df[[3, 4]] # Salary and skills columns
df.columns = ['cost', 'skills']
df = df[(df.cost != "$0") & (df.skills != "UNKNOWN")]
df = df.reset_index(drop=True)
df = df.assign(user_id=df.index.values)
df = df.assign(skills=df.apply(lambda x: x['skills'][:-1].split(','), axis=1))
# Convert cost to integers
user_df = df.assign(cost=df.apply(lambda x: int(x['cost'][1:]), axis=1))
# Read skills data
df = data_provider.read_guru_skill_data()
df = df[[1]]
df.columns = ['skill']
skill_df = df.assign(skill_id=df.index.values)
# Create multilabel binarizer
mlb = MultiLabelBinarizer(classes=skill_df.skill.values)
# One hot encoding of user skills
skills = mlb.fit_transform(user_df['skills'])
# Create dataset
users = user_df.to_dict('records')
for i in range(len(users)):
users[i]['skills_array'] = skills[i]
# Export csv files
data_exporter.export_csv_file(user_df, "guru/guru_user_df.csv")
data_exporter.export_csv_file(skill_df, "guru/guru_skill_df.csv")
# Scaling factor for submodular function
scaling_factor = 1
# Create and export data object to be used in experiments
# containing all methods related to guru data
guru = GuruData(self.config, user_df, skill_df, users, scaling_factor)
data_exporter.export_dill_file(guru, "guru/guru_data.dill")
self.logger.info("Finished job: GuruDataProcessor")
def run(self):
"""
This method executes the job
:param:
:return:
"""
self.logger.info("Starting job: BuildRegressionModelsJob\n")
# Export the regression models data
for model in self.config['regression_models']:
year = model['year'][-2:]
month = model['month'].lower()
weekday = list(calendar.day_name).index(model['weekday'])
(self.logger.info(
"Creating regression model data {}-{}-{}s".format(
year, month, model['weekday'])))
rmb = RegressionModelBuilder(self.config, year, month, weekday)
dist_df = rmb.get_bins_distance_dataframe()
trips_df = rmb.create_trips_data_with_distance()
(self.logger.info(
"Exporting regression model data {}-{}-{}s\n".format(
year, month, model['weekday'])))
data_exporter = DataExporter(self.config)
data_exporter.export_bin_distances(dist_df)
data_exporter.export_rmb_data(
trips_df, model['weekday'] + '_' + month + '_' + year)
self.logger.info("Finished job: BuildRegressionModelsJob")
class RunRLTrainingJob(object):
"""
This class implements a job to run the RL training
"""
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.logger = logging.getLogger("cuda_logger")
self.data_exporter = DataExporter(self.config)
def run(self):
"""
This method executes the job
:param:
:return:
"""
self.logger.info("Starting job: RunRLTrainingJob\n")
self.logger.info("RL training parameters:")
pp = pprint.PrettyPrinter(indent=4)
self.logger.info(pp.pprint(self.config['RL_parameters']))
# Create RL trainer
rl_trainer = RLTrainer(self.config)
# Runs RL episodes
result = rl_trainer.run()
best_episode = result[0]
best_model = result[1]
training_tracker = result[2]
# Export best_model
best_model_filename = self.config['RL_parameters'].get(
'best_model_filename', False)
best_episode_filename = self.config['RL_parameters'].get(
'best_episode_filename', False)
training_tracker_filename = self.config['RL_parameters'].get(
'training_tracker_filename', False)
if best_model_filename:
self.data_exporter.export_model(best_model, best_model_filename)
if best_episode_filename:
self.data_exporter.export_episode(best_episode,
best_episode_filename)
if training_tracker_filename:
self.data_exporter.export_training_tracker(
training_tracker, training_tracker_filename)
self.logger.info("Finished job: RunRLTrainingJob")
return best_episode
def run(self):
"""
This method executes the job
:param:
:return:
"""
self.logger.info("Starting job: NeighborhoodDataExportJob\n")
data_provider = DataProvider(self.config)
data_exporter = DataExporter(self.config)
hex_attr_df = data_provider.read_hex_bin_attributes()
hex_bins = hex_attr_df['hex_id'].values
data = {}
for r in xrange(self.radius + 1):
data[r] = {}
for hex_bin in hex_bins:
neighbors = hex_neighborhood(hex_bin, hex_attr_df, r)
zero_vector = np.zeros(len(hex_bins))
np.put(zero_vector, neighbors, 1)
one_hot_encoding_vector = zero_vector
data[r][hex_bin] = one_hot_encoding_vector
data_exporter.export_neighborhood_data(data)
self.logger.info("Finished job: NeighborhoodDataExportJob")
class Experiment01(object):
"""
Experiment01 class
"""
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.data_exporter = DataExporter(self.config)
self.logger = logging.getLogger("cuda_logger")
self.expt_name = "expt_01"
self.config['RL_parameters']['experiment'] = self.expt_name
@staticmethod
def run_rl_training(config):
rl_trainer = RunRLTrainingJob(config)
data = rl_trainer.run()
return data
def run(self):
"""
Run experiment
"""
ind_percent = np.arange(0., 1.1, 0.1)
reb_percent = np.arange(0., 1.1, 0.1)
ind_percent[0] = 0.01
reb_percent[0] = 0.01
combinations = list(itertools.product(ind_percent, reb_percent))
num_episodes = self.config['RL_parameters']['num_episodes']
# Create a pool of processes
num_processes = mp.cpu_count()
pool = ProcessPool(nodes=num_processes)
configs = []
count = 0
for comb in combinations:
self.config['RL_parameters']['experiment'] = self.expt_name + "_" + str(count)
ind_episodes = int(comb[0] * num_episodes)
reb_episodes = int(comb[1] * num_episodes)
if (ind_episodes + reb_episodes) < num_episodes:
self.config['RL_parameters']['ind_episodes'] = ind_episodes
self.config['RL_parameters']['reb_episodes'] = reb_episodes
configs.append(deepcopy(self.config))
count += 1
self.logger.info("Starting expt_01")
results = pool.amap(self.run_rl_training, configs).get()
pool.close()
pool.join()
pool.clear()
self.logger.info("Finished expt_01")
# Export best episode
self.data_exporter.export_episode(results, self.expt_name + ".dill")
class Experiment02(object):
"""
Experiment02 class
"""
def __init__(self, config):
"""
Constructor
:param config:
:return:
"""
self.config = config
self.logger = logging.getLogger("so_logger")
self.data_provider = DataProvider(self.config)
self.data_exporter = DataExporter(self.config)
@staticmethod
def run_algorithm(args):
# Run algorithm
alg = AlgorithmDriver()
data = alg.run(*args)
return data
def set_scaling_factor(self,data):
# Find appropriate scaling factor
alg = SetCoverGreedy(self.config, data.submodular_func, data.E)
sol = alg.run()
submodular_val = data.submodular_func(sol)
cost = data.cost_func(sol)
scaling_factor = data.scaling_func(submodular_val, cost)
# Update scaling factor
data.scaling_factor = scaling_factor
def run(self):
"""
Run experiment
:param:
:return:
"""
self.logger.info("Starting experiment 02")
self.expt_config = self.config['experiment_configs']['experiment_02']
popular_threshold = self.expt_config['popular_threshold']
rare_threshold = self.expt_config['rare_threshold']
user_sample_ratios = [0.4,1]
seeds = [i for i in range(6,10)]
ks = [1,5,10,15,20,25,30,35,40,45,50]
sampling_epsilon_values_stochastic = [0.1,0.05,0.01,0.005]
error_epsilon_values_scaled_threshold = [0.2,0.15,0.1,0.05]
num_sampled_skills = 50
rare_sample_fraction = 0.1
popular_sample_fraction = 0.1
scaling_factor = 800
alg = AlgorithmDriver()
results = []
for seed in seeds:
for user_sample_ratio in user_sample_ratios:
self.logger.info("Experiment for user sample ratio: {} and scaling factor: {} and seed: {}".format(user_sample_ratio,scaling_factor,seed))
# Load dataset
data = self.data_provider.read_freelancer_data_obj()
config = self.config.copy()
alg.create_sample(config, data, num_sampled_skills, rare_sample_fraction, popular_sample_fraction,
rare_threshold,popular_threshold, user_sample_ratio, seed)
self.logger.info("Scaling factor for submodular function is: {}".format(scaling_factor))
# Distorted Greedy
for k in ks:
# Run algorithm
start = timer()
result = alg.run(self.config, data, "distorted_greedy",
None, None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction, rare_threshold, popular_threshold,
user_sample_ratio, seed, k)
end = timer()
result['runtime'] = end - start
results.append(result)
self.logger.info("Algorithm: {} and k: {} and runtime: {}".format("distorted_greedy",k,end - start))
self.logger.info("\n")
# Stochastic Distorted Greedy
for k in ks:
for sample_epsilon in sampling_epsilon_values_stochastic:
# Run algorithm
start = timer()
result = alg.run(config, data, "stochastic_distorted_greedy",
sample_epsilon, None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction, rare_threshold, popular_threshold,
user_sample_ratio, seed, k)
end = timer()
result['runtime'] = end - start
results.append(result)
self.logger.info("Algorithm: {} and epsilon: {} and k: {} and runtime: {}".format("stochastic_distorted_greedy",sample_epsilon,k,end - start))
self.logger.info("\n")
# Cost Scaled Greedy
for k in ks:
# Run algorithm
start = timer()
result = alg.run(self.config, data, "cost_scaled_greedy",
None, None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction, rare_threshold, popular_threshold,
user_sample_ratio, seed, k)
end = timer()
result['runtime'] = end - start
results.append(result)
self.logger.info("Algorithm: {} and k: {} and runtime: {}".format("cost_scaled_greedy",k,end - start))
self.logger.info("\n")
# Cost scaled lazy exact greedy
for k in ks:
# Run algorithm
start = timer()
result = alg.run(self.config, data, "cost_scaled_lazy_greedy",
None, None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction, rare_threshold, popular_threshold,
user_sample_ratio, seed, k)
end = timer()
result['runtime'] = end - start
results.append(result)
self.logger.info("Algorithm: {} and k: {} and runtime: {}".format("cost_scaled_lazy_greedy",k,end - start))
self.logger.info("\n")
# Greedy
for k in ks:
# Run algorithm
start = timer()
result = alg.run(self.config, data, "greedy",
None, None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction, rare_threshold, popular_threshold,
user_sample_ratio, seed, k)
end = timer()
result['runtime'] = end - start
results.append(result)
self.logger.info("Algorithm: {} and k: {} and runtime: {}".format("greedy",k,end - start))
self.logger.info("\n")
# Scaled Single Threshold Greedy
for k in ks:
for error_epsilon in error_epsilon_values_scaled_threshold:
# Run algorithm
start = timer()
result = alg.run(self.config, data, "scaled_single_threshold_greedy",
None, error_epsilon, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction, rare_threshold, popular_threshold,
user_sample_ratio, seed, k)
end = timer()
result['runtime'] = end - start
results.append(result)
self.logger.info("Algorithm: {} and epsilon: {} and k: {} and runtime: {}".format("scaled_single_threshold_greedy",error_epsilon,k,end - start))
self.logger.info("\n")
# Baseline Top k
for k in ks:
# Run algorithm
start = timer()
result = alg.run(self.config, data, "baseline_topk",
None, None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction, rare_threshold, popular_threshold,
user_sample_ratio, seed, k)
end = timer()
result['runtime'] = end - start
results.append(result)
self.logger.info("Algorithm: {} and k: {} and runtime: {}".format("baseline_topk",k,end - start))
self.logger.info("\n")
self.logger.info("Finished experiment 02")
# Export results
df = pd.DataFrame(results)
self.data_exporter.export_csv_file(df, "experiment_02_freelancer_pop01_rare01_greedy.csv")
self.logger.info("Exported experiment_02 results")
class Experiment00(object):
"""
Experiment00 class
"""
def __init__(self, config):
"""
Constructor
:param config:
:return:
"""
self.config = config
self.logger = logging.getLogger("so_logger")
self.data_provider = DataProvider(self.config)
self.data_exporter = DataExporter(self.config)
@staticmethod
def run_algorithm(args):
# Run algorithm
alg = AlgorithmDriver()
data = alg.run(*args)
return data
def set_scaling_factor(self, data):
# Find appropriate scaling factor
alg = SetCoverGreedy(self.config, data.submodular_func, data.E)
sol = alg.run()
submodular_val = data.submodular_func(sol)
cost = data.cost_func(sol)
scaling_factor = data.scaling_func(submodular_val, cost)
# Update scaling factor
data.scaling_factor = scaling_factor
def run(self):
"""
Run experiment
:param:
:return:
"""
self.logger.info("Starting experiment 00")
self.expt_config = self.config['experiment_configs']['experiment_00']
popular_threshold = self.expt_config['popular_threshold']
rare_threshold = self.expt_config['rare_threshold']
user_sample_ratios = [
0.001, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1
]
seeds = [i for i in range(6, 11)]
sampling_epsilon_values = [0.1, 0.05, 0.01, 0.005]
num_sampled_skills = 50
rare_sample_fraction = 0.1
popular_sample_fraction = 0.1
scaling_factor = 800
alg = AlgorithmDriver()
results = []
for seed in seeds:
for user_sample_ratio in user_sample_ratios:
self.logger.info(
"Experiment for user sample ratio: {} and scaling factor: {} and seed: {}"
.format(user_sample_ratio, scaling_factor, seed))
# Load dataset
data = self.data_provider.read_freelancer_data_obj()
config = self.config.copy()
alg.create_sample(config, data, num_sampled_skills,
rare_sample_fraction,
popular_sample_fraction, rare_threshold,
popular_threshold, user_sample_ratio, seed)
# # Create controlled samples dataset
# data.sample_skills_to_be_covered_controlled(num_sampled_skills, rare_sample_fraction,
# popular_sample_fraction, rare_threshold,
# popular_threshold, user_sample_ratio)
# # Setting scaling factor of coverage as coverage(S)/cost(S) for set cover solution S
# self.set_scaling_factor(data)
self.logger.info(
"Scaling factor for submodular function is: {}".format(
scaling_factor))
# Distorted greedy - ICML
start = timer()
result = alg.run(config, data, "distorted_greedy", None, None,
scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"distorted_greedy", None, end - start))
results.append(result)
self.logger.info("\n")
# Cost scaled greedy
start = timer()
result = alg.run(config, data, "cost_scaled_greedy", None,
None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"cost_scaled_greedy", None, end - start))
results.append(result)
self.logger.info("\n")
# Cost scaled lazy exact greedy
start = timer()
result = alg.run(config, data, "cost_scaled_lazy_greedy", None,
None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"cost_scaled_lazy_greedy", None, end - start))
results.append(result)
self.logger.info("\n")
# Greedy
start = timer()
result = alg.run(config, data, "greedy", None, None,
scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"greedy", None, end - start))
results.append(result)
self.logger.info("\n")
# Unconstrained Linear
start = timer()
result = alg.run(config, data, "unconstrained_linear", None,
None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"unconstrained_linear", None, end - start))
results.append(result)
self.logger.info("\n")
# Unconstrained distorted greedy
start = timer()
result = alg.run(config, data,
"unconstrained_distorted_greedy", None, None,
scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"unconstrained_distorted_greedy", None, end - start))
results.append(result)
self.logger.info("\n")
# Stochastic distorted greedy
for sample_epsilon in sampling_epsilon_values:
start = timer()
config['algorithms']['stochastic_distorted_greedy_config'][
'epsilon'] = sample_epsilon
result = alg.run(config, data,
"stochastic_distorted_greedy",
sample_epsilon, None, scaling_factor,
num_sampled_skills, rare_sample_fraction,
popular_sample_fraction, rare_threshold,
popular_threshold, user_sample_ratio,
seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and epsilon: {} and k: {} and runtime: {}"
.format("stochastic_distorted_greedy", sample_epsilon,
None, end - start))
results.append(result)
self.logger.info("\n")
# Baseline top k
start = timer()
result = alg.run(config, data, "baseline_topk", None, None,
scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"baseline_topk", None, end - start))
results.append(result)
self.logger.info("Finished experiment 00")
# Export results
df = pd.DataFrame(results)
self.data_exporter.export_csv_file(
df, "experiment_00_freelancer_pop01_rare01_greedy.csv")
self.logger.info("Exported experiment_00 results")
class Experiment04(object):
"""
Experiment04 class
"""
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.data_exporter = DataExporter(self.config)
self.logger = logging.getLogger("cuda_logger")
self.expt_name = "expt_04"
self.config['RL_parameters']['experiment'] = self.expt_name
@staticmethod
def run_rl_training(config):
rl_trainer = RunRLTrainingJob(config)
try:
data = rl_trainer.run()
except BaseException:
print config
raise ValueError
return data
def run(self):
"""
Run experiment
"""
num_drivers = np.arange(1000, 6500, 500)
thresholds = np.arange(5, 55, 5)
thresholds = np.insert(thresholds, 0, 2)
combinations = list(itertools.product(num_drivers, thresholds))
# Create a pool of processes
num_processes = mp.cpu_count()
self.logger.info("Processes: {}".format(num_processes))
pool = ProcessPool(nodes=num_processes)
configs = []
count = 0
for comb in combinations:
self.config['RL_parameters'][
'experiment'] = self.expt_name + "_" + str(count)
self.config['RL_parameters']['num_drivers'] = comb[0]
self.config['RL_parameters']['imbalance_threshold'] = comb[1]
configs.append(deepcopy(self.config))
count += 1
self.logger.info("Starting expt_04")
results = pool.amap(self.run_rl_training, configs).get()
pool.close()
pool.join()
pool.clear()
self.logger.info("Finished expt_04")
# Export best episode
self.data_exporter.export_episode(results, self.expt_name + ".dill")
def __init__(self, config):
self.config = config
self.logger = logging.getLogger("baseline_logger")
self.data_provider = DataProvider(self.config)
self.data_exporter = DataExporter(self.config)
class Experiment06(object):
"""
Experiment06 class
"""
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.data_exporter = DataExporter(self.config)
self.logger = logging.getLogger("cuda_logger")
self.expt_name = "expt_06"
self.config['RL_parameters']['experiment'] = self.expt_name
@staticmethod
def run_rl_training(config):
rl_trainer = RunRLTrainingJob(config)
data = rl_trainer.run()
return data
def run(self):
"""
Run experiment
"""
days = [
'Sunday_00_', 'Monday_00_', 'Tuesday_00_', 'Wednesday_00_',
'Thursday_00_', 'Friday_00_', 'Saturday_00_', 'Sunday_01_',
'Monday_01_', 'Tuesday_01_', 'Wednesday_01_', 'Thursday_01_',
'Friday_01_', 'Saturday_01_', 'Sunday_02_', 'Monday_02_',
'Tuesday_02_', 'Wednesday_02_', 'Thursday_02_', 'Friday_02_',
'Saturday_02_', 'Sunday_03_', 'Monday_03_', 'Tuesday_03_',
'Wednesday_03_', 'Thursday_03_', 'Friday_03_', 'Saturday_03_'
]
# Create a pool of processes
num_processes = mp.cpu_count()
pool = ProcessPool(nodes=num_processes)
configs = []
count = 0
for day in days:
self.config['RL_parameters'][
'experiment'] = self.expt_name + "_" + str(count)
self.config['RL_parameters'][
'city_states_filename'] = day + 'city_states.dill'
self.config['RL_parameters'][
'best_model_filename'] = day + 'model.dill'
configs.append(deepcopy(self.config))
count += 1
self.logger.info("Starting expt_06")
results = pool.amap(self.run_rl_training, configs).get()
pool.close()
pool.join()
pool.clear()
self.logger.info("Finished expt_06")
# Export best episode
self.data_exporter.export_episode(results, self.expt_name + ".dill")
def run(self):
"""
Execute the job
:param:
:return:
"""
self.logger.info("Starting job: FreelancerDataProcessor\n")
data_provider = DataProvider(self.config)
data_exporter = DataExporter(self.config)
# Read freelancer data
df = data_provider.read_freelancer_user_data()
df_cost = df[[1]] # Salary/Hour
df_skills = df[df.columns[4::2]]
df_skills.replace(to_replace=["Other Skills"], value="", inplace=True)
df_skills = (df_skills.iloc[:, 0].map(str) + ',' +
df_skills.iloc[:, 1].map(str) + ',' +
df_skills.iloc[:, 2].map(str) + ',' +
df_skills.iloc[:, 3].map(str) + ',' +
df_skills.iloc[:, 4].map(str) + ',' +
df_skills.iloc[:, 5].map(str)) # Skills
user_df = pd.DataFrame()
user_df['cost'] = df_cost.iloc[:, 0].tolist()
# Converting all strings to lower case
user_df['skills'] = df_skills.str.lower().tolist()
user_df = user_df.reset_index(drop=True)
user_df = user_df.assign(user_id=user_df.index.values)
user_df = user_df.assign(skills=user_df.apply(
lambda x: x['skills'][:-1].split(','), axis=1))
# Convert cost to integers
user_df.cost = user_df.cost.astype(int)
# Read skills data
df = data_provider.read_freelancer_skill_data()
df = df[[1]]
df.columns = ['skill']
skill_df = df.assign(skill_id=df.index.values)
# Create multilabel binarizer
mlb = MultiLabelBinarizer(classes=skill_df.skill.values)
# One hot encoding of user skills
skills = mlb.fit_transform(user_df['skills'])
# Create dataset
users = user_df.to_dict('records')
for i in range(len(users)):
users[i]['skills_array'] = skills[i]
# Export csv files
data_exporter.export_csv_file(user_df,
"freelancer/freelancer_user_df.csv")
data_exporter.export_csv_file(skill_df,
"freelancer/freelancer_skill_df.csv")
# Scaling factor for submodular function
scaling_factor = 1
# Create and export data object to be used in experiments
# containing all methods related to freelancer data
freelancer = FreelancerData(self.config, user_df, skill_df, users,
scaling_factor)
data_exporter.export_dill_file(freelancer,
"freelancer/freelancer_data.dill")
self.logger.info("Finished job: FreelancerDataProcessor")
class Experiment08(object):
"""
Experiment08 class
"""
def __init__(self, config_):
"""
Constructor
:param config_:
:return:
"""
self.config = config_
self.data_exporter = DataExporter(self.config)
self.logger = logging.getLogger("cuda_logger")
self.expt_name = "expt_08"
self.config['Model_testing']['experiment'] = self.expt_name
@staticmethod
def run_rl_testing(config):
rl_tester = RunRLTestingJob(config)
data = rl_tester.run()
return data
def run(self):
"""
Run experiment
"""
days = [
'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',
'Saturday'
]
weeks_of_month = ['00', '01', '02', '03', '04']
imbalance_thresholds = [2]
model_num_drivers = [4000, 5000, 6000, 7000, 8000, 9000, 10000]
test_combinations = []
for model_day in days:
for model_wom in weeks_of_month:
for model_threshold in imbalance_thresholds:
for model_drivers in model_num_drivers:
model_args = [
model_day, model_wom,
str(model_drivers),
str(model_threshold)
]
model_filename = "_".join(model_args) + "_model.dill"
if os.path.isfile(self.config['app']['DATA_DIR'] +
'models/' + model_filename):
for test_wom in weeks_of_month:
for test_drivers in range(
model_drivers - 3000,
model_drivers + 4000, 1000):
test_file = model_day + '_' + test_wom + '_city_states.dill'
if os.path.isfile(
self.config['app']['DATA_DIR'] +
'city_states/' + test_file):
test_combinations.append({
'model':
model_filename,
'test_dow':
model_day,
'test_wom':
test_wom,
'test_drivers':
test_drivers
})
self.logger.info("Total test combinations: {}".format(
len(test_combinations)))
# Create a pool of processes
num_processes = mp.cpu_count()
pool = ProcessPool(nodes=num_processes)
configs = []
count = 0
for comb in test_combinations:
self.config['Model_testing'][
'experiment'] = self.expt_name + "_" + str(count)
self.config['Model_testing']['city_states_filename'] = (
comb['test_dow'] + '_' + comb['test_wom'] +
'_city_states.dill')
self.config['Model_testing']['model_filename'] = comb['model']
self.config['RL_parameters']['num_drivers'] = comb['test_drivers']
self.config['RL_parameters']['num_strategic_drivers'] = comb[
'test_drivers']
configs.append(deepcopy(self.config))
count += 1
self.logger.info("Starting expt_08")
results = pool.amap(self.run_rl_testing, configs).get()
pool.close()
pool.join()
pool.clear()
self.logger.info("Finished expt_08")
# Export best episode
self.data_exporter.export_episode(results, self.expt_name + ".dill")
class Experiment04(object):
"""
Experiment04 class
"""
def __init__(self, config):
"""
Constructor
:param config:
:return:
"""
self.config = config
self.logger = logging.getLogger("so_logger")
self.data_provider = DataProvider(self.config)
self.data_exporter = DataExporter(self.config)
@staticmethod
def run_algorithm(args):
# Run algorithm
alg = AlgorithmDriver()
data = alg.run(*args)
return data
def set_scaling_factor(self, data):
# Find appropriate scaling factor
alg = SetCoverGreedy(self.config, data.submodular_func, data.E)
sol = alg.run()
submodular_val = data.submodular_func(sol)
cost = data.cost_func(sol)
scaling_factor = data.scaling_func(submodular_val, cost)
# Update scaling factor
data.scaling_factor = scaling_factor
def run(self):
"""
Run experiment
:param:
:return:
"""
self.logger.info("Starting experiment 04")
self.expt_config = self.config['experiment_configs']['experiment_04']
popular_threshold = self.expt_config['popular_threshold']
rare_threshold = self.expt_config['rare_threshold']
num_of_partitions = self.expt_config['num_of_partitions']
partition_type = self.expt_config['partition_type']
cardinality_constraint = self.expt_config['cardinality_constraint']
user_sample_ratios = [1]
seeds = [i for i in range(6, 10)]
cardinality_constraints = [i for i in range(1, 11)]
num_of_partitions = [i for i in range(1, 6)]
num_sampled_skills = 50
rare_sample_fraction = 0.1
popular_sample_fraction = 0.8
scaling_factor = 800
alg = AlgorithmDriver()
results = []
for seed in seeds:
for user_sample_ratio in user_sample_ratios:
for cardinality_constraint in cardinality_constraints:
for num_of_partition in num_of_partitions:
self.logger.info(
"Experiment for user sample ratio: {} and scaling factor: {} and seed: {} and cardinality constraint:{} and num of partitions:{} "
.format(user_sample_ratio, scaling_factor, seed,
cardinality_constraint, num_of_partition))
# Load dataset
data = self.data_provider.read_guru_data_obj()
config = self.config.copy()
# Creating the ground set of users
alg.create_sample(config, data, num_sampled_skills,
rare_sample_fraction,
popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed)
# Assigning users to partitions uniformly at random
alg.create_partitions(data, num_of_partition,
partition_type,
cardinality_constraint)
self.logger.info(
"Scaling factor for submodular function is: {}".
format(scaling_factor))
# Partition matroid greedy
start = timer()
result = alg.run(
config, data, "partition_matroid_greedy", None,
None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
result[
'cardinality_constraint'] = cardinality_constraint
result['num_of_partitions'] = num_of_partition
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"partition_matroid_greedy", None, end - start))
results.append(result)
self.logger.info("\n")
# Cost scaled partition matroid greedy
start = timer()
result = alg.run(
config, data,
"cost_scaled_partition_matroid_greedy", None, None,
scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
result[
'cardinality_constraint'] = cardinality_constraint
result['num_of_partitions'] = num_of_partition
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"cost_scaled_partition_matroid_greedy", None,
end - start))
results.append(result)
self.logger.info("\n")
# Cost scaled partition matroid lazy exact greedy
start = timer()
result = alg.run(
config, data,
"cost_scaled_partition_matroid_lazy_greedy", None,
None, scaling_factor, num_sampled_skills,
rare_sample_fraction, popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
result[
'cardinality_constraint'] = cardinality_constraint
result['num_of_partitions'] = num_of_partition
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"cost_scaled_partition_matroid_lazy_greedy",
None, end - start))
results.append(result)
self.logger.info("\n")
# Baseline Top k
start = timer()
result = alg.run(config, data, "baseline_topk_matroid",
None, None, scaling_factor,
num_sampled_skills,
rare_sample_fraction,
popular_sample_fraction,
rare_threshold, popular_threshold,
user_sample_ratio, seed, None)
end = timer()
result['runtime'] = end - start
result[
'cardinality_constraint'] = cardinality_constraint
result['num_of_partitions'] = num_of_partition
self.logger.info(
"Algorithm: {} and k: {} and runtime: {}".format(
"baseline_topk_matroid", None, end - start))
results.append(result)
self.logger.info("\n")
self.logger.info("Finished experiment 04")
# Export results
df = pd.DataFrame(results)
self.data_exporter.export_csv_file(
df, "experiment_04_guru_salary_pop08_rare01.csv")
self.logger.info("Exported experiment 04 results")
class BaselineDriver(object):
def __init__(self, config):
self.config = config
self.logger = logging.getLogger("baseline_logger")
self.data_provider = DataProvider(self.config)
self.data_exporter = DataExporter(self.config)
@staticmethod
def run_baseline(baseline_config):
baseline_name = baseline_config['name']
baseline_count = baseline_config['count']
config = baseline_config['config']
city_states = baseline_config['city_states']
episode_rewards = []
if baseline_name == "cDQN":
baseline = cDQN(config)
rewards = baseline.run(city_states)
for _ in range(len(rewards)):
episode_rewards.append({
'agent': 'cDQN',
'episode': _,
'run': baseline_count,
'earnings': rewards[_]
})
if baseline_name == "cA2C":
baseline = cA2C(config)
rewards = baseline.run(city_states)
for _ in range(len(rewards)):
episode_rewards.append({
'agent': 'cA2C',
'episode': _,
'run': baseline_count,
'earnings': rewards[_]
})
if baseline_name == "A2C":
baseline = A2C(config)
rewards = baseline.run(city_states)
for _ in range(len(rewards)):
episode_rewards.append({
'agent': 'A2C',
'episode': _,
'run': baseline_count,
'earnings': rewards[_]
})
return episode_rewards
def run(self):
self.logger.info("Starting baselines")
city_states = self.data_provider.read_city_states()
baseline_list = self.config['baselines']['baseline_list']
# Create a pool of processes
num_processes = mp.cpu_count()
self.logger.info("Processes: {}".format(num_processes))
pool = ProcessPool(nodes=num_processes)
configs = []
for count in range(10):
for name in baseline_list:
configs.append({
'name': name,
'count': count,
'config': self.config,
'city_states': city_states
})
results = pool.amap(self.run_baseline, configs).get()
pool.close()
pool.join()
pool.clear()
episode_rewards = []
for result in results:
episode_rewards += result
self.data_exporter.export_baseline_data(episode_rewards)
self.logger.info("Finished baselines")
