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airware_main.py
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import numpy as np
import argparse
import utils.models as model
from utils.deep_model_helper import *
def run_split_model_1(gest_set, cv_strategy):
model_fn = model.split_model_1
hyper_param_path = "./gridSearch/split_model_1/"
model_result_path = "/split_model_1/Model"
if cv_strategy == 'loso':
loso_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./leave_one_subject/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'personalized':
personalized_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./personalized_cv/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'user_calibrated':
train_size_percent = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
for train_size in train_size_percent:
user_split_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path, train_size=train_size,
results_file_path="./user_split_cv/gest_set_" + str(gest_set) + model_result_path + str(
train_size))
else:
raise ValueError("Cross-validation strategy not defined")
def run_split_model_2(gest_set, cv_strategy):
model_fn = model.split_model_2
hyper_param_path = "./gridSearch/split_model_2/"
model_result_path = "/split_model_2/Model"
if cv_strategy == 'loso':
loso_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./leave_one_subject/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'personalized':
personalized_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./personalized_cv/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'user_calibrated':
train_size_percent = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
for train_size in train_size_percent:
user_split_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path, train_size=train_size,
results_file_path="./user_split_cv/gest_set_" + str(gest_set) + model_result_path + str(
train_size))
else:
raise ValueError("Cross-validation strategy not defined")
def run_split_model_3(gest_set, cv_strategy):
model_fn = model.split_model_3
hyper_param_path = "./gridSearch/split_model_3/"
model_result_path = "/split_model_3/Model"
if cv_strategy == 'loso':
loso_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./leave_one_subject/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'personalized':
personalized_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./personalized_cv/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'user_calibrated':
if gest_set ==1:
train_size_percent = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
else:
train_size_percent = [0.6]
for train_size in train_size_percent:
user_split_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path, train_size=train_size,
results_file_path="./user_split_cv/gest_set_" + str(gest_set) + model_result_path + str(
train_size))
else:
raise ValueError("Cross-validation strategy not defined")
def run_split_model_4(gest_set, cv_strategy):
model_fn = model.split_model_4
hyper_param_path = "./gridSearch/split_model_4/"
model_result_path = "/split_model_4/Model"
if cv_strategy == 'loso':
loso_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./leave_one_subject/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'personalized':
personalized_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path,
results_file_path="./personalized_cv/gest_set_" + str(gest_set) + model_result_path)
elif cv_strategy == 'user_calibrated':
train_size_percent = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
for train_size in train_size_percent:
user_split_cv(model_fn, gest_set=gest_set, hyper_param_path=hyper_param_path, train_size=train_size,
results_file_path="./user_split_cv/gest_set_" + str(gest_set) + model_result_path + str(
train_size))
else:
raise ValueError("Cross-validation strategy not defined")
if __name__ == '__main__':
model_map = {'model_1': run_split_model_1,
'model_2': run_split_model_2,
'model_3': run_split_model_3,
'model_4': run_split_model_4}
parser = argparse.ArgumentParser(description="AirWare grid search and train model using different CV strategies")
# "?" one argument consumed from the command line and produced as a single item
# Positional arguments
parser.add_argument('-model',
help="Define model architecture",
choices=['model_1', 'model_2', 'model_3', 'model_4'])
parser.add_argument('-cv_strategy',
help="Define CV Strategy. loso: Leave one subject out, user_calibrated: Partial train and test "
"user, personalized_cv: Train and test only for a given user",
choices=['loso', 'user_calibrated',
'personalized'])
parser.add_argument('-gesture_set', type=int, default=1,
help="Gesture set. 1: All gestures, 2: Reduced Gesture 1, 3: Reduced Gesture 2, 4: Reduced "
"Gesture 3, 5: Reduced Gesture 4. Default is full gesture set",
choices=range(1, 6))
args = parser.parse_args()
run_model = model_map[args.model]
print("Cross Validation Strategy: ", args.cv_strategy, " for model: ", args.model)
run_model(gest_set=args.gesture_set, cv_strategy=args.cv_strategy)