def exp2():
report.set_save_file("experiment2_ccd_%s.csv" % start_time_str)
for run in range(3):
report.run = run
for alg in ["ccd"]:
for vis in ["st", "sh", "kp", "as"]:
for dataset in ["ds_with_duplicates", "ds_no_duplicates"]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode, False)
report.reset()
def exp1():
report.set_save_file("experiment1_cc_%s.csv" % start_time_str)
for run in range(3):
report.run = run
print("-CSV-Run: %s" % run)
for resnet in ["resnet18", "resnet50"]:
for vis in ["st"]:
for dataset in ["oj"]:
try_learn("cc", learn, dataset, vis, datasets_path, resnet,
epochs, lazy_mode)
report.reset()
def exp1():
report.set_save_file("experiment1_%s.csv" % start_time_str)
for run in range(3):
report.run = run
print("-CSV-Run: %s" % run)
for alg in ["bdsvm"]:
for vis in ["st", "sh", "kp", "as"]:
for dataset in ["viscode_t4_limited_art"]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode)
report.reset()
def exptest():
report.set_save_file("test.csv")
for run in range(3):
report.run = run
print("--- Run: %s" % run)
for alg in ["fc"]:
for vis in ["st", "sh", "kp", "as"]:
for dataset in ["viscode_t4_limited_art", "gen"]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode)
report.reset()
def exp4():
report.set_save_file("experiment4_%s.csv" % start_time_str)
for run in range(3):
report.run = run
print("--- Run: %s" % run)
for alg in ["bdsvm", "astnn"]:
for vis in ["as"]:
for dataset in ["gen"]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode)
report.reset()
def all(
): # this runs all possible combinations, enable/disable the calls to the method according to your needs
report.set_save_file("experiment_all_ccd_%s.csv" % start_time_str)
for run in range(3):
report.run = run
for alg in ["ccd", "astnn"]:
for dataset in ["astnn", "ds_with_duplicates", "ds_no_duplicates"]:
for vis in ["st", "sh", "kp", "as"]:
for retrain in [True, False]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode, retrain)
report.reset()
def exp1():
report.set_save_file("experiment1_ccd_%s.csv" % start_time_str)
for run in range(3):
report.run = run
for alg in [
"ccd", "astnn"
]: # ccd includes all 3 binary classifiers, the results need to be filtered from the csv accordingly
for vis in ["as"]:
for dataset in ["astnn_t4"]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode, False)
report.reset()
def exp3():
report.set_save_file("experiment3_%s.csv" % start_time_str)
for run in range(3):
report.run = run
print("--- Run: %s" % run)
for alg in ["astnn"]:
for vis in ["as"]:
for dataset in [
"viscode_t4_limited", "viscode_t4_limited_art",
"astnn_t4"
]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode)
report.reset()
def exp2():
report.set_save_file("experiment2_%s.csv" % start_time_str)
for run in range(3):
report.run = run
print("-CSV-Run: %s" % run)
for alg in [
"bdsvm"
]: # bdnn bundled with bdsvm now to only generate vectors once...
for vis in ["as"]:
for dataset in [
"viscode_t4_limited", "viscode_t4_limited_art",
"astnn_t4"
]:
try_learn(alg, algos[alg], dataset, vis, datasets_path,
epochs, lazy_mode)
report.reset()