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run_server_copy.py
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47 lines (38 loc) · 3.9 KB
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import subprocess
from constants import *
from itertools import product
EEG_EYE_STATE = "EEG_Eye_State"
SEMIRANDOM = "SemiRandom"
SHOPPER_INTENTION = "Shopper_Intention"
SHOPPER_INTENTION_BALANCED = "Shopper_Intention_Balanced"
datasets = [EEG_EYE_STATE, SEMIRANDOM, SHOPPER_INTENTION, SHOPPER_INTENTION_BALANCED] # "Shopper_Intention" EEG_Eye_State
num_holdouts = [100]
holdout_sizes = [5]
starting_experiment = 300
# exp_params = [{"model": "KNN", "lower_range": 1, "upper_range":4}]
exp_params = [{"model": ModelNamesMetrics.KNN_NEIGHBORS.value, "lower_range": 1, "upper_range":26, "step": 1, "x-axis": "neighbors", "y-axis": "metric"},
# {"model": ModelNamesMetrics.GAUSSIAN_PROCESS_MAX_ITER.value, "lower_range": 1, "upper_range":200, "step": 10, "x-axis": "max iterations", "y-axis": "metric"},
{"model": ModelNamesMetrics.DECISION_TREE_MAX_DEPTH.value, "lower_range": 1, "upper_range":16, "step": 1, "x-axis": "max depth", "y-axis": "metric"},
{"model": ModelNamesMetrics.LINEAR_SVC_MAX_ITER.value, "lower_range": 1, "upper_range":50, "step": 5, "x-axis": "max iterations", "y-axis": "metric"},
{"model": ModelNamesMetrics.C_SUPPORT_SVC_MAX_ITER.value, "lower_range": 1, "upper_range":250, "step": 10, "x-axis": "max iterations", "y-axis": "metric"},
{"model": ModelNamesMetrics.LOGISTIC_REGRESSION_MAX_ITER.value, "lower_range": 1, "upper_range":70, "step": 2, "x-axis": "max iterations", "y-axis": "metric"},
{"model": ModelNamesMetrics.RANDOM_FOREST_DEPTH.value, "lower_range": 1, "upper_range":15, "step": 1, "x-axis": "depth", "y-axis": "metric"},
{"model": ModelNamesMetrics.RANDOM_FOREST_ESTIMATORS.value, "lower_range": 1, "upper_range":21, "step": 1, "x-axis": "estimators", "y-axis": "metric"},
{"model": ModelNamesMetrics.ADABOOST_ESTIMATORS.value, "lower_range": 1, "upper_range":21, "step": 1, "x-axis": "estimators", "y-axis": "metric"}]
for i, (dataset, num_holdout, holdout_size, exp_param) in enumerate(product(datasets, num_holdouts, holdout_sizes, exp_params)):
current_experiment = i+starting_experiment
training_args = ["python3", "Trial_Setup_Utils.py", "--dataset", dataset, "--num_holdout", str(num_holdout), "--size_holdout",
str(holdout_size), "--model_name", exp_param["model"], "--mode", "training", "--lower", str(exp_param["lower_range"]),
"--upper", str(exp_param["upper_range"]), "--step", str(exp_param["step"]), "--model_num", str(current_experiment)]
subprocess.run(training_args)
inference_args = ["python3", "Trial_Setup_Utils.py", "--dataset", dataset, "--num_holdout", str(num_holdout), "--size_holdout", str(holdout_size), "--model_name", exp_param["model"], "--mode", "inference", "--lower", str(exp_param["lower_range"]), "--upper", str(exp_param["upper_range"]), "--model_num", str(current_experiment), "--step", str(exp_param["step"])]
subprocess.run(inference_args)
analysis_args = ["python3", "run_analysis.py", "--trial_num", str(current_experiment), "--model", str(current_experiment), "--holdout_size", str(holdout_size)]
subprocess.run(analysis_args)
graphing_args = ["python3", "run_graphing.py", dataset, str(current_experiment), exp_param["model"], exp_param["model"], exp_param["x-axis"], exp_param["y-axis"]]
subprocess.run(graphing_args)
accuracies_args = ["python3", "graph_accuracies.py", "--dataset", dataset, "--model_num", str(current_experiment)]
subprocess.run(accuracies_args)
# RUNS BOTH TRAINING AND INFERENCE
# python3 Trial_Setup_Utils.py --dataset $DATASET --num_holdout $NUMBER_HOLDOUT --size_holdout $HOLDOUT_SIZE --model_name $MODEL --mode training --lower $LOWER_RANGE --upper $UPPER_RANGE
# python3 Trial_Setup_Utils.py --dataset $DATASET --num_holdout $NUMBER_HOLDOUT --size_holdout $HOLDOUT_SIZE --model_name $MODEL --mode inference --lower $LOWER_RANGE --upper $UPPER_RANGE --model_num $MODEL_NUMBER