add error bar vis, comment unused parameters from parser
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b24fa770f9
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345afbaef5
24
arguments.py
24
arguments.py
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@ -6,14 +6,14 @@ parser = argparse.ArgumentParser()
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parser.add_argument("--mode", action="store", dest="mode",
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default="")
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parser.add_argument("--train", action="store", dest="train_data",
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# parser.add_argument("--train", action="store", dest="train_data",
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# default="data/full_dataset.csv.tar.gz")
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parser.add_argument("--data", action="store", dest="data",
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default="data/full_dataset.csv.tar.gz")
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parser.add_argument("--data", action="store", dest="train_data",
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default="data/full_dataset.csv.tar.gz")
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parser.add_argument("--test", action="store", dest="test_data",
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default="data/full_future_dataset.csv.tar.gz")
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# parser.add_argument("--test", action="store", dest="test_data",
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# default="data/full_future_dataset.csv.tar.gz")
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parser.add_argument("--hyper_result", action="store", dest="hyperband_results",
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default="")
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@ -117,9 +117,9 @@ def get_model_args(args):
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"embedding_model": os.path.join(model_path, "embd.h5"),
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"clf_model": os.path.join(model_path, "clf.h5"),
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"train_log": os.path.join(model_path, "train.log.csv"),
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"train_h5data": args.train_data,
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"test_h5data": args.test_data,
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"future_prediction": os.path.join(model_path, f"{os.path.basename(args.test_data)}_pred")
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# "train_h5data": args.train_data,
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# "test_h5data": args.test_data,
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"future_prediction": os.path.join(model_path, f"{os.path.basename(args.data)}_pred")
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} for model_path in args.model_paths]
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@ -130,7 +130,7 @@ def parse():
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args.embedding_model = os.path.join(args.model_path, "embd.h5")
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args.clf_model = os.path.join(args.model_path, "clf.h5")
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args.train_log = os.path.join(args.model_path, "train.log.csv")
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args.train_h5data = args.train_data
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args.test_h5data = args.test_data
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args.future_prediction = os.path.join(args.model_path, f"{os.path.basename(args.test_data)}_pred")
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# args.train_h5data = args.train_data
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# args.test_h5data = args.test_data
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args.future_prediction = os.path.join(args.model_path, f"{os.path.basename(args.data)}_pred")
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return args
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24
fancy.sh
24
fancy.sh
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@ -3,22 +3,22 @@
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RESDIR=$1
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DATADIR=$2
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python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_final --test ${DATADIR}/futureData.csv --model_output both
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python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_inter --test ${DATADIR}/futureData.csv --model_output both
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python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_staggered --test ${DATADIR}/futureData.csv --model_output both
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python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_final --test ${DATADIR}/futureData.csv --model_output client
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_inter --test ${DATADIR}/futureData.csv --model_output client
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python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_final --data ${DATADIR}/futureData.csv --model_output both
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python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_inter --data ${DATADIR}/futureData.csv --model_output both
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_staggered --data ${DATADIR}/futureData.csv --model_output both
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python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_final --data ${DATADIR}/futureData.csv --model_output client
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_inter --data ${DATADIR}/futureData.csv --model_output client
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_final --test ${DATADIR}/futureData.csv --model_output both
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_inter --test ${DATADIR}/futureData.csv --model_output both
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_final --test ${DATADIR}/futureData.csv --model_output client
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_inter --test ${DATADIR}/futureData.csv --model_output client
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_final --data ${DATADIR}/futureData.csv --model_output both
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_inter --data ${DATADIR}/futureData.csv --model_output both
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_final --data ${DATADIR}/futureData.csv --model_output client
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#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_inter --data ${DATADIR}/futureData.csv --model_output client
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#python3 main.py --mode all_fancy --batch 256 --test ${DATADIR}/futureData.csv \
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#python3 main.py --mode all_fancy --batch 256 --data ${DATADIR}/futureData.csv \
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# --models ${RESDIR}/*_small_*/ --out-prefix ${RESDIR}/small
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#python3 main.py --mode all_fancy --batch 256 --test ${DATADIR}/futureData.csv \
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#python3 main.py --mode all_fancy --batch 256 --data ${DATADIR}/futureData.csv \
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# --models ${RESDIR}/*_medium_*/ --out-prefix ${RESDIR}/medium
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python3 main.py --mode all_fancy --batch 256 --test ${DATADIR}/futureData.csv \
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python3 main.py --mode all_fancy --batch 256 --data ${DATADIR}/futureData.csv \
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--models ${RESDIR}/*/ --out-prefix ${RESDIR}/all
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8
main.py
8
main.py
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@ -589,6 +589,12 @@ def plot_overall_result():
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visualize.plot_legend()
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visualize.plot_save(f"{path}/{vis}_all.png")
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for cat, models in results.items():
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visualize.plot_clf()
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visualize.plot_error_bars(models)
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visualize.plot_legend()
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visualize.plot_save(f"{path}/error_bars_{cat}.png")
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def main():
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if "train" == args.mode:
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@ -605,6 +611,8 @@ def main():
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main_visualize_all()
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if "beta" == args.mode:
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main_beta()
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if "all_beta" == args.mode:
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plot_overall_result()
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if __name__ == "__main__":
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13
visualize.py
13
visualize.py
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@ -2,6 +2,7 @@ import os
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from scipy import interpolate
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from sklearn.decomposition import TruncatedSVD
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from sklearn.manifold import TSNE
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@ -211,6 +212,18 @@ def plot_training_curve(logs, key, path, dpi=600):
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plt.close()
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def plot_error_bars(results):
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rates = []
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for m, r in results.items():
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if m == "all": continue
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rates.append((r / r.sum(axis=0, keepdims=True)).flatten())
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rates = pd.DataFrame(np.vstack(rates), columns=("TN", "FP", "FN", "TP"))
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ax = rates.mean().plot.bar(yerr=rates.std())
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for p in ax.patches:
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ax.annotate(str(np.round(p.get_height(), 4)), (p.get_x(), 0.5))
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def plot_embedding(domain_embedding, labels, path, dpi=600, method="svd"):
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if method == "svd":
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red = TruncatedSVD(n_components=2)
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