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add error bar vis, comment unused parameters from parser

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This commit is contained in:
René Knaebel 2017-10-05 14:50:59 +02:00
parent b24fa770f9
commit 345afbaef5
4 changed files with 45 additions and 24 deletions
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24
arguments.py
View File

@ -6,14 +6,14 @@ parser = argparse.ArgumentParser()
parser.add_argument("--mode", action="store", dest="mode", parser.add_argument("--mode", action="store", dest="mode",
default="") default="")
parser.add_argument("--train", action="store", dest="train_data", # parser.add_argument("--train", action="store", dest="train_data",
# default="data/full_dataset.csv.tar.gz")
parser.add_argument("--data", action="store", dest="data",
default="data/full_dataset.csv.tar.gz") default="data/full_dataset.csv.tar.gz")
parser.add_argument("--data", action="store", dest="train_data", # parser.add_argument("--test", action="store", dest="test_data",
default="data/full_dataset.csv.tar.gz") # default="data/full_future_dataset.csv.tar.gz")
parser.add_argument("--test", action="store", dest="test_data",
default="data/full_future_dataset.csv.tar.gz")
parser.add_argument("--hyper_result", action="store", dest="hyperband_results", parser.add_argument("--hyper_result", action="store", dest="hyperband_results",
default="") default="")
@ -117,9 +117,9 @@ def get_model_args(args):
"embedding_model": os.path.join(model_path, "embd.h5"), "embedding_model": os.path.join(model_path, "embd.h5"),
"clf_model": os.path.join(model_path, "clf.h5"), "clf_model": os.path.join(model_path, "clf.h5"),
"train_log": os.path.join(model_path, "train.log.csv"), "train_log": os.path.join(model_path, "train.log.csv"),
"train_h5data": args.train_data, # "train_h5data": args.train_data,
"test_h5data": args.test_data, # "test_h5data": args.test_data,
"future_prediction": os.path.join(model_path, f"{os.path.basename(args.test_data)}_pred") "future_prediction": os.path.join(model_path, f"{os.path.basename(args.data)}_pred")
} for model_path in args.model_paths] } for model_path in args.model_paths]
@ -130,7 +130,7 @@ def parse():
args.embedding_model = os.path.join(args.model_path, "embd.h5") args.embedding_model = os.path.join(args.model_path, "embd.h5")
args.clf_model = os.path.join(args.model_path, "clf.h5") args.clf_model = os.path.join(args.model_path, "clf.h5")
args.train_log = os.path.join(args.model_path, "train.log.csv") args.train_log = os.path.join(args.model_path, "train.log.csv")
args.train_h5data = args.train_data # args.train_h5data = args.train_data
args.test_h5data = args.test_data # args.test_h5data = args.test_data
args.future_prediction = os.path.join(args.model_path, f"{os.path.basename(args.test_data)}_pred") args.future_prediction = os.path.join(args.model_path, f"{os.path.basename(args.data)}_pred")
return args return args

24
fancy.sh
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@ -3,22 +3,22 @@
RESDIR=$1 RESDIR=$1
DATADIR=$2 DATADIR=$2
python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_final --test ${DATADIR}/futureData.csv --model_output both python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_final --data ${DATADIR}/futureData.csv --model_output both
python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_inter --test ${DATADIR}/futureData.csv --model_output both python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_inter --data ${DATADIR}/futureData.csv --model_output both
python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_staggered --test ${DATADIR}/futureData.csv --model_output both #python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_staggered --data ${DATADIR}/futureData.csv --model_output both
python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_final --test ${DATADIR}/futureData.csv --model_output client python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_final --data ${DATADIR}/futureData.csv --model_output client
#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_inter --test ${DATADIR}/futureData.csv --model_output client #python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_inter --data ${DATADIR}/futureData.csv --model_output client
#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_final --test ${DATADIR}/futureData.csv --model_output both #python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_final --data ${DATADIR}/futureData.csv --model_output both
#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_inter --test ${DATADIR}/futureData.csv --model_output both #python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/both_medium_inter --data ${DATADIR}/futureData.csv --model_output both
#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_final --test ${DATADIR}/futureData.csv --model_output client #python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_final --data ${DATADIR}/futureData.csv --model_output client
#python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_inter --test ${DATADIR}/futureData.csv --model_output client #python3 main.py --mode fancy --batch 1024 --model ${RESDIR}/client_medium_inter --data ${DATADIR}/futureData.csv --model_output client
#python3 main.py --mode all_fancy --batch 256 --test ${DATADIR}/futureData.csv \ #python3 main.py --mode all_fancy --batch 256 --data ${DATADIR}/futureData.csv \
# --models ${RESDIR}/*_small_*/ --out-prefix ${RESDIR}/small # --models ${RESDIR}/*_small_*/ --out-prefix ${RESDIR}/small
#python3 main.py --mode all_fancy --batch 256 --test ${DATADIR}/futureData.csv \ #python3 main.py --mode all_fancy --batch 256 --data ${DATADIR}/futureData.csv \
# --models ${RESDIR}/*_medium_*/ --out-prefix ${RESDIR}/medium # --models ${RESDIR}/*_medium_*/ --out-prefix ${RESDIR}/medium
python3 main.py --mode all_fancy --batch 256 --test ${DATADIR}/futureData.csv \ python3 main.py --mode all_fancy --batch 256 --data ${DATADIR}/futureData.csv \
--models ${RESDIR}/*/ --out-prefix ${RESDIR}/all --models ${RESDIR}/*/ --out-prefix ${RESDIR}/all

8
main.py
View File

@ -589,6 +589,12 @@ def plot_overall_result():
visualize.plot_legend() visualize.plot_legend()
visualize.plot_save(f"{path}/{vis}_all.png") visualize.plot_save(f"{path}/{vis}_all.png")
for cat, models in results.items():
visualize.plot_clf()
visualize.plot_error_bars(models)
visualize.plot_legend()
visualize.plot_save(f"{path}/error_bars_{cat}.png")
def main(): def main():
if "train" == args.mode: if "train" == args.mode:
@ -605,6 +611,8 @@ def main():
main_visualize_all() main_visualize_all()
if "beta" == args.mode: if "beta" == args.mode:
main_beta() main_beta()
if "all_beta" == args.mode:
plot_overall_result()
if __name__ == "__main__": if __name__ == "__main__":

13
visualize.py
View File

@ -2,6 +2,7 @@ import os
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
import pandas as pd
from scipy import interpolate from scipy import interpolate
from sklearn.decomposition import TruncatedSVD from sklearn.decomposition import TruncatedSVD
from sklearn.manifold import TSNE from sklearn.manifold import TSNE
@ -211,6 +212,18 @@ def plot_training_curve(logs, key, path, dpi=600):
plt.close() plt.close()
def plot_error_bars(results):
rates = []
for m, r in results.items():
if m == "all": continue
rates.append((r / r.sum(axis=0, keepdims=True)).flatten())
rates = pd.DataFrame(np.vstack(rates), columns=("TN", "FP", "FN", "TP"))
ax = rates.mean().plot.bar(yerr=rates.std())
for p in ax.patches:
ax.annotate(str(np.round(p.get_height(), 4)), (p.get_x(), 0.5))
def plot_embedding(domain_embedding, labels, path, dpi=600, method="svd"): def plot_embedding(domain_embedding, labels, path, dpi=600, method="svd"):
if method == "svd": if method == "svd":
red = TruncatedSVD(n_components=2) red = TruncatedSVD(n_components=2)