change hyperband to count minimal val_loss over all losses
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Makefile
42
Makefile
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@ -1,65 +1,65 @@
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run:
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_1 --epochs 2 --depth flat1 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_both_1 --epochs 2 --depth flat1 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final --model_output both
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_2 --epochs 2 --depth flat1 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_both_2 --epochs 2 --depth flat1 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --model_output both
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_3 --epochs 2 --depth deep1 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_both_3 --epochs 2 --depth deep1 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final --model_output both
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_4 --epochs 2 --depth deep1 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_both_4 --epochs 2 --depth deep1 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --model_output both
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_both_5 --epochs 2 --depth flat2 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_both_5 --epochs 2 --depth flat2 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type staggered --model_output both
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_1 --epochs 2 --depth flat2 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_client_1 --epochs 2 --depth flat2 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type final --model_output client
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_2 --epochs 2 --depth flat2 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_client_2 --epochs 2 --depth flat2 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --type inter --model_output client
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_3 --epochs 2 --depth deep1 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_client_3 --epochs 2 --depth deep1 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --type final --model_output client
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python3 main.py --mode train --train data/rk_mini.csv.gz --model results/test/test_client_4 --epochs 2 --depth deep1 \
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python3 main.py --mode train --data data/rk_mini.csv.gz --model results/test/test_client_4 --epochs 2 --depth deep1 \
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--filter_embd 32 --kernel_embd 3 --filter_main 16 --kernel_main 3 --dense_main 32 \
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--dense_embd 16 --domain_embd 8 --batch 64 --balanced_weights --type inter --model_output client
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test:
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python3 main.py --mode test --batch 128 --models results/test/test_both_* --test data/rk_mini.csv.gz --model_output both
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python3 main.py --mode test --batch 128 --models results/test/test_client_* --test data/rk_mini.csv.gz --model_output client
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python3 main.py --mode test --batch 128 --models results/test/test_both_* --data data/rk_mini.csv.gz --model_output both
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python3 main.py --mode test --batch 128 --models results/test/test_client_* --data data/rk_mini.csv.gz --model_output client
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fancy:
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_1 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_1 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_2 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_2 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_3 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_3 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_4 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_4 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_5 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_both_5 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_1 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_1 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_2 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_2 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_3 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_3 --data data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_4 --test data/rk_mini.csv.gz
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python3 main.py --mode fancy --batch 128 --model results/test/test_client_4 --data data/rk_mini.csv.gz
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all-fancy:
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python3 main.py --mode all_fancy --batch 128 --models results/test/test* --test data/rk_mini.csv.gz \
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python3 main.py --mode all_fancy --batch 128 --models results/test/test* --data data/rk_mini.csv.gz \
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--out-prefix results/test/
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hyper:
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@ -71,7 +71,7 @@ class Hyperband:
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shuffle=True,
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validation_split=0.4)
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return {"loss": history.history['val_loss'][-1],
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return {"loss": np.min(history.history['val_loss']),
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"early_stop": len(history.history["loss"]) < n_iterations}
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# can be called multiple times
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169
main.py
169
main.py
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@ -5,7 +5,8 @@ import numpy as np
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import pandas as pd
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import tensorflow as tf
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from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint
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from keras.models import Model, load_model as load_keras_model
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from keras.models import Model
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from sklearn.metrics import confusion_matrix
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import arguments
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import dataset
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@ -14,7 +15,7 @@ import models
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# create logger
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import visualize
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from arguments import get_model_args
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from utils import exists_or_make_path, get_custom_class_weights
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from utils import exists_or_make_path, get_custom_class_weights, load_model
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logger = logging.getLogger('logger')
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logger.setLevel(logging.DEBUG)
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@ -85,19 +86,13 @@ def create_model(model, output_type):
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raise Exception("unknown model output")
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def load_model(path, custom_objects=None):
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clf = load_keras_model(path, custom_objects)
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embd = clf.get_layer("domain_cnn").layer
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return embd, clf
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def main_paul_best():
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pauls_best_params = models.pauls_networks.best_config
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main_train(pauls_best_params)
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def main_hyperband():
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params = {
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param_dist = {
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# static params
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"type": [args.model_type],
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"depth": [args.model_depth],
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@ -117,22 +112,25 @@ def main_hyperband():
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"kernel_main": [1, 3, 5, 7, 9],
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"dense_main": [2 ** x for x in range(1, 12)],
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}
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logger.info("create training dataset")
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.train_h5data,
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args.train_data,
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.data,
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args.data,
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args.domain_length,
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args.window)
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server_tr = np.max(server_windows_tr, axis=1)
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if args.model_type in ("inter", "staggered"):
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server_tr = np.expand_dims(server_windows_tr, 2)
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hp = hyperband.Hyperband(params,
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hp = hyperband.Hyperband(param_dist,
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[domain_tr, flow_tr],
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[client_tr, server_tr])
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[client_tr, server_tr],
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max_iter=81,
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savefile=args.hyperband_results)
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results = hp.run()
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joblib.dump(results, args.hyperband_results)
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return results
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def main_train(param=None):
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@ -140,8 +138,8 @@ def main_train(param=None):
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exists_or_make_path(args.model_path)
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logger.info(f"Use command line arguments: {args}")
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.train_h5data,
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args.train_data,
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.data,
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args.data,
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args.domain_length,
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args.window)
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logger.info("define callbacks")
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@ -236,9 +234,9 @@ def main_retrain():
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logger.info(f"Use command line arguments: {args}")
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exists_or_make_path(args.model_destination)
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.train_h5data,
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args.train_data,
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.data,
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args.data,
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args.domain_length,
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args.window)
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logger.info("define callbacks")
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@ -265,7 +263,7 @@ def main_retrain():
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custom_class_weights = None
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logger.info(f"Load pretrained model")
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embedding, model = load_model(source, custom_objects=models.get_metrics())
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embedding, model = load_model(source, custom_objects=models.get_custom_objects())
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if args.model_type in ("inter", "staggered"):
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server_tr = np.expand_dims(server_windows_tr, 2)
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@ -293,17 +291,17 @@ def main_retrain():
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def main_test():
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logger.info("start test: load data")
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domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.test_h5data,
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args.test_data,
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domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.data,
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args.data,
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args.domain_length,
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args.window)
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domain_encs, _ = dataset.load_or_generate_domains(args.test_data, args.domain_length)
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domain_encs, _ = dataset.load_or_generate_domains(args.data, args.domain_length)
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for model_args in get_model_args(args):
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results = {}
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logger.info(f"process model {model_args['model_path']}")
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embd_model, clf_model = load_model(model_args["clf_model"], custom_objects=models.get_metrics())
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embd_model, clf_model = load_model(model_args["clf_model"], custom_objects=models.get_custom_objects())
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pred = clf_model.predict([domain_val, flow_val],
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batch_size=args.batch_size,
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verbose=1)
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@ -324,8 +322,28 @@ def main_test():
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def main_visualization():
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_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.test_h5data,
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args.test_data,
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def plot_model(clf_model, path):
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embd, model = load_model(clf_model, custom_objects=models.get_custom_objects())
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visualize.plot_model_as(embd, os.path.join(path, "model_embd.pdf"))
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visualize.plot_model_as(model, os.path.join(path, "model_clf.pdf"))
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def vis(model_name, model_path, df, df_paul, aggregation, curve):
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visualize.plot_clf()
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if aggregation == "user":
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df = df.groupby(df.names).max()
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df_paul = df_paul.groupby(df_paul.names).max()
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if curve == "prc":
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visualize.plot_precision_recall(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
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visualize.plot_precision_recall(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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elif curve == "roc":
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visualize.plot_roc_curve(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
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visualize.plot_roc_curve(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save("{}/{}_{}.png".format(model_path, aggregation, curve))
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_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.data,
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args.data,
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args.domain_length,
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args.window)
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@ -336,19 +354,17 @@ def main_visualization():
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})
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df["client_val"] = np.logical_or(df.hits_vt == 1.0, df.hits_trusted >= 3)
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df_user = df.groupby(df.names).max()
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paul = dataset.load_predictions("results/paul/")
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df_paul = pd.DataFrame(data={
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"names": paul["testNames"].flatten(), "client_pred": paul["testScores"].flatten(),
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"hits_vt": paul["testLabel"].flatten(), "hits_trusted": paul["testHits"].flatten()
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})
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df_paul["client_val"] = np.logical_or(df_paul.hits_vt == 1.0, df_paul.hits_trusted >= 3)
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df_paul_user = df_paul.groupby(df_paul.names).max()
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logger.info("plot model")
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embd, model = load_model(args.clf_model, custom_objects=models.get_metrics())
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visualize.plot_model_as(model, os.path.join(args.model_path, "model.png"))
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plot_model(args.clf_model, args.model_path)
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# logger.info("plot training curve")
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# logs = pd.read_csv(args.train_log)
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# if "acc" in logs.keys():
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@ -359,31 +375,15 @@ def main_visualization():
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# else:
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# logger.warning("Error while plotting training curves")
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logger.info("plot pr curve")
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visualize.plot_clf()
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visualize.plot_precision_recall(df.client_val.as_matrix(), df.client_pred.as_matrix(), args.model_name)
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visualize.plot_precision_recall(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save("{}/window_client_prc.png".format(args.model_path))
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logger.info("plot window prc")
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vis(args.model_name, args.model_path, df, df_paul, "window", "prc")
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logger.info("plot window roc")
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vis(args.model_name, args.model_path, df, df_paul, "window", "roc")
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logger.info("plot user prc")
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vis(args.model_name, args.model_path, df, df_paul, "user", "prc")
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logger.info("plot user roc")
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vis(args.model_name, args.model_path, df, df_paul, "user", "roc")
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logger.info("plot roc curve")
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visualize.plot_clf()
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visualize.plot_roc_curve(df.client_val.as_matrix(), df.client_pred.as_matrix(), args.model_name)
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visualize.plot_roc_curve(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save("{}/window_client_roc.png".format(args.model_path))
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visualize.plot_clf()
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visualize.plot_precision_recall(df_user.client_val.as_matrix(), df_user.client_pred.as_matrix(), args.model_name)
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visualize.plot_precision_recall(df_paul_user.client_val.as_matrix(), df_paul_user.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save("{}/user_client_prc.png".format(args.model_path))
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visualize.plot_clf()
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visualize.plot_roc_curve(df_user.client_val.as_matrix(), df_user.client_pred.as_matrix(), args.model_name)
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visualize.plot_roc_curve(df_paul_user.client_val.as_matrix(), df_paul_user.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save("{}/user_client_roc.png".format(args.model_path))
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# absolute values
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visualize.plot_confusion_matrix(df.client_val.as_matrix(), df.client_pred.as_matrix().round(),
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"{}/client_cov.png".format(args.model_path),
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@ -398,25 +398,18 @@ def main_visualization():
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visualize.plot_confusion_matrix(df_user.client_val.as_matrix(), df_user.client_pred.as_matrix().round(),
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"{}/user_cov_norm.png".format(args.model_path),
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normalize=True, title="User Confusion Matrix")
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logger.info("visualize embedding")
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domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length)
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domain_embedding = results["domain_embds"]
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visualize.plot_embedding(domain_embedding, labels, path="{}/embd_svd.png".format(args.model_path), method="svd")
|
||||
visualize.plot_embedding(domain_embedding, labels, path="{}/embd_tsne.png".format(args.model_path), method="tsne")
|
||||
plot_embedding(args.model_path, results["domain_embds"], args.data, args.domain_length)
|
||||
|
||||
|
||||
def plot_embedding():
|
||||
def plot_embedding(model_path, domain_embedding, data, domain_length):
|
||||
logger.info("visualize embedding")
|
||||
results = dataset.load_predictions(args.model_path)
|
||||
domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length)
|
||||
domain_embedding = results["domain_embds"]
|
||||
visualize.plot_embedding(domain_embedding, labels, path="{}/embd_svd.png".format(args.model_path), method="svd")
|
||||
visualize.plot_embedding(domain_embedding, labels, path="{}/embd_tsne.png".format(args.model_path), method="tsne")
|
||||
domain_encs, labels = dataset.load_or_generate_domains(data, domain_length)
|
||||
visualize.plot_embedding(domain_embedding, labels, path="{}/embd_svd.png".format(model_path), method="svd")
|
||||
|
||||
|
||||
def main_visualize_all():
|
||||
_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.test_h5data,
|
||||
args.test_data,
|
||||
_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.data,
|
||||
args.data,
|
||||
args.domain_length,
|
||||
args.window)
|
||||
|
||||
|
|
@ -428,7 +421,7 @@ def main_visualize_all():
|
|||
})
|
||||
res["client_val"] = np.logical_or(res.hits_vt == 1.0, res.hits_trusted >= 3)
|
||||
return res
|
||||
|
||||
|
||||
paul = dataset.load_predictions("results/paul/")
|
||||
df_paul = pd.DataFrame(data={
|
||||
"names": paul["testNames"].flatten(), "client_pred": paul["testScores"].flatten(),
|
||||
|
|
@ -480,8 +473,8 @@ import joblib
|
|||
|
||||
|
||||
def main_beta():
|
||||
_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.test_h5data,
|
||||
args.test_data,
|
||||
_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.data,
|
||||
args.data,
|
||||
args.domain_length,
|
||||
args.window)
|
||||
path, model_prefix = os.path.split(os.path.normpath(args.output_prefix))
|
||||
|
|
@ -489,7 +482,7 @@ def main_beta():
|
|||
results = joblib.load(f"{path}/curves.joblib")
|
||||
except Exception:
|
||||
results = {}
|
||||
results[model_prefix] = {}
|
||||
results[model_prefix] = {"all": {}}
|
||||
|
||||
def load_df(path):
|
||||
res = dataset.load_predictions(path)
|
||||
|
|
@ -514,7 +507,9 @@ def main_beta():
|
|||
for model_args in get_model_args(args):
|
||||
df = load_df(model_args["model_path"])
|
||||
predictions.append(df.client_pred.as_matrix())
|
||||
results[model_prefix]["window_prc"] = visualize.calc_pr_mean(df.client_val.as_matrix(), predictions)
|
||||
results[model_prefix][model_args["model_name"]] = confusion_matrix(df.client_val.as_matrix(),
|
||||
df.client_pred.as_matrix().round())
|
||||
results[model_prefix]["all"]["window_prc"] = visualize.calc_pr_mean(df.client_val.as_matrix(), predictions)
|
||||
visualize.plot_pr_mean(df.client_val.as_matrix(), predictions, "mean")
|
||||
visualize.plot_pr_mean(df_paul.client_val.as_matrix(), [df_paul.client_pred.as_matrix()], "paul")
|
||||
visualize.plot_legend()
|
||||
|
|
@ -526,7 +521,9 @@ def main_beta():
|
|||
for model_args in get_model_args(args):
|
||||
df = load_df(model_args["model_path"])
|
||||
predictions.append(df.client_pred.as_matrix())
|
||||
results[model_prefix]["window_roc"] = visualize.calc_roc_mean(df.client_val.as_matrix(), predictions)
|
||||
results[model_prefix][model_args["model_name"]] = confusion_matrix(df.client_val.as_matrix(),
|
||||
df.client_pred.as_matrix().round())
|
||||
results[model_prefix]["all"]["window_roc"] = visualize.calc_roc_mean(df.client_val.as_matrix(), predictions)
|
||||
visualize.plot_roc_mean(df.client_val.as_matrix(), predictions, "mean")
|
||||
visualize.plot_roc_mean(df_paul.client_val.as_matrix(), [df_paul.client_pred.as_matrix()], "paul")
|
||||
visualize.plot_legend()
|
||||
|
|
@ -539,7 +536,9 @@ def main_beta():
|
|||
df = load_df(model_args["model_path"])
|
||||
df = df.groupby(df.names).max()
|
||||
predictions.append(df.client_pred.as_matrix())
|
||||
results[model_prefix]["user_prc"] = visualize.calc_pr_mean(df.client_val.as_matrix(), predictions)
|
||||
results[model_prefix][model_args["model_name"]] = confusion_matrix(df.client_val.as_matrix(),
|
||||
df.client_pred.as_matrix().round())
|
||||
results[model_prefix]["all"]["user_prc"] = visualize.calc_pr_mean(df.client_val.as_matrix(), predictions)
|
||||
visualize.plot_pr_mean(df.client_val.as_matrix(), predictions, "mean")
|
||||
visualize.plot_pr_mean(df_paul_user.client_val.as_matrix(), [df_paul_user.client_pred.as_matrix()], "paul")
|
||||
visualize.plot_legend()
|
||||
|
|
@ -552,14 +551,14 @@ def main_beta():
|
|||
df = load_df(model_args["model_path"])
|
||||
df = df.groupby(df.names).max()
|
||||
predictions.append(df.client_pred.as_matrix())
|
||||
results[model_prefix]["user_roc"] = visualize.calc_roc_mean(df.client_val.as_matrix(), predictions)
|
||||
results[model_prefix]["all"]["user_roc"] = visualize.calc_roc_mean(df.client_val.as_matrix(), predictions)
|
||||
visualize.plot_roc_mean(df.client_val.as_matrix(), predictions, "mean")
|
||||
visualize.plot_roc_mean(df_paul_user.client_val.as_matrix(), [df_paul_user.client_pred.as_matrix()], "paul")
|
||||
visualize.plot_legend()
|
||||
visualize.plot_save(f"{args.output_prefix}_user_client_roc_all.png")
|
||||
|
||||
joblib.dump(results, f"{path}/curves.joblib")
|
||||
|
||||
|
||||
plot_overall_result()
|
||||
|
||||
|
||||
|
|
@ -576,7 +575,7 @@ def plot_overall_result():
|
|||
logger.info(f"plot {vis}")
|
||||
visualize.plot_clf()
|
||||
for model_key in results.keys():
|
||||
ys_mean, ys_std, score = results[model_key][vis]
|
||||
ys_mean, ys_std, score = results[model_key]["all"][vis]
|
||||
plt.plot(x, ys_mean, label=f"{model_key} - {score:5.4}")
|
||||
plt.fill_between(x, ys_mean - ys_std, ys_mean + ys_std, alpha=0.2)
|
||||
if vis.endswith("prc"):
|
||||
|
|
@ -604,14 +603,8 @@ def main():
|
|||
main_visualization()
|
||||
if "all_fancy" == args.mode:
|
||||
main_visualize_all()
|
||||
if "embd" == args.mode:
|
||||
plot_embedding()
|
||||
if "paul" == args.mode:
|
||||
main_paul_best()
|
||||
if "beta" == args.mode:
|
||||
main_beta()
|
||||
if "beta_all" == args.mode:
|
||||
plot_overall_result()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
13
utils.py
13
utils.py
|
|
@ -3,6 +3,7 @@ from operator import itemgetter
|
|||
|
||||
import joblib
|
||||
import numpy as np
|
||||
from keras.models import load_model as load_keras_model
|
||||
from sklearn.utils import class_weight
|
||||
|
||||
|
||||
|
|
@ -27,3 +28,15 @@ def get_custom_sample_weights(client, server):
|
|||
def load_ordered_hyperband_results(path):
|
||||
results = joblib.load(path)
|
||||
return sorted(results, itemgetter("loss"))
|
||||
|
||||
|
||||
def load_model(path, custom_objects=None):
|
||||
clf = load_keras_model(path, custom_objects)
|
||||
try:
|
||||
embd = clf.get_layer("domain_cnn").layer
|
||||
except Exception:
|
||||
# in some version i forgot to specify domain_cnn
|
||||
# this bug fix is for certain compatibility
|
||||
embd = clf.layers[1].layer
|
||||
|
||||
return embd, clf
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ def plot_clf():
|
|||
|
||||
|
||||
def plot_save(path, dpi=300):
|
||||
plt.title(path)
|
||||
fig = plt.gcf()
|
||||
fig.set_size_inches(18.5, 10.5)
|
||||
fig.savefig(path, dpi=dpi)
|
||||
|
|
@ -48,6 +49,10 @@ def plot_legend():
|
|||
plt.legend()
|
||||
|
||||
|
||||
def mathews_correlation_curve(y, y_pred):
|
||||
pass
|
||||
|
||||
|
||||
def plot_precision_recall(y, y_pred, label=""):
|
||||
y = y.flatten()
|
||||
y_pred = y_pred.flatten()
|
||||
|
|
|
|||
Loading…
Reference in New Issue