add parser argument for naming in multi model modes, minor fixes, re-set fix vals for network - need to make them flexible
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2080444fb7
102
main.py
102
main.py
@ -66,13 +66,12 @@ PARAMS = {
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'dropout': 0.5,
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'domain_features': args.domain_embedding,
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'embedding_size': args.embedding,
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'filter_main': 64,
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'flow_features': 3,
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# 'dense_main': 512,
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'dense_main': 64,
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'filter_embedding': args.hidden_char_dims,
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'hidden_embedding': args.domain_embedding,
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'kernel_embedding': 3,
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'filter_main': 128,
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'dense_main': 128,
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'kernels_main': 3,
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'input_length': 40,
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'model_output': args.model_output
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@ -154,34 +153,63 @@ def main_train(param=None):
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custom_class_weights = None
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logger.info(f"select model: {args.model_type}")
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if args.model_type == "inter":
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if args.model_type == "staggered":
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server_tr = np.expand_dims(server_windows_tr, 2)
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model = new_model
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logger.info("compile and train model")
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embedding.summary()
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model.summary()
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logger.info(model.get_config())
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'] + custom_metrics)
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logger.info("compile and train model")
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embedding.summary()
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model.summary()
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logger.info(model.get_config())
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model.outputs
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'] + custom_metrics)
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if args.model_output == "both":
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labels = [client_tr, server_tr]
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else:
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raise ValueError("unknown model output")
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model.fit([domain_tr, flow_tr],
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labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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callbacks=callbacks,
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shuffle=True,
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validation_split=0.2,
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class_weight=custom_class_weights)
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if args.model_output == "both":
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labels = [client_tr, server_tr]
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elif args.model_output == "client":
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labels = [client_tr]
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elif args.model_output == "server":
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labels = [server_tr]
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else:
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raise ValueError("unknown model output")
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if args.model_type == "inter":
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server_tr = np.expand_dims(server_windows_tr, 2)
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model = new_model
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logger.info("compile and train model")
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embedding.summary()
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model.summary()
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logger.info(model.get_config())
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'] + custom_metrics)
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model.fit([domain_tr, flow_tr],
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labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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callbacks=callbacks,
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shuffle=True,
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validation_split=0.2,
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class_weight=custom_class_weights)
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if args.model_output == "both":
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labels = [client_tr, server_tr]
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elif args.model_output == "client":
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labels = [client_tr]
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elif args.model_output == "server":
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labels = [server_tr]
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else:
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raise ValueError("unknown model output")
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model.fit([domain_tr, flow_tr],
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labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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callbacks=callbacks,
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shuffle=True,
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validation_split=0.2,
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class_weight=custom_class_weights)
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logger.info("save embedding")
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embedding.save(args.embedding_model)
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@ -225,9 +253,9 @@ def main_visualization():
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# client_val, server_val = client_val.value, server_val.value
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client_val = client_val.value
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# logger.info("plot model")
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# model = load_model(model_args.clf_model, custom_objects=models.get_metrics())
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# visualize.plot_model(model, os.path.join(args.model_path, "model.png"))
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logger.info("plot model")
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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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try:
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logger.info("plot training curve")
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@ -276,10 +304,10 @@ def main_visualization():
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# visualize.plot_confusion_matrix(server_val.argmax(1), server_pred.argmax(1),
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# "{}/server_cov.png".format(args.model_path),
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# normalize=False, title="Server 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 = np.load(args.model_path + "/domain_embds.npy")
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# visualize.plot_embedding(domain_embedding, labels, path="{}/embd.png".format(args.model_path))
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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 = np.load(args.model_path + "/domain_embds.npy")
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visualize.plot_embedding(domain_embedding, labels, path="{}/embd.png".format(args.model_path))
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def main_visualize_all():
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@ -293,7 +321,7 @@ def main_visualize_all():
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client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
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visualize.plot_precision_recall(client_val.value, client_pred.value, model_args["model_path"])
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visualize.plot_legend()
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visualize.plot_save("all_client_prc.png")
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visualize.plot_save(f"{args.output_prefix}_client_prc.png")
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logger.info("plot roc curves")
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visualize.plot_clf()
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@ -301,7 +329,7 @@ def main_visualize_all():
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client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
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visualize.plot_roc_curve(client_val.value, client_pred.value, model_args["model_path"])
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visualize.plot_legend()
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visualize.plot_save("all_client_roc.png")
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visualize.plot_save(f"{args.output_prefix}_client_roc.png")
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df_val = pd.DataFrame(data={"names": name_val, "client_val": client_val})
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user_vals = df_val.groupby(df_val.names).max().client_val.as_matrix().astype(float)
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@ -314,7 +342,7 @@ def main_visualize_all():
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user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
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visualize.plot_precision_recall(user_vals, user_preds, model_args["model_path"])
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visualize.plot_legend()
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visualize.plot_save("all_user_client_prc.png")
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visualize.plot_save(f"{args.output_prefix}_user_client_prc.png")
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logger.info("plot user roc curves")
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visualize.plot_clf()
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@ -324,7 +352,7 @@ def main_visualize_all():
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user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
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visualize.plot_roc_curve(user_vals, user_preds, model_args["model_path"])
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visualize.plot_legend()
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visualize.plot_save("all_user_client_roc.png")
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visualize.plot_save(f"{args.output_prefix}_user_client_roc.png")
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def main_data():
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12
run.sh
12
run.sh
@ -1,6 +1,10 @@
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#!/usr/bin/env bash
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RESDIR=$1
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mkdir -p /tmp/rk/RESDIR
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DATADIR=$2
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for output in client both
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do
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for depth in small medium
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@ -9,8 +13,8 @@ do
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do
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python main.py --mode train \
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--train /tmp/rk/currentData.csv \
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--model /tmp/rk/results/${output}_${depth}_${mtype} \
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--train ${DATADIR}/currentData.csv \
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--model ${RESDIR}/${output}_${depth}_${mtype} \
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--epochs 50 \
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--embd 64 \
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--hidden_char_dims 128 \
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@ -28,8 +32,8 @@ done
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for depth in small medium
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do
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python main.py --mode train \
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--train /tmp/rk/currentData.csv \
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--model /tmp/rk/results/both_${depth}_inter \
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--train ${DATADIR}/currentData.csv \
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--model ${RESDIR}/both_${depth}_inter \
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--epochs 50 \
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--embd 64 \
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--hidden_char_dims 128 \
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6
test.sh
6
test.sh
@ -1,10 +1,12 @@
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#!/usr/bin/env bash
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RESDIR=$1
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DATADIR=$2
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for output in client both
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do
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python3 main.py --mode test --batch 1024 \
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--models tm/rk/${output}_* \
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--test data/futureData.csv \
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--models ${RESDIR}/${output}_*/ \
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--test ${DATADIR}/futureData.csv \
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--model_output ${output}
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done
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