refactor main functions - separate things into different functions

hyperband.py

@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+# implementation of hyperband:
+# https://arxiv.org/pdf/1603.06560.pdf
+import numpy as np
+
+
+def get_hyperparameter_configuration(configGenerator, n):
+    configurations = []
+    for i in np.arange(0, n, 1):
+        configurations.append(configGenerator())
+    return configurations
+
+
+def run_then_return_val_loss(config, r_i, modelGenerator, trainData, trainLabel,
+                             testData, testLabel):
+    # parameter
+    batch_size = 128
+    model = modelGenerator(config)
+    if model != None:
+        model.fit(x=trainData, y=trainLabel,
+                  epochs=int(r_i), shuffle=True, initial_epoch=0,
+                  batch_size=batch_size)
+        score = model.evaluate(testData, testLabel,
+                               batch_size=batch_size)
+        score = score[0]
+    else:
+        score = np.infty
+    return score
+
+
+def top_k(configurations, L, k):
+    outConfigs = []
+    sortIDs = np.argsort(np.array(L))
+    for i in np.arange(0, k, 1):
+        outConfigs.append(configurations[sortIDs[i]])
+    return outConfigs
+
+
+def hyperband(R, nu, modelGenerator,
+              configGenerator,
+              trainData, trainLabel,
+              testData, testLabel,
+              outputFile=''):
+    allLosses = []
+    allConfigs = []
+    # input
+
+    # initialization
+    s_max = np.floor(np.log(R) / np.log(nu))
+    B = (s_max + 1) * R
+
+    for s in np.arange(s_max, -1, -1):
+        n = np.ceil(np.float(B) / np.float(R) * (np.float(np.power(nu, s)) / np.float(s + 1)))
+        r = np.float(R) * np.power(nu, -s)
+        configurations = get_hyperparameter_configuration(configGenerator, n)
+        for i in np.arange(0, s + 1, 1):
+            n_i = np.floor(np.float(n) * np.power(nu, -i))
+            r_i = np.float(r) * np.power(nu, i)
+            L = []
+            for config in configurations:
+                curLoss = run_then_return_val_loss(config, r_i, modelGenerator,
+                                                   trainData, trainLabel,
+                                                   testData, testLabel)
+                L.append(curLoss)
+                allLosses.append(curLoss)
+                allConfigs.append(config)
+                if outputFile != '':
+                    with open(outputFile, 'a') as myfile:
+                        myfile.write(str(config) + '\t' + str(curLoss) + \
+                                     '\t' + str(r_i) + '\n')
+            configurations = top_k(configurations, L, np.floor(np.float(n_i) / nu))
+
+            # print('n_i: ' + str(n_i))
+            # print('r_i: ' + str(r_i))
+    bestConfig = top_k(allConfigs, allLosses, 1)
+    return (bestConfig[0], allConfigs, allLosses)

main.py

@@ -79,13 +79,11 @@ args = parser.parse_args()
 # session = tf.Session(config=config)
 
 
-def main():
+def main_train():
     # parameter
     cnnDropout = 0.5
-    cnnHiddenDims = 1024
-    numCiscoFeatures = 30
+    cnnHiddenDims = 512
     kernel_size = 3
-    drop_out = 0.5
     filters = 128
     network = models.pauls_networks
 
@@ -120,10 +118,6 @@ def main():
               validation_split=0.2)
 
 
-def main_train():
-    pass
-
-
 def main_test():
     char_dict = dataset.get_character_dict()
     user_flow_df = dataset.get_user_flow_data(args.test_data)
@@ -133,5 +127,35 @@ def main_test():
     # TODO: get model and exec model.evaluate(...)
 
 
+def main_visualization():
+    mask = dataset.load_mask_eval(args.data, args.test_image)
+    y_pred_path = args.model_path + "pred.npy"
+    print("plot model")
+    model = load_model(args.model_path + "model.h5",
+                       custom_objects=evaluation.get_metrics())
+    visualize.plot_model(model, args.model_path + "model.png")
+    print("plot training curve")
+    logs = pd.read_csv(args.model_path + "train.log")
+    visualize.plot_training_curve(logs, "{}/train.png".format(args.model_path))
+    pred = np.load(y_pred_path)
+    print("plot pr curve")
+    visualize.plot_precision_recall(mask, pred, "{}/prc.png".format(args.model_path))
+    visualize.plot_precision_recall_curves(mask, pred, "{}/prc2.png".format(args.model_path))
+    print("plot roc curve")
+    visualize.plot_roc_curve(mask, pred, "{}/roc.png".format(args.model_path))
+    print("store prediction image")
+    visualize.save_image_as(pred, "{}/pred.png".format(args.model_path))
+
+
+def main_score():
+    mask = dataset.load_mask_eval(args.data, args.test_image)
+    pred = np.load(args.pred)
+    visualize.score_model(mask, pred)
+
+
+def main():
+    main_train()
+
+
 if __name__ == "__main__":
     main()

models/__init__.py

@@ -1,2 +1,32 @@
 from . import pauls_networks
 from . import renes_networks
+
+
+def get_models_by_params(params: dict):
+    # decomposing param section
+    # mainly embedding model
+    network_type = params.get("type")
+    vocab_size = params.get("vocab_size")
+    embedding_size = params.get("embedding_size")
+    input_length = params.get("input_length")
+    filter_embedding = params.get("filter_embedding")
+    kernel_embedding = params.get("kernel_embedding")
+    hidden_embedding = params.get("hidden_embedding")
+    dropout = params.get("dropout")
+    # mainly prediction model
+    flow_features = params.get("flow_features")
+    domain_features = params.get("domain_features")
+    window_size = params.get("window_size")
+    domain_length = params.get("domain_length")
+    filter_main = params.get("filter_main")
+    kernel_main = params.get("kernels_main")
+    dense_dim = params.get("dense_main")
+    # create models
+    networks = renes_networks if network_type == "rene" else pauls_networks
+    embedding_model = networks.get_embedding(vocab_size, embedding_size, input_length,
+                                             filter_embedding, kernel_embedding, hidden_embedding, drop_out=dropout)
+
+    predict_model = networks.get_model(dropout, flow_features, domain_features, window_size, domain_length,
+                                       filter_main, kernel_main, dense_dim, embedding_model)
+
+    return embedding_model, predict_model

models/renes_networks.py

@@ -4,14 +4,14 @@ from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout,
 
 
 def get_embedding(vocab_size, embedding_size, input_length,
-                  hidden_char_dims, kernel_size, hidden_dims, drop_out=0.5):
+                  filter_size, kernel_size, hidden_dims, drop_out=0.5):
     x = y = Input(shape=(input_length,))
     y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
-    y = Conv1D(hidden_char_dims, kernel_size=5, activation='relu')(y)
+    y = Conv1D(filter_size, kernel_size=5, activation='relu')(y)
     y = MaxPool1D(pool_size=3, strides=1)(y)
-    y = Conv1D(hidden_char_dims, kernel_size=3, activation='relu')(y)
+    y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
     y = MaxPool1D(pool_size=3, strides=1)(y)
-    y = Conv1D(hidden_char_dims, kernel_size=3, activation='relu')(y)
+    y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
     y = GlobalMaxPooling1D()(y)
     y = Dropout(drop_out)(y)
     y = Dense(hidden_dims, activation="relu")(y)
@@ -35,6 +35,7 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
     y = GlobalMaxPooling1D()(y)
     y = Dropout(cnnDropout)(y)
     y = Dense(dense_dim, activation='relu')(y)
+    y = Dense(dense_dim // 2, activation='relu')(y)
     y1 = Dense(2, activation='softmax', name="client")(y)
     y2 = Dense(2, activation='softmax', name="server")(y)