separating logical sections into dataset, models and main.

continued initial refactoring

main.py

@@ -0,0 +1,68 @@
+import numpy as np
+from keras.utils import np_utils
+
+import dataset
+import models
+
+
+def main():
+    # parameter
+    innerCNNFilters = 512
+    innerCNNKernelSize = 2
+    cnnDropout = 0.5
+    cnnHiddenDims = 1024
+    domainFeatures = 512
+    flowFeatures = 3
+    numCiscoFeatures = 30
+    windowSize = 10
+    maxLen = 40
+    embeddingSize = 100
+    kernel_size = 2
+    drop_out = 0.5
+    filters = 2
+    hidden_dims = 100
+    vocabSize = 40
+    threshold = 3
+    minFlowsPerUser = 10
+    numEpochs = 100
+    timesNeg = -1
+
+    char_dict = dataset.get_character_dict()
+    user_flow_df = dataset.get_user_flow_data()
+
+    print("create training dataset")
+    (X_tr, y_tr, hits_tr, names_tr) = dataset.create_dataset_from_flows(
+        user_flow_df, char_dict,
+        maxLen=maxLen, threshold=threshold, windowSize=windowSize)
+
+    pos_idx = np.where(y_tr == 1.0)[0]
+    neg_idx = np.where(y_tr == 0.0)[0]
+
+    use_idx = np.concatenate((pos_idx, neg_idx))
+
+    y_tr = y_tr[use_idx]
+    # hits_tr = hits_tr[use_idx]
+    # names_tr = names_tr[use_idx]
+    for i in range(len(X_tr)):
+        X_tr[i] = X_tr[i][use_idx]
+
+    # TODO: WTF? I don't get it...
+    shared_cnn = models.get_shared_cnn(len(char_dict) + 1, embeddingSize, maxLen,
+                                       domainFeatures, kernel_size, domainFeatures, 0.5)
+
+    model = models.get_top_cnn(shared_cnn, flowFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size,
+                               cnnHiddenDims, cnnDropout)
+
+    model.compile(optimizer='adam',
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'])
+
+    epochNumber = 0
+    y_tr = np_utils.to_categorical(y_tr, 2)
+    model.fit(x=X_tr, y=y_tr, batch_size=128,
+              epochs=epochNumber + 1, shuffle=True, initial_epoch=epochNumber)  # ,
+    # validation_data=(testData,testLabel))
+
+
+if __name__ == "__main__":
+    main()