add parser argument for naming in multi model modes, minor fixes,

arguments.py

@@ -58,6 +58,9 @@ parser.add_argument("--domain_length", action="store", dest="domain_length",
 parser.add_argument("--domain_embd", action="store", dest="domain_embedding",
                     default=512, type=int)
 
+parser.add_argument("--out-prefix", action="store", dest="output_prefix",
+                    default="", type=str)
+
 
 # parser.add_argument("--queue", action="store", dest="queue_size",
 #                     default=50, type=int)

models/pauls_networks.py

@@ -75,7 +75,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
     y = Conv1D(cnn_dims,
                kernel_size,
                activation='relu',
-               input_shape=(window_size, domain_features + flow_features))(merged)
+               input_shape=(window_size, domain_features + flow_features))(y)
     # remove temporal dimension by global max pooling
     y = GlobalMaxPooling1D()(y)
     y = Dropout(dropout)(y)

run.sh

@@ -5,7 +5,7 @@ for output in client both
 do
     for depth in small medium
     do
-        for mtype in inter final staggered
+        for mtype in inter final
         do
 
             python main.py --mode train \
@@ -13,7 +13,7 @@ do
                         --model /tmp/rk/results/${output}_${depth}_${mtype} \
                         --epochs 50 \
                         --embd 64 \
-                        --hidden_chaar_dims 128 \
+                        --hidden_char_dims 128 \
                         --domain_embd 32 \
                         --batch 256 \
                         --balanced_weights \
@@ -24,3 +24,19 @@ do
         done
     done
 done
+
+for depth in small medium
+do
+    python main.py --mode train \
+                --train /tmp/rk/currentData.csv \
+                --model /tmp/rk/results/both_${depth}_inter \
+                --epochs 50 \
+                --embd 64 \
+                --hidden_char_dims 128 \
+                --domain_embd 32 \
+                --batch 256 \
+                --balanced_weights \
+                --model_output both \
+                --type inter \
+                --depth ${depth}
+done

test.sh

@@ -3,17 +3,8 @@
 
 for output in client both
 do
-    for depth in small medium
-    do
-        for mtype in inter final staggered
-        do
-
-            python main.py --mode test --batch 1024 \
-                           --model /tmp/rk/results/${output}_${depth}_${mtype} \
-                           --test /tmp/rk/futureData.csv \
-                           --model_output ${output} \
-                           --type ${mtype} \
-                           --depth ${depth}
-        done
-    done
+    python3 main.py --mode test --batch 1024 \
+                    --models tm/rk/${output}_* \
+                    --test data/futureData.csv \
+                    --model_output ${output}
 done

visualize.py

@@ -5,7 +5,7 @@ import numpy as np
 from sklearn.decomposition import TruncatedSVD
 from sklearn.metrics import (
     auc, classification_report, confusion_matrix, fbeta_score, precision_recall_curve,
-    roc_auc_score, roc_curve
+    roc_auc_score, roc_curve, average_precision_score
 )
 
 
@@ -52,7 +52,9 @@ def plot_precision_recall(y, y_pred, label=""):
 
     # fig, ax = plt.subplots(1, 1)
     # ax.hold(True)
-    plt.plot(recall, precision, '--', label=label)
+    score = fbeta_score(y, y_pred.round(), 1)
+    # prc_ap = average_precision_score(y, y_pred)
+    plt.plot(recall, precision, '--', label=f"{label} - {score}")
     # ax.step(recall[::-1], decreasing_max_precision, '-r')
     plt.xlabel('Recall')
     plt.ylabel('Precision')
@@ -87,6 +89,7 @@ def plot_roc_curve(mask, prediction, label=""):
     y_pred = prediction.flatten()
     fpr, tpr, thresholds = roc_curve(y, y_pred)
     roc_auc = auc(fpr, tpr)
+    plt.xscale('log')
     plt.plot(fpr, tpr, label=f"{label} - {roc_auc}")
 
 
@@ -154,7 +157,8 @@ def plot_embedding(domain_embedding, labels, path, dpi=600):
                 domain_reduced[:, 1],
                 c=(labels * (1, 2)).sum(1).astype(int),
                 cmap=plt.cm.plasma,
-                s=3)
+                s=3,
+                alpha=0.2)
     plt.colorbar()
     plt.savefig(path, dpi=dpi)