tikz/validation-curve: Update

tikz/validation-curve/baseline_cifar_test_acc.csv

@@ -1,127 +1,147 @@
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tikz/validation-curve/cifar100_baseline_20170419-1617-12_history.csv

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+57,2.2480,0.4605,0.5360
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+59,2.2115,0.4696,0.5402
+60,2.2043,0.4702,0.5376
+61,2.2028,0.4705,0.5350
+62,2.1886,0.4691,0.5434
+63,2.1842,0.4748,0.5462
+64,2.1797,0.4700,0.5442
+65,2.1574,0.4746,0.5488
+66,2.1489,0.4801,0.5430
+67,2.1429,0.4789,0.5552
+68,2.1318,0.4832,0.5496
+69,2.1254,0.4828,0.5472
+70,2.1195,0.4825,0.5556
+71,2.0999,0.4870,0.5556
+72,2.0988,0.4880,0.5508
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tikz/validation-curve/edit_curve.py

@@ -4,8 +4,10 @@ import csv
 import glob
 import numpy as np
 
-history_files = glob.glob("*.csv")
+history_files = glob.glob("cifar100_*.csv")
+print(history_files)
 data = []
+loss = []
 
 for filename in history_files:
     print(filename)
@@ -18,7 +20,7 @@ for filename in history_files:
             if i == 0:
                 continue
             data.append([float(el[2])])
-        print(data)
+            loss.append([float(el[1])])
     else:
         for i, el in enumerate(datathis):
             if i == 0:
@@ -26,20 +28,60 @@ for filename in history_files:
             if i == len(data):
                 break
             data[i - 1].append(float(el[2]))
+            loss[i - 1].append(float(el[1]))
 
-print("!" * 80)
-print(data)
-print("-" * 80)
+# crop to where all are trained
+print(len(data))
+orderings = []
+for i, el in enumerate(data):
+    if len(el) != len(history_files):
+        break
+data = data[:i]
+loss = loss[:i]
+print(len(data))
+
+
+# orderings
+def get_changes(ord1, ord2):
+    """Count how often the order changes between two elements."""
+    changes = 0
+    for i in range(10):
+        for j in range(i + 1, 10):
+            o1go2 = (ord1.index(i) > ord1.index(j) and
+                     ord2.index(i) > ord2.index(j))
+            o1lo2 = (ord1.index(i) < ord1.index(j) and
+                     ord2.index(i) < ord2.index(j))
+            if not (o1go2 or o1lo2):
+                changes += 1
+
+    return changes
+
+if len(history_files) > 1:
+    orderings = []
+    for row in data:
+        ordering = zip(range(10), row)
+        ordering = sorted(ordering, key=lambda n: n[1])
+        ordering = [el[0] for el in ordering]
+        orderings.append(ordering)
+    get_changes(orderings[0], orderings[1])
+
+    change_list = []
+    for ord1, ord2 in zip(orderings, orderings[1:]):
+        changes = get_changes(ord1, ord2)
+        change_list.append(changes)
+    change_list = np.array(change_list)
+
+    print("change_list = {}".format(change_list.mean()))
+
+# write
 max_range = 0
 with open('baseline_cifar_test_acc.csv', 'w') as fp:
     writer = csv.writer(fp, delimiter=',')
-    writer.writerow(["epoch", "min_acc", "max_acc", "mean_acc"])
+    writer.writerow(["epoch", "min_acc", "max_acc", "mean_acc", "mean_loss"])
     for epoch, row in enumerate(data):
-        if len(row) < 10:
-            print(row)
-            print(len(row))
+        if len(row) < len(history_files):
             break
         max_range = max(max_range, max(row) - min(row))
-        print("max range={}, epoch={}".format(max_range, epoch))
-        writer.writerow([epoch, min(row), max(row), np.array(row).mean()])
-print(max_range)
+        writer.writerow([epoch, min(row), max(row), np.array(row).mean(),
+                         np.array(loss[epoch]).mean()])
+print("max_range={}".format(max_range))

tikz/validation-curve/validation-curve.tex

@@ -1,4 +1,4 @@
-\documentclass[varwidth=true, border=2pt]{standalone}
+\documentclass[border=2pt]{standalone}
 \usepackage[utf8]{inputenc} % this is needed for umlauts
 \usepackage[ngerman]{babel} % this is needed for umlauts
 \usepackage[T1]{fontenc}    % this is needed for correct output of umlauts in pdf
@@ -9,33 +9,48 @@
 \pgfplotsset{compat=1.13}
 
 \begin{document}
+\definecolor{c1}{HTML}{0072B2}
+\definecolor{c2}{HTML}{009E73}
+\definecolor{c3}{HTML}{CB0000}
 \begin{tikzpicture}
+\pgfplotsset{
+    scale only axis,
+    % scaled x ticks=base 10:3,
+    xmin=0, xmax=171,
+    width=15cm, height=8cm,     % size of the image
+}
     \begin{axis}[
-            axis x line=middle,
-            axis y line=middle,
-            enlarge y limits=true,
-            xmin=0,
-            %xmax=2014,
+            axis y line*=left,
             ymin=0.15,
             % ymax=26000,
-            width=15cm, height=8cm,     % size of the image
             grid = major,
             grid style={dashed, gray!30},
-            ylabel=validation error,
-            ylabel style={at={(0,1.0)}},
+            ylabel=validation accuracy,
+            legend style={at={(0.5,0.1)},anchor=south},
+            % ylabel style={at={(0,1.0)}    },
             xlabel=epoch,
-            legend style={at={(0.8,0.71)}, anchor=north},
-            legend cell align=right,
+            % legend style={at={(0.3,0.2)}, anchor=north},
+            % legend cell align=right,
             xticklabel style=
             {/pgf/number format/1000 sep=,rotate=60,anchor=east,font=\scriptsize},
-            % yticklabel style=
-            % {scaled ticks=false,/pgf/number format/1000 sep=\,,rotate=0,anchor=east,font=\scriptsize},
-            % legend style={draw=none, legend columns=-1}
          ]
-          \addplot[thick, red, mark=., densely dashed] table [x=epoch, y=max_acc, col sep=comma] {baseline_cifar_test_acc.csv};
-          \addplot[thick, blue, mark=.] table [x=epoch,y=min_acc, col sep=comma] {baseline_cifar_test_acc.csv};
-          \legend{maximum validation accuracy, minimum validation accuracy}
+          \addplot[thick, c1, mark=., densely dashed] table [x=epoch, y=max_acc, col sep=comma] {baseline_cifar_test_acc.csv};\label{plot_one}\addlegendentry{maximum validation accuracy}
+          \addplot[thick, c2, mark=.] table [x=epoch,y=min_acc, col sep=comma] {baseline_cifar_test_acc.csv};\label{plot_two}\addlegendentry{minimum validation accuracy}
     \end{axis}
-\end{tikzpicture}d
-
+    \begin{axis}[
+      axis y line*=right,
+      axis x line=none,
+      % xmin=0,
+      % xmax=171,
+      % ymin=0, ymax=100,
+      ylabel=loss,
+      legend style={at={(0.5,0.1)},anchor=south},
+      y dir=reverse
+    ]
+          \addlegendimage{thick, c1, mark=., densely dashed}\addlegendentry{maximum validation accuracy}
+          \addlegendimage{thick, c2, mark=.}\addlegendentry{minimum validation accuracy}
+          \addplot[thick, c3, mark=., dotted] table [x=epoch,y=mean_loss, col sep=comma] {baseline_cifar_test_acc.csv};
+          \addlegendentry{mean loss}
+    \end{axis}
+\end{tikzpicture}
 \end{document}