2 files changed, 75 insertions, 0 deletions
diff --git a/src/analysis.py b/src/analysis.py
new file mode 100644
index 0000000..f7d82d9
--- /dev/null
+++ b/src/analysis.py
@@ -0,0 +1,36 @@
+import pandas as pd
+import torch
+import yaml
+import parse_config
+from net import Net
+
+class ExperimentData:
+    def __init__(self, directory):
+        self.directory = directory
+        self.config = self.get_config()
+        self.metrics = self.get_metrics()
+
+    def get_config(self):
+        with open('{}/config.yaml'.format(self.directory)) as file:
+            return yaml.safe_load(file)
+
+    def get_metrics(self):
+        return pd.read_csv('{}/metrics.csv'.format(self.directory))
+
+    def get_net(self):
+        (
+            net_config,
+            lr,
+            self.epochs,
+            batch_size,
+            augmentations,
+            target_transform,
+            self.loss_function,
+            self.count_correct
+        ) = parse_config.parse(self.config)
+
+        net = Net(**net_config)
+        model_file = './{}/net.pt'.format(self.directory)
+        net.load_state_dict(torch.load(model_file,
+            map_location=torch.device('cpu')))
+        return net
diff --git a/src/confusion.py b/src/confusion.py
new file mode 100644
index 0000000..dd6fe81
--- /dev/null
+++ b/src/confusion.py
@@ -0,0 +1,39 @@
+import sys
+import torch
+from sklearn.metrics import multilabel_confusion_matrix
+from analysis import ExperimentData
+from data import get_loaders
+from classification import target_transform, twoargmax
+
+if len(sys.argv) < 2:
+    print('provide results directory as argument')
+    exit(1)
+
+def predict(ys):
+    predictions = torch.zeros(ys.shape)
+    for i in range(ys.shape[0]):
+        maxes = twoargmax(ys[i])
+        predictions[i][maxes[0]] = 1
+        predictions[i][maxes[1]] = 1
+    return predictions
+
+def make_matrix(net, data, targets):
+    matrix = None
+    net.eval()
+    torch.no_grad()
+    predictions = net(data)
+    predictions = predict(predictions)
+    matrix = multilabel_confusion_matrix(targets.detach().int(), predictions.detach().int())
+    return matrix
+
+experiment = ExperimentData(sys.argv[1])
+net = experiment.get_net()
+
+data = None
+targets = None
+_, train_loader = get_loaders([(0, False, False)], target_transform, 100)
+for idx, (data, targets) in enumerate(train_loader):
+    pass
+
+matrix = make_matrix(net, data, targets)
+print(matrix)