From 5d54ccd75159934d097de03ae0f82df2f85cf41c Mon Sep 17 00:00:00 2001
From: hasslesstech <hasslesstech@tutanota.com>
Date: Thu, 20 Nov 2025 16:13:05 +0200
Subject: [PATCH] stats making and reading files

---
 stats-cm.py      | 47 +++++++++++++++++++++++++++++++++++++++++++++++
 stats-generic.py | 20 ++++++++++++++++++++
 stats-other.py   | 30 ++++++++++++++++++++++++++++++
 stats-plot-1.py  | 13 +++++++++++++
 4 files changed, 110 insertions(+)
 create mode 100644 stats-cm.py
 create mode 100644 stats-generic.py
 create mode 100644 stats-other.py
 create mode 100644 stats-plot-1.py

diff --git a/stats-cm.py b/stats-cm.py
new file mode 100644
index 0000000..23d3e01
--- /dev/null
+++ b/stats-cm.py
@@ -0,0 +1,47 @@
+from m import *
+
+from matplotlib import pyplot as plt
+
+def __prep_conf_matr(data):
+    output_matrix = np.zeros([2, 2])
+
+    for p, r in zip(*data):
+        print(p, r)
+        output_matrix[np.argmax(p)][np.argmax(r)] += 1
+
+    return output_matrix
+
+
+def __plot_conf_matr(data):
+    matr = __prep_conf_matr(data)
+
+    _, ax = plt.subplots()
+
+    ax.matshow(matr, cmap = plt.cm.Blues)
+
+    for i, x in enumerate(matr):
+        for j, y in enumerate(x):
+            ax.text(i,
+                    j,
+                    str(round(y)),
+                    va = "center",
+                    ha = "center")
+
+    plt.show()
+
+ds = tf.keras.preprocessing.image_dataset_from_directory(
+    '../dataset-orig-aug-1-mini-1/',
+    labels = 'inferred',
+    label_mode = 'categorical',
+    color_mode = 'rgb',
+    image_size = (300, 300),
+    batch_size = 32,
+    verbose = True
+)
+
+#ds_short = ds.take(1)
+
+p = mod.predict(ds)
+r = np.concatenate([y for x, y in ds])
+
+__plot_conf_matr([p, r])
diff --git a/stats-generic.py b/stats-generic.py
new file mode 100644
index 0000000..3fdf2ef
--- /dev/null
+++ b/stats-generic.py
@@ -0,0 +1,20 @@
+from m import *
+
+from matplotlib import pyplot as plt
+
+ds = tf.keras.preprocessing.image_dataset_from_directory(
+    '../dataset-orig-aug-1-mini-1/',
+    labels = 'inferred',
+    label_mode = 'categorical',
+    color_mode = 'rgb',
+    image_size = (300, 300),
+    batch_size = 32,
+    verbose = True
+)
+
+p = mod.predict(ds)
+r = np.concatenate([y for x, y in ds])
+
+with open('results.txt', 'w') as f:
+    for i in zip(p, r):
+        f.write(f"{i[0][0]} {i[0][1]} {i[1][0]} {i[1][1]}\n")
diff --git a/stats-other.py b/stats-other.py
new file mode 100644
index 0000000..1e07e3d
--- /dev/null
+++ b/stats-other.py
@@ -0,0 +1,30 @@
+from m import *
+from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
+
+
+ds = tf.keras.preprocessing.image_dataset_from_directory(
+    '../dataset-orig-aug-1-mini-1/',
+    labels = 'inferred',
+    label_mode = 'categorical',
+    color_mode = 'rgb',
+    image_size = (300, 300),
+    batch_size = 32,
+    verbose = True
+)
+
+p = []
+r = []
+
+with open('results.txt', 'r') as f:
+    for i in f.read().split('\n'):
+        if i == '':
+            continue
+        
+        res = tuple(map(float, i.split()))
+        p.append([res[0] > 0.5, res[1] > 0.5])
+        r.append([res[2], res[3]])
+
+print(f"Accuracy  : {accuracy_score(p, r)}")
+print(f"Precision : {precision_score(p, r, average = 'micro')}")
+print(f"Recall    : {recall_score(p, r, average = 'micro')}")
+print(f"F1 Score  : {f1_score(p, r, average = 'micro')}")
diff --git a/stats-plot-1.py b/stats-plot-1.py
new file mode 100644
index 0000000..2da5f02
--- /dev/null
+++ b/stats-plot-1.py
@@ -0,0 +1,13 @@
+from matplotlib import pyplot as plt
+
+ta = [0.5396, 0.6266, 0.7137, 0.6768, 0.7114, 0.7821, 0.7373, 0.6866, 0.5987, 0.6861, 0.7236, 0.7147]
+va = [0.4273, 0.4714, 0.4273, 0.4229, 0.4317, 0.4229, 0.5022, 0.4449, 0.5947, 0.7489, 0.5815, 0.7048]
+
+x = [i+1 for i in range(12)]
+
+plt.plot(x, ta)
+plt.plot(x, va)
+
+plt.legend(["train_acc", "valid_acc"])
+
+plt.show()