tools, lockfile, deps

tools/plot_scripts/background_ml_unsupervised.py

@@ -0,0 +1,105 @@
+"""
+Downloads the cats_vs_dogs dataset, then generates an unsupervised clusters image:
+ - unsupervised_clusters.png
+
+This script saves outputs in a datetime folder and also copies the latest outputs to a "latest" folder.
+All versions of the outputs and scripts are archived.
+"""
+
+import shutil
+from datetime import datetime
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+
+# -----------------------------------------------------------------------------
+# CONFIGURATION
+# -----------------------------------------------------------------------------
+UNSUP_SAMPLES = 200
+
+output_path = Path("/home/fedex/mt/plots/background_ml_unsupervised")
+datetime_folder_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
+latest_folder_path = output_path / "latest"
+archive_folder_path = output_path / "archive"
+output_datetime_path = output_path / datetime_folder_name
+
+
+# -----------------------------------------------------------------------------
+# UTILITIES
+# -----------------------------------------------------------------------------
+def ensure_dir(directory: Path):
+    directory.mkdir(exist_ok=True, parents=True)
+
+
+# Create required output directories
+ensure_dir(output_path)
+ensure_dir(output_datetime_path)
+ensure_dir(latest_folder_path)
+ensure_dir(archive_folder_path)
+
+
+# -----------------------------------------------------------------------------
+# Unsupervised Clustering Plot (PCA + KMeans)
+# -----------------------------------------------------------------------------
+def plot_unsupervised_clusters(ds, outpath):
+    """
+    Processes a subset of images from the dataset, reduces their dimensionality with PCA,
+    applies KMeans clustering, and saves a scatterplot of the clusters.
+    """
+    imgs = []
+    for img, _ in ds.take(UNSUP_SAMPLES):
+        # resize to 64x64, convert to grayscale by averaging channels
+        arr = tf.image.resize(img, (64, 64)).numpy().astype("float32").mean(axis=2)
+        imgs.append(arr.ravel() / 255.0)
+    X = np.stack(imgs)
+    pca = PCA(n_components=2, random_state=0)
+    X2 = pca.fit_transform(X)
+
+    km = KMeans(n_clusters=2, random_state=0)
+    clusters = km.fit_predict(X2)
+
+    plt.figure(figsize=(6, 6))
+    plt.scatter(X2[:, 0], X2[:, 1], c=clusters, s=15, alpha=0.6)
+    plt.title("Unsupervised: PCA + KMeans")
+    plt.xlabel("PCA 1")
+    plt.ylabel("PCA 2")
+    plt.tight_layout()
+    plt.savefig(outpath, dpi=150)
+    plt.close()
+    print(f"✔ Saved unsupervised clusters → {outpath}")
+
+
+# -----------------------------------------------------------------------------
+# MAIN
+# -----------------------------------------------------------------------------
+if __name__ == "__main__":
+    # Load and prepare the dataset
+    print("▶ Loading cats_vs_dogs dataset...")
+    ds, _ = tfds.load("cats_vs_dogs", split="train", with_info=True, as_supervised=True)
+    ds = ds.shuffle(1000, reshuffle_each_iteration=False).cache()
+
+    # Generate the unsupervised clusters image
+    unsupervised_outfile = output_datetime_path / "unsupervised_clusters.png"
+    plot_unsupervised_clusters(ds, unsupervised_outfile)
+
+    # -----------------------------------------------------------------------------
+    # Update the 'latest' results folder: remove previous and copy current outputs
+    # -----------------------------------------------------------------------------
+    shutil.rmtree(latest_folder_path, ignore_errors=True)
+    ensure_dir(latest_folder_path)
+    for file in output_datetime_path.iterdir():
+        shutil.copy2(file, latest_folder_path)
+
+    # Copy this script to the output folder and to the latest folder to preserve the used code
+    script_path = Path(__file__)
+    shutil.copy2(script_path, output_datetime_path)
+    shutil.copy2(script_path, latest_folder_path)
+
+    # Move the output datetime folder to the archive folder for record keeping
+    shutil.move(output_datetime_path, archive_folder_path)