Update app.py

app.py

@@ -1,29 +1,45 @@
 import numpy as np
-import pandas as pd
-from sklearn.datasets import make_classification, make_blobs, make_moons
-from sklearn.ensemble import IsolationForest
+import matplotlib.pyplot as plt
+from threading import Thread
+from matplotlib.colors import ListedColormap
+from sklearn.datasets import make_moons, make_circles, make_classification
+from sklearn.datasets import make_blobs, make_circles, make_moons
+import gradio as gr
+import math
+from functools import partial
+import time
+
+import matplotlib
+
+from sklearn import svm
+from sklearn.datasets import make_moons, make_blobs
 from sklearn.covariance import EllipticEnvelope
+from sklearn.ensemble import IsolationForest
 from sklearn.neighbors import LocalOutlierFactor
 from sklearn.linear_model import SGDOneClassSVM
-from sklearn.pipeline import make_pipeline
 from sklearn.kernel_approximation import Nystroem
-from sklearn import svm
-import matplotlib.pyplot as plt
-import gradio as gr
-import time
-from functools import partial
+from sklearn.pipeline import make_pipeline
+
+def get_groundtruth_model(X, labels):
+    # dummy model to show true label distribution
+    class Dummy:
+        def __init__(self, y):
+            self.labels_ = labels
+
+    return Dummy(labels)
 
+#### PLOT
+FIGSIZE = 10,10
+figure = plt.figure(figsize=(25, 10))
 
-# Function to train and visualize anomaly detection models
-def train_models(input_data, outliers_fraction, n_samples, clf_name):
-    """Train anomaly detection models and plot results."""
+
+def train_models(input_data,  outliers_fraction, n_samples, clf_name):
     n_outliers = int(outliers_fraction * n_samples)
     n_inliers = n_samples - n_outliers
     blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2)
-    NAME_CLF_MAPPING = {
-        "Robust covariance": EllipticEnvelope(contamination=outliers_fraction),
-        "One-Class SVM": svm.OneClassSVM(nu=outliers_fraction, kernel="rbf", gamma=0.1),
-        "One-Class SVM (SGD)": make_pipeline(
+    NAME_CLF_MAPPING = {"Robust covariance": EllipticEnvelope(contamination=outliers_fraction),
+    "One-Class SVM": svm.OneClassSVM(nu=outliers_fraction, kernel="rbf", gamma=0.1),
+    "One-Class SVM (SGD)":make_pipeline(
             Nystroem(gamma=0.1, random_state=42, n_components=150),
             SGDOneClassSVM(
                 nu=outliers_fraction,
@@ -35,76 +51,110 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
         ),
         "Isolation Forest": IsolationForest(contamination=outliers_fraction, random_state=42),
         "Local Outlier Factor": LocalOutlierFactor(n_neighbors=35, contamination=outliers_fraction),
-    }
+    }   
     DATA_MAPPING = {
-        "Central Blob": make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5, **blobs_params)[0],
-        "Two Blobs": make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5], **blobs_params)[0],
-        "Blob with Noise": make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, 0.3], **blobs_params)[0],
-        "Moons": 4.0
-        * (make_moons(n_samples=n_samples, noise=0.05, random_state=0)[0] - np.array([0.5, 0.25])),
-        "Noise": 14.0 * (np.random.RandomState(42).rand(n_samples, 2) - 0.5),
+    "Central Blob":make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5, **blobs_params)[0],
+    "Two Blobs": make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5], **blobs_params)[0],
+    "Blob with Noise": make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, 0.3], **blobs_params)[0],
+    "Moons": 4.0
+    * (
+        make_moons(n_samples=n_samples, noise=0.05, random_state=0)[0]
+        - np.array([0.5, 0.25])
+    ),
+    "Noise": 14.0 * (np.random.RandomState(42).rand(n_samples, 2) - 0.5),
     }
+    DATASETS = [
+    make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5, **blobs_params)[0],
+    make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5], **blobs_params)[0],
+    make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, 0.3], **blobs_params)[0],
+    4.0
+    * (
+        make_moons(n_samples=n_samples, noise=0.05, random_state=0)[0]
+        - np.array([0.5, 0.25])
+    ),
+    14.0 * (np.random.RandomState(42).rand(n_samples, 2) - 0.5),
+    ]
+    
     xx, yy = np.meshgrid(np.linspace(-7, 7, 150), np.linspace(-7, 7, 150))
     clf = NAME_CLF_MAPPING[clf_name]
-    plt.figure(figsize=(10, 8))
-    X = DATA_MAPPING[input_data]
-    rng = np.random.RandomState(42)
-    X = np.concatenate([X, rng.uniform(low=-6, high=6, size=(n_outliers, 2))], axis=0)
+    plt.figure(figsize=(len(NAME_CLF_MAPPING) * 2 + 4, 12.5))
 
-    # Convert X to DataFrame if using IsolationForest to ensure feature names
-    if clf_name == "Isolation Forest":
-        X = pd.DataFrame(X, columns=["Feature1", "Feature2"])
 
+    plot_num = 1
+    rng = np.random.RandomState(42)
+    X = DATA_MAPPING[input_data]
+    X = np.concatenate([X, rng.uniform(low=-6, high=6, size=(n_outliers, 2))], axis=0)
+    
     t0 = time.time()
     clf.fit(X)
     t1 = time.time()
-
+    # fit the data and tag outliers
     if clf_name == "Local Outlier Factor":
         y_pred = clf.fit_predict(X)
     else:
         y_pred = clf.fit(X).predict(X)
 
-    if clf_name != "Local Outlier Factor":
+    # plot the levels lines and the points
+    if clf_name != "Local Outlier Factor":  
         Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
         Z = Z.reshape(xx.shape)
-        plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors="black")
+        plt.contour(xx, yy, Z, levels=[0], linewidths=10, colors="black")
 
     colors = np.array(["#377eb8", "#ff7f00"])
-    plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=30, color=colors[(y_pred + 1) // 2])
+    plt.scatter(X[:, 0], X[:, 1], s=100, color=colors[(y_pred + 1) // 2])
 
     plt.xlim(-7, 7)
     plt.ylim(-7, 7)
     plt.xticks(())
     plt.yticks(())
-    plt.title(f"{clf_name} (time: {t1 - t0:.2f}s)")
+    plt.text(
+        0.99,
+        0.01,
+        ("%.2fs" % (t1 - t0)).lstrip("0"),
+        transform=plt.gca().transAxes,
+        size=60,
+        horizontalalignment="right",
+    )
+    plot_num += 1
+
     return plt
 
+description = "Learn how different anomaly detection algorithms perform in different datasets."
 
-# Gradio interface setup
+def iter_grid(n_rows, n_cols):
+    # create a grid using gradio Block
+    for _ in range(n_rows):
+        with gr.Row():
+            for _ in range(n_cols):
+                with gr.Column():
+                    yield
+
+title = "🕵️‍♀️ compare anomaly detection algorithms 🕵️‍♂️"
 with gr.Blocks() as demo:
-    gr.Markdown("# Anomaly Detection Algorithms Comparison")
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
 
-    input_models = [
-        "Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"
-    ]
+    input_models = ["Robust covariance","One-Class SVM","One-Class SVM (SGD)","Isolation Forest",
+    "Local Outlier Factor"]
     input_data = gr.Radio(
         choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
-        value="Moons",
-        label="Dataset Type"
-    )
-    n_samples = gr.Slider(
-        minimum=100, maximum=500, step=25, value=300, label="Number of Samples"
-    )
-    outliers_fraction = gr.Slider(
-        minimum=0.1, maximum=0.9, step=0.1, value=0.2, label="Outlier Fraction"
+        value="Moons"
     )
+    n_samples = gr.Slider(minimum=100, maximum=500, step=25, label="Number of Samples")
+    outliers_fraction = gr.Slider(minimum=0.1, maximum=0.9, step=0.1, label="Fraction of Outliers")
+    counter = 0
+
+
+    for _ in iter_grid(5, 5):
+        if counter >= len(input_models):
+            break
 
-    for clf_name in input_models:
-        plot = gr.Plot(label=clf_name)
-        fn = partial(train_models, clf_name=clf_name)
+        input_model = input_models[counter]
+        plot = gr.Plot(label=input_model)
+        fn = partial(train_models, clf_name=input_model)
         input_data.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
         n_samples.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
         outliers_fraction.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
+        counter += 1
 
-# Launch the app
-demo.launch()
+demo.launch(enable_queue=True, debug=True)