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app.py

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+import numpy as np
+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.kernel_approximation import Nystroem
+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))
+
+
+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(
+            Nystroem(gamma=0.1, random_state=42, n_components=150),
+            SGDOneClassSVM(
+                nu=outliers_fraction,
+                shuffle=True,
+                fit_intercept=True,
+                random_state=42,
+                tol=1e-6,
+            ),
+        ),
+        "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),
+    }
+    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=(len(NAME_CLF_MAPPING) * 2 + 4, 12.5))
+
+
+    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)
+
+    # 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=10, colors="black")
+
+    colors = np.array(["#377eb8", "#ff7f00"])
+    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.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."
+
+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(f"## {title}")
+    gr.Markdown(description)
+
+    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"
+    )
+    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
+
+        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
+
+demo.launch(enable_queue=True, debug=True)
+

gitattributes

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requirements.txt

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+scikit-learn
+matplotlib
+numpy