Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.font_manager
+from sklearn import svm
+
+xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
+# Generate train data
+X = 0.3 * np.random.randn(100, 2)
+X_train = np.r_[X + 2, X - 2]
+# Generate some regular novel observations
+X = 0.3 * np.random.randn(20, 2)
+X_test = np.r_[X + 2, X - 2]
+# Generate some abnormal novel observations
+X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
+
+def createPlotAndPlotPoint(x_new=9, y_new=9):
+  clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
+  clf.fit(X_train)
+  y_pred_train = clf.predict(X_train)
+  y_pred_test = clf.predict(X_test)
+  y_pred_outliers = clf.predict(X_outliers)
+  n_error_train = y_pred_train[y_pred_train == -1].size
+  n_error_test = y_pred_test[y_pred_test == -1].size
+  n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
+
+  # plot the line, the points, and the nearest vectors to the plane
+  Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
+  Z = Z.reshape(xx.shape)
+
+  plt.title("Novelty Detection")
+  plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)
+  a = plt.contour(xx, yy, Z, levels=[0], linewidths=3, colors="darkred")
+  plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors="palevioletred")
+
+  s = 40
+  b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
+  b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
+  c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
+  plt.axis("tight")
+  plt.xlim((-5, 5))
+  plt.ylim((-5, 5))
+  plt.legend(
+      [a.collections[0], b1, b2, c],
+      [
+          "learned frontier",
+          "training observations",
+          "new regular observations",
+          "new abnormal observations",
+      ],
+      loc="upper left",
+      prop=matplotlib.font_manager.FontProperties(size=11),
+  )
+
+  isAbnormal = (clf.predict([[x_new,y_new]])[0] == -1)
+  markerfacecolor = "gold" if isAbnormal else "blueviolet"
+  outputText = "abnormal" if isAbnormal else "regular"
+  plt.plot(x_new, y_new, marker="o", markersize=15, markeredgecolor="m", markerfacecolor=markerfacecolor)
+
+  plt.xlabel(
+      "error train: %d/200 ; errors novel regular: %d/40 ; errors novel abnormal: %d/40"
+      % (n_error_train, n_error_test, n_error_outliers)
+  )
+  return plt, outputText.capitalize()
+
+with gr.Blocks() as demo:
+    link = "https://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html#sphx-glr-auto-examples-svm-plot-oneclass-py"
+    gr.Markdown("# Novelty detection using One-class SVM")
+    gr.Markdown(f"## This demo is based on this [scikit-learn example]({link}).")
+    gr.Markdown("In this demo, we use One-class SVM (Support Vector Machine) to learn the decision function for novelty detection.")
+    gr.Markdown("Furthermore, we **test** the algorithm on new data that would be classified as similar or different to the training set.")
+
+    gr.Markdown("#### You can define the coordinates of the new data point below!")
+
+    x_new = gr.Slider(-5,5,0, label="X", info="Choose the X coordinate")
+    y_new = gr.Slider(-5,5,0, label="Y", info="Choose the Y coordinate")
+    
+    with gr.Row():
+        with gr.Column(scale=2):        
+            plot = gr.Plot(label=f"Decision function plot")
+        with gr.Column(scale=1):
+            prediction = gr.Textbox(label="Is the new data point regular or abormal?")
+
+    x_new.change(createPlotAndPlotPoint,  inputs=[x_new, y_new], outputs=[plot, prediction])
+    y_new.change(createPlotAndPlotPoint,  inputs=[x_new, y_new], outputs=[plot, prediction])
+    demo.load(createPlotAndPlotPoint, inputs=[x_new, y_new], outputs=[plot, prediction])
+
+if __name__ == "__main__":
+    demo.launch()