improvements

app.py

@@ -22,12 +22,6 @@ from sklearn.pipeline import make_pipeline
 
 
 
-# Example settings
-n_samples = 300
-outliers_fraction = 0.15
-n_outliers = int(outliers_fraction * n_samples)
-n_inliers = n_samples - n_outliers
-
 #### MODELS
 
 def get_groundtruth_model(X, labels):
@@ -39,21 +33,26 @@ def get_groundtruth_model(X, labels):
     return Dummy(labels)
 ############
 # Define datasets
-blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2)
-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),
-}
+# Example settings
 
 
-NAME_CLF_MAPPING = {"Robust covariance": EllipticEnvelope(contamination=outliers_fraction),
+
+
+#### PLOT
+FIGSIZE = 10,10
+figure = plt.figure(figsize=(25, 10))
+i = 1
+
+
+
+
+def train_models(input_data,  outliers_fraction, n_samples, clf_name):
+    # n_samples=300
+    # outliers_fraction = 0.15
+    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),
@@ -67,15 +66,19 @@ NAME_CLF_MAPPING = {"Robust covariance": EllipticEnvelope(contamination=outliers
         ),
         "Isolation Forest": IsolationForest(contamination=outliers_fraction, random_state=42),
         "Local Outlier Factor": LocalOutlierFactor(n_neighbors=35, contamination=outliers_fraction),
-}
-
-
-
-###########################################################
-
-# Compare given classifiers under given settings
-
-DATASETS = [
+    }   
+    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],
@@ -85,21 +88,8 @@ DATASETS = [
         - np.array([0.5, 0.25])
     ),
     14.0 * (np.random.RandomState(42).rand(n_samples, 2) - 0.5),
-]
-########################################################
-
-
-###########
-
-#### PLOT
-FIGSIZE = 10,10
-figure = plt.figure(figsize=(25, 10))
-i = 1
-
-
-
-
-def train_models(selected_data, clf_name):
+    ]
+    
     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))
@@ -107,7 +97,7 @@ def train_models(selected_data, clf_name):
 
     plot_num = 1
     rng = np.random.RandomState(42)
-    X = DATA_MAPPING[selected_data]
+    X = DATA_MAPPING[input_data]
     X = np.concatenate([X, rng.uniform(low=-6, high=6, size=(n_outliers, 2))], axis=0)
     
     t0 = time.time()
@@ -161,11 +151,14 @@ with gr.Blocks() as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description)
 
-    input_models = list(NAME_CLF_MAPPING)
+    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
 
 
@@ -176,7 +169,9 @@ with gr.Blocks() as demo:
         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], outputs=plot)
+        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)