adjustments

app.py

@@ -2,19 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 from threading import Thread
 from matplotlib.colors import ListedColormap
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
 from sklearn.datasets import make_moons, make_circles, make_classification
-from sklearn.neural_network import MLPClassifier
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.svm import SVC
-from sklearn.gaussian_process import GaussianProcessClassifier
-from sklearn.gaussian_process.kernels import RBF
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
-from sklearn.naive_bayes import GaussianNB
-from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
-from sklearn.inspection import DecisionBoundaryDisplay
 from sklearn.datasets import make_blobs, make_circles, make_moons
 import gradio as gr
 import math
@@ -33,10 +21,6 @@ from sklearn.kernel_approximation import Nystroem
 from sklearn.pipeline import make_pipeline
 
 
-### DATASETS
-
-def normalize(X):
-    return StandardScaler().fit_transform(X)
 
 # Example settings
 n_samples = 300
@@ -108,7 +92,7 @@ DATASETS = [
 ###########
 
 #### PLOT
-FIGSIZE = 7,7
+FIGSIZE = 10,10
 figure = plt.figure(figsize=(25, 10))
 i = 1
 
@@ -136,13 +120,13 @@ def train_models(selected_data, clf_name):
         y_pred = clf.fit(X).predict(X)
 
     # plot the levels lines and the points
-    if clf_name != "Local Outlier Factor":  # LOF does not implement predict
+    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[:, 0], X[:, 1], s=10, 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)
@@ -153,7 +137,7 @@ def train_models(selected_data, clf_name):
         0.01,
         ("%.2fs" % (t1 - t0)).lstrip("0"),
         transform=plt.gca().transAxes,
-        size=15,
+        size=60,
         horizontalalignment="right",
     )
     plot_num += 1