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app.py

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+import gradio as gr
+
+
+
+def plot_figure(N, u1, std1, u2, std2, show_dist, classifier=None):
+  #N = 100
+  import numpy as np
+  import matplotlib.pyplot as pp
+  pp.style.use('default')
+  #val = 0. # this is the value where you want the data to appear on the y-axis.
+
+  points_class1 = [stats.norm.rvs(loc=u1, scale = std1) for x in range(N)]
+  points_class2 = [stats.norm.rvs(loc=u2, scale = std2) for x in range(N)]
+
+  pd_class1 = pd.DataFrame({'Feature 1 (X)': points_class1, 'Label (Y)': np.repeat(0,len(points_class1))})
+  pd_class2 = pd.DataFrame({'Feature 1 (X)': points_class2, 'Label (Y)': np.repeat(1,len(points_class2))})
+
+
+  pd_all = pd.concat([pd_class1, pd_class2]).reset_index(drop=True)
+
+  import numpy as np
+  X_data= pd_all['Feature 1 (X)'].to_numpy().reshape((len(pd_all),1))
+  y_labels= pd_all['Label (Y)']
+
+
+  # define x, y limits
+  x_min, x_max = X_data[:, 0].min() - 1, X_data[:, 0].max() + 1
+  y_min, y_max = 0-1, 1 + 1
+
+  fig = pp.figure(figsize=(8, 6)) # figure size in inches
+  fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.3, wspace=0.05) 
+
+
+  pp.tick_params(left = False, right = False , labelleft = False ,
+                  labelbottom = True, bottom = False)
+
+  #reference = [stats.uniform.rvs(loc=1, scale = 1) for x in range(N)]
+  pp.plot(points_class1, np.zeros_like(points_class1) + val, 'x', label = 'Class 1', markersize = 10)
+  pp.plot(points_class2, np.zeros_like(points_class2) + val, 'o', label = 'Class 2', markersize = 10)
+
+
+  if show_dist:
+    x = np.arange(x_min, x_max, 0.01, dtype=np.float) # define range of x
+    x, y, u, s = gaussian(x, 10000, np.mean(points_class1), np.std(points_class1) )
+    pp.plot(x, y)
+    #pp.plot(x, y, label=r'$Gaussian (\mu=%.2f,\ \sigma=%.2f)$' % (u, s))
+
+
+    x = np.arange(x_min, x_max, 0.01, dtype=np.float) # define range of x
+    x, y, u, s = gaussian(x, 10000, np.mean(points_class2), np.std(points_class2) )
+    pp.plot(x, y)
+    #pp.plot(x, y, label=r'$Gaussian (\mu=%.2f,\ \sigma=%.2f)$' % (u, s))
+
+
+
+  ### draw decision boundary on knn 
+
+  import numpy as np
+  from matplotlib import pyplot as plt
+  from sklearn import neighbors, datasets
+  from matplotlib.colors import ListedColormap
+
+  # Create color maps for 3-class classification problem, as with iris
+  cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA'])
+  cmap_bold = ListedColormap(['#FF0000', '#00FF00'])
+
+
+  xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
+                          np.linspace(y_min, y_max, 100))
+
+  
+  if classifier == 'LDA':
+    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+    model_sk = LinearDiscriminantAnalysis()
+    model_sk.fit(X_data,y_labels)
+    Z = model_sk.predict(np.c_[xx.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    pp.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.2)
+  elif classifier == 'QDA':
+    from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
+    model_sk = QuadraticDiscriminantAnalysis()
+    model_sk.fit(X_data,y_labels)
+    Z = model_sk.predict(np.c_[xx.ravel()])
+    Z = Z.reshape(xx.shape)
+    print("Z: ",Z)
+    pp.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.2)
+  elif classifier == 'NaiveBayes':
+    from sklearn.naive_bayes import GaussianNB
+    model_sk = GaussianNB(priors = None)
+    model_sk.fit(X_data,y_labels)
+    Z = model_sk.predict(np.c_[xx.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    pp.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.2)
+
+  
+
+  pp.xlim([x_min, x_max])
+  pp.ylim([y_min, y_max])
+  pp.xlabel("Feature 1 (X)", size=20)
+  pp.xticks(fontsize=20)
+  pp.xlabel("Feature 1 (X)")
+  pp.legend(loc='upper right', borderpad=0, handletextpad=0, fontsize = 20)
+  pp.savefig('plot.png')
+
+  return 'plot.png', pd_all
+
+
+# 1. define mean and standard deviation for class 1
+
+set_mean_class1 = gr.inputs.Slider(-20, 20, step=0.5, default=1, label = 'Mean (Class 1)')
+set_std_class1 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Standard Deviation (Class 1)')
+
+# 2. define mean and standard deviation for class 2
+
+set_mean_class2 = gr.inputs.Slider(-20, 20, step=0.5, default=10, label = 'Mean (Class 2)')
+set_std_class2 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Standard Deviation (Class 2)')
+
+# 3. Define the number of data points 
+set_number_points = gr.inputs.Slider(10, 100, step=5, default=20, label = 'Number of samples in each class')
+
+# 4. show distribution or not
+set_show_dist = gr.inputs.Checkbox(label="Show data distribution")
+
+# 5. set classifier type
+set_classifier = gr.inputs.Dropdown(["None", "LDA", "QDA", "NaiveBayes"])
+
+# 6. define output imagem model
+set_out_plot_images = gr.outputs.Image(label="Data visualization")
+
+set_out_plot_table = gr.outputs.Dataframe(type='pandas', label ='Simulated Dataset')
+
+
+
+### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider
+interface = gr.Interface(fn=plot_figure, 
+                         inputs=[set_number_points,set_mean_class1,set_std_class1,set_mean_class2,set_std_class2, set_show_dist, set_classifier], 
+                         outputs=[set_out_plot_images,set_out_plot_table],
+                         examples_per_page = 2,
+                         #examples = get_sample_data(10), 
+                         title="CSCI4750/5750 Demo: Web Application for Probabilistic Classifier (Single feature)", 
+                         description= "Click examples below for a quick demo",
+                         theme = 'huggingface',
+                         layout = 'vertical', live=True
+                         )
+interface.launch(debug=True)