### CSCI 4750/5750- SLU ### Jie Hou, Oct 2022 import gradio as gr import numpy as np import pandas as pd from matplotlib import pyplot as plt import matplotlib.colors as colors import itertools from scipy.stats import norm from scipy import stats from sklearn.naive_bayes import GaussianNB def gaussian(x, n, u, s): #u = x.mean() #s = x.std() # divide [x.min(), x.max()] by n x = np.linspace(x.min(), x.max(), n) a = ((x - u) ** 2) / (2 * (s ** 2)) y = 1 / (s * np.sqrt(2 * np.pi)) * np.exp(-a) return x, y, u, s import gradio as gr # 1. define mean and standard deviation for class 1 set_fea1_mean_class1 = gr.inputs.Slider(0, 20, step=0.5, default=1, label = 'Feature_1 Mean (Class 1)') set_fea1_var_class1 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Feature_1 Variance (Class 1)') set_fea2_mean_class1 = gr.inputs.Slider(0, 20, step=0.5, default=2, label = 'Feature_2 Mean (Class 1)') set_fea2_var_class1 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Feature_2 Variance (Class 1)') set_fea_covariance_class1 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Feature_1_2 Co-Variance (Class 1)') # 2. define mean and standard deviation for class 2 set_fea1_mean_class2 = gr.inputs.Slider(0, 20, step=0.5, default=5, label = 'Feature_1 Mean (Class 2)') set_fea1_var_class2 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Feature_1 Variance (Class 2)') set_fea2_mean_class2 = gr.inputs.Slider(0, 20, step=0.5, default=6, label = 'Feature_2 Mean (Class 2)') set_fea2_var_class2 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Feature_2 Variance (Class 2)') set_fea_covariance_class2 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Feature_1_2 Co-Variance (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') # 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') def plot_figure_twofeature(N, fea1_u1, fea1_var1, fea2_u1, fea2_var1, covariance1, fea1_u2, fea1_var2, fea2_u2, fea2_var2, covariance2, 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. np.random.seed(seed = 3) mu1 = [fea1_u1, fea2_u1] sigma1 = [[np.sqrt(fea1_var1), np.sqrt(covariance1)], [np.sqrt(covariance1), np.sqrt(fea2_var1)]] points_class1_fea1, points_class1_fea2 = np.random.multivariate_normal(mu1, sigma1, N).T mu2 = [fea1_u2, fea2_u2] sigma2 = [[np.sqrt(fea1_var2), np.sqrt(covariance2)], [np.sqrt(covariance2), np.sqrt(fea2_var2)]] points_class2_fea1, points_class2_fea2 = np.random.multivariate_normal(mu2, sigma2, N).T mu_list = [mu1,mu2] sigma_list = [sigma1,sigma2] color_list = ['darkblue','darkgreen'] pd_class1 = pd.DataFrame({'Feature 1 (X)': points_class1_fea1,'Feature 2 (X)': points_class1_fea2, 'Label (Y)': np.repeat(0,len(points_class1_fea1))}) pd_class2 = pd.DataFrame({'Feature 1 (X)': points_class2_fea1,'Feature 2 (X)': points_class2_fea2, 'Label (Y)': np.repeat(1,len(points_class2_fea1))}) pd_all = pd.concat([pd_class1, pd_class2]).reset_index(drop=True) import numpy as np #X_data= pd_all['Feature 1 (X)','Feature 2 (X)'].to_numpy().reshape((len(pd_all),2)) #y_labels= pd_all['Label (Y)'] #Setup X and y data X_data = np.asarray(np.vstack((np.hstack((points_class1_fea1, points_class1_fea2)),np.hstack((points_class2_fea1, points_class2_fea2)))).T) y_labels = np.hstack((np.zeros(N),np.ones(N))) print("X_data: ",X_data.shape) 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 = True , # labelbottom = True, bottom = False) #reference = [stats.uniform.rvs(loc=1, scale = 1) for x in range(N)] pp.plot(points_class1_fea1, points_class1_fea2 + val, 'x', label = 'Class 1', markersize = 10) pp.plot(points_class2_fea1, points_class2_fea2 + val, 'o', label = 'Class 2', markersize = 10) # define x, y limits #x_min, x_max = X_data[:, 0].min() - 1, X_data[:, 0].max() + 1 #y_min, y_max = X_data[:, 1].min() - 1, X_data[:, 1].max() + 1 x_min, x_max = -5, 15 y_min, y_max = -5, 15 X_grid, Y_grid = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100)) ### draw decision boundary 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']) if classifier == 'LDA': from sklearn.discriminant_analysis import LinearDiscriminantAnalysis model_sk = LinearDiscriminantAnalysis() model_sk.fit(X_data,y_labels) X_grid, Y_grid = np.meshgrid(np.linspace(x_min, x_max, N), np.linspace(y_min, y_max, N)) #Predictions for each point on meshgrid zz = np.array( [model_sk.predict( [[xx,yy]])[0] for xx, yy in zip(np.ravel(X_grid), np.ravel(Y_grid)) ] ) Z = zz.reshape(X_grid.shape) pp.pcolormesh(X_grid, Y_grid, Z, cmap=cmap_light, alpha=0.2) #pp.contour( X_grid, Y_grid, Z, 1, alpha = .3, colors = ('red')) elif classifier == 'QDA': from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis model_sk = QuadraticDiscriminantAnalysis() model_sk.fit(X_data,y_labels) X_grid, Y_grid = np.meshgrid(np.linspace(x_min, x_max, N), np.linspace(y_min, y_max, N)) #Predictions for each point on meshgrid zz = np.array( [model_sk.predict( [[xx,yy]])[0] for xx, yy in zip(np.ravel(X_grid), np.ravel(Y_grid)) ] ) Z = zz.reshape(X_grid.shape) pp.pcolormesh(X_grid, Y_grid, 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) X_grid, Y_grid = np.meshgrid(np.linspace(x_min, x_max, N), np.linspace(y_min, y_max, N)) #Predictions for each point on meshgrid zz = np.array( [model_sk.predict( [[xx,yy]])[0] for xx, yy in zip(np.ravel(X_grid), np.ravel(Y_grid)) ] ) Z = zz.reshape(X_grid.shape) pp.pcolormesh(X_grid, Y_grid, Z, cmap=cmap_light, alpha=0.2) #Reshaping the predicted class into the meshgrid shape #Plot the contours print(x_min, x_max) print(y_min, y_max) #pp.xlim([x_min-0.8*x_max, x_max+0.8*x_max]) #pp.ylim([y_min-0.8*y_max, y_max+0.8*y_max]) pp.xlim([x_min, x_max]) pp.ylim([y_min, y_max]) pp.xlabel("Feature 1 (X)", size=20) pp.xticks(fontsize=20) pp.yticks(fontsize=20) pp.ylabel("Feature 2 (X)", size=20) pp.legend(loc='upper right', borderpad=0, handletextpad=0, fontsize = 20) pp.savefig('plot.png') return 'plot.png', pd_all ### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider interface = gr.Interface(fn=plot_figure_twofeature, inputs=[set_number_points,set_fea1_mean_class1,set_fea1_var_class1,set_fea2_mean_class1,set_fea2_var_class1,set_fea_covariance_class1,set_fea1_mean_class2,set_fea1_var_class2,set_fea2_mean_class2,set_fea2_var_class2,set_fea_covariance_class2, 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 (Two feature)", description= "Click examples below for a quick demo", theme = 'huggingface', layout = 'vertical', live=True ) interface.launch(debug=True)