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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.datasets import fetch_california_housing | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score,mean_absolute_percentage_error | |
| from matplotlib import pyplot as plt | |
| import matplotlib.image as mpimg | |
| import torch | |
| load_reg_data = False | |
| load_class_data = False | |
| def conformal_Predict(cal_err,alpha = 0.8): | |
| assert alpha != None, " Provide a value of alpha " | |
| idx = int(alpha*len(cal_err)) | |
| return cal_err[idx] | |
| if __name__ == '__main__': | |
| st.set_page_config(layout="wide") | |
| if not(load_reg_data): | |
| x_test = np.load("./Reg_Test_X.npy") | |
| y_test = np.load("./Reg_Test_y.npy") | |
| err_calib = np.load("./Reg_calib_err.npy") | |
| y_pred = np.load("./Reg_y_pred.npy") | |
| california_img=plt.imread("./california.png") | |
| load_reg_data = True | |
| if not(load_class_data): | |
| img = np.load("./final_images.npy") | |
| pred = np.load("./final_pred.npy") | |
| cls_calib = np.load("./cerr.npy") | |
| load_class_data = True | |
| st.title("Conformal Prediction") | |
| intro_tab , reg_tab , class_tab = st.tabs(["Introduction","Regression", "Classification"]) | |
| css = ''' | |
| <style> | |
| .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p { | |
| font-size:2rem; | |
| } | |
| </style> | |
| ''' | |
| st.markdown(css, unsafe_allow_html=True) | |
| with intro_tab: | |
| f = open("Introduction.md",'r') | |
| st.markdown(f.read()) | |
| with reg_tab: | |
| with st.container(): | |
| left,right = st.columns([3,2]) | |
| with left: | |
| st.write(" ") | |
| st.markdown("For Regression, we are using California Housing Dataset. It serves as an excellent introduction to implementing machine learning algorithms because it has an easily understandable list of variables and sits at an optimal size between being too toyish and too cumbersome. The dataset pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data.") | |
| st.write("---") | |
| st.markdown("Lets assume you are a buyer in California who is intrested in buying a house. You will most likely have a budget in mind. We have trained a Random forest regressor on the california dataset that predicts the price of a property. This model will help you determine where you will be able to buy a house in california given your budget estimates") | |
| budget = st.slider('Your Budget (in Millions)',min_value=0.3,max_value=5.0,value=2.0,step=0.05) | |
| st.markdown("Now, Please select how certain you want the model to be. More the value of alpha, more certain the model will be and hence more accurate will the reading be for the price estimate") | |
| alpha = st.slider(' Select a value of alpha',min_value=0.1,max_value=.99,value=0.5,step=0.05) | |
| st.markdown("The green points indicate that your budget it greater than the upper bound of model's prediction and hence these properties could be bought. Red points however, are the are the areas where you wont be able to buy a house") | |
| with right: | |
| sigma = conformal_Predict(err_calib,1-alpha) | |
| in_range = (y_pred+sigma)<budget | |
| fig1, ax1 = plt.figure(figsize=(10,7),dpi=150), plt.gca() | |
| ax1.imshow(california_img, alpha=0.3,cmap=plt.get_cmap("jet"),extent=[-124.55, -113.80, 32.45, 42.05],zorder=1) | |
| ax1.scatter(x_test[in_range,7],x_test[in_range,6],s=10,alpha=0.5,label='Can be Bought',c='C2',zorder=3) | |
| ax1.scatter(x_test[~in_range,7],x_test[~in_range,6],s=10,alpha=0.5,label='Cannot Buy',c='r',zorder=3) | |
| ax1.set_title("California Housing Locations (Test-set)") | |
| ax1.set_xlabel("Latitude") | |
| ax1.set_ylabel("Longitude") | |
| ax1.spines['top'].set_visible(False) | |
| ax1.spines['bottom'].set_visible(False) | |
| ax1.spines['right'].set_visible(False) | |
| ax1.spines['left'].set_visible(False) | |
| ax1.set_xticks([]) | |
| ax1.set_yticks([]) | |
| ax1.legend() | |
| ax1.patch.set_alpha(0.0) | |
| st.pyplot(fig1) | |
| with class_tab: | |
| st.write("For Classification we are using Fashion-MNIST dataset. Fashion-MNIST is a dataset of Zalando's article images. Zalando intends Fashion-MNIST to serve as a direct drop-in for benchmarking machine learning algorithms. Each example is assigned to one of the following labels: 0 T-shirt/top, 1 Trouser,2 Pullover, 3 Dress, 4 Coat, 5 Sandal, 6 Shirt, 7 Sneaker, 8 Bag, 9 Ankle boot") | |
| st.write("---") | |
| st.write("Lets assume you have a model trained for Object detection but you cant just rely on the softmax output for that model. This is where conformal prediction comes into play. We can use the alpha value to pick up a threshold. When softmax scores go beyond this threshold score then onlt that label is considered as the predicted class.") | |
| st.write("The higher the value of alpha more the model is certain about its prediction") | |
| c1,c2,c3 = st.columns(3) | |
| with c2: | |
| alpha1 = st.slider('Select a value of alpha for the Model',min_value=0.1,max_value=.99,value=0.5,step=0.05) | |
| sigma = conformal_Predict(cls_calib,alpha1) | |
| labels = np.array(['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']) | |
| with st.container(): | |
| c1,col1, col2, col3,c2 = st.columns([0.2,0.3,0.3,0.3,0.2]) | |
| with col1: | |
| fig1, ax1 = plt.figure(), plt.gca() | |
| ax1.imshow(torch.tensor(img[0]).permute(1,2,0),cmap='gray') | |
| ax1.spines['top'].set_visible(False) | |
| ax1.spines['bottom'].set_visible(False) | |
| ax1.spines['right'].set_visible(False) | |
| ax1.spines['left'].set_visible(False) | |
| ax1.set_xticks([]) | |
| ax1.set_yticks([]) | |
| st.pyplot(fig1) | |
| fig1, ax1 = plt.figure(), plt.gca() | |
| ax1.bar(range(10),pred[0]) | |
| ax1.axhline(y=sigma,linestyle='dashed',c='r') | |
| ax1.set_xlabel("Classe Labels") | |
| ax1.set_ylabel("SoftMax Probabilities") | |
| ax1.set_title("Class Scores with Threshold") | |
| ax1.set_xticks([i for i in range(10)]) | |
| st.pyplot(fig1) | |
| out_labels = labels[pred[0]>sigma] | |
| if len(out_labels)==0: | |
| out_labels = ["None"] | |
| out_labels = ",".join(out_labels) | |
| st.write("Ouput Labels : "+out_labels) | |
| st.write("True Label : Coat") | |
| with col2: | |
| fig1, ax1 = plt.figure(), plt.gca() | |
| ax1.imshow(torch.tensor(img[1]).permute(1,2,0),cmap='gray') | |
| ax1.spines['top'].set_visible(False) | |
| ax1.spines['bottom'].set_visible(False) | |
| ax1.spines['right'].set_visible(False) | |
| ax1.spines['left'].set_visible(False) | |
| ax1.set_xticks([]) | |
| ax1.set_yticks([]) | |
| st.pyplot(fig1) | |
| fig1, ax1 = plt.figure(), plt.gca() | |
| ax1.bar(range(10),pred[1]) | |
| ax1.axhline(y=sigma,linestyle='dashed',c='r') | |
| ax1.set_xlabel("Classe Labels") | |
| ax1.set_ylabel("SoftMax Probabilities") | |
| ax1.set_title("Class Scores with Threshold") | |
| ax1.set_xticks([i for i in range(10)]) | |
| st.pyplot(fig1) | |
| out_labels = labels[pred[1]>sigma] | |
| if len(out_labels)==0: | |
| out_labels = ["None"] | |
| out_labels = ",".join(out_labels) | |
| st.write("Ouput Labels : "+out_labels) | |
| st.write("True Label : Ankle Boot") | |
| with col3: | |
| fig1, ax1 = plt.figure(), plt.gca() | |
| ax1.imshow(torch.tensor(img[2]).permute(1,2,0),cmap='gray') | |
| ax1.spines['top'].set_visible(False) | |
| ax1.spines['bottom'].set_visible(False) | |
| ax1.spines['right'].set_visible(False) | |
| ax1.spines['left'].set_visible(False) | |
| ax1.set_xticks([]) | |
| ax1.set_yticks([]) | |
| st.pyplot(fig1) | |
| fig1, ax1 = plt.figure(), plt.gca() | |
| ax1.bar(range(10),pred[2]) | |
| ax1.axhline(y=sigma,linestyle='dashed',c='r') | |
| ax1.set_xlabel("Classe Labels") | |
| ax1.set_ylabel("SoftMax Probabilities") | |
| ax1.set_title("Class Scores with Threshold") | |
| ax1.set_xticks([i for i in range(10)]) | |
| st.pyplot(fig1) | |
| out_labels = labels[pred[2]>sigma] | |
| if len(out_labels)==0: | |
| out_labels = ["None"] | |
| out_labels = ",".join(out_labels) | |
| st.write("Ouput Labels : "+out_labels) | |
| st.write("True Label : Bag") | |