Create app.py

app.py

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+import numpy as np
+import matplotlib.pylab as plt
+import ruptures as rpt
+import streamlit as st
+from ruptures.metrics import precision_recall
+from ruptures.metrics import hausdorff
+from ruptures.metrics import randindex
+
+st.title("Change Point Detection")
+# Generating Signal
+
+def pw_constant_input(n,dim,n_bkps,sigma):
+    """Piecewise constant (pw_constant)"""
+    # n, dim  # number of samples, dimension
+    # n_bkps, sigma # number of change points, noise standard deviation
+    signal, bkps = rpt.pw_constant(n, dim, n_bkps, noise_std=sigma)
+    rpt.display(signal, bkps)
+    return signal,bkps
+
+def pw_linear_input(n,dim,n_bkps,sigma):
+    """Piecewise Linear"""
+    # creation of data
+    # n, dim = 500, 3  # number of samples, dimension of the covariates
+    # n_bkps, sigma = 3, 5  # number of change points, noise standart deviation
+    signal, bkps = rpt.pw_linear(n, dim, n_bkps, noise_std=sigma)
+    rpt.display(signal, bkps)
+    return signal,bkps
+
+def pw_normal_input(n,dim,n_bkps,sigma):
+    """Piecewise 2D Gaussian process (pw_normal)#"""
+    # creation of data
+    #n = 500  # number of samples
+    #n_bkps = 3  # number of change points
+    signal, bkps = rpt.pw_normal(n, n_bkps)
+    rpt.display(signal, bkps)
+    return signal,bkps
+
+def pw_wavy_input(n,dim,n_bkps,sigma):
+    # creation of data
+    #n, dim = 500, 3  # number of samples, dimension
+    #n_bkps, sigma = 3, 5  # number of change points, noise standart deviation
+    signal, bkps = rpt.pw_wavy(n, n_bkps, noise_std=sigma)
+    rpt.display(signal, bkps)
+    return signal,bkps
+
+input_list = ['piecewiseConstant','piecewiseLinear','piecewiseNormal','piecewiseSinusoidal']
+generate_signal = st.selectbox(label = "Choose an input signal", options = input_list)
+
+
+
+n,dim,n_bkps,sigma = st.columns(4)
+with n:
+    n= st.number_input('No of Samples',min_value=100,step=1)
+with dim:
+    dim = st.number_input('No of dimesions',min_value=1,max_value = 5,step=1)
+with n_bkps:
+    n_bkps = st.number_input('No of breakpoints',min_value=2,step=1)
+with sigma:
+    sigma = st.number_input('Variance',min_value=1,max_value=4,step=1)
+
+if generate_signal == 'piecewiseConstant':
+     signal,bkps = pw_constant_input(n,dim,n_bkps,sigma)
+elif generate_signal== 'piecewiseLinear':
+    signal,bkps = pw_linear_input(n,dim,n_bkps,sigma)
+elif generate_signal == 'piecewiseNormal':
+    signal,bkps = pw_normal_input(n,dim,n_bkps,sigma)
+else:
+    signal,bkps= pw_wavy_input(n,dim,n_bkps,sigma)
+
+fig, axarr = rpt.display(signal,bkps)
+st.pyplot(fig)
+
+def dynp_method(signal,bkps,n_bkps):
+    # change point detection
+    model = "l1"  # "l2", "rbf"
+    algo = rpt.Dynp(model=model, min_size=3, jump=5).fit(signal)
+    my_bkps = algo.predict(n_bkps)
+    # show results
+    fig,axarr = rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))
+    #plt.show()
+    st.pyplot(fig)
+    return my_bkps
+
+def pelt_method(signal,bkps,n_bkps):
+    # change point detection
+    model = "l1"  # "l2", "rbf"
+    algo = rpt.Pelt(model=model, min_size=3, jump=5).fit(signal)
+    my_bkps = algo.predict(pen=3)
+
+    # show results
+    fig, ax_arr = rpt.display(signal, bkps, my_bkps, figsize=(10, 6))
+    st.pyplot(fig)
+    return my_bkps
+    
+
+def bin_seg_method(signal,bkps,n_bkps):
+    # change point detection
+    model = "l2"  # "l1", "rbf", "linear", "normal", "ar",...
+    algo = rpt.Binseg(model=model).fit(signal)
+    my_bkps = algo.predict(n_bkps)
+
+    # show results
+    fg,axxarr = rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))
+    st.pyplot(fig)
+    return my_bkps
+
+def bot_up_seg(signal,bkps,n_bkps):
+    # change point detection
+    model = "l2"  # "l1", "rbf", "linear", "normal", "ar",...
+    algo = rpt.Binseg(model=model).fit(signal)
+    my_bkps = algo.predict(n_bkps)
+
+    # show results
+    fig,axxar = rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))
+    st.pyplot(fig)
+    return my_bkps
+
+def win_sli_seg(signal,bkps,n_bkps):
+    # change point detection
+    model = "l2"  # "l1", "rbf", "linear", "normal", "ar"
+    algo = rpt.Window(width=40, model=model).fit(signal)
+    my_bkps = algo.predict(n_bkps)
+
+    # show results
+    fig,axxar= rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))
+    st.pyplot(fig)
+    return my_bkps
+    
+
+searchmethod_list = ['Dynamic Programming','Pelt','Binary Segmentation','Bottom-up Segmentation','Window sliding segmentation']
+detection_model = st.selectbox(label = "Choose a Detection Method",options = searchmethod_list)
+
+if detection_model== 'Dynamic Programming':
+    bkps1 = dynp_method(signal,bkps,n_bkps)
+    
+elif detection_model=='Pelt':
+    bkps1 = pelt_method(signal,bkps,n_bkps)
+elif detection_model=='Binary Segmentation':
+    bkps1 = bin_seg_method(signal,bkps,n_bkps)
+elif detection_model=='Bottom-up Segmentation':
+    bkps1 = bot_up_seg(signal,bkps,n_bkps)
+else:
+    bkps1 = win_sli_seg(signal,bkps,n_bkps)
+
+p, r = precision_recall(bkps, bkps1)
+st.header('Precision and Recall')
+st.write(p, r)
+
+st.header('Hausdorff metric')
+st.write(hausdorff(bkps, bkps1))
+    
+st.header('Rand index')
+
+
+st.write(randindex(bkps, bkps1))