Update app.py

app.py

@@ -9,147 +9,3 @@ 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))