# Fitting mixture of Gamma variate functions at once (with python)

I am trying to automate the fitting of a signal composed of several Gamma variate functions with some added noise. However, I face some troubles and I do not know how to deal with it. First I do not know what solver to choose and why the ones I have tried fail (SLSQP, COBYLA, ...).

It is the first time I try to fit such a complex function. The fit is highly sensitive to the preset parameters (detected by interval segmentation using rupture), which does not make the method robust at all...

I guess the form of the function (with if and for loops) is also an issue... Maybe there is something better than scipy to do the job ? (such as CPLEX, Gurobi, Mosek, Xpress, etc.)

Any help is welcome !

Here is a part of the code output:

raw signal:

If I try to find the initial parameters like this:

def Initiate_Parameters(peaks):

# Initiate the parameters based on the found curves
params = np.zeros(4 * len(peaks))
for pk in range(len(peaks)):
# initial guesses
params[pk * 4 + 0] = list(peaks)[pk]                    # tmax
params[pk * 4 + 1] = peaks[params[pk * 4 + 0]] *0.7     # ymax
params[pk * 4 + 2] = 1                                  # alpha
params[pk * 4 + 3] = params[pk * 4 + 0] - 1000          # AT
return params


Here is the results:

  • Initial Objective score: 486948083.344168
• Final Objective score:   544857.2020755907
• R² correlation = 0.9822807114129106
Solution for gamma cuvrve 1 :
• tmax   = 56.0398491915798
• ymax   = 289.60192865412944
• alpha  = 23.09316050448387
• AT     = -895.1055522009805
Solution for gamma cuvrve 2 :
• tmax   = 233.33786128620045
• ymax   = 455.3681441365064
• alpha  = 70.95859468175692
• AT     = -775.6995035505241
Solution for gamma cuvrve 3 :
• tmax   = 598.2349150407774
• ymax   = 482.0524394851544
• alpha  = 34.893347990437825
• AT     = -438.0504007789465


But if I decided to change the function to find initial parameters, affecting only AT:

def Initiate_Parameters(peaks):

# Initiate the parameters based on the found curves
params = np.zeros(4 * len(peaks))
for pk in range(len(peaks)):
# initial guesses
params[pk * 4 + 0] = list(peaks)[pk]                    # tmax
params[pk * 4 + 1] = peaks[params[pk * 4 + 0]] *0.7     # ymax
params[pk * 4 + 2] = 1                                  # alpha
params[pk * 4 + 3] = params[pk * 4 + 0] - 100           # AT

return params


The result looks pretty different...

  • Initial Objective score: 46560853.304969855
• Final Objective score:   19519888.326930575
• R² correlation = 0.5703701950750049
Solution for gamma cuvrve 1 :
• tmax   = 102.18255199219803
• ymax   = 324.16745138185007
• alpha  = 2.074917963036321
• AT     = 2.7375869275750824
Solution for gamma cuvrve 2 :
• tmax   = 226.67108538687984
• ymax   = 487.43411063438265
• alpha  = 1.0056653550394392
• AT     = 125.87136501547698
Solution for gamma cuvrve 3 :
• tmax   = 347.90715935325585
• ymax   = 390.6541617771044
• alpha  = 0.1685425172833932
• AT     = 245.32587623362775


Here is the code:

# -*- coding: utf-8 -*-
"""
Created on Tue Nov  5 15:11:38 2019

@author: ancollet
"""

import numpy as np
from math import exp
import matplotlib.pyplot as plt
from scipy.optimize import minimize
import ruptures as rpt
from scipy import stats

def Gamma_Variate_Function_Madsen(t, tmax, ymax, alpha, AT):

"""
Madsen, M. T., “A simplified formulation of the gamma variate function,
”Physics in Medicine and Biol-ogy37(7), 1597–1600 (1992).

"The fit of the gamma variate function has been used in numerous studies.
The main benefitsof the gamma variate function are the convenient
mathematical properties.
We found that the fitting of the gamma variate function leads to problems
because of the slow ”wash out” of the bolus".

t: time value
tmax: time of the peak
ymax: peak intensity (absolute value)
alpha: shape parameter
AT: appearance time --> should be lower that tmax

"""
if t <= AT:
return 0
if tmax <= AT:
return float('nan')

#t should always be positive
t = abs((t - AT) / (tmax - AT))
#print(t)
f = ymax * pow(t, alpha) * exp(alpha * (1 - t))

return f

"""
Madsen, M. T., “A simplified formulation of the gamma variate function,
”Physics in Medicine and Biol-ogy37(7), 1597–1600 (1992).

t: time value
tmax: time of the peak
ymax: peak intensity (absolute value)
alpha: shape parameter
AT: appearance time --> should be lower that tmax

"""

f = 0

for i in range(int(len(params)/4)):
tmax = params[i*4]
ymax = params[i*4 + 1]
alpha = params[i*4 + 2]
AT = params[i*4 + 3]

if t <= AT:
f += 0
elif tmax <= AT:
f += 0
else:
#t should always be positive
t2 = abs((t - AT) / (tmax - AT))
#print(t)
f += ymax  * pow(t2, alpha) * exp(alpha * (1 - t2))
return f

# define objective function: SSE
def Objective_Fun(params, y_raw):

# calculate y
# calculate objective
obj = 0.0
for t in range(1, len(y_raw)):
if y_raw[t] != 0: #and not isnan(y_raw[t]):
obj = obj + (y_fit-y_raw[t])**2
# return result
return obj

def Moving_Average(a, n=3) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n

def Detect_Multiple_Gamma_Breakpoints(signal, n_bkps = 1, min_size = 10,
jump = 1, model = 'rbf',
pen = 1):

# Need to ensure a numpy array
signal = np.array(signal)

# Smoothing (to get rid of the noise), getting the base and removing it
signal_smooth = Moving_Average(signal, n = min_size)

# change point detection: Window segmentation
algo = rpt.Window(width = min_size, model=model).fit(signal_smooth)

# predict the ruptures with a given number of breakpoints
breakpoints = algo.predict(n_bkps)

# display the results
rpt.display(signal, breakpoints)
plt.plot(signal_smooth)
plt.show()

return breakpoints

def Find_Multiple_Gamma_Peaks_2(signal, n_peaks):

# Case with only a single Gamma variate:
if n_peaks == 1:
i_max, j_max = 0, 0
for i, j in enumerate(signal):
if j > j_max:
j_max = j
i_max = i
peaks_indices = {i_max: j_max}
return peaks_indices

# Else: Two Gamma to fit at least
peaks_indices = {}

#Detect the breakpoints in the series
breakpoints = \
Detect_Multiple_Gamma_Breakpoints(y_raw, n_bkps = n_peaks - 1,
min_size = 40, jump = 1, model = 'rbf',
pen = 1)

i1 = 0
# Iterate over breakpoints
for i2 in breakpoints:
# Take the interval between breakpoints
sub_signal = signal[i1 + 1 : i2 - 1]
# Add the max of this sub_signal to the peak_index dico
peak_index = \
[i + i1 + 1 for i, j in enumerate(sub_signal) if j == max(sub_signal)]
peaks_indices[peak_index[0]] = signal[peak_index[0]]
# Set the lower bound for the next iteration as the current
# interval upperbound
i1 = i2

return peaks_indices

def Fit_TS_Multiple_Gamma_Variates(y_raw, n_peaks):

# Find the peaks
peaks = Find_Multiple_Gamma_Peaks_2(y_raw, n_peaks)
# Initiate a preset of parameters from the peaks found
params_raw = Initiate_Parameters(peaks)
# cons = f(nb of peaks)
cons = Initiate_Parameters_Constraints(peaks)
# bonds = f(nb of peaks)
bnds = Initiate_Parameters_Bounds(peaks)

tol = 1e-7

"""
#Solving
solution = minimize(Objective_Fun, x0 = params, method='CG', bounds=bnds,
args = (y_raw), constraints = None)
"""

"""
#Solving
solution = minimize(Objective_Fun, x0 = params, method='SLSQP', bounds=bnds,
args = (y_raw), constraints = cons)

"""

# Solving
solution = minimize(Objective_Fun, x0 = params_raw, method='COBYLA',
bounds=bnds, args = (y_raw), tol = tol)

# Get the optimized parameters
params_fit = solution.x

# Generate the fitted curve (general + individual)
y_fits = Get_Y_Fits(y_raw, params_fit, n_peaks)

return y_fits, params_raw, params_fit

def Get_Y_Fits(y_raw, params, n_peaks):

# Store the curves as a dico
y_fits = {}

y_fit = np.zeros(len(y_raw))
for t in range(len(y_raw)):

y_fits['Global gamma fit curve'] = y_fit

if n_peaks > 1:
for pk in range(1, n_peaks + 1):
y_fit = np.zeros(len(y_raw))
sub_params = params[(pk - 1) * 4: pk * 4]
#print(sub_params)
for t in range(len(y_raw)):
y_fit[t] = \
y_fits['Gamma fit cruve ' + str(pk)] = y_fit

return y_fits

def Initiate_Parameters(peaks):

# Initiate the parameters based on the found curves
params = np.zeros(4 * len(peaks))
for pk in range(len(peaks)):
# initial guesses
params[pk * 4 + 0] = list(peaks)[pk]                    # tmax
params[pk * 4 + 1] = peaks[params[pk * 4 + 0]] * 0.7    # ymax
params[pk * 4 + 2] = 1                                  # alpha
params[pk * 4 + 3] = params[pk * 4 + 0] - 1000          # AT

return params

def Initiate_Parameters_Constraints(peaks):

#Empty list
cons = []
#Setting the constraints as dictionnaries
for pk in range(len(peaks)):
cons.append({'type': 'ineq',
'fun': lambda x: x[pk * 4 + 0] - x[pk * 4 + 3]})
return cons

def Initiate_Parameters_Bounds(peaks):

bnds = ()
for pk in range(len(peaks)):
# bounds on variables
# tmax belongs to R
# ymax belongs to R+
# alpha belongs to R+
# AT belongs to R
bnds += ((-10000, 10000), (0, 5000), (0, 50), (-10000, 10000))

return bnds

def Get_Variance_Explaination_R2(y_raw, y_fit):

slope, intercept, r_value, p_value, std_err = \
stats.linregress(y_raw, y_fit)
r2 = r_value**2

return r2

def Display(r2, params_raw, params_fit):

# show initial objective
print('  • Initial Objective score: ' \
+ str(Objective_Fun(params_raw, y_raw)))

# show final objective
print('  • Final Objective score:   ' \
+ str(Objective_Fun(params_fit, y_raw)))

cR2 = "  • R² correlation = " + str(r2)

print(cR2)

n_peaks = int(len(params_fit)/4)

for pk in range(n_peaks):
# print solution
print('Solution for gamma cuvrve', str(pk + 1), ':')
ctmax =  '  • tmax   = ' + str(params_fit[pk * 4])
print(ctmax)
cymax =  '  • ymax   = ' + str(params_fit[pk * 4 + 1])
print(cymax)
calpha = '  • alpha  = ' + str(params_fit[pk * 4 + 2])
print(calpha)
cAT =    '  • AT     = ' + str(params_fit[pk * 4 + 3])
print(cAT)

#Test
if __name__ == '__main__':

# Generate a multi - Gamma variate curve
x_raw = range(1000)

params = [100, 450, 0.9, -100, 250, 300, 1.2, 100, 600, 300, 2, 400]
y_raw = [Multiple_Gamma_Variate_Function_Madsen(x, params) + \
float(np.random.normal(0,2,1)) for x in x_raw]
#y_raw = [Multiple_Gamma_Variate_Function_Madsen(x, params) for x in x_raw]

#Show the raw curve
print('raw signal:')
plt.plot(y_raw) #.fig_title = 'Raw noisy data'
plt.show()

# Fit successively with one, two and then three Gamma - Variate
for n_peaks in [1, 2, 3]:

print('Fitting the curve with ', n_peaks, ' Gamma functions...')

#Fit the functions
y_fits, params_raw, params_fit = \
Fit_TS_Multiple_Gamma_Variates(y_raw, n_peaks)

#Calculate R2
r2 = \
Get_Variance_Explaination_R2(y_raw, y_fits['Global gamma fit curve'])

#Display the regression parameters and info:
Display(r2, params_raw, params_fit)

#Plot the results
plt.plot(y_raw)
for key in y_fits.keys():
plt.plot(y_fits[key])
plt.show()


• Exactly what is the problem? Your illustration looks like a success, not a failure. The concern about robustness is legitimate, because mixture models are notoriously difficult to fit and unstable. Are you perhaps fishing for information on making the model more robust? Or maybe on how to identify good starting values for the solution?
– whuber
Nov 13, 2019 at 13:43
• You are right, this is not very clear... I updated my question. The main issue is that I want to apply the code to real data, with noise and other parameters affecting the distribution. Here is a perfect mixture of distribution, with very little noise. The model should be able to handle it whatever the initial parameters are right ? Nov 14, 2019 at 3:34
• Mathematically the model can accommodate any data, but as a practical matter that's expecting far too much. Even in simpler cases it may be impossible to discover the global minimum of the objective function automatically.
– whuber
Nov 14, 2019 at 14:51
• I found a solver which is working quite well (but takes a bit too much time):  #Solving solution = minimize(Objective_Fun, x0 = params_raw, method='trust-constr', bounds = bnds_t, args = (y_raw), constraints = cons)  Now I need to have a look to deconvolution techniques to speed-up the process. I will update my post with some results from the lastest solver. Nov 15, 2019 at 5:49