在我的工作中,我尝试使用基于可能性的方法使模型适合数据。我以前为稍微不同的模型编写过这段代码,但由于某种原因,它在与此模型一起使用时抛出了这个错误:“RuntimeError:类似于map的callable必须是f(func,iterable)形式,返回一个与“iterable”长度相同的数字序列”。我不确定是模型还是代码有问题,但如果您能帮助我理解这个错误消息的含义以及如何修复它,我将不胜感激
import pandas as pd
import numpy as np
from scipy.integrate import odeint
from scipy.optimize import curve_fit
from lmfit import minimize, Parameters, Parameter, report_fit
#==============================================================================
''' Make new data matrix, same as csv except infected cells are one total for convience '''
DataFrame = pd.read_csv('Cell_count_data_Tromas_2014.csv') # Read data from file
TROMAS_DATA = np.empty((DataFrame.shape[0], 5), int)
for i in range(DataFrame.shape[0]): # Number of rows
for j in range(5): # Number of columns desired
TROMAS_DATA[i][j] = DataFrame.iloc[i, j]
if j == 4:
TROMAS_DATA[i][j] = DataFrame.ix[i, 'Venus_only'] + DataFrame.ix[i, 'BFP_only'] + DataFrame.ix[i, 'Mixed']
'''
Col 0: Days post infection
Col 1: Leaf number
Col 2: Replicate plant number
Col 3: Number of unifected cells
Col 4: Number of total infected cells
'''
#==============================================================================
''' Make axis for negative log likelihood '''
ZERO_DAYS_AXIS = [0, 3, 5, 7, 10]
#==============================================================================
''' Parameter list for model '''
likeParams = Parameters()
likeParams.add('I0', value = .00372, min = .0000001, max = 1.0000)
likeParams.add('b', value = .5, min = .0001, max = 20.0000)
likeParams.add('x5', value = .5, min = .0001, max = 20.0000)
likeParams.add('x6', value = .5, min = .0001, max = 20.0000)
likeParams.add('x7', value = .5, min = .0001, max = 20.0000)
likeParams.add('psi3', value = .5, min = .0001, max = 1.0000)
likeParams.add('psi5', value = .5, min = .0001, max = 1.0000)
likeParams.add('psi6', value = .5, min = .0001, max = 1.0000)
likeParams.add('psi7', value = .5, min = .0001, max = 1.0000)
#==============================================================================
def model(Mk, t, parameters):
M3 = Mk[0]
M5 = Mk[1]
M6 = Mk[2]
M7 = Mk[3]
try: # Get parameters
b = parameters['b'].value
x3 = parameters['x3'].value
x5 = parameters['x5'].value
x6 = parameters['x6'].value
psi3 = parameters['psi3'].value
psi5 = parameters['psi5'].value
psi6 = parameters['psi6'].value
psi7 = parameters['psi7'].value
except KeyError:
b, x3, x5, x6, psi3, psi5, psi6, psi7 = parameters
if (M3 < psi3):
S3 = (1 - (M3 / psi3))
else:
S3 = 0
if (M5 < psi5):
S5 = (1 - (M5 / psi5))
else:
S5 = 0
if (M6 < psi6):
S6 = (1 - (M6 / psi6))
else:
S6 = 0
if (M7 < psi7):
S7 = (1 - (M7 / psi7))
else:
S7 = 0
dM3dt = b * M3 * S3 + x3 * S3 * M7
dM5dt = b * M5 * S5 + x5 * S5 * M7
dM6dt = b * M6 * S6 + x6 * S6 * M7
dM7dt = b * M7 * S7
return [dM3dt, dM5dt, dM6dt, dM7dt]
#==============================================================================
''' Compute negative log likelihood of Tromas' data given the model, see eq. (3) pg. 11 '''
def negLogLike(parameters):
# Solve ODE system to get model values; parameters are not yet fitted
Lk0 = [0, 0, 0, parameters['I0'].value]
MM = odeint(model, Lk0, ZERO_DAYS_AXIS, args=(parameters,))
nll = 0
epsilon = 10**-10
for t in range(4): # Iterate through days
for p in range(5): # Iterate through replicates
for k in range(4): # Iterate through leaves
Vktp = TROMAS_DATA[20 * t + 4 * p + k][4] # Number of infected cells
Aktp = TROMAS_DATA[20 * t + 4 * p + k][3] + Vktp # Total number of cells observed
Iktp = MM[t + 1][k] # Frequency of cellular infection
if (Iktp <= 0):
Iktp = epsilon
elif (Iktp >= 1):
Iktp = 1 - epsilon
nll += Vktp * np.log(Iktp) + (Aktp - Vktp) * np.log(1 - Iktp)
return [-nll]
#==============================================================================
''' Miminize negative log likelihood with differential evolution algorithm '''
result_likelihood = minimize(negLogLike, likeParams, method = 'differential_evolution')
report_fit(result_likelihood)
我想出来了。我没有在likeParams中定义“x3”。我猜这个错误传播开来,并以差异进化错误的形式传播出去
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