CODEWITHSUNDEEP
pythonscipyminimize
DataByDavid
DataByDavid

Reputation: 1069

Having trouble with scipy Minimize function, it is giving me odd results

Created an objective function
Added constraints

The problem is no matter what initial guess I use, the minimize functions just keeps on using that number. for example: If I use 15 for the initial guess, the solver will not try any other number and say the answer is 15. I'm sure the ere is an issue with the code but I am not sure where.

CODE BELOW:

from scipy.optimize import minimize
import numpy as np
from pandas import *

#----------------------------------------------------
#-------- Create Function ------------
#----------------------------------------------------
def MovingAverage(Input,N,test=0):

    # Create data frame
    df = DataFrame(Input, columns=['Revenue'])

    # Add columns
    df['CummSum'] = df['Revenue'].cumsum()
    df['Mavg'] = rolling_mean(df['Revenue'], N)
    df['Error'] = df['Revenue'] - df['Mavg']
    df['MFE'] = (df['Error']).mean()
    df['MAD'] = np.fabs(df['Error']).mean()
    df['MSE'] = np.sqrt(np.square(df['Error']).mean())
    df['TS'] = np.sum(df['Error'])/df['MAD']

    print N, df.MAD[0]

    if test == 0:
        return df.MAD[0]
    else: return df

#----------------------------------------------------
#-------- Input ------------
#----------------------------------------------------
data = [1,2,3,4,5,5,5,5,5,5,5,5,5,5,5]


#----------------------------------------------------
#-------- SOLVER ------------
#----------------------------------------------------

## Objective Function
fun = lambda x: MovingAverage(data, x[0])

## Contraints
cons = ({'type': 'ineq', 'fun': lambda x:  x[0] - 2}, # N>=2
        {'type': 'ineq', 'fun': lambda x:  len(data) - x[0]}) # N<=len(data)


## Bounds (note sure what this is yet)
bnds = (None,None)

## Solver
res = minimize(fun, 15, method='SLSQP', bounds=bnds, constraints=cons)

##print res
##print res.status
##print res.success
##print res.njev
##print res.nfev
##print res.fun
##for i in res.x:
##    print i
##print res.message
##for i in res.jac:
##    print i
##print res.nit

# print final results
result = MovingAverage(data,res.x,1)
print result

List of possible values:
2 = 0.142857142857,
3 = 0.25641025641,
4 = 0.333333333333,
5 = 0.363636363636,
6 = 0.333333333333,
7 = 0.31746031746,
8 = 0.3125,
9 = 0.31746031746,
10 = 0.333333333333,
11 = 0.363636363636,
12 = 0.416666666667,
13 = 0.487179487179,
14 = 0.571428571429,
15 = 0.666666666667

Upvotes: 4

Views: 2730

Answers (1)

silvado
silvado

Reputation: 18137

Your function is piecewise constant between integer input values, as seen in the plot below (plotted in steps of 0.1 on the x axis):

function plot

So the derivative is zero at almost all points, and that's why a gradient based minimization method will return any given initial point as a local minimum.

To rescue the situation, you could think about using interpolation in the objective function to get intermediate function values for non-integer input values. If you combine this with a gradient-based minimization, it might find some point around 8 as a local minimum when starting at 15.

Upvotes: 7

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