146 lines
4.3 KiB
Python
146 lines
4.3 KiB
Python
'''Trying out tree structure for nested logit
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sum is standing for likelihood calculations
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should collect and aggregate likelihood contributions bottom up
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'''
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from statsmodels.compat.python import lrange
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import numpy as np
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tree = [[0,1],[[2,3],[4,5,6]],[7]]
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#singleton/degenerate branch needs to be list
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xb = 2*np.arange(8)
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testxb = 1 #0
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def branch(tree):
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'''walking a tree bottom-up
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'''
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if not isinstance(tree[0], int): #assumes leaves are int for choice index
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branchsum = 0
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for b in tree:
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branchsum += branch(b)
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else:
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print(tree)
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print('final branch with', tree, sum(tree))
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if testxb:
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return sum(xb[tree])
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else:
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return sum(tree)
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print('working on branch', tree, branchsum)
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return branchsum
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print(branch(tree))
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#new version that also keeps track of branch name and allows V_j for a branch
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# as in Greene, V_j + lamda * IV does not look the same as including the
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# explanatory variables in leaf X_j, V_j is linear in X, IV is logsumexp of X,
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testxb = 0#1#0
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def branch2(tree):
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'''walking a tree bottom-up based on dictionary
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'''
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if isinstance(tree, tuple): #assumes leaves are int for choice index
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name, subtree = tree
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print(name, data2[name])
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print('subtree', subtree)
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if testxb:
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branchsum = data2[name]
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else:
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branchsum = name #0
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for b in subtree:
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#branchsum += branch2(b)
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branchsum = branchsum + branch2(b)
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else:
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leavessum = sum(data2[bi] for bi in tree)
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print('final branch with', tree, ''.join(tree), leavessum) #sum(tree)
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if testxb:
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return leavessum #sum(xb[tree])
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else:
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return ''.join(tree) #sum(tree)
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print('working on branch', tree, branchsum)
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return branchsum
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tree = [[0,1],[[2,3],[4,5,6]],[7]]
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tree2 = ('top',
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[('B1',['a','b']),
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('B2',
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[('B21',['c', 'd']),
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('B22',['e', 'f', 'g'])
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]
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),
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('B3',['h'])]
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)
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data2 = dict([i for i in zip('abcdefgh',lrange(8))])
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#data2.update({'top':1000, 'B1':100, 'B2':200, 'B21':300,'B22':400, 'B3':400})
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data2.update({'top':1000, 'B1':100, 'B2':200, 'B21':21,'B22':22, 'B3':300})
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#data2
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#{'a': 0, 'c': 2, 'b': 1, 'e': 4, 'd': 3, 'g': 6, 'f': 5, 'h': 7,
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#'top': 1000, 'B22': 22, 'B21': 21, 'B1': 100, 'B2': 200, 'B3': 300}
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print('\n tree with dictionary data')
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print(branch2(tree2)) # results look correct for testxb=0 and 1
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#parameters/coefficients map coefficient names to indices, list of indices into
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#a 1d params one for each leave and branch
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#Note: dict looses ordering
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paramsind = {
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'B1': [],
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'a': ['consta', 'p'],
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'b': ['constb', 'p'],
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'B2': ['const2', 'x2'],
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'B21': [],
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'c': ['consta', 'p', 'time'],
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'd': ['consta', 'p', 'time'],
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'B22': ['x22'],
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'e': ['conste', 'p', 'hince'],
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'f': ['constt', 'p', 'hincf'],
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'g': [ 'p', 'hincg'],
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'B3': [],
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'h': ['consth', 'p', 'h'],
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'top': []}
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#unique, parameter array names,
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#sorted alphabetically, order is/should be only internal
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paramsnames = sorted({i for j in paramsind.values() for i in j})
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#mapping coefficient names to indices to unique/parameter array
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paramsidx = {name: idx for (idx,name) in enumerate(paramsnames)}
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#mapping branch and leaf names to index in parameter array
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inddict = {k:[paramsidx[j] for j in v] for k,v in paramsind.items()}
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'''
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>>> paramsnames
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['const2', 'consta', 'constb', 'conste', 'consth', 'constt', 'h', 'hince',
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'hincf', 'hincg', 'p', 'time', 'x2', 'x22']
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>>> parmasidx
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{'conste': 3, 'consta': 1, 'constb': 2, 'h': 6, 'time': 11, 'consth': 4,
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'p': 10, 'constt': 5, 'const2': 0, 'x2': 12, 'x22': 13, 'hince': 7,
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'hincg': 9, 'hincf': 8}
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>>> inddict
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{'a': [1, 10], 'c': [1, 10, 11], 'b': [2, 10], 'e': [3, 10, 7],
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'd': [1, 10, 11], 'g': [10, 9], 'f': [5, 10, 8], 'h': [4, 10, 6],
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'top': [], 'B22': [13], 'B21': [], 'B1': [], 'B2': [0, 12], 'B3': []}
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>>> paramsind
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{'a': ['consta', 'p'], 'c': ['consta', 'p', 'time'], 'b': ['constb', 'p'],
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'e': ['conste', 'p', 'hince'], 'd': ['consta', 'p', 'time'],
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'g': ['p', 'hincg'], 'f': ['constt', 'p', 'hincf'], 'h': ['consth', 'p', 'h'],
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'top': [], 'B22': ['x22'], 'B21': [], 'B1': [], 'B2': ['const2', 'x2'],
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'B3': []}
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'''
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