def generateHeadings(OffenceArray=c2n.offcat):
"""Generate the pairwise comparisons on an array, OffenceArray
Returns
-------
array of strings
"""
OffenceArrayLen=len(OffenceArray)
HeadingArray=[]
for ci,C in enumerate(OffenceArray):
for P in range(ci+1,OffenceArrayLen):
HeadingArray.append(c2n.upcaseFirstLetter(C)
+c2n.upcaseFirstLetter(OffenceArray[P]))
return HeadingArray
def deathornot(CatEmp):
"""Create the raw data for death or not as punishment per offence.
Use generateDependentModelLaplace(deathornot) to generate the probability estimates
"""
#Grouping arrays, statically as it is easier to understand. 63 columns, combine by category for Death and Not:
Groupings = [ c2n.upcaseFirstLetter(x)+y for x in c2n.offcat for y in ['Not']*2 + ['Death'] + ['Not']*4]
DeathOrNotEmp = CatEmp.groupby(Groupings,axis=1,sort=False).sum()
return DeathOrNotEmp
def validatePartitioning():
""" Validate the partition searching.
Parameters
----------
DumyEmp : pandas DataFrame, Dummy emperical data frame as generated by generateDummyDataFrame()
BestPartition: bool, Whether to return the partition AIC scores or the best partition.
Returns:
pandas DataFrame, Partition AIC scores, or the best partition
Try also:
x = [15,85,44,56,49,51,46,54,50,50,50,50,56,44,46,54,4,96] NNNNope
"""
#Death Or Not test
#For partitions of typ: A, B, A, C, A, A, A, C, A, where we should get
# Which is [[0,2,4,5,6,8],[1],[3,7]]
#A = [ 'breakingPeace','deception', 'miscellaneous', 'royalOffences', 'sexual', 'violentTheft']
#B = [ 'damage' ]
#C = [ 'kill', 'theft']
# We also ensure that the occurence of the offences are different within the same partition
TestRow = [40, 160, 70, 30, 20, 80, 50, 50, 20, 80, 20, 80, 10, 40, 100, 100, 20, 80]
#For not Death or Not, have 7 punishmnets
A = [ 10, 20, 30, 40, 50, 60, 70 ]
B = [ 70, 60, 50, 40, 30, 20, 10 ]
C = [ 20, 10, 40, 30, 60, 50, 70 ]
#For 9 offences
#A, B, A, C, A, A, A, C, A as above
TestFullRowA = A+listMul(B,2)+listMul(A,3)+C+A+A+listMul(A,2)+C+A
#C, A, A, B, B, C, A, A, C
TestFullRowB = C+A+listMul(A,2)+B+listMul(B,3)+C+A+listMul(A,3)+C
TestFrame = pd.DataFrame([TestRow,TestRow], columns=list(range(18)), index=[0,1])
TestFullFrame = pd.DataFrame([TestFullRowA,TestFullRowA,TestFullRowB,TestFullRowB,TestFullRowB], columns=list(range(63)), index=list(range(5)))
partitions = partition.Partition([c2n.upcaseFirstLetter(x) for x in c2n.offcat])
print('Testing Death Or Not partitioning')
DeathAICtable = oboP.generateAICtable(TestFrame)
DeathAICmin = DeathAICtable.idxmin(axis=1).apply(lambda x: partitions[int(x)])
print('Found minimal partitions:')
print(DeathAICmin)
print('Testing full partitioning')
AICtable = oboP.generateAICtable(TestFullFrame)
AICmin = AICtable.idxmin(axis=1).apply(lambda x: partitions[int(x)])
print('Found minimal partitions:')
print(AICmin)
def generateRowRange(StartDate, EndDate, Gender='None', http='None',Sockets=2):
"""Generate one row of data for a given date and defendent gender
We are not atomising the code to generalise URL gets such that
the socket opened by urllib3 can stay open. This may be messy
looking...
"""
import CategoryToNumberAssignment as c2n
import urllib3
import json
if http is 'None':
http=urllib3.PoolManager(maxsize=Sockets)
sStartDate = str(StartDate)
sEndDate = str(EndDate)
_Columns = c2n.generateCategories()
#Generate empty columns
Row = [sStartDate]
"""Get not breakdown of punishments by category and subcategory for the period"""
# Categories
for Category in c2n.offcat:
_TempCategories = initialiseEmptyCatArray()
#Get the Json data
#print("Generating URL: {}".format(generateURLCategoryRange(sStartDate,sEndDate,Category, Gender)))
_Json = URLtoJSON(generateURLCategoryRange(sStartDate,sEndDate,Category, Gender),http)
#Find the punishment totals and place in the correct position in the array
# adding 1 place for the Not guilty
for Totals in _Json['breakdown']:
_TempCategories[c2n.puncat.index(Totals['term'])+1]=Totals['total']
# Append _Temps to the Rows
Row = Row + _TempCategories
#Associate Offence Subcategories with Punishment Subategories
for Category in c2n.offsubcat:
_TempCategories = initialiseEmptySubCatArray()
#Get the Json data
_Json = URLtoJSON(generateURLSubCategoryRange(sStartDate,sEndDate,Category, Gender),http)
#Find the punishment totals and place in the correct position in the array
# adding 1 place for the Not guilty
for Totals in _Json['breakdown']:
_TempCategories[c2n.punsubcat.index(Totals['term'])+1]=Totals['total']
# Append _Temps to the Rows
Row = Row + _TempCategories
"""Get not guilties by category and subcategory for the period"""
#Get not guilties:
_JsonNotGuiltyCat = URLtoJSON(generateURLCategoryNotGuiltyRange(sStartDate,sEndDate,Gender),http)
_JsonNotGuiltySubCat = URLtoJSON(generateURLSubCategoryNotGuiltyRange(sStartDate,sEndDate,Gender),http)
# Place values associated with locations in Row:
for Totals in _JsonNotGuiltyCat['breakdown']:
Row[_Columns.index(c2n.upcaseFirstLetter(Totals['term'])+'NotGuilty')] = Totals['total']
for Totals in _JsonNotGuiltySubCat['breakdown']:
Row[_Columns.index(c2n.upcaseFirstLetter(Totals['term'])+'NotGuilty')] = Totals['total']
return Row
import pandas as pd
import numpy as np
from ast import literal_eval
from partitionsets import partition
import CategoryToNumberAssignment as c2n
import OBOModelling as oboM
#The following dependency is from CythonGSL from https://github.com/twiecki/CythonGSL
# The interfaces must be installed as:
# sudo python3 setup_interface.py install
# in the CythonGSL directory. (you will need gcc and libgsl-dev or equivalent installed)
import probability_distribution as gslPDD
Deltas = [1,2,3,4,5,10,50,100,240]
#Initilise partitions:
partitions = partition.Partition([c2n.upcaseFirstLetter(x) for x in c2n.offcat])
partitioN = partition.Partition(list(range(0,9)))
def partitionAIC(EmpFrame, part, OffenceEstimateFrame = [], ReturnDeathEstimate=False, BlockPunishment='Death', Verbose=True):
"""Calculate AIC score between the EmpFrame and the model where offences are partitioned as `part'.
Parameters:
-----------
EmpFrame : DataFrame
DataFrame of emperical data, pre processed, maybe.
part : nested list, 2 levels
Partition formatted as: [[0, 3], [1, 2, 6, 7], [4, 5, 8]]
ReturnDeathEstimate : bool
Whether to return the DeathEstimate frame
Returns:
