CIC Subpopulation Construction¶

To create a representative model of the agent interactions, we will use subpopulation modeling. We take all of the agents and cluster them based off of the following features from full population actual transactional data from Jan - May 11 2020 xDai data (s means source, t means target):

s_location - source individual location
s_business_type - source individual business type
t_location - target individual location
t_business_type - target individual business type.
weight, which is tokens, exchange amount
s_bal - source individual CIC wallet balance
t_bal - target individual CIC wallet balance

Essentially, we are taking a graph zoom operation, bundling nodes together based off of their likeness. Nodes are constant with edges being transative. The algorithm we use for this graph zoom operation is Kmeans clustering. Based off our descriptive statistical analysis and use of th Gap Statistic created by Stanford researchers Tibshirani, Walther and Hastie in their 2001 paper, we determined 50 clusters are representative of the subpopulations. All of the flows inside of the bundle become part of the self-loop flow. For example, within cluster 1, agent a can transaction with as b. This will not be reflected within our model as this is intra not inter cluster interactions.

Graph Model of Current Spend Activity¶

We created a network graph of the CIC transaction data as a $G(N,E)$ weighted directed graph with source and target agents as nodes, $N$ and the edges as $E$ . Tokens are used as the edge weight to denote the actual CIC flow between agents, as $i,j \in E$ .

The observed data shows the actual payments between network actors that are transacting in CIC. The observed data does not show us shillings payments between actors, actors utility, or demand. We only know actual CIC spends between agents.

Saving Clustering Results¶

At the bottom of this notebook, we calculate the median, 1st quartile, 3rd quartile, mean, standard deviation, utility types ordering, and utility types probability. These values can then be copied into the subpopulation_clusters.py in the simulation folders for use in the simulations.

In [1]:

# import libraries
import networkx as nx
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from gap_statistic import OptimalK
from sklearn.decomposition import PCA

import matplotlib.pyplot as plt
%matplotlib inline

Data Dump as of 5-15-2020¶

Jan - May 11 2020 xDai Blockchain data https://www.grassrootseconomics.org/research

In [2]:

# import the data
transactions = pd.read_csv('data/sarafu_xDAI_tx_all_pub_all_time_12May2020.csv')
users = pd.read_csv('data/sarafu_xDAI_users_all_pub_all_time_12May2020.csv')

In [3]:

transactions.head()

Out[3]:

	id	timeset	transfer_subtype	source	s_gender	s_location	s_business_type	target	t_gender	t_location	t_business_type	tx_token	weight	type	token_name	token_address
0	1	2020-01-25 19:13:17.731529	DISBURSEMENT	0xBDB3Bc887C3b70586BC25D04d89eC802b897fC5F	NaN	None	System	0x245fc81fe385450Dc0f4787668e47c903C00b0A1	female	GE Office	Savings Group	NaN	18000.000000	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
1	2	2020-01-25 19:13:19.056070	DISBURSEMENT	0xBDB3Bc887C3b70586BC25D04d89eC802b897fC5F	NaN	None	System	0xC1697C1326fD192438515fE2F7E4cCb0C705C5d2	male	GE Nairobi	Farming/Labour	NaN	9047.660892	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
2	3	2020-01-25 19:13:20.288346	DISBURSEMENT	0xBDB3Bc887C3b70586BC25D04d89eC802b897fC5F	NaN	None	System	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	NaN	25378.726002	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
3	4	2020-01-25 19:13:21.478850	DISBURSEMENT	0xBDB3Bc887C3b70586BC25D04d89eC802b897fC5F	NaN	None	System	0xD95954e3fCd2f09A6Be5931D24f731eFa63BF435	male	G.E	Farming/Labour	NaN	4495.932576	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
4	5	2020-01-26 07:48:43.042684	DISBURSEMENT	0xBDB3Bc887C3b70586BC25D04d89eC802b897fC5F	NaN	None	System	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72	male	Home	Farming/Labour	NaN	400.000000	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4

In [4]:

transactions.transfer_subtype.value_counts(normalize=True).to_dict()

Out[4]:

{'STANDARD': 0.5085207861177824,
 'DISBURSEMENT': 0.35574873997902784,
 'RECLAMATION': 0.13483070053783444,
 'AGENT_OUT': 0.0008997733653553429}

Based on the data dictionary provided by Grassroots Economics, we know that the transfer subtype codes are:

DISBURSEMENT = from Grassroots Economics
RECLAMATION = Back to GE,
STANDARD = a trade between users,
AGENT = when a group account is cashing out

For purposes of our analysis, we will subset to STANDARD transactions.

In [3]:

transactions_subset = transactions[transactions['transfer_subtype'] == 'STANDARD']
transactions_subset = transactions_subset[transactions_subset['token_name']=='Sarafu']

In [6]:

users.head()

Out[6]:

	id	start	label	gender	location	held_roles	business_type	bal	xDAI_blockchain_address	confidence	...	otxns_in	otxns_out	ounique_in	ounique_out	svol_in	svol_out	stxns_in	stxns_out	sunique_in	sunique_out
0	1	2020-01-25 19:10:50.218686	1	NaN	None	ADMIN	System	8.916761e+06	0xBDB3Bc887C3b70586BC25D04d89eC802b897fC5F	0.000000	...	19917	52610	9	19862	0.0	0.0	0	0	0	0
1	2	2018-10-23 09:09:58	2	female	GE Office	TOKEN_AGENT	Savings Group	1.800000e+05	0x245fc81fe385450Dc0f4787668e47c903C00b0A1	0.000000	...	134	16	68	0	0.0	0.0	0	0	0	0
2	3	2018-10-21 14:20:57	3	male	GE Nairobi	BENEFICIARY	Farming/Labour	5.666089e+01	0xC1697C1326fD192438515fE2F7E4cCb0C705C5d2	0.000000	...	2	2	1	0	0.0	9007.0	0	1	0	1
3	4	2018-10-21 15:38:30	4	male	GE Nairobi	BENEFICIARY	Farming/Labour	1.173773e+04	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	0.100000	...	6	1	1	0	20619.0	50449.0	20	15	11	5
4	5	2018-10-23 14:10:27	5	male	G.E	BENEFICIARY	Farming/Labour	7.297263e+03	0xD95954e3fCd2f09A6Be5931D24f731eFa63BF435	0.405063	...	15	1	1	0	127393.3	168905.0	158	208	84	65

5 rows × 22 columns

In [4]:

users['business_type'].value_counts(normalize=True).to_dict()

Out[4]:

{'Farming/Labour': 0.43367860016090104,
 'Food/Water': 0.22863032984714401,
 'Shop': 0.1406878519710378,
 'Fuel/Energy': 0.06365647626709574,
 'None': 0.0621983105390185,
 'Transport': 0.04379525341914722,
 'Education': 0.014380530973451327,
 'Savings Group': 0.006335478680611424,
 'Health': 0.00331858407079646,
 'Environment': 0.001910699919549477,
 'System': 0.0012067578439259854,
 'Staff': 0.00010056315366049879,
 'Chama': 5.0281576830249393e-05,
 'Game': 5.0281576830249393e-05}

Combine user and transaction tables¶

Combine user and transaction tables on both the source and target features.

In [5]:

user_subset = users[['bal','xDAI_blockchain_address']]

In [6]:

transactions_subset

Out[6]:

	id	timeset	transfer_subtype	source	s_gender	s_location	s_business_type	target	t_gender	t_location	t_business_type	tx_token	weight	type	token_name	token_address
72647	170140	2020-04-30 10:43:45.170528	STANDARD	0xC1697C1326fD192438515fE2F7E4cCb0C705C5d2	male	GE Nairobi	Farming/Labour	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	NaN	9007.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
72648	10	2020-01-26 08:26:22.521902	STANDARD	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72	male	Home	Farming/Labour	NaN	100.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
72649	11	2020-01-26 08:27:26.757372	STANDARD	0xD95954e3fCd2f09A6Be5931D24f731eFa63BF435	male	G.E	Farming/Labour	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	NaN	2.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
72650	13	2020-01-26 08:32:05.154096	STANDARD	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72	male	Home	Farming/Labour	NaN	23.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
72651	15	2020-01-26 08:38:42.186525	STANDARD	0x4AfD04b9eD17759B362c8C929207Fe7ad81C39d3	male	Test	Health	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	NaN	12.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
147810	208035	2020-05-11 08:52:34.504171	STANDARD	0x97F5165b544e0869ba3Be80D7eEe8b73a0270Dfe	Unknown gender	kilibole	Farming/Labour	0x5CAaA1f7dC13235Fe181D0307e682c387e75a6ec	Unknown gender	Kilibole	Food/Water	NaN	20.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
147811	208021	2020-05-11 08:49:20.768559	STANDARD	0x9a05d12df366cE3aa1420c6DFFD0db9ce4ba77Fc	Unknown gender	Kikomani	Food/Water	0xb44279a1d11A2bc4b1b3D08D3BEAb8278cc86985	Unknown gender	Bofu	Shop	NaN	350.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
147812	208459	2020-05-11 10:11:18.699013	STANDARD	0x2e44845BE57687bFdcdd26044bB7CdD575781336	male	Miyani	Shop	0xfCF20a412eB6DD345237C7BEeBab53B424b98297	male	Miyani	Shop	NaN	400.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
147813	208395	2020-05-11 10:01:04.805823	STANDARD	0xAc4DB7728940e76BCd98Bb8E60671916f3B7576A	male	Kilifi	Farming/Labour	0x2f99a653F5dc201eA97578A6a203BC4db1eaD2FF	Unknown gender	KIlifi	Education	NaN	20.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4
147814	208396	2020-05-11 10:01:10.449068	STANDARD	0x2f99a653F5dc201eA97578A6a203BC4db1eaD2FF	Unknown gender	KIlifi	Education	0xAc4DB7728940e76BCd98Bb8E60671916f3B7576A	male	Kilifi	Farming/Labour	NaN	20.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4

75167 rows × 16 columns

In [7]:

transactions_subset_v1 = transactions_subset.merge(user_subset, how='left', left_on='source', right_on='xDAI_blockchain_address')
transactions_subset_v1['s_bal'] = transactions_subset_v1['bal']
del transactions_subset_v1['bal']
transactions_subset_v1['s_xDAI_blockchain_address'] = transactions_subset_v1['xDAI_blockchain_address']
del transactions_subset_v1['xDAI_blockchain_address']

In [8]:

transactions_subset_v2 = transactions_subset_v1.merge(user_subset, how='left', left_on='target', right_on='xDAI_blockchain_address')

In [9]:

transactions_subset_v2 = transactions_subset_v1.merge(user_subset, how='left', left_on='target', right_on='xDAI_blockchain_address')
transactions_subset_v2['t_bal'] = transactions_subset_v2['bal']
del transactions_subset_v2['bal']
transactions_subset_v2['t_xDAI_blockchain_address'] = transactions_subset_v2['xDAI_blockchain_address']
del transactions_subset_v2['xDAI_blockchain_address']

In [13]:

transactions_subset_v2.head()

Out[13]:

	id	timeset	transfer_subtype	source	s_gender	s_location	s_business_type	target	t_gender	t_location	t_business_type	tx_token	weight	type	token_name	token_address	s_bal	s_xDAI_blockchain_address	t_bal	t_xDAI_blockchain_address
0	170140	2020-04-30 10:43:45.170528	STANDARD	0xC1697C1326fD192438515fE2F7E4cCb0C705C5d2	male	GE Nairobi	Farming/Labour	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	NaN	9007.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4	56.660892	0xC1697C1326fD192438515fE2F7E4cCb0C705C5d2	11737.726002	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31
1	10	2020-01-26 08:26:22.521902	STANDARD	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72	male	Home	Farming/Labour	NaN	100.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4	11737.726002	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	902.500000	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72
2	11	2020-01-26 08:27:26.757372	STANDARD	0xD95954e3fCd2f09A6Be5931D24f731eFa63BF435	male	G.E	Farming/Labour	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	NaN	2.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4	7297.262576	0xD95954e3fCd2f09A6Be5931D24f731eFa63BF435	11737.726002	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31
3	13	2020-01-26 08:32:05.154096	STANDARD	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72	male	Home	Farming/Labour	NaN	23.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4	11737.726002	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	902.500000	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72
4	15	2020-01-26 08:38:42.186525	STANDARD	0x4AfD04b9eD17759B362c8C929207Fe7ad81C39d3	male	Test	Health	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	male	GE Nairobi	Farming/Labour	NaN	12.0	directed	Sarafu	0x0Fd6e8F2320C90e9D4b3A5bd888c4D556d20AbD4	448.000000	0x4AfD04b9eD17759B362c8C929207Fe7ad81C39d3	11737.726002	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31

In [10]:

# subset the data into the needed columns for clustering
combined = transactions_subset_v2[['source','s_location','s_business_type','target','t_location',
            't_business_type','weight','s_bal','t_bal']]

In [15]:

combined

Out[15]:

	source	s_location	s_business_type	target	t_location	t_business_type	weight	s_bal	t_bal
0	0xC1697C1326fD192438515fE2F7E4cCb0C705C5d2	GE Nairobi	Farming/Labour	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	GE Nairobi	Farming/Labour	9007.0	56.660892	11737.726002
1	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	GE Nairobi	Farming/Labour	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72	Home	Farming/Labour	100.0	11737.726002	902.500000
2	0xD95954e3fCd2f09A6Be5931D24f731eFa63BF435	G.E	Farming/Labour	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	GE Nairobi	Farming/Labour	2.0	7297.262576	11737.726002
3	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	GE Nairobi	Farming/Labour	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72	Home	Farming/Labour	23.0	11737.726002	902.500000
4	0x4AfD04b9eD17759B362c8C929207Fe7ad81C39d3	Test	Health	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	GE Nairobi	Farming/Labour	12.0	448.000000	11737.726002
...	...	...	...	...	...	...	...	...	...
75162	0x97F5165b544e0869ba3Be80D7eEe8b73a0270Dfe	kilibole	Farming/Labour	0x5CAaA1f7dC13235Fe181D0307e682c387e75a6ec	Kilibole	Food/Water	20.0	0.000000	5.000000
75163	0x9a05d12df366cE3aa1420c6DFFD0db9ce4ba77Fc	Kikomani	Food/Water	0xb44279a1d11A2bc4b1b3D08D3BEAb8278cc86985	Bofu	Shop	350.0	0.000000	800.000000
75164	0x2e44845BE57687bFdcdd26044bB7CdD575781336	Miyani	Shop	0xfCF20a412eB6DD345237C7BEeBab53B424b98297	Miyani	Shop	400.0	0.000000	800.000000
75165	0xAc4DB7728940e76BCd98Bb8E60671916f3B7576A	Kilifi	Farming/Labour	0x2f99a653F5dc201eA97578A6a203BC4db1eaD2FF	KIlifi	Education	20.0	400.000000	500.000000
75166	0x2f99a653F5dc201eA97578A6a203BC4db1eaD2FF	KIlifi	Education	0xAc4DB7728940e76BCd98Bb8E60671916f3B7576A	Kilifi	Farming/Labour	20.0	500.000000	400.000000

75167 rows × 9 columns

In [11]:

source = combined.source.values
target = combined.target.values
# remove the source and target variables for clustering
del combined['source']
del combined['target']

In [12]:

# create dummy variables of the categorical variables 
updated = pd.get_dummies(combined)

In [ ]:

# define how many clusters to test
clustersToTest = [10,20,25,30,40,50]
# calculate the optimal number of clusters using the Gap Statistic -https://statweb.stanford.edu/~gwalther/gap
optimalK = OptimalK(parallel_backend='joblib')
n_clusters = optimalK(X=updated, cluster_array=clustersToTest)

In [42]:

optimalK.gap_df.head()

Out[42]:

	n_clusters	gap_value	gap*	ref_dispersion_std	sk	sk*	diff	diff*
0	10.0	3.363615	1.414036e+14	2.510135e+12	0.019816	1.633046e+14	0.200252	1.555745e+14
1	20.0	3.177815	9.154344e+13	1.200491e+12	0.014451	1.057143e+14	0.187308	1.033105e+14
2	25.0	3.001553	7.603468e+13	7.675192e+11	0.011046	8.780176e+13	0.106216	8.643467e+13
3	30.0	2.904411	6.720920e+13	5.602132e+11	0.009074	7.760920e+13	0.230329	7.539618e+13
4	40.0	2.686358	5.289592e+13	6.017818e+11	0.012277	6.108290e+13	-1.248115	6.018132e+13

In [60]:

plt.plot(optimalK.gap_df.n_clusters, optimalK.gap_df.gap_value, linewidth=3)
plt.scatter(optimalK.gap_df[optimalK.gap_df.n_clusters == n_clusters].n_clusters,
            optimalK.gap_df[optimalK.gap_df.n_clusters == n_clusters].gap_value, s=250, c='r')
plt.grid(True)
plt.text(20, 4, 'Clusters: {}'.format(str(clustersToTest)), horizontalalignment='center',verticalalignment='center')
plt.xlabel('Cluster Count')
plt.ylabel('Gap Value')
plt.title('Gap Values by Cluster Count')
plt.savefig('gap_statistic.png')
plt.show()

Compute clusters based off of the following features:

s_location
s_business_type
t_location
t_business_type
weight, which is tokens exchange
s_bal
t_bal

"The KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares (see below). This algorithm requires the number of clusters to be specified. It scales well to large number of samples and has been used across a large range of application areas in many different fields.

The k-means algorithm divides a set of samples into disjoint clusters , each described by the mean of the samples in the cluster. The means are commonly called the cluster “centroids”; note that they are not, in general, points from , although they live in the same space." - https://scikit-learn.org/stable/modules/clustering.html#k-means

In [13]:

kmeans = KMeans(n_clusters=50, random_state=1,n_jobs=-1).fit(updated.values)

/home/aclarkdata/anaconda3/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py:974: FutureWarning: 'n_jobs' was deprecated in version 0.23 and will be removed in 0.25.
  " removed in 0.25.", FutureWarning)

In [14]:

# add the clusters back to the combined dataframe
combined['cluster'] = kmeans.labels_

/home/aclarkdata/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

In [15]:

# add back the source and target variables
combined['source'] = source
combined['target'] = target

/home/aclarkdata/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/home/aclarkdata/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until

In [16]:

combined.head()

Out[16]:

	s_location	s_business_type	t_location	t_business_type	weight	s_bal	t_bal	cluster	source	target
0	GE Nairobi	Farming/Labour	GE Nairobi	Farming/Labour	9007.0	56.660892	11737.726002	13	0xC1697C1326fD192438515fE2F7E4cCb0C705C5d2	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31
1	GE Nairobi	Farming/Labour	Home	Farming/Labour	100.0	11737.726002	902.500000	12	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72
2	G.E	Farming/Labour	GE Nairobi	Farming/Labour	2.0	7297.262576	11737.726002	48	0xD95954e3fCd2f09A6Be5931D24f731eFa63BF435	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31
3	GE Nairobi	Farming/Labour	Home	Farming/Labour	23.0	11737.726002	902.500000	12	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31	0x4AB73CfaC1732a9DcD74BdB4C9605f21832D7C72
4	Test	Health	GE Nairobi	Farming/Labour	12.0	448.000000	11737.726002	13	0x4AfD04b9eD17759B362c8C929207Fe7ad81C39d3	0xBAB77A20a757e8438DfaBF01D5F36DD12d862B31

Calculate and plot Two PCA components of the data.¶

In [165]:

# Create a PCA instance: pca
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(updated
                                       )
df = pd.DataFrame(principalComponents)

df['label'] = kmeans.labels_
colors = plt.cm.Spectral(np.linspace(0, 1, len(df.label.unique())))

for color, label in zip(colors, df.label.unique()):
    
    tempdf = df[df.label == label]
    plt.scatter(tempdf[0], tempdf[1], c=color)
    
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:, 1], c='r', s=500, alpha=0.5,)
plt.grid(True)
plt.text(200000, 260000, 'Clusters are the red dots', horizontalalignment='center',verticalalignment='center')
plt.savefig('pca.png')
plt.show()

Descriptive statistics¶

Calculate relevant statistics, such as median, mean, etc for creating probability distributions in the subpopulation model.

In [17]:

combined.groupby('cluster').mean()

Out[17]:

	weight	s_bal	t_bal
cluster
0	217.737536	332.674357	3122.710726
1	588.111940	793.864819	251651.998315
2	957.820312	7089.179214	21755.181601
3	349.925309	516.042937	64166.491418
4	455.317844	64995.360511	751.148124
5	2443.890625	251651.998315	1746.597767
6	586.917409	1022.866799	38404.821609
7	1533.448040	23214.200804	1469.670862
8	408.100000	895.817336	100579.182676
9	1468.701299	64148.612782	22314.167052
10	271.261258	573.611940	1152.574038
11	365.099390	406.400941	14410.774177
12	790.907135	11235.023756	956.054517
13	433.822322	537.032863	9837.111302
14	8074.000000	251651.998315	121082.429001
15	1187.972973	9402.471887	55225.482860
16	15562.500000	121082.429001	50899.750788
17	389.501989	401.109603	25436.848041
18	4298.095238	34136.137502	62137.100395
19	80000.000000	63145.960000	121082.429001
20	8978.181818	44220.000308	121082.429001
21	1039.047619	100579.182676	1313.687026
22	302.688709	2335.376506	576.133882
23	374.248656	529.628969	31342.309668
24	1631.845550	38576.683107	1335.521733
25	1962.083333	63573.309892	43986.716465
26	285.848711	848.769339	18260.218613
27	37712.500000	15293.127761	6721.732034
28	1941.818182	45110.659482	251651.998315
29	468.716263	17535.443217	791.779968
30	3240.503052	862.117155	993.258934
31	1236.973333	15340.314634	39194.641356
32	1408.134556	15404.220436	68353.725751
33	317.344987	336.022102	6084.374342
34	404.778700	594.851729	44619.093968
35	482.002628	4479.433066	4557.371287
36	3593.148148	3569.248851	121082.429001
37	9354.545455	121082.429001	2014.247057
38	14338.571429	251651.998315	60154.599923
39	503.700787	928.528786	71742.725832
40	175.678483	294.939972	271.114703
41	1302.072607	12999.924780	7072.339809
42	387.122709	697.305678	21694.464157
43	437.794249	568.978270	55748.221328
44	11678.857143	4067.108100	4226.813826
45	548.573207	6283.897324	679.334775
46	13100.037736	4238.100340	28746.781097
47	32377.777778	59578.441515	14515.326330
48	772.342169	4772.491359	12047.885243
49	3661.085106	30063.054365	19776.065227

In [18]:

# compute median, Q1,Q3, mean, and sigma
clustersMedianSourceBalance = []
clusters1stQSourceBalance = []
clusters3rdQSourceBalance = []
clustersMu = []
clustersSigma = []
for i in range(0,len(combined.cluster.unique())):
    temp = combined[combined['cluster']==i]
    clustersMu.append(round(temp.weight.mean(),2))
    clustersSigma.append(round(temp.weight.std(),2))
    clustersMedianSourceBalance.append(round(temp.weight.median(),2))
    clusters1stQSourceBalance.append(round(temp.s_bal.quantile(0.25),2))
    clusters3rdQSourceBalance.append(round(temp.s_bal.quantile(0.75),2))
    

In [23]:

clusters = []
for i in range(0,len(combined.cluster.unique())):
    clusters.append(str(i))
    
    
mixingAgents = clusters.copy()
mixingAgents.append('external')

In [26]:

UtilityTypesOrdered = {}
for i in range(0,len(combined.cluster.unique())):
    UtilityTypesOrdered[str(i)] = dict(zip(list(combined[combined['cluster']==i].t_business_type.value_counts(normalize=True).to_dict().keys()),list(combined[combined['cluster']==i].t_business_type.value_counts(normalize=True).to_dict().values())))
    
UtilityTypesOrdered['external'] =  {'Food/Water':1,
                                            'Fuel/Energy':2,
                                            'Health':3,
                                            'Education':4,
                                            'Savings Group':5,
                                            'Shop':6}

In [28]:

utilityTypesProbability = {}
for i in range(0,len(combined.cluster.unique())):
    utilityTypesProbability[str(i)] = combined[combined['cluster']==i].t_business_type.value_counts(normalize=True).to_dict()
    
    
utilityTypesProbability['external'] = {'Food/Water':0.6,
                                            'Fuel/Energy':0.10,
                                            'Health':0.03,
                                            'Education':0.015,
                                            'Savings Group':0.065,
                                            'Shop':0.19}

In [ ]:

# Create initilization file (copy from here) 

clusters = ['0',
 '1',
 '2',
 '3',
 '4',
 '5',
 '6',
 '7',
 '8',
 '9',
 '10',
 '11',
 '12',
 '13',
 '14',
 '15',
 '16',
 '17',
 '18',
 '19',
 '20',
 '21',
 '22',
 '23',
 '24',
 '25',
 '26',
 '27',
 '28',
 '29',
 '30',
 '31',
 '32',
 '33',
 '34',
 '35',
 '36',
 '37',
 '38',
 '39',
 '40',
 '41',
 '42',
 '43',
 '44',
 '45',
 '46',
 '47',
 '48',
 '49']

mixingAgents = ['0',
 '1',
 '2',
 '3',
 '4',
 '5',
 '6',
 '7',
 '8',
 '9',
 '10',
 '11',
 '12',
 '13',
 '14',
 '15',
 '16',
 '17',
 '18',
 '19',
 '20',
 '21',
 '22',
 '23',
 '24',
 '25',
 '26',
 '27',
 '28',
 '29',
 '30',
 '31',
 '32',
 '33',
 '34',
 '35',
 '36',
 '37',
 '38',
 '39',
 '40',
 '41',
 '42',
 '43',
 '44',
 '45',
 '46',
 '47',
 '48',
 '49',
 'external']


clustersMedianSourceBalance = [150.0,
 340.0,
 250.0,
 20.0,
 330.0,
 320.0,
 240.0,
 300.0,
 300.0,
 50.0,
 900.0,
 120.0,
 400.0,
 180.0,
 300.0,
 6000.0,
 132.5,
 130.0,
 160.0,
 5000.0,
 150.0,
 10000.0,
 200.0,
 10000.0,
 200.0,
 200.0,
 35000.0,
 20000.0,
 100.0,
 500.0,
 425.0,
 13320.0,
 500.0,
 500.0,
 1000.0,
 390.0,
 150.0,
 250.0,
 45000.0,
 36300.0,
 960.0,
 120.0,
 200.0,
 100.0,
 220.0,
 600.0,
 62000.0,
 500.0,
 900.0,
 486.0]

clusters1stQSourceBalance = [56.0,
 118.46,
 105.0,
 64767.51,
 251652.0,
 124.5,
 4139.28,
 146.1,
 1002.5,
 17145.78,
 52676.2,
 100.0,
 121082.43,
 112.0,
 28849.43,
 27619.22,
 66.36,
 251652.0,
 148.0,
 38653.54,
 67.22,
 121082.43,
 6429.46,
 555.04,
 104.48,
 96.43,
 52676.2,
 251652.0,
 64.73,
 36824.5,
 15182.03,
 485.94,
 21660.89,
 11210.0,
 100579.18,
 100.46,
 2845.01,
 3338.98,
 1274.91,
 6724.88,
 38653.54,
 114.5,
 68.0,
 100.0,
 20.93,
 14050.3,
 63145.96,
 9276.23,
 63234.8,
 64767.51]

clusters3rdQSourceBalance = [403.96,
 506.6,
 592.96,
 64767.51,
 251652.0,
 1501.41,
 7214.9,
 869.82,
 1557.01,
 18304.36,
 55142.93,
 419.96,
 121082.43,
 816.3,
 38653.54,
 37106.89,
 770.65,
 251652.0,
 838.46,
 38653.54,
 315.0,
 121082.43,
 9074.79,
 5726.66,
 602.02,
 437.96,
 63234.8,
 251652.0,
 425.0,
 40953.15,
 17145.78,
 6349.27,
 25695.83,
 13156.46,
 100579.18,
 819.33,
 4158.5,
 5597.38,
 2823.81,
 20030.91,
 51710.52,
 537.94,
 542.92,
 415.43,
 895.66,
 18304.36,
 63145.96,
 14050.3,
 64767.51,
 64767.51]

clustersMu = [329.98,
 588.11,
 469.93,
 492.32,
 2443.89,
 565.21,
 1120.5,
 408.1,
 550.09,
 503.42,
 2478.89,
 349.93,
 9354.55,
 453.69,
 4298.1,
 7508.1,
 376.86,
 8074.0,
 333.75,
 7691.43,
 362.68,
 15562.5,
 672.28,
 10809.6,
 274.98,
 405.46,
 34555.56,
 14338.57,
 255.48,
 1229.44,
 1470.23,
 14590.61,
 1527.75,
 770.73,
 1039.05,
 503.7,
 362.11,
 499.51,
 45000.0,
 37504.55,
 1941.82,
 262.96,
 702.23,
 168.57,
 2000.58,
 1383.32,
 65333.33,
 1454.43,
 1483.11,
 1853.03]

clustersSigma = [583.23,
 1501.26,
 966.32,
 1452.2,
 6789.39,
 847.29,
 2228.12,
 483.5,
 852.2,
 1170.38,
 3256.26,
 1174.55,
 16235.99,
 841.35,
 7696.91,
 6814.68,
 785.21,
 10886.9,
 712.65,
 8713.11,
 708.54,
 18542.24,
 1164.0,
 3682.08,
 340.99,
 624.76,
 8171.77,
 15060.34,
 461.52,
 1774.39,
 4617.97,
 4770.82,
 2641.75,
 1133.41,
 767.87,
 437.68,
 652.72,
 761.07,
 7071.07,
 5274.96,
 2716.8,
 572.43,
 1553.21,
 210.61,
 4477.94,
 1798.73,
 31134.12,
 2147.9,
 1900.27,
 2909.68]


# nested dictionary
UtilityTypesOrdered = {'0': {'Food/Water': 0.4119323241317899,
  'Farming/Labour': 0.26090828138913624,
  'Shop': 0.17916295636687443,
  'Savings Group': 0.07266251113089937,
  'Fuel/Energy': 0.034194122885129116,
  'Transport': 0.02617987533392698,
  'Health': 0.006767586821015138,
  'Education': 0.004096170970614425,
  'None': 0.004096170970614425},
 '1': {'Food/Water': 1.0},
 '2': {'Savings Group': 0.87890625,
  'Health': 0.08984375,
  'Food/Water': 0.03125},
 '3': {'Savings Group': 0.4905964535196131,
  'Farming/Labour': 0.3610961848468565,
  'Food/Water': 0.14830736163353037},
 '4': {'Farming/Labour': 0.2843866171003718,
  'Shop': 0.25650557620817843,
  'Fuel/Energy': 0.17843866171003717,
  'Food/Water': 0.16171003717472118,
  'None': 0.10966542750929369,
  'Savings Group': 0.0055762081784386614,
  'Transport': 0.0037174721189591076},
 '5': {'Farming/Labour': 0.421875,
  'Food/Water': 0.421875,
  'Shop': 0.0625,
  'Savings Group': 0.03125,
  'Fuel/Energy': 0.03125,
  'Transport': 0.03125},
 '6': {'Savings Group': 0.6008097165991902,
  'Food/Water': 0.35870445344129553,
  'Shop': 0.04048582995951417},
 '7': {'Farming/Labour': 0.4346590909090909,
  'Food/Water': 0.2869318181818182,
  'Shop': 0.1278409090909091,
  'Fuel/Energy': 0.07670454545454546,
  'Savings Group': 0.03977272727272727,
  'Education': 0.017045454545454544,
  'None': 0.011363636363636364,
  'Transport': 0.002840909090909091,
  'Health': 0.002840909090909091},
 '8': {'Savings Group': 1.0},
 '9': {'Savings Group': 0.7142857142857143,
  'Food/Water': 0.18181818181818182,
  'Farming/Labour': 0.07792207792207792,
  'Education': 0.025974025974025976},
 '10': {'Food/Water': 0.3499875508340941,
  'Farming/Labour': 0.3162088140094614,
  'Shop': 0.21047389824881732,
  'Transport': 0.03950535314133953,
  'None': 0.03386173126400531,
  'Fuel/Energy': 0.022491493069964313,
  'Education': 0.01709685451074778,
  'Savings Group': 0.006473566271059839,
  'Environment': 0.002157855423686613,
  'Health': 0.0016598887874512407,
  'Chama': 8.299443937256204e-05},
 '11': {'Savings Group': 0.4873417721518987,
  'Food/Water': 0.3377445339470656,
  'Education': 0.09723820483314154,
  'Farming/Labour': 0.06271576524741082,
  'Shop': 0.014959723820483314},
 '12': {'Food/Water': 0.34994337485843713,
  'Shop': 0.2332955832389581,
  'Farming/Labour': 0.19592298980747452,
  'Fuel/Energy': 0.057757644394110984,
  'Savings Group': 0.053227633069082674,
  'Education': 0.05096262740656852,
  'None': 0.026047565118912798,
  'Transport': 0.020385050962627407,
  'Health': 0.011325028312570781,
  'Environment': 0.0011325028312570782},
 '13': {'Savings Group': 0.3712871287128713,
  'Food/Water': 0.247974797479748,
  'Shop': 0.19801980198019803,
  'Fuel/Energy': 0.08235823582358236,
  'Health': 0.07605760576057606,
  'Farming/Labour': 0.024302430243024302},
 '14': {'Savings Group': 1.0},
 '15': {'Savings Group': 1.0},
 '16': {'Savings Group': 0.5, 'Food/Water': 0.5},
 '17': {'Savings Group': 0.7335701598579041,
  'Shop': 0.17584369449378331,
  'Food/Water': 0.0905861456483126},
 '18': {'Savings Group': 0.6984126984126984,
  'Food/Water': 0.23809523809523808,
  'Farming/Labour': 0.06349206349206349},
 '19': {'Savings Group': 1.0},
 '20': {'Savings Group': 1.0},
 '21': {'Farming/Labour': 0.47619047619047616,
  'Food/Water': 0.3333333333333333,
  'Shop': 0.09523809523809523,
  'Fuel/Energy': 0.047619047619047616,
  'Transport': 0.047619047619047616},
 '22': {'Food/Water': 0.33040588654165676,
  'Farming/Labour': 0.3209114645145977,
  'Shop': 0.164016140517446,
  'None': 0.06147638262520769,
  'Fuel/Energy': 0.05008307619273677,
  'Transport': 0.028957987182530263,
  'Savings Group': 0.023973415618324233,
  'Education': 0.014478993591265131,
  'Health': 0.0035604082601471635,
  'Environment': 0.0011868027533823878,
  'Staff': 0.00047472110135295516,
  'Chama': 0.00023736055067647758,
  'Game': 0.00023736055067647758},
 '23': {'Savings Group': 0.8323424494649228,
  'Farming/Labour': 0.16765755053507728},
 '24': {'Farming/Labour': 0.38481675392670156,
  'Food/Water': 0.3717277486910995,
  'Shop': 0.1387434554973822,
  'Fuel/Energy': 0.05235602094240838,
  'Transport': 0.02356020942408377,
  'Savings Group': 0.01832460732984293,
  'Education': 0.007853403141361256,
  'Staff': 0.002617801047120419},
 '25': {'Savings Group': 0.7916666666666666,
  'Food/Water': 0.20833333333333334},
 '26': {'Savings Group': 0.7442348008385744, 'Food/Water': 0.2557651991614256},
 '27': {'Food/Water': 0.3333333333333333,
  'Farming/Labour': 0.25,
  'Health': 0.25,
  'Savings Group': 0.08333333333333333,
  'Fuel/Energy': 0.08333333333333333},
 '28': {'Food/Water': 1.0},
 '29': {'Food/Water': 0.27335640138408307,
  'Farming/Labour': 0.23529411764705882,
  'Shop': 0.21972318339100347,
  'Fuel/Energy': 0.21280276816608998,
  'None': 0.03806228373702422,
  'Education': 0.006920415224913495,
  'Transport': 0.006920415224913495,
  'Savings Group': 0.005190311418685121,
  'Staff': 0.0017301038062283738},
 '30': {'Food/Water': 0.36228287841191065,
  'Shop': 0.2679900744416873,
  'Farming/Labour': 0.21712158808933002,
  'Savings Group': 0.08436724565756824,
  'Education': 0.02481389578163772,
  'Fuel/Energy': 0.018610421836228287,
  'Transport': 0.017369727047146403,
  'None': 0.0037220843672456576,
  'Health': 0.0024813895781637717,
  'Environment': 0.0012406947890818859},
 '31': {'Savings Group': 0.8,
  'Food/Water': 0.13333333333333333,
  'Shop': 0.06666666666666667},
 '32': {'Savings Group': 0.7444444444444445,
  'Farming/Labour': 0.2,
  'Food/Water': 0.05555555555555555},
 '33': {'Food/Water': 0.33343474292668085,
  'Farming/Labour': 0.28414968055978096,
  'Savings Group': 0.18892607240644965,
  'Shop': 0.1146942500760572,
  'Fuel/Energy': 0.06936416184971098,
  'None': 0.006693033160937024,
  'Education': 0.0027380590203833284},
 '34': {'Savings Group': 1.0},
 '35': {'Food/Water': 0.3829787234042553,
  'Farming/Labour': 0.2390488110137672,
  'Shop': 0.1902377972465582,
  'Savings Group': 0.07259073842302878,
  'Transport': 0.060075093867334166,
  'Health': 0.030037546933667083,
  'Fuel/Energy': 0.016270337922403004,
  'None': 0.0050062578222778474,
  'Education': 0.0037546933667083854},
 '36': {'Savings Group': 1.0},
 '37': {'Farming/Labour': 0.5454545454545454,
  'Food/Water': 0.36363636363636365,
  'Savings Group': 0.045454545454545456,
  'Shop': 0.045454545454545456},
 '38': {'Savings Group': 1.0},
 '39': {'Savings Group': 1.0},
 '40': {'Farming/Labour': 0.3595236417447678,
  'Food/Water': 0.3165386512578395,
  'Shop': 0.18842928616728913,
  'Fuel/Energy': 0.05108871820167712,
  'None': 0.0360439715312522,
  'Transport': 0.022443802409978154,
  'Education': 0.01039391163413431,
  'Savings Group': 0.00842083010358678,
  'Health': 0.004545134240011275,
  'Staff': 0.0011627087590726517,
  'Environment': 0.0010570079627933197,
  'System': 0.00035233598759777326},
 '41': {'Food/Water': 0.33003300330033003,
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  'Shop': 0.1782178217821782,
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  'Health': 0.0429042904290429,
  'Fuel/Energy': 0.0165016501650165,
  'Transport': 0.0165016501650165,
  'Education': 0.0033003300330033004},
 '42': {'Savings Group': 0.8661740558292282, 'Health': 0.13382594417077176},
 '43': {'Savings Group': 1.0},
 '44': {'Food/Water': 0.4805194805194805,
  'Shop': 0.14285714285714285,
  'Savings Group': 0.14285714285714285,
  'Farming/Labour': 0.13636363636363635,
  'Health': 0.06493506493506493,
  'Transport': 0.012987012987012988,
  'Environment': 0.012987012987012988,
  'Fuel/Energy': 0.006493506493506494},
 '45': {'Food/Water': 0.35471100554235946,
  'Farming/Labour': 0.2414885193982581,
  'Shop': 0.23198733174980204,
  'Education': 0.03800475059382423,
  'None': 0.035629453681710214,
  'Transport': 0.035629453681710214,
  'Fuel/Energy': 0.028503562945368172,
  'Savings Group': 0.02454473475851148,
  'Health': 0.006334125098970704,
  'Environment': 0.001583531274742676,
  'Staff': 0.000791765637371338,
  'System': 0.000791765637371338},
 '46': {'Savings Group': 0.6981132075471698,
  'Health': 0.18867924528301888,
  'Food/Water': 0.09433962264150944,
  'Shop': 0.018867924528301886},
 '47': {'Savings Group': 0.5555555555555556,
  'Farming/Labour': 0.2222222222222222,
  'Food/Water': 0.2222222222222222},
 '48': {'Food/Water': 0.38795180722891565,
  'Savings Group': 0.38313253012048193,
  'Health': 0.10120481927710843,
  'Shop': 0.09879518072289156,
  'Fuel/Energy': 0.016867469879518072,
  'Farming/Labour': 0.012048192771084338},
 '49': {'Food/Water': 0.3829787234042553,
  'Savings Group': 0.3829787234042553,
  'Education': 0.19148936170212766,
  'Fuel/Energy': 0.0425531914893617},
 'external': {'Food/Water': 1,
  'Fuel/Energy': 2,
  'Health': 3,
  'Education': 4,
  'Savings Group': 5,
  'Shop': 6}}
    
#  nested dictionary 
utilityTypesProbability = {'0': {'Food/Water': 0.4119323241317899,
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  'Shop': 0.17916295636687443,
  'Savings Group': 0.07266251113089937,
  'Fuel/Energy': 0.034194122885129116,
  'Transport': 0.02617987533392698,
  'Health': 0.006767586821015138,
  'Education': 0.004096170970614425,
  'None': 0.004096170970614425},
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 '2': {'Savings Group': 0.87890625,
  'Health': 0.08984375,
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 '3': {'Savings Group': 0.4905964535196131,
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  'Food/Water': 0.14830736163353037},
 '4': {'Farming/Labour': 0.2843866171003718,
  'Shop': 0.25650557620817843,
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  'Food/Water': 0.16171003717472118,
  'None': 0.10966542750929369,
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  'Transport': 0.0037174721189591076},
 '5': {'Farming/Labour': 0.421875,
  'Food/Water': 0.421875,
  'Shop': 0.0625,
  'Savings Group': 0.03125,
  'Fuel/Energy': 0.03125,
  'Transport': 0.03125},
 '6': {'Savings Group': 0.6008097165991902,
  'Food/Water': 0.35870445344129553,
  'Shop': 0.04048582995951417},
 '7': {'Farming/Labour': 0.4346590909090909,
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  'Shop': 0.1278409090909091,
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  'Savings Group': 0.03977272727272727,
  'Education': 0.017045454545454544,
  'None': 0.011363636363636364,
  'Transport': 0.002840909090909091,
  'Health': 0.002840909090909091},
 '8': {'Savings Group': 1.0},
 '9': {'Savings Group': 0.7142857142857143,
  'Food/Water': 0.18181818181818182,
  'Farming/Labour': 0.07792207792207792,
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 '10': {'Food/Water': 0.3499875508340941,
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  'Shop': 0.21047389824881732,
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  'None': 0.03386173126400531,
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  'Environment': 0.002157855423686613,
  'Health': 0.0016598887874512407,
  'Chama': 8.299443937256204e-05},
 '11': {'Savings Group': 0.4873417721518987,
  'Food/Water': 0.3377445339470656,
  'Education': 0.09723820483314154,
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 '13': {'Savings Group': 0.3712871287128713,
  'Food/Water': 0.247974797479748,
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  'Farming/Labour': 0.024302430243024302},
 '14': {'Savings Group': 1.0},
 '15': {'Savings Group': 1.0},
 '16': {'Savings Group': 0.5, 'Food/Water': 0.5},
 '17': {'Savings Group': 0.7335701598579041,
  'Shop': 0.17584369449378331,
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 '18': {'Savings Group': 0.6984126984126984,
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  'Farming/Labour': 0.06349206349206349},
 '19': {'Savings Group': 1.0},
 '20': {'Savings Group': 1.0},
 '21': {'Farming/Labour': 0.47619047619047616,
  'Food/Water': 0.3333333333333333,
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  'Transport': 0.047619047619047616},
 '22': {'Food/Water': 0.33040588654165676,
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 '23': {'Savings Group': 0.8323424494649228,
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 '24': {'Farming/Labour': 0.38481675392670156,
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  'Staff': 0.002617801047120419},
 '25': {'Savings Group': 0.7916666666666666,
  'Food/Water': 0.20833333333333334},
 '26': {'Savings Group': 0.7442348008385744, 'Food/Water': 0.2557651991614256},
 '27': {'Food/Water': 0.3333333333333333,
  'Farming/Labour': 0.25,
  'Health': 0.25,
  'Savings Group': 0.08333333333333333,
  'Fuel/Energy': 0.08333333333333333},
 '28': {'Food/Water': 1.0},
 '29': {'Food/Water': 0.27335640138408307,
  'Farming/Labour': 0.23529411764705882,
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  'None': 0.03806228373702422,
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 '30': {'Food/Water': 0.36228287841191065,
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  'Transport': 0.017369727047146403,
  'None': 0.0037220843672456576,
  'Health': 0.0024813895781637717,
  'Environment': 0.0012406947890818859},
 '31': {'Savings Group': 0.8,
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  'Shop': 0.06666666666666667},
 '32': {'Savings Group': 0.7444444444444445,
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 '33': {'Food/Water': 0.33343474292668085,
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 '35': {'Food/Water': 0.3829787234042553,
  'Farming/Labour': 0.2390488110137672,
  'Shop': 0.1902377972465582,
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  'Transport': 0.060075093867334166,
  'Health': 0.030037546933667083,
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 '36': {'Savings Group': 1.0},
 '37': {'Farming/Labour': 0.5454545454545454,
  'Food/Water': 0.36363636363636365,
  'Savings Group': 0.045454545454545456,
  'Shop': 0.045454545454545456},
 '38': {'Savings Group': 1.0},
 '39': {'Savings Group': 1.0},
 '40': {'Farming/Labour': 0.3595236417447678,
  'Food/Water': 0.3165386512578395,
  'Shop': 0.18842928616728913,
  'Fuel/Energy': 0.05108871820167712,
  'None': 0.0360439715312522,
  'Transport': 0.022443802409978154,
  'Education': 0.01039391163413431,
  'Savings Group': 0.00842083010358678,
  'Health': 0.004545134240011275,
  'Staff': 0.0011627087590726517,
  'Environment': 0.0010570079627933197,
  'System': 0.00035233598759777326},
 '41': {'Food/Water': 0.33003300330033003,
  'Farming/Labour': 0.2739273927392739,
  'Shop': 0.1782178217821782,
  'Savings Group': 0.13861386138613863,
  'Health': 0.0429042904290429,
  'Fuel/Energy': 0.0165016501650165,
  'Transport': 0.0165016501650165,
  'Education': 0.0033003300330033004},
 '42': {'Savings Group': 0.8661740558292282, 'Health': 0.13382594417077176},
 '43': {'Savings Group': 1.0},
 '44': {'Food/Water': 0.4805194805194805,
  'Shop': 0.14285714285714285,
  'Savings Group': 0.14285714285714285,
  'Farming/Labour': 0.13636363636363635,
  'Health': 0.06493506493506493,
  'Transport': 0.012987012987012988,
  'Environment': 0.012987012987012988,
  'Fuel/Energy': 0.006493506493506494},
 '45': {'Food/Water': 0.35471100554235946,
  'Farming/Labour': 0.2414885193982581,
  'Shop': 0.23198733174980204,
  'Education': 0.03800475059382423,
  'None': 0.035629453681710214,
  'Transport': 0.035629453681710214,
  'Fuel/Energy': 0.028503562945368172,
  'Savings Group': 0.02454473475851148,
  'Health': 0.006334125098970704,
  'Environment': 0.001583531274742676,
  'Staff': 0.000791765637371338,
  'System': 0.000791765637371338},
 '46': {'Savings Group': 0.6981132075471698,
  'Health': 0.18867924528301888,
  'Food/Water': 0.09433962264150944,
  'Shop': 0.018867924528301886},
 '47': {'Savings Group': 0.5555555555555556,
  'Farming/Labour': 0.2222222222222222,
  'Food/Water': 0.2222222222222222},
 '48': {'Food/Water': 0.38795180722891565,
  'Savings Group': 0.38313253012048193,
  'Health': 0.10120481927710843,
  'Shop': 0.09879518072289156,
  'Fuel/Energy': 0.016867469879518072,
  'Farming/Labour': 0.012048192771084338},
 '49': {'Food/Water': 0.3829787234042553,
  'Savings Group': 0.3829787234042553,
  'Education': 0.19148936170212766,
  'Fuel/Energy': 0.0425531914893617},
 'external': {'Food/Water': 0.6,
  'Fuel/Energy': 0.1,
  'Health': 0.03,
  'Education': 0.015,
  'Savings Group': 0.065,
  'Shop': 0.19}}

# agent:[centrality,allocationValue]
agentAllocation = {'0': [1, 1],
 '1': [1, 1],
 '2': [1, 1],
 '3': [1, 1],
 '4': [1, 1],
 '5': [1, 1],
 '6': [1, 1],
 '7': [1, 1],
 '8': [1, 1],
 '9': [1, 1],
 '10': [1, 1],
 '11': [1, 1],
 '12': [1, 1],
 '13': [1, 1],
 '14': [1, 1],
 '15': [1, 1],
 '16': [1, 1],
 '17': [1, 1],
 '18': [1, 1],
 '19': [1, 1],
 '20': [1, 1],
 '21': [1, 1],
 '22': [1, 1],
 '23': [1, 1],
 '24': [1, 1],
 '25': [1, 1],
 '26': [1, 1],
 '27': [1, 1],
 '28': [1, 1],
 '29': [1, 1],
 '30': [1, 1],
 '31': [1, 1],
 '32': [1, 1],
 '33': [1, 1],
 '34': [1, 1],
 '35': [1, 1],
 '36': [1, 1],
 '37': [1, 1],
 '38': [1, 1],
 '39': [1, 1],
 '40': [1, 1],
 '41': [1, 1],
 '42': [1, 1],
 '43': [1, 1],
 '44': [1, 1],
 '45': [1, 1],
 '46': [1, 1],
 '47': [1, 1],
 '48': [1, 1],
 '49': [1, 1]}