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eba_dataloader.py
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eba_dataloader.py
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import networkx as nx
import numpy as np
import seaborn as sns
import scipy.stats
import matplotlib.pyplot as plt
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
sns.set_theme()
def expon_fit_helper(data):
params = scipy.stats.expon.fit(data)
return 'Exp Fit: scale = {}, loc = {}'.format(round(params[1], 4), round(params[0], 1))
def preprocess_eba_data():
data = pd.read_csv('data/glasserman_young_data.csv')
data.sort_values('w_i', ascending=False, inplace=True)
data['cum_w_i'] = data['w_i'].cumsum()
# Assets from other nodes in the network
internal_assets = data['Assets'] - data['c_i']
internal_assets.to_csv('data/glasserman_young_data_internal_assets.csv')
# Construct internal liabilities
random_petrubations = np.random.normal(0, 100, size=len(internal_assets))
peturbed_internal_assets = internal_assets + random_petrubations
peturbed_internal_assets_sum = peturbed_internal_assets.sum()
internal_assets_sum = internal_assets.sum()
internal_liabilities = peturbed_internal_assets * internal_assets_sum / peturbed_internal_assets_sum
internal_liabilities = internal_liabilities.round(1)
internal_liabilities.iloc[-1] = internal_assets_sum - internal_liabilities[:-1].sum()
internal_liabilities.to_csv('data/glasserman_young_data_internal_liabilities.csv')
# Calculate external liabilities
external_liabilities = internal_liabilities / data['beta_i'] - internal_liabilities
external_liabilities.to_csv('data/glasserman_young_data_external_liabilities.csv')
# External assets
external_assets = data['c_i']
external_assets.to_csv('data/glasserman_young_data_external_assets.csv')
plt.figure(figsize=(12, 10))
pearson_corr = scipy.stats.pearsonr(internal_liabilities, external_liabilities)[0]
jointplot = sns.jointplot(internal_liabilities, external_liabilities, kind='reg')
coeffs = np.round(np.polyfit(internal_liabilities.to_numpy().flatten(), external_liabilities.to_numpy().flatten(), deg=1), 2)
jointplot.fig.suptitle('$R^2 = {}$, $a = {}$, $b = {}$'.format(
round(pearson_corr, 3), round(coeffs[0], 2), round(coeffs[1], 2)), fontsize=12)
plt.ylabel('Int. Liabilities')
plt.xlabel('Ext. Liabilities')
plt.savefig('glasserman_young_internal_liabilities_external_liabilities_reg.png')
plt.figure(figsize=(12, 10))
pearson_corr = scipy.stats.pearsonr(external_assets, internal_liabilities)[0]
jointplot = sns.jointplot(external_assets, external_liabilities, kind='reg')
coeffs = np.round(np.polyfit(external_assets.to_numpy().flatten(), external_liabilities.to_numpy().flatten(), deg=1), 2)
jointplot.fig.suptitle('$R^2 = {}$, $a = {}$, $b = {}$'.format(
round(pearson_corr, 3), round(coeffs[0], 2), round(coeffs[1], 2)), fontsize=12)
plt.ylabel('Ext. Liabilities')
plt.xlabel('Ext. Assets')
plt.savefig('glasserman_young_external_assets_external_liabilities_reg.png')
plt.figure(figsize=(10, 10))
sns.distplot(data['w_i'], kde=False, fit=scipy.stats.expon,
label='Wealth {}'.format(expon_fit_helper(data['w_i'])))
plt.xlim(0, data['w_i'].max())
plt.legend()
plt.savefig('glasserman_young_wealths.png')
plt.figure(figsize=(10, 10))
sns.distplot(external_assets, kde=False, fit=scipy.stats.expon,
label='External Assets {}'.format(expon_fit_helper(external_assets)))
sns.distplot(external_liabilities, kde=False, fit=scipy.stats.expon,
label='External Liabilities {}'.format(expon_fit_helper(external_liabilities)))
plt.xlim(0, external_assets.max())
plt.legend()
plt.savefig('glasserman_young_external_assets_external_liabilities_dist.png')
plt.figure(figsize=(10, 10))
sns.distplot(internal_assets, kde=False, fit=scipy.stats.expon,
label='Internal Assets {}'.format(expon_fit_helper(internal_assets)))
# sns.distplot(internal_liabilities, kde=False, fit=scipy.stats.expon, label='Internal Liabilities (after pertubation)')
plt.xlim(0, max(internal_assets.max(), internal_liabilities.max()))
plt.legend()
plt.savefig('glasserman_young_data_internal_assets.png')
plt.figure(figsize=(10, 10))
plt.plot(np.linspace(0, 1, len(data)), data['cum_w_i'].to_numpy())
plt.ylabel('Cummulative Wealth')
plt.xlabel('Percentile')
plt.savefig('glasserman_young_cummwealth.png')
plt.show()
def load_eba_dataset():
data = pd.read_csv('data/glasserman_young_data.csv')
external_liabilities = pd.read_csv(
'data/glasserman_young_data_external_liabilities.csv').to_numpy()[:, -1]
external_assets = pd.read_csv(
'data/glasserman_young_data_external_assets.csv').to_numpy()[:, -1]
for i in range(1, 51):
liabilities = np.genfromtxt(
'data/glasserman_young_data_liabilities_matrix_{}.csv'.format(i), delimiter=' ', dtype=np.float64)
adj = (liabilities > 0).astype(np.float64)
outdegree = adj.sum(0)
indegree = adj.sum(-1)
G = nx.from_numpy_matrix(adj, create_using=nx.DiGraph)
internal_assets = liabilities.sum(-1).reshape((len(G), 1))
internal_liabilities = liabilities.sum(0).reshape((len(G), 1))
external_assets = external_assets.reshape((len(G), 1))
external_liabilities = external_liabilities.reshape((len(G), 1))
wealth = external_assets + internal_assets - external_liabilities - internal_liabilities
P_bar = internal_liabilities + external_liabilities
p = adj.sum() / (len(data)**2 - len(data))
A = np.copy(liabilities)
for i in range(liabilities.shape[0]):
A[i] /= P_bar[i]
yield data, A, P_bar, liabilities, adj, internal_assets, internal_liabilities, outdegree, indegree, p, external_assets, external_liabilities, wealth, G
if __name__ == '__main__':
preprocess_eba_data()