def main():
start_time = time.time()
print("Reading input parameters..")
configParams = config.readMainConfigs()
#try:
config.setGlobals(configParams)
N = configParams[config.mode]['n']
companyIDSeed = configParams[config.mode]['company_id_seed']
#print('Choose what you wan to do')
#print('1. Generate metadata\n2. Generate transactions from existing metadata\n3. Perform sanity check')
m.generateMetadata(N, companyIDSeed)
print("Metadata generation successful!! Check output file at " +
config.metadataFile)
#except:
#print ("Error is generating metadata..")
#try:
t.generateTransactionData(companyIDSeed)
end_time = time.time()
print("Execution time: ", end_time - start_time)
#except:
#print ("Error is generating transaction data..")
start_time = time.time()
ds.summarizeData()
end_time = time.time()
print("Execution time: ", end_time - start_time)
start_time = time.time()
#dc.sanityCheck()
end_time = time.time()
print("Execution time: ", end_time - start_time)
def postProcessor():
# Read config files
print('Reading config parameters for post-processing')
try:
mainConfig = con.readMainConfigs()
con.setGlobals(mainConfig)
configParams = con.readConfigFile(con.paramConfigFile)
configDistributions = con.readConfigFile(con.distributionsFile)
modeparams = mainConfig[con.mode]
except:
print(
'Error in reading config files.. Make sure you have not made any mistake in editing parameters'
)
if con.mode != 'M':
sys.exit(
'BTS is not operating in mixining mode. Please change the mode in mainConfig.ini file to M.'
)
tran_info_path = os.path.join(modeparams['mixed_data'],
modeparams['traninfo_folder'])
# File paths
vertices = tran_info_path + 'vertices.csv'
edges = tran_info_path + 'edges.csv'
metadata_file = modeparams['mixed_data'] + 'combinedMetadata.csv'
print('Reading combined metadata file..')
metadata_df = pd.read_csv(metadata_file)
print('Metadata read for ', len(metadata_df), ' companies.')
print('Reading vertices and edges..')
# Read vertices and edges
#v_set = pd.read_csv(vertices)
all_files_v = glob.glob(tran_info_path + 'vertices*.csv')
df_list_v = list()
for file in all_files_v:
df = pd.read_csv(file)
df_list_v.append(df)
v_set = pd.concat(df_list_v, ignore_index=True)
all_files_v.clear()
df_list_v.clear()
all_files = glob.glob(tran_info_path + 'edge*.csv')
df_list = list()
for file in all_files:
df = pd.read_csv(file)
df_list.append(df)
e_set = pd.concat(df_list, ignore_index=True)
all_files.clear()
df_list.clear()
acc_under_study = list(metadata_df.loc[:, 'acc_no'])
print('Number of edges read = ', len(e_set))
print('Number of vertices read = ', len(v_set))
if modeparams['remove_edges'] == 'Y':
print('Removing edges for outside accounts')
# Graph with just accounts under study
v_set_under_study = v_set.loc[v_set.vertex.isin(acc_under_study),
['vertex']]
e_set_under_study = e_set.loc[(e_set.src_acc.isin(acc_under_study))
& (e_set.dest_acc.isin(acc_under_study))]
# Write reduced vertices and edges
v_set_under_study.to_csv(modeparams['mixed_data'] +
'\\reducedVertices.csv',
index=False)
e_set_under_study.to_csv(modeparams['mixed_data'] +
'\\reducedEdges.csv',
index=False)
else:
print('Collapsing edges for outside accounts')
# Collapse edges by adding a dummy account
n = len(v_set)
#v_set.set_index(np.arange(n))
v_set_under_study = v_set.loc[v_set.vertex.isin(acc_under_study),
['vertex']]
#v_set_under_study.set_index(np.arange(len(v_set_under_study)))
v_set_under_study.loc[len(v_set_under_study), 'vertex'] = 'outside'
e_set_under_study = e_set.loc[(e_set.src_acc.isin(acc_under_study)) |
(e_set.dest_acc.isin(acc_under_study))]
e_set_under_study.loc[(e_set.src_acc.isin(acc_under_study))
& ~(e_set.dest_acc.isin(acc_under_study)),
'dest_acc'] = 'outside'
e_set_under_study.loc[(e_set.dest_acc.isin(acc_under_study))
& ~(e_set.src_acc.isin(acc_under_study)),
'src_acc'] = 'outside'
e_set_under_study = e_set_under_study.groupby(
by=['src_acc', 'dest_acc'], as_index=False).agg({
'amount': np.sum,
'n_tran': np.sum
})
#e_set_under_study.columns = e_set_under_study.columns.droplevel(level=0)
e_set_under_study.columns = ['src_acc', 'dest_acc', 'amount', 'n_tran']
# Write reduced vertices and edges
v_set_under_study.to_csv(modeparams['mixed_data'] +
'collapsedVertices.csv',
index=False)
e_set_under_study.to_csv(modeparams['mixed_data'] +
'collapsedEdges.csv',
index=False)
print('Number of edges reduced to ', len(e_set_under_study))
print('Number of vertices reduced to ', len(v_set_under_study))
def summarizeData ():
# Read config files
print('Reading config parameters for data summary')
try:
mainConfig = con.readMainConfigs ()
con.setGlobals(mainConfig)
configParams = con.readConfigFile(con.paramConfigFile)
configDistributions = con.readConfigFile(con.distributionsFile)
modeparams = mainConfig[con.mode]
distributions = configDistributions['distributions']
commonParams = configParams['common']
home_location = commonParams['home_location']
except:
print('Error in reading config files.. Make sure you have not made any mistake in editing parameters')
# Read metadata
metadata_df = pd.DataFrame()
try:
metadata_df = pd.read_csv(con.metadataFile, converters={'in_amt_wt':ast.literal_eval, 'supply_amt_wt':ast.literal_eval, 'utility_proportions': ast.literal_eval, 'inside_customers': ast.literal_eval, 'inside_suppliers':ast.literal_eval, 'utility_accs':ast.literal_eval},index_col='index')
except:
print('Error in reading metadata')
metadata_df['industry_sector'] = metadata_df.industry_sector.astype('str')
company_acc = metadata_df['acc_no']
N = len(metadata_df)
print('Metadata for ', N, ' companies is read successfully!')
# Load tran data in chunks prepare summary for small data
allFiles = glob.glob(con.tranData + '\*.csv')
n_files = len(allFiles)
if n_files == 0:
print('No transaction data found!! Make sure trandatapath parameter is set appropriately.')
else:
chunk_size = 8
n_iterations = math.ceil(n_files / chunk_size)
# Prepare folder for summarized data
companyDataPath = modeparams['company_data_path']
summaryDataPath = companyDataPath + 'SummaryData\\'
tranInfoPath = companyDataPath + 'TranInfo\\'
if not os.path.exists(summaryDataPath):
os.makedirs(summaryDataPath)
if not os.path.exists(tranInfoPath):
os.makedirs(tranInfoPath)
i = 0
for i in range(n_iterations):
# Read chunk
start_index = i * chunk_size
end_index = start_index + chunk_size - 1 if i < n_iterations - 1 else n_files -1
print('Files getting read from ', start_index, end_index)
tran_data_df = readTransactionData(con.tranData, start_index, end_index)
# Summarize chunk
partial_summary_df = metadata_df[['acc_no']]
partial_summary_df, partial_traninfo_df = prepareSummary (partial_summary_df, tran_data_df, home_location)
# Write summary to disk
file_path = summaryDataPath + 'partialSummary' + str(i) + '.csv'
partial_summary_df.to_csv (file_path)
file_path = tranInfoPath + 'partialTranInfo' + str(i) + '.csv'
partial_traninfo_df.to_csv (file_path)
print('Partial summary files are written..')
# Propare final summary
summarized_df = metadata_df[['acc_no', 'annual_revenue']]
summarized_df = combinePartialSummaries(summaryDataPath, summarized_df)
combinedFiles_df_grouped = combineTranInfoFiles (tranInfoPath)
# Find out clients and expense account from both
# Get n_clients_total, n_clients_within, n_expense_accs_total, n_expense_acc_within
acc_no_list = list(metadata_df.loc[:,'acc_no'])
for_clients_total = combinedFiles_df_grouped.loc[(combinedFiles_df_grouped.src_acc != 'CASHC') & (combinedFiles_df_grouped.dest_acc.isin(acc_no_list))]
for_clients_total_grouped = for_clients_total.groupby(by='dest_acc', as_index=False).agg({'src_acc':pd.Series.nunique})
for_clients_total_grouped.columns = ['acc_no', 'n_clients_total']
summarized_df = summarized_df.merge(for_clients_total_grouped, on='acc_no',how='left')
for_clients_within = for_clients_total.loc[for_clients_total.src_acc.isin(acc_no_list)]
for_clients_within = for_clients_within.groupby(by='dest_acc', as_index=False).agg({'src_acc':pd.Series.nunique})
for_clients_within.columns = ['acc_no', 'n_clients_within']
summarized_df = summarized_df.merge(for_clients_within, on='acc_no',how='left')
for_exp_acc_total = combinedFiles_df_grouped.loc[(~combinedFiles_df_grouped.dest_acc.isin(['CASHS', 'CASHU'])) & (combinedFiles_df_grouped.src_acc.isin(acc_no_list))]
for_exp_acc_total_grouped = for_exp_acc_total.groupby(by='src_acc', as_index=False).agg({'dest_acc':pd.Series.nunique})
for_exp_acc_total_grouped.columns = ['acc_no', 'n_exp_acc_total']
summarized_df = summarized_df.merge(for_exp_acc_total_grouped, on='acc_no',how='left')
for_exp_acc_within = for_exp_acc_total.loc[for_exp_acc_total.dest_acc.isin(acc_no_list)]
for_exp_acc_within = for_exp_acc_within.groupby(by='src_acc', as_index=False).agg({'dest_acc':pd.Series.nunique})
for_exp_acc_within.columns = ['acc_no', 'n_exp_acc_within']
summarized_df = summarized_df.merge(for_exp_acc_within, on='acc_no',how='left')
summarized_df.to_csv (summaryDataPath + 'finalSummary.csv', index=False)
#src_acc = set(combinedFiles_df_grouped.loc[:,'src_acc'])
#dest_acc = set(combinedFiles_df_grouped.loc[:,'dest_acc'])
#v = src_acc.union(dest_acc)
#v_df = pd.DataFrame(data = list(v), columns=['vertex'])
#v_df.to_csv(tranInfoPath + 'vertices.csv', index=False)
#e = list(combinedFiles_df_grouped.itertuples(index=False,name=None))
#combinedFiles_df_grouped.to_csv(tranInfoPath + 'edges.csv', index = False)
# Print msg
print('First level data summarization for sanity check is successful! Check out summary file at ', summaryDataPath)
#summarizeData()
def mixer():
# Read config files
print('Reading config parameters for data summary')
try:
mainConfig = con.readMainConfigs()
con.setGlobals(mainConfig)
configParams = con.readConfigFile(con.paramConfigFile)
configDistributions = con.readConfigFile(con.distributionsFile)
modeparams = mainConfig[con.mode]
except:
print(
'Error in reading config files.. Make sure you have not made any mistake in editing parameters'
)
# Read metadata files and join them
print('Reading both metadata files..')
metadata_df = pd.DataFrame()
for file in con.metadataFile:
df = pd.read_csv(file,
converters={
'in_amt_wt': ast.literal_eval,
'supply_amt_wt': ast.literal_eval,
'utility_proportions': ast.literal_eval,
'inside_customers': ast.literal_eval,
'inside_suppliers': ast.literal_eval,
'utility_accs': ast.literal_eval
},
index_col='index')
metadata_df = pd.concat([metadata_df, df])
# Write joint metadata file
combined_metadata_path = con.mixedData + 'combinedMetadata.csv'
metadata_df.to_csv(combined_metadata_path, index=False)
print(
'Metadata is combined successfully! Metadata file can be accessed at ',
combined_metadata_path)
# Combine tranInfo
print('Combining transaction information files')
combinedFiles_df_grouped = pd.DataFrame()
for path in con.tranInfo:
df = old_combineTranInfoFiles(path)
combinedFiles_df_grouped = pd.concat([combinedFiles_df_grouped, df])
# get vertices and edges
mixed_data_path = os.path.join(modeparams['mixed_data'],
modeparams['traninfo_folder'])
if not os.path.exists(mixed_data_path):
os.makedirs(mixed_data_path)
src_acc = set(combinedFiles_df_grouped.loc[:, 'src_acc'])
dest_acc = set(combinedFiles_df_grouped.loc[:, 'dest_acc'])
v = src_acc.union(dest_acc)
v_df = pd.DataFrame(data=list(v), columns=['vertex'])
#v_df.to_csv(mixed_data_path + 'vertices.csv', index=False)
#Write vertices
file_size_v = 50000
n_files_v = ceil(len(v_df) / file_size_v)
i = 0
while i < n_files_v:
start_index = i * file_size_v
end_index = start_index + file_size_v
vSet = v_df[start_index:end_index]
vSet.to_csv(mixed_data_path + 'vertices' + str(i) + '.csv',
index=False)
i = i + 1
file_size = 50000
n_files = ceil(len(combinedFiles_df_grouped) / file_size)
i = 0
while i < n_files:
start_index = i * file_size
end_index = start_index + file_size
edgeSet = combinedFiles_df_grouped[start_index:end_index]
edgeSet.to_csv(mixed_data_path + 'edges' + str(i) + '.csv',
index=False)
i = i + 1
#e = list(combinedFiles_df_grouped.itertuples(index=False,name=None))
#combinedFiles_df_grouped.to_csv(mixed_data_path + 'edges.csv', index = False)
print(
'Transaction data combined successfully! Vertices and edges can be found at ',
mixed_data_path)
def sanityCheck():
# Read config files
print('Reading config parameters')
try:
mainConfig = con.readMainConfigs()
con.setGlobals(mainConfig)
configParams = con.readConfigFile(con.paramConfigFile)
configDistributions = con.readConfigFile(con.distributionsFile)
modeparams = mainConfig[con.mode]
distributions = configDistributions['distributions']
commonParams = configParams['common']
home_location = commonParams['home_location']
except:
print(sys.exc_info()[0], ' error occured!')
print(
'Error in reading config files.. Make sure you have not made any mistake in editing parameters'
)
sys.exit(1)
# Read metadata
try:
metadata_df = pd.read_csv(con.metadataFile,
converters={
'in_amt_wt': ast.literal_eval,
'supply_amt_wt': ast.literal_eval,
'utility_proportions': ast.literal_eval,
'inside_customers': ast.literal_eval,
'inside_suppliers': ast.literal_eval,
'utility_accs': ast.literal_eval
},
index_col='index')
# Read summary
companyDataPath = modeparams['company_data_path']
summaryDataPath = companyDataPath + 'SummaryData\\finalSummary.csv'
summary_Df = pd.read_csv(summaryDataPath)
tranInfoPath = companyDataPath + '\\TranInfo'
edgeList = pd.read_csv(tranInfoPath + '\\edges.csv')
vertexList = pd.read_csv(tranInfoPath + '\\vertices.csv')
N_tran_total = np.sum(edgeList['n_tran'])
group_id_list = list(metadata_df.groupby('group_id').groups.keys())
n_accounts = len(vertexList)
n_edges = len(edgeList)
n_groups = len(group_id_list)
except:
print(sys.exc_info()[0], ' error occured!')
print('Error in reading metadata')
sys.exit(1)
metadata_df['industry_sector'] = metadata_df.industry_sector.astype('str')
company_acc = metadata_df['acc_no']
N = len(metadata_df)
print('Metadata for ', N, ' companies is read successfully!')
# Save to file
metafile_path = con.additionalFiles + '\\metafile.txt'
meta_file = open(metafile_path, 'w')
meta_file.write('Data set type :' + con.mode)
meta_file.write('\nNumber of companies :' + str(N))
meta_file.write('\nTotal transactions generated : ' + str(N_tran_total))
meta_file.write('\nNumber of vertices : ' + str(n_accounts))
meta_file.write('\nNumber of edges (connections) : ' + str(n_edges))
meta_file.write('\nNumber of groups : ' + str(n_groups))
meta_file.close()
# Read sanity check file
sanityCheck_df = pd.read_csv('sanityCheck.csv',
converters={
'param_bins': ast.literal_eval,
'distribution': ast.literal_eval
})
n_checks = len(sanityCheck_df)
sanityCheck_df['KL_divergence'] = [0.0] * n_checks
sanityCheck_df['status'] = ['F'] * n_checks
for i in range(n_checks):
param_name = sanityCheck_df.loc[i, 'parameter']
print('Checking for parameter ', param_name)
type = sanityCheck_df.loc[i, 'type']
if type == 'm':
param_dist = commonParams[param_name]
paramValue = {k: v for k, v in sorted(param_dist.items())}
expected_dist = np.array(list(paramValue.values()))
param_grp = metadata_df.groupby(
by=param_name, as_index=False).comp_id.agg('count')
param_grp = param_grp.sort_values(by=param_name)
param_grp['comp_id'] = param_grp['comp_id'] / N
param_grp = {str(k): v for k, v in param_grp.items()}
actual_dist = param_grp['comp_id'].get_values()
KLD = entropy(expected_dist, actual_dist)
else:
bins = sanityCheck_df.loc[i, 'param_bins']
distribution = sanityCheck_df.loc[i, 'distribution']
if param_name in metadata_df.columns:
slice = metadata_df.loc[:, param_name]
else:
slice = summary_Df.loc[:, param_name]
bin_len = len(bins)
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111)
arr = ax1.hist(slice,
bins,
facecolor='red',
alpha=0.5,
edgecolor='brown')
observed_distribution = list(arr[0] / N)
KLD = entropy(distribution, observed_distribution)
sanityCheck_df.loc[i, 'KL_divergence'] = KLD
if abs(KLD * 100) < 5.0:
sanityCheck_df.loc[i, 'status'] = 'P'
print('KL divergence is ', KLD, ' Status - Successful')
else:
print('KL divergence is ', KLD, ' Status - Failed')
sanityCheck_df.to_csv(con.additionalFiles + '\\sanityCheckReport.csv')
## Visualizations
# Industry sector
fig1 = plt.figure(1)
sector_grps = metadata_df.groupby(
by='industry_sector', as_index=False).agg({'comp_id': 'count'})
industry_sector_names = list(sector_grps.loc[:, 'industry_sector'])
industry_sectors = np.arange(len(industry_sector_names))
cnt_per_sector = list(sector_grps.loc[:, 'comp_id'])
ax1 = fig1.add_subplot(111)
ax1.bar(industry_sectors,
cnt_per_sector,
label='Number of companies per sector',
facecolor='green',
edgecolor='gray')
ax1.set_xticks(industry_sectors)
ax1.set_xticklabels(industry_sector_names, rotation=45)
ax1.set_title('No. of companies per sector', fontsize=10)
ax1.set_xlabel('Industry sector', fontsize=8)
ax1.set_ylabel('No. of companies', fontsize=8)
fig1.savefig(con.additionalFiles + '\\SectrwiseNoCompanies.png')
# Employee base
fig2 = plt.figure(2)
emp_base_grps = metadata_df.groupby(
by='employee_base', as_index=False).agg({'comp_id': 'count'})
emp_base_names = list(emp_base_grps.loc[:, 'employee_base'])
base_ticks = np.arange(len(emp_base_names))
cnt_per_size = list(emp_base_grps.loc[:, 'comp_id'])
ax1 = fig2.add_subplot(111)
ax1.bar(base_ticks,
cnt_per_size,
label='Number of companies per base size',
facecolor='green',
edgecolor='gray')
ax1.set_xticks(base_ticks)
ax1.set_xticklabels(emp_base_names, rotation=45)
ax1.set_title('No. of companies per base size', fontsize=10)
ax1.set_xlabel('Employee base sizes', fontsize=8)
ax1.set_ylabel('No. of companies', fontsize=8)
fig2.savefig(con.additionalFiles + '\\EmployeeBaseDistribution.png')
# Number of employees
fig3 = plt.figure(3)
num_employees = metadata_df.loc[:, 'num_employees']
bins = [0, 50, 100, 200, 500, 2000]
ax1 = fig3.add_subplot(111)
arr = ax1.hist(num_employees,
bins,
facecolor='lightblue',
edgecolor='gray')
ax1.set_xlabel('Number of employees', fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title('Distribution of number of employees', fontsize=10)
for i in range(len(arr[0])):
plt.text(arr[1][i], arr[0][i], str(arr[0][i] / N * 100))
#ax1.set_xscale('log')
max_no = max(num_employees)
min_no = min(num_employees)
text = 'Max. = ' + str(max_no) + '\nMin. = ' + str(min_no)
plt.text(0, 40, text, fontsize=10)
fig3.savefig(con.additionalFiles + '\\NumEmployees.png')
# Employee profitability
fig4 = plt.figure(4)
emp_profitability = metadata_df.loc[:, 'rev_per_emp_per_month']
bins = [1e3, 1e4, 1e5, 1e6, 1e7, 1e8]
ax1 = fig4.add_subplot(111)
arr = ax1.hist(emp_profitability,
bins,
facecolor='lightblue',
edgecolor='gray')
ax1.set_xlabel('Employee profitability in Rs.', fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title('Distribution of employee profitability', fontsize=10)
ax1.set_xscale('log')
for i in range(len(arr[0])):
plt.text(arr[1][i], arr[0][i], str(arr[0][i] / N * 100))
max_no = max(emp_profitability)
min_no = min(emp_profitability)
text = 'Max. = ' + str(max_no) + '\nMin. = ' + str(min_no)
plt.text(1e3, 100, text, fontsize=10)
fig4.savefig(con.additionalFiles + '\\EmployeeProfitability.png')
# Annual revenue
fig5 = plt.figure(5)
annual_rev = summary_Df.loc[:, 'generated_annual_revenue']
bins = [1e5, 1e6, 1e7, 1e8, 1e9, 1e10, 1e11, 1e12]
ax1 = fig5.add_subplot(111)
arr = ax1.hist(annual_rev,
bins,
facecolor='lightblue',
edgecolor='gray')
ax1.set_xlabel('Genearted annual revenue by BTS in Rs.', fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title('Distribution of annual revenue', fontsize=10)
#ax3.set_yscale('log')
ax1.set_xscale('log')
for i in range(len(arr[0])):
plt.text(arr[1][i], arr[0][i], str(arr[0][i] / N * 100))
max_revenue = max(annual_rev)
min_revenue = min(annual_rev)
text = 'Max. = ' + str(max_revenue) + '\nMin. = ' + str(min_revenue)
plt.text(1e5, 100, text, fontsize=10)
fig5.savefig(con.additionalFiles + '\\AnnualRevenue.png')
# deviation in annual revenue
fig6 = plt.figure(6)
dev_annual_rev = summary_Df.loc[:, 'revenue_deviation_per']
bins = 11
ax1 = fig6.add_subplot(111)
arr = ax1.hist(dev_annual_rev,
bins,
facecolor='lightblue',
edgecolor='gray')
ax1.set_xlabel('Deviation in generated annual revenue in Rs.',
fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title('Distribution of deviation in generated annual revenue',
fontsize=10)
for i in range(len(arr[0])):
plt.text(arr[1][i], arr[0][i], str(arr[0][i] / N * 100))
max_deviation = min(dev_annual_rev)
text = 'Max. = ' + str(max_deviation)
plt.text(arr[1][0], 100, text, fontsize=10)
fig6.savefig(con.additionalFiles + '\\AnnualRevenueDeviation.png')
# Generated expenses
fig7 = plt.figure(7)
generated_expenses = summary_Df.loc[:, 'generated_expenses']
bins = [1e5, 1e6, 1e7, 1e8, 1e9, 1e10, 1e11, 1e12, 1e13]
ax1 = fig7.add_subplot(111)
arr = ax1.hist(generated_expenses,
bins,
facecolor='lightblue',
edgecolor='gray')
ax1.set_xlabel('Generated expenses in Rs.', fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title('Distribution of generation of expenses', fontsize=10)
ax1.set_xscale('log')
for i in range(len(arr[0])):
plt.text(arr[1][i], arr[0][i], str(arr[0][i]))
max_expenses = max(generated_expenses)
max_expenses = min(generated_expenses)
text = 'Max. = ' + str(max_expenses) + '\nMin. = ' + str(max_expenses)
plt.text(1e5, 100, text, fontsize=10)
fig7.savefig(con.additionalFiles + '\\GeneratedExpenses.png')
# Profit loss
fig8 = plt.figure(8)
profit_loss_per = summary_Df.loc[:, 'profit_loss_per']
bins = [-100, -75, -50, -25, 0, 10, 20, 30, 100]
ax1 = fig8.add_subplot(111)
arr = ax1.hist(profit_loss_per,
bins,
facecolor='red',
alpha=0.5,
edgecolor='brown')
#ax1.yticks([0, 10, 20, 30, 40, 50, 60, 70, 80,90])
ax1.set_xlabel('Net profit/loss in %', fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title('Distribution of net profit/loss', fontsize=10)
#ax1.set_yscale('log')
for i in range(len(arr[0])):
ax1.text(arr[1][i], arr[0][i], str(arr[0][i]))
max_profit = max(profit_loss_per)
max_loss = abs(min(profit_loss_per))
text = 'Maximum profit = ' + str(
max_profit) + '%' + '\nMaximum loss = ' + str(
max_loss) + '%' + '\nN=' + str(N)
ax1.text(-100, 40, text, fontsize=12)
fig8.savefig(con.additionalFiles + '\\ProfitLoss.png')
# Percentage of incoming transactions
fig9 = plt.figure(9)
nan_indices = np.argwhere(
np.isnan(summary_Df['i_tran_within_percentage']))
nan_indices = list(nan_indices[:, 0])
summary_Df.loc[nan_indices, 'i_tran_within_percentage'] = 0
i_tran_within = summary_Df.loc[:, 'i_tran_within_percentage']
bins = 11
ax1 = fig9.add_subplot(111)
arr = ax1.hist(i_tran_within,
bins,
facecolor='red',
alpha=0.5,
edgecolor='brown')
ax1.set_xlabel('Percentage of incoming transations from within set',
fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title(
'Distribution of percenatge of incoming transactions from within set',
fontsize=10)
#ax1.set_yscale('log')
for i in range(len(arr[0])):
ax1.text(arr[1][i], arr[0][i], str(arr[0][i]))
max_per = max(i_tran_within)
min_per = min(i_tran_within)
text = 'Maximum = ' + str(max_per) + '%' + '\nMaximum loss = ' + str(
min_per) + '%' + '\nN=' + str(N)
ax1.text(0, 40, text, fontsize=12)
fig9.savefig(con.additionalFiles + '\\PercentageTranWithin.png')
# Percentage of outgoing transactions
fig10 = plt.figure(10)
nan_indices = np.argwhere(
np.isnan(summary_Df['o_tran_within_percentage']))
nan_indices = list(nan_indices[:, 0])
summary_Df.loc[nan_indices, 'o_tran_within_percentage'] = 0
o_tran_within = summary_Df.loc[:, 'o_tran_within_percentage']
bins = [0, 10, 20, 30, 40, 50, 70, 80, 90, 100]
ax1 = fig10.add_subplot(111)
arr = ax1.hist(o_tran_within,
bins,
facecolor='red',
alpha=0.5,
edgecolor='brown')
ax1.set_xlabel('Percentage of outgoing transations to within set',
fontsize=8)
ax1.set_ylabel('Number of companies', fontsize=8)
ax1.set_title(
'Distribution of percenatge of outgoing transactions to companies within set',
fontsize=10)
#ax1.set_yscale('log')
for i in range(len(arr[0])):
ax1.text(arr[1][i], arr[0][i], str(arr[0][i]))
max_per = max(o_tran_within)
min_per = min(o_tran_within)
text = 'Maximum = ' + str(max_per) + '%' + '\nMaximum loss = ' + str(
min_per) + '%' + '\nN=' + str(N)
ax1.text(0, 40, text, fontsize=12)
fig10.savefig(con.additionalFiles +
'\\PercentageTranWithinOutgoing.png')