# Example 2: Minimal -> Three state transition matrix
M = tm.TransitionMatrix(values=Generic)
# Lets take a look at the values
M.print()
M.validate()
# The size of the rating scale
Ratings = M.dimension
# The Default (absorbing state)
Default = Ratings - 1
# Lets extend the matrix into multi periods
Periods = 5
T = tm.TransitionMatrixSet(values=M, periods=Periods, method='Power', temporal_type='Cumulative')
# Initialize a threshold set
As = ThresholdSet(TMSet=T)
print("> Fit Multiperiod Thresholds")
for ri in range(0, Ratings):
print("RI: ", ri)
As.fit(AR_Model, ri)
print("> Validate Multiperiod Thresholds against Input Transition Matrix Set")
Q = As.validate(AR_Model)
print("> Save Multiperiod Thresholds in JSON Format")
As.to_json('generic_thresholds.json')
# The size of the rating scale
Ratings = M.dimension
# The Default (absorbing state)
Default = Ratings - 1
# Lets extend the matrix into multi periods
Periods = 10
T = tm.TransitionMatrixSet(values=M,
periods=Periods,
method='Power',
temporal_type='Cumulative')
# Initialize a threshold set
As = ThresholdSet(TMSet=T)
for ri in range(0, Ratings):
print("RI: ", ri)
As.fit(AR_Model, ri)
lines = []
ri = 3
for rf in range(0, Ratings):
for k in range(0, Periods):
if rf != ri:
value = As.A[ri, rf, k]
line = [(k, value), (k + 1.0, value)]
lines.append(line)
lc = mc.LineCollection(lines, linewidths=2)
# The size of the rating scale
Ratings = M.dimension
# The Default (absorbing state)
Default = Ratings - 1
# Lets extend the matrix into multi periods
Periods = 5
T = tm.TransitionMatrixSet(values=M,
periods=Periods,
method='Power',
temporal_type='Cumulative')
# Initialize a threshold set
As = ThresholdSet(TMSet=T)
print("> Fit Multiperiod Thresholds")
for ri in range(0, Ratings):
print("RI: ", ri)
As.fit(AR_Model, ri)
print(
"> Validate Multiperiod Thresholds against Input Transition Matrix Set"
)
Q = As.validate(AR_Model)
print("> Save Multiperiod Thresholds in JSON Format")
As.to_json('generic_thresholds.json')
elif example == 2:
from portfolioAnalytics import source_path
dataset_path = source_path + "datasets/"
# A Generic matrix with 7 non-absorbing and one absorbing state
Generic = [[0.92039, 0.0709, 0.0063, 0.0015, 0.0006, 0.0002, 0.0001, 1e-05],
[0.0062, 0.9084, 0.0776, 0.0059, 0.0006, 0.001, 0.0002, 0.0001],
[0.0005, 0.0209, 0.9138, 0.0579, 0.0044, 0.0016, 0.0004, 0.0005],
[0.0004, 0.0021, 0.041, 0.8936, 0.0482, 0.0086, 0.0024, 0.0037],
[0.0003, 0.0008, 0.014, 0.0553, 0.8225, 0.0815, 0.0111, 0.0145],
[0.0001, 0.0004, 0.0057, 0.0134, 0.0539, 0.8114, 0.0492, 0.0659],
[1e-05, 0.0002, 0.0029, 0.0058, 0.0155, 0.1054, 0.52879, 0.3414],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]]
# Initialize a threshold set from file
As = ThresholdSet(json_file=dataset_path + 'generic_thresholds.json')
# Inspect values (we assume these inputs have already been validated after generation!)
# As.print(accuracy=4)
# Specify the initial rating of interest
ri = 3
# As.plot(ri)
# Initialize a conditional migration matrix with the given thresholds
Q = ConditionalTransitionMatrix(thresholds=As)
# # Q.print()
#
Ratings = M.dimension
# The Default (absorbing state)
Default = Ratings - 1
# Lets extend the matrix into multi periods
Periods = 10
T = tm.TransitionMatrixSet(values=M,
periods=Periods,
method='Power',
temporal_type='Cumulative')
print("> Extend the matrix into 10 periods")
elif example == 2:
T = tm.TransitionMatrixSet(periods=120,
temporal_type='Cumulative',
json_file=dataset_path + "generic_monthly.json")
Ratings = T.entries[0].dimension
# Initialize a threshold set
As = ThresholdSet(TMSet=T)
# Calculate thresholds per initial rating state
print("> Calculate thresholds per initial rating state")
for ri in range(0, Ratings):
print("Initial Rating: ", ri)
As.fit(AR_Model, ri, dt=1.0 / 12.0)
As.to_json(json_file="monthly_thresholds.json")
# Display the calculated thresholds
# print("> Display the calculated thresholds")
# As.print()
# Example 2: Minimal -> Three state transition matrix
M = tm.TransitionMatrix(values=Generic)
# The size of the rating scale
Ratings = M.dimension
# The Default (absorbing state)
Default = Ratings - 1
# Lets extend the matrix into multi periods
Periods = 10
T = tm.TransitionMatrixSet(values=M, periods=Periods, method='Power', temporal_type='Cumulative')
# Initialize a threshold set
As = ThresholdSet(TMSet=T)
for ri in range(0, Ratings):
print("RI: ", ri)
As.fit(AR_Model, ri)
lines = []
ri = 3
for rf in range(0, Ratings):
for k in range(0, Periods):
if rf != ri:
value = As.A[ri, rf, k]
line = [(k, value), (k + 1.0, value)]
lines.append(line)
lc = mc.LineCollection(lines, linewidths=2)