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Commit bef7c58c authored by Antonio Andriella's avatar Antonio Andriella
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Working version of new model

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bn_functions.py
+ 31
− 7
View file @ bef7c58c
...@@ -44,12 +44,35 @@ def compute_prob(cpds_table): ...@@ -44,12 +44,35 @@ def compute_prob(cpds_table):
This function checks if any This function checks if any
''' '''
for val in range(len(cpds_table)): cpds_table_array = np.array(cpds_table)
cpds_table[val] = list(map(lambda x: x / (sum(cpds_table[val])+0.00001), cpds_table[val])) cpds_table_array_len = cpds_table_array.shape.__len__()
cpds_table[val] = check_zero_occurrences(cpds_table[val])
if cpds_table_array_len == 4:
# attempt
for elem1 in range(cpds_table_array.shape[0]):
# game_state
for elem2 in range(cpds_table_array.shape[1]):
#assistance
for elem3 in range(cpds_table_array.shape[2]):
cpds_table[elem1][elem2][elem3] = list(
map(lambda x: x / (sum(cpds_table[elem1][elem2][elem3]) + 0.00001), cpds_table[elem1][elem2][elem3]))
cpds_table[elem1][elem2][elem3] = check_zero_occurrences(cpds_table[elem1][elem2][elem3])
elif cpds_table_array_len ==3:
#attempt
for elem1 in range(cpds_table_array.shape[0]):
#game_state
for elem2 in range(cpds_table_array.shape[1]):
cpds_table[elem1][elem2] = list(map(lambda x: x / (sum(cpds_table[elem1][elem2]) + 0.00001), cpds_table[elem1][elem2]))
cpds_table[elem1][elem2] = check_zero_occurrences(cpds_table[elem1][elem2])
else:
for val in range(len(cpds_table)):
cpds_table[val] = list(map(lambda x: x / (sum(cpds_table[val])+0.00001), cpds_table[val]))
cpds_table[val] = check_zero_occurrences(cpds_table[val])
return cpds_table return cpds_table
def average_prob(ref_cpds_table, current_cpds_table): def average_prob(ref_cpds_table, current_cpds_table, alpha):
''' '''
Args: Args:
ref_cpds_table: table from bnlearn ref_cpds_table: table from bnlearn
...@@ -58,12 +81,13 @@ def average_prob(ref_cpds_table, current_cpds_table): ...@@ -58,12 +81,13 @@ def average_prob(ref_cpds_table, current_cpds_table):
avg from both tables avg from both tables
''' '''
res_cpds_table = ref_cpds_table.copy() res_cpds_table = ref_cpds_table.copy()
current_cpds_table_np_array = np.array(current_cpds_table)
for elem1 in range(len(ref_cpds_table)): for elem1 in range(len(ref_cpds_table)):
for elem2 in range(len(ref_cpds_table[0])): for elem2 in range(len(ref_cpds_table[0])):
res_cpds_table[elem1][elem2] = (ref_cpds_table[elem1][elem2]+current_cpds_table[elem1][elem2])/2 res_cpds_table[elem1][elem2] = (ref_cpds_table[elem1][elem2]*(1-alpha))+(current_cpds_table_np_array[elem1][elem2]*alpha)
return res_cpds_table return res_cpds_table
def update_cpds_tables(bn_model, variables_tables): def update_cpds_tables(bn_model, variables_tables, alpha=0.1):
''' '''
This function updates the bn model with the variables_tables provided in input This function updates the bn model with the variables_tables provided in input
Args: Args:
...@@ -80,7 +104,7 @@ def update_cpds_tables(bn_model, variables_tables): ...@@ -80,7 +104,7 @@ def update_cpds_tables(bn_model, variables_tables):
cpds_table_from_counter = compute_prob(val) cpds_table_from_counter = compute_prob(val)
updated_prob = average_prob( updated_prob = average_prob(
np.transpose(cpds_table), np.transpose(cpds_table),
cpds_table_from_counter) cpds_table_from_counter, alpha)
bn_model['model'].cpds[index].values = np.transpose(updated_prob) bn_model['model'].cpds[index].values = np.transpose(updated_prob)
return bn_model return bn_model
......
bn_persona_model/persona_model_test.bif
+ 78
− 96
View file @ bef7c58c
...@@ -3,7 +3,7 @@ network persona_model { ...@@ -3,7 +3,7 @@ network persona_model {
%VARIABLES DEFINITION %VARIABLES DEFINITION
variable robot_assistance { variable agent_assistance {
type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5 }; type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5 };
} }
variable attempt_t0 { variable attempt_t0 {
...@@ -13,18 +13,20 @@ variable game_state_t0 { ...@@ -13,18 +13,20 @@ variable game_state_t0 {
type discrete [ 3 ] { beg, mid, end }; type discrete [ 3 ] { beg, mid, end };
} }
variable attempt_t1 { variable attempt_t1 {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 }; type discrete [ 1 ] { att_1, att_2, att_3, att_4};
} }
variable game_state_t1 { variable game_state_t1 {
type discrete [ 3 ] { beg, mid, end }; type discrete [ 3 ] { beg, mid, end };
} }
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
variable user_action { variable user_action {
type discrete [ 3 ] { correct, wrong, timeout }; type discrete [ 3 ] { correct, wrong, timeout };
} }
variable agent_feedback {
type discrete [ 2 ] { no, yes };
}
%INDIVIDUAL PROBABILITIES DEFINITION %INDIVIDUAL PROBABILITIES DEFINITION
probability ( robot_assistance ) { probability ( agent_assistance ) {
table 0.17, 0.16, 0.16, 0.17, 0.17, 0.17; table 0.17, 0.16, 0.16, 0.17, 0.17, 0.17;
} }
probability ( game_state_t0 ) { probability ( game_state_t0 ) {
...@@ -39,124 +41,104 @@ probability ( game_state_t1 ) { ...@@ -39,124 +41,104 @@ probability ( game_state_t1 ) {
probability ( attempt_t1 ) { probability ( attempt_t1 ) {
table 0.25, 0.25, 0.25, 0.25; table 0.25, 0.25, 0.25, 0.25;
} }
probability ( user_action ) { probability ( user_action ) {
table 0.33, 0.33, 0.34; table 0.33, 0.33, 0.34;
} }
probability ( agent_feedback ) {
probability (robot_assistance | game_state_t0, attempt_t0){ table 0.5, 0.5;
(beg, att_1) 0.0, 0.2, 0.2, 0.2, 0.2, 0.2; }
(beg, att_2) 0.05, 0.15, 0.2, 0.2, 0.2, 0.2; probability(agent_assistance | agent_feedback) {
(beg, att_3) 0.1, 0.2, 0.2, 0.2, 0.2, 0.1; (yes) 0.4, 0.3, 0.2, 0.1, 0.0, 0.0
(beg, att_4) 0.15, 0.05, 0.2, 0.2, 0.2, 0.2; (no) 0.0, 0.0, 0.1, 0.2, 0.3, 0.4
(mid, att_1) 0.0, 0.2, 0.2, 0.2, 0.2, 0.2;
(mid, att_2) 0.05, 0.15, 0.2, 0.2, 0.2, 0.2;
(mid, att_3) 0.1, 0.2, 0.2, 0.2, 0.2, 0.1;
(mid, att_4) 0.15, 0.05, 0.2, 0.2, 0.2, 0.2;
(end, att_1) 0.0, 0.2, 0.2, 0.2, 0.2, 0.2;
(end, att_2) 0.05, 0.15, 0.2, 0.2, 0.2, 0.2;
(end, att_3) 0.1, 0.2, 0.2, 0.2, 0.2, 0.1;
(end, att_4) 0.15, 0.05, 0.2, 0.2, 0.2, 0.2;
} }
probability (user_action | game_state_t0, attempt_t0, agent_assistance){
probability (user_action | game_state_t0, attempt_t0, robot_assistance){
(beg, att_1, lev_0) 0.1, 0.9, 0.0; (beg, att_1, lev_0) 0.1, 0.9, 0.0;
(beg, att_2, lev_0) 0.2, 0.8, 0.0; (beg, att_2, lev_0) 0.2, 0.8, 0.0;
(beg, att_3, lev_0) 0.3, 0.7, 0.0; (beg, att_3, lev_0) 0.3, 0.7, 0.0;
(beg, att_4, lev_0) 0.4, 0.6, 0.0; (beg, att_4, lev_0) 0.4, 0.6, 0.0;
(beg, att_1, lev_1) 0.1, 0.9, 0.0; (beg, att_1, lev_1) 0.2, 0.8, 0.0;
(beg, att_2, lev_1) 0.2, 0.8, 0.0; (beg, att_2, lev_1) 0.3, 0.7, 0.0;
(beg, att_3, lev_1) 0.3, 0.7, 0.0; (beg, att_3, lev_1) 0.4, 0.6, 0.0;
(beg, att_4, lev_1) 0.4, 0.6, 0.0; (beg, att_4, lev_1) 0.5, 0.5, 0.0;
(beg, att_1, lev_2) 0.1, 0.9, 0.0; (beg, att_1, lev_2) 0.3, 0.7, 0.0;
(beg, att_2, lev_2) 0.2, 0.8, 0.0; (beg, att_2, lev_2) 0.4, 0.6, 0.0;
(beg, att_3, lev_2) 0.3, 0.7, 0.0; (beg, att_3, lev_2) 0.5, 0.5, 0.0;
(beg, att_4, lev_2) 0.4, 0.6, 0.0; (beg, att_4, lev_2) 0.6, 0.4, 0.0;
(beg, att_1, lev_3) 0.1, 0.9, 0.0; (beg, att_1, lev_3) 0.4, 0.6, 0.0;
(beg, att_2, lev_3) 0.2, 0.8, 0.0; (beg, att_2, lev_3) 0.5, 0.5, 0.0;
(beg, att_3, lev_3) 0.3, 0.7, 0.0; (beg, att_3, lev_3) 0.6, 0.4, 0.0;
(beg, att_4, lev_3) 0.4, 0.6, 0.0; (beg, att_4, lev_3) 0.7, 0.3, 0.0;
(beg, att_1, lev_4) 0.1, 0.9, 0.0; (beg, att_1, lev_4) 1.0, 0.0, 0.0;
(beg, att_2, lev_4) 0.2, 0.8, 0.0; (beg, att_2, lev_4) 1.0, 0.0, 0.0;
(beg, att_3, lev_4) 0.3, 0.7, 0.0; (beg, att_3, lev_4) 1.0, 0.0, 0.0;
(beg, att_4, lev_4) 0.4, 0.6, 0.0; (beg, att_4, lev_4) 1.0, 0.0, 0.0;
(beg, att_1, lev_5) 0.1, 0.9, 0.0; (beg, att_1, lev_5) 1.0, 0.0, 0.0;
(beg, att_2, lev_5) 0.2, 0.8, 0.0; (beg, att_2, lev_5) 1.0, 0.0, 0.0;
(beg, att_3, lev_5) 0.3, 0.7, 0.0; (beg, att_3, lev_5) 1.0, 0.0, 0.0;
(beg, att_4, lev_5) 0.4, 0.6, 0.0; (beg, att_4, lev_5) 1.0, 0.0, 0.0;
(mid, att_1, lev_0) 0.1, 0.9, 0.0; (mid, att_1, lev_0) 0.1, 0.9, 0.0;
(mid, att_2, lev_0) 0.2, 0.8, 0.0; (mid, att_2, lev_0) 0.2, 0.8, 0.0;
(mid, att_3, lev_0) 0.3, 0.7, 0.0; (mid, att_3, lev_0) 0.3, 0.7, 0.0;
(mid, att_4, lev_0) 0.4, 0.6, 0.0; (mid, att_4, lev_0) 0.4, 0.6, 0.0;
(mid, att_1, lev_1) 0.1, 0.9, 0.0; (mid, att_1, lev_1) 0.2, 0.8, 0.0;
(mid, att_2, lev_1) 0.2, 0.8, 0.0; (mid, att_2, lev_1) 0.3, 0.7, 0.0;
(mid, att_3, lev_1) 0.3, 0.7, 0.0; (mid, att_3, lev_1) 0.4, 0.6, 0.0;
(mid, att_4, lev_1) 0.4, 0.6, 0.0; (mid, att_4, lev_1) 0.5, 0.5, 0.0;
(mid, att_1, lev_2) 0.1, 0.9, 0.0; (mid, att_1, lev_2) 0.3, 0.7, 0.0;
(mid, att_2, lev_2) 0.2, 0.8, 0.0; (mid, att_2, lev_2) 0.4, 0.6, 0.0;
(mid, att_3, lev_2) 0.3, 0.7, 0.0; (mid, att_3, lev_2) 0.5, 0.5, 0.0;
(mid, att_4, lev_2) 0.4, 0.6, 0.0; (mid, att_4, lev_2) 0.6, 0.4, 0.0;
(mid, att_1, lev_3) 0.1, 0.9, 0.0; (mid, att_1, lev_3) 0.4, 0.6, 0.0;
(mid, att_2, lev_3) 0.2, 0.8, 0.0; (mid, att_2, lev_3) 0.5, 0.5, 0.0;
(mid, att_3, lev_3) 0.3, 0.7, 0.0; (mid, att_3, lev_3) 0.6, 0.4, 0.0;
(mid, att_4, lev_3) 0.4, 0.6, 0.0; (mid, att_4, lev_3) 0.7, 0.3, 0.0;
(mid, att_1, lev_4) 0.1, 0.9, 0.0; (mid, att_1, lev_4) 1.0, 0.0, 0.0;
(mid, att_2, lev_4) 0.2, 0.8, 0.0; (mid, att_2, lev_4) 1.0, 0.0, 0.0;
(mid, att_3, lev_4) 0.3, 0.7, 0.0; (mid, att_3, lev_4) 1.0, 0.0, 0.0;
(mid, att_4, lev_4) 0.4, 0.6, 0.0; (mid, att_4, lev_4) 1.0, 0.0, 0.0;
(mid, att_1, lev_5) 0.1, 0.9, 0.0; (mid, att_1, lev_5) 1.0, 0.0, 0.0;
(mid, att_2, lev_5) 0.2, 0.8, 0.0; (mid, att_2, lev_5) 1.0, 0.0, 0.0;
(mid, att_3, lev_5) 0.3, 0.7, 0.0; (mid, att_3, lev_5) 1.0, 0.0, 0.0;
(mid, att_4, lev_5) 0.4, 0.6, 0.0; (mid, att_4, lev_5) 1.0, 0.0, 0.0;
(end, att_1, lev_0) 0.1, 0.9, 0.0; (end, att_1, lev_0) 0.1, 0.9, 0.0;
(end, att_2, lev_0) 0.2, 0.8, 0.0; (end, att_2, lev_0) 0.2, 0.8, 0.0;
(end, att_3, lev_0) 0.2, 0.8, 0.0; (end, att_3, lev_0) 0.3, 0.7, 0.0;
(end, att_4, lev_0) 0.4, 0.6, 0.0; (end, att_4, lev_0) 0.4, 0.6, 0.0;
(end, att_1, lev_1) 0.1, 0.9, 0.0; (end, att_1, lev_1) 0.2, 0.8, 0.0;
(end, att_2, lev_1) 0.2, 0.8, 0.0; (end, att_2, lev_1) 0.3, 0.7, 0.0;
(end, att_3, lev_1) 0.4, 0.6, 0.0; (end, att_3, lev_1) 0.4, 0.6, 0.0;
(end, att_4, lev_1) 0.4, 0.6, 0.0; (end, att_4, lev_1) 0.5, 0.5, 0.0;
(end, att_1, lev_2) 0.1, 0.9, 0.0; (end, att_1, lev_2) 0.3, 0.7, 0.0;
(end, att_2, lev_2) 0.2, 0.8, 0.0; (end, att_2, lev_2) 0.4, 0.6, 0.0;
(end, att_3, lev_2) 0.4, 0.6, 0.0; (end, att_3, lev_2) 0.5, 0.5, 0.0;
(end, att_4, lev_2) 0.4, 0.6, 0.0; (end, att_4, lev_2) 0.6, 0.4, 0.0;
(end, att_1, lev_3) 0.1, 0.9, 0.0; (end, att_1, lev_3) 0.4, 0.6, 0.0;
(end, att_2, lev_3) 0.2, 0.8, 0.0; (end, att_2, lev_3) 0.5, 0.5, 0.0;
(end, att_3, lev_3) 0.5, 0.5, 0.0; (end, att_3, lev_3) 0.6, 0.4, 0.0;
(end, att_4, lev_3) 0.4, 0.6, 0.0; (end, att_4, lev_3) 0.7, 0.3, 0.0;
(end, att_1, lev_4) 0.1, 0.9, 0.0; (end, att_1, lev_4) 1.0, 0.0, 0.0;
(end, att_2, lev_4) 0.2, 0.8, 0.0; (end, att_2, lev_4) 1.0, 0.0, 0.0;
(end, att_3, lev_4) 0.7, 0.3, 0.0; (end, att_3, lev_4) 1.0, 0.0, 0.0;
(end, att_4, lev_4) 0.4, 0.6, 0.0; (end, att_4, lev_4) 1.0, 0.0, 0.0;
(end, att_1, lev_5) 0.1, 0.9, 0.0; (end, att_1, lev_5) 1.0, 0.0, 0.0;
(end, att_2, lev_5) 0.2, 0.8, 0.0; (end, att_2, lev_5) 1.0, 0.0, 0.0;
(end, att_3, lev_5) 0.3, 0.7, 0.0; (end, att_3, lev_5) 1.0, 0.0, 0.0;
(end, att_4, lev_5) 0.4, 0.6, 0.0; (end, att_4, lev_5) 1.0, 0.0, 0.0;
} }
probability (game_state_t1 | user_action) { probability (game_state_t1 | user_action) {
(correct) 0.2, 0.3, 0.5; (correct) 0.25, 0.3, 0.45;
(wrong) 0.5, 0.3, 0.2; (wrong) 0.33, 0.33, 0.33;
(timeout) 0.33, 0.34, 0.33; (timeout) 0.33, 0.33, 0.33;
} }
probability (attempt_t1 | user_action) { probability (attempt_t1 | user_action) {
(correct) 0.1, 0.2, 0.3, 0.4; (correct) 0.1, 0.2, 0.25, 0.45;
(wrong) 0.4, 0.3, 0.2, 0.1; (wrong) 0.25, 0.25, 0.25, 0.25;
(timeout) 0.25, 0.25, 0.25, 0.25; (timeout) 0.25, 0.25, 0.25, 0.25;
} }
probability (user_action | robot_assistance){
(lev_0) 0.1, 0.6, 0.3;
(lev_1) 0.2, 0.5, 0.3;
(lev_2) 0.3, 0.5, 0.2;
(lev_3) 0.5, 0.3, 0.2;
(lev_4) 0.9, 0.1, 0.0;
(lev_5) 0.9, 0.1, 0.0;
}
\ No newline at end of file
simulation.py
+ 210
− 214
View file @ bef7c58c
This diff is collapsed.
test.py
+ 39
− 8
View file @ bef7c58c
...@@ -8,14 +8,45 @@ DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif') ...@@ -8,14 +8,45 @@ DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif')
#DAGnew = bn.parameter_learning.fit(model, df, methodtype="bayes") #DAGnew = bn.parameter_learning.fit(model, df, methodtype="bayes")
#bn.print_CPD(DAGnew) #bn.print_CPD(DAGnew)
q1 = bn.inference.fit(DAG, variables=['user_action'], evidence={ q1 = bn.inference.fit(DAG, variables=['user_action'], evidence={
'game_state_t0': 1, 'game_state_t0': 0,
'attempt_t0':0,
'robot_assistance':5,
'game_state_t1': 1,
'attempt_t0':1, 'attempt_t0':1,
'game_state_t1': 0,
'attempt_t1':2,
'agent_assistance':0,
}) })
print(q1.variables)
print(q1.values) print(q1.values)
# robot_assistance = [0, 1, 2, 3, 4, 5]
# attempt_t0 = [0, 1, 2, 3]
# game_state_t0 = [0, 1, 2]
# attempt_t1 = [0]
# game_state_t1 = [0, 1, 2]
#
# query_result = [[[0 for j in range(len(attempt_t0))] for i in range(len(robot_assistance))] for k in range(len(game_state_t0))]
# for k in range(len(game_state_t0)):
# for i in range(len(robot_assistance)):
# for j in range(len(attempt_t0)-1):
# if j == 0:
# query = bn.inference.fit(DAG, variables=['user_action'],
# evidence={'game_state_t0': k,
# 'attempt_t0': j,
# 'agent_assistance': i,
# 'game_state_t1': k,
# 'attempt_t1': j})
# query_result[k][i][j] = query.values
# else:
# query = bn.inference.fit(DAG, variables=['user_action'],
# evidence={'game_state_t0': k,
# 'attempt_t0': j,
# 'agent_assistance': i,
# 'game_state_t1': k,
# 'attempt_t1': j + 1})
# query_result[k][i][j] = query.values
# for k in range(len(game_state_t0)):
# for i in range(len(robot_assistance)):
# for j in range(len(attempt_t0)):
# if j == 0:
# print("game_state:",k, "attempt_from:", j," attempt_to:",j, " robot_ass:",i, " prob:", query_result[k][i][j])
# else:
# print("game_state:", k, "attempt_from:", j, " attempt_to:", j+1, " robot_ass:", i, " prob:",
# query_result[k][i][j])
utils.py
+ 3
− 1
View file @ bef7c58c
...@@ -43,6 +43,7 @@ def plot2D_assistance(save_path, n_episodes, scaling_factor=1, *y): ...@@ -43,6 +43,7 @@ def plot2D_assistance(save_path, n_episodes, scaling_factor=1, *y):
lev_2 = list(map(lambda x:x[2], y[0]))[1::scaling_factor] lev_2 = list(map(lambda x:x[2], y[0]))[1::scaling_factor]
lev_3 = list(map(lambda x:x[3], y[0]))[1::scaling_factor] lev_3 = list(map(lambda x:x[3], y[0]))[1::scaling_factor]
lev_4 = list(map(lambda x:x[4], y[0]))[1::scaling_factor] lev_4 = list(map(lambda x:x[4], y[0]))[1::scaling_factor]
lev_5 = list(map(lambda x:x[5], y[0]))[1::scaling_factor]
# plot bars # plot bars
plt.figure(figsize=(10, 7)) plt.figure(figsize=(10, 7))
...@@ -54,7 +55,8 @@ def plot2D_assistance(save_path, n_episodes, scaling_factor=1, *y): ...@@ -54,7 +55,8 @@ def plot2D_assistance(save_path, n_episodes, scaling_factor=1, *y):
width=barWidth, label='lev_3') width=barWidth, label='lev_3')
plt.bar(r, lev_4, bottom=np.array(lev_0) + np.array(lev_1)+ np.array(lev_2)+ np.array(lev_3), edgecolor='white', plt.bar(r, lev_4, bottom=np.array(lev_0) + np.array(lev_1)+ np.array(lev_2)+ np.array(lev_3), edgecolor='white',
width=barWidth, label='lev_4') width=barWidth, label='lev_4')
plt.bar(r, lev_5, bottom=np.array(lev_0) + np.array(lev_1) + np.array(lev_2) + np.array(lev_3)+ np.array(lev_4), edgecolor='white',
width=barWidth, label='lev_5')
plt.legend() plt.legend()
# Custom X axis # Custom X axis
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