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Commit c4da8237 authored by Antonio Andriella's avatar Antonio Andriella
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new model that creates from logs

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bn_persona_model/persona_model_test.bif
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...@@ -6,39 +6,36 @@ network persona_model { ...@@ -6,39 +6,36 @@ network persona_model {
variable agent_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 {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 }; type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
} }
variable game_state_t0 { variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
variable attempt_t1 {
type discrete [ 1 ] { att_1, att_2, att_3, att_4};
}
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 { variable agent_feedback {
type discrete [ 2 ] { no, yes }; type discrete [ 2 ] { no, yes };
} }
variable user_memory {
type discrete [ 3 ] {low, medium, high};
}
variable user_reactivity {
type discrete [ 3 ] {low, medium, high};
}
%INDIVIDUAL PROBABILITIES DEFINITION %INDIVIDUAL PROBABILITIES DEFINITION
probability ( agent_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) {
table 0.34, 0.33, 0.33; table 0.34, 0.33, 0.33;
} }
probability ( attempt_t0 ) { probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability ( game_state_t1 ) {
table 0.34, 0.33, 0.33;
}
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 ) {
...@@ -47,98 +44,65 @@ probability ( user_action ) { ...@@ -47,98 +44,65 @@ probability ( user_action ) {
probability ( agent_feedback ) { probability ( agent_feedback ) {
table 0.5, 0.5; table 0.5, 0.5;
} }
probability(agent_assistance | agent_feedback) { probability ( user_memory ) {
(yes) 0.4, 0.3, 0.2, 0.1, 0.0, 0.0 table 0.33, 0.34, 0.33;
(no) 0.0, 0.0, 0.1, 0.2, 0.3, 0.4 }
} probability ( user_reactivity ) {
table 0.33, 0.34, 0.33;
probability (user_action | game_state_t0, attempt_t0, agent_assistance){ }
(beg, att_1, lev_0) 0.1, 0.9, 0.0;
(beg, att_2, lev_0) 0.2, 0.8, 0.0;
(beg, att_3, lev_0) 0.3, 0.7, 0.0; probability (game_state | user_memory){
(beg, att_4, lev_0) 0.4, 0.6, 0.0; (low) 0.1, 0.3, 0.6;
(beg, att_1, lev_1) 0.2, 0.8, 0.0; (medium) 0.2, 0.4, 0.4;
(beg, att_2, lev_1) 0.3, 0.7, 0.0; (high) 0.33, 0.33, 0.34;
(beg, att_3, lev_1) 0.4, 0.6, 0.0; }
(beg, att_4, lev_1) 0.5, 0.5, 0.0;
(beg, att_1, lev_2) 0.3, 0.7, 0.0; probability (attempt | user_memory){
(beg, att_2, lev_2) 0.4, 0.6, 0.0; (low) 0.1,0.1,0.2, 0.6;
(beg, att_3, lev_2) 0.5, 0.5, 0.0; (medium) 0.1, 0.2, 0.3, 0.4;
(beg, att_4, lev_2) 0.6, 0.4, 0.0; (high) 0.25, 0.25, 0.25, 0.25;
(beg, att_1, lev_3) 0.4, 0.6, 0.0; }
(beg, att_2, lev_3) 0.5, 0.5, 0.0;
(beg, att_3, lev_3) 0.6, 0.4, 0.0; probability (game_state | user_reactivity){
(beg, att_4, lev_3) 0.7, 0.3, 0.0; (low) 0.1, 0.3, 0.6;
(beg, att_1, lev_4) 1.0, 0.0, 0.0; (medium) 0.2, 0.4, 0.4;
(beg, att_2, lev_4) 1.0, 0.0, 0.0; (high) 0.33, 0.33, 0.34;
(beg, att_3, lev_4) 1.0, 0.0, 0.0; }
(beg, att_4, lev_4) 1.0, 0.0, 0.0;
(beg, att_1, lev_5) 1.0, 0.0, 0.0; probability (attempt | user_reactivity){
(beg, att_2, lev_5) 1.0, 0.0, 0.0; (low) 0.1,0.1,0.2, 0.6;
(beg, att_3, lev_5) 1.0, 0.0, 0.0; (medium) 0.1, 0.2, 0.3, 0.4;
(beg, att_4, lev_5) 1.0, 0.0, 0.0; (high) 0.25, 0.25, 0.25, 0.25;
(mid, att_1, lev_0) 0.1, 0.9, 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_4, lev_0) 0.4, 0.6, 0.0;
(mid, att_1, lev_1) 0.2, 0.8, 0.0;
(mid, att_2, lev_1) 0.3, 0.7, 0.0;
(mid, att_3, lev_1) 0.4, 0.6, 0.0;
(mid, att_4, lev_1) 0.5, 0.5, 0.0;
(mid, att_1, lev_2) 0.3, 0.7, 0.0;
(mid, att_2, lev_2) 0.4, 0.6, 0.0;
(mid, att_3, lev_2) 0.5, 0.5, 0.0;
(mid, att_4, lev_2) 0.6, 0.4, 0.0;
(mid, att_1, lev_3) 0.4, 0.6, 0.0;
(mid, att_2, lev_3) 0.5, 0.5, 0.0;
(mid, att_3, lev_3) 0.6, 0.4, 0.0;
(mid, att_4, lev_3) 0.7, 0.3, 0.0;
(mid, att_1, lev_4) 1.0, 0.0, 0.0;
(mid, att_2, lev_4) 1.0, 0.0, 0.0;
(mid, att_3, lev_4) 1.0, 0.0, 0.0;
(mid, att_4, lev_4) 1.0, 0.0, 0.0;
(mid, att_1, lev_5) 1.0, 0.0, 0.0;
(mid, att_2, lev_5) 1.0, 0.0, 0.0;
(mid, att_3, lev_5) 1.0, 0.0, 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_2, 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_1, 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_4, lev_1) 0.5, 0.5, 0.0;
(end, att_1, lev_2) 0.3, 0.7, 0.0;
(end, att_2, lev_2) 0.4, 0.6, 0.0;
(end, att_3, lev_2) 0.5, 0.5, 0.0;
(end, att_4, lev_2) 0.6, 0.4, 0.0;
(end, att_1, lev_3) 0.4, 0.6, 0.0;
(end, att_2, lev_3) 0.5, 0.5, 0.0;
(end, att_3, lev_3) 0.6, 0.4, 0.0;
(end, att_4, lev_3) 0.7, 0.3, 0.0;
(end, att_1, lev_4) 1.0, 0.0, 0.0;
(end, att_2, lev_4) 1.0, 0.0, 0.0;
(end, att_3, lev_4) 1.0, 0.0, 0.0;
(end, att_4, lev_4) 1.0, 0.0, 0.0;
(end, att_1, lev_5) 1.0, 0.0, 0.0;
(end, att_2, lev_5) 1.0, 0.0, 0.0;
(end, att_3, lev_5) 1.0, 0.0, 0.0;
(end, att_4, lev_5) 1.0, 0.0, 0.0;
}
probability (game_state_t1 | user_action) {
(correct) 0.25, 0.3, 0.45;
(wrong) 0.33, 0.33, 0.33;
(timeout) 0.33, 0.33, 0.33;
}
probability (attempt_t1 | user_action) {
(correct) 0.1, 0.2, 0.25, 0.45;
(wrong) 0.25, 0.25, 0.25, 0.25;
(timeout) 0.25, 0.25, 0.25, 0.25;
} }
probability (game_state | user_action) {
(correct) 0.30, 0.30, 0.4;
(wrong) 0.35, 0.35, 0.3;
(timeout) 0.33, 0.33, 0.34;
}
probability (attempt | user_action) {
(correct) 0.25, 0.25, 0.25, 0.25;
(wrong) 0.4, 0.3, 0.2, 0.1;
(timeout) 0.4, 0.3, 0.2, 0.1;
}
probability (user_action | agent_assistance, agent_feedback) {
(lev_0, no) 0.05 0.85 0.1;
(lev_1, no) 0.1 0.8 0.1;
(lev_2, no) 0.2 0.7 0.1;
(lev_3, no) 0.33 0.57 0.1;
(lev_4, no) 0.9 0.1 0.0;
(lev_5, no) 1.0 0.0 0.0;
(lev_0, yes) 0.5 0.4 0.1;
(lev_1, yes) 0.6 0.3 0.1;
(lev_2, yes) 0.7 0.2 0.1;
(lev_3, yes) 0.8 0.1 0.1;
(lev_4, yes) 0.9 0.1 0.0;
(lev_5, yes) 1.0 0.0 0.0;
}
test.py
+ 86
− 9
View file @ c4da8237
import bnlearn as bn import bnlearn as bn
import pandas as pd
DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif')
def import_data_from_csv(csv_filename, dag_filename):
print("/************************************************************/")
print("Init model")
DAG = bn.import_DAG(dag_filename)
df_caregiver = bn.sampling(DAG, n= 10000)
print("/************************************************************/")
print("real_user Model")
DAG_ = bn.import_DAG(dag_filename, CPD=False)
df_real_user = pd.read_csv(csv_filename)
DAG_real_user = bn.parameter_learning.fit(DAG_, df_real_user, methodtype='bayes')
df_real_user = bn.sampling(DAG_real_user, n=10000)
print("/************************************************************/")
print("Shared knowledge")
DAG_ = bn.import_DAG(dag_filename, CPD=False)
shared_knowledge = [df_real_user, df_caregiver]
conc_shared_knowledge = pd.concat(shared_knowledge)
DAG_shared = bn.parameter_learning.fit(DAG_, conc_shared_knowledge)
df_conc_shared_knowledge = bn.sampling(DAG_shared, n=10000)
return DAG_shared
import_data_from_csv(csv_filename='bn_persona_model/cognitive_game.csv', dag_filename='bn_persona_model/persona_model_test.bif')
# DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif')
# G = bn.plot(DAG)
# q1 = bn.inference.fit(DAG, variables=[ 'user_action'], evidence={
# 'game_state': 0,
# 'attempt':0,
# 'agent_feedback':1,
# 'memory': 0,
# 'reactivity':0,
# 'agent_assistance':0,
#
# })
# df = pd.read_csv('bn_persona_model/cognitive_game.csv')
# df = bn.sampling(DAG, n=10000)
# #model_sl = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
# #bn.plot(model_sl, pos=G['pos'])
# DAG_update = bn.parameter_learning.fit(DAG, df)
# n_game_state = 3
# n_attempt = 4
# n_aas = 6
# n_af = 2
# for gs in range(n_game_state):
# for att in range(n_attempt):
# for aas in range(n_aas):
# for af in range(n_af):
# q1 = bn.inference.fit(DAG_update, variables=[ 'user_action'], evidence={
# 'game_state': gs,
# 'attempt':att,
# 'agent_feedback':af,
# 'user_memory':0,
# 'user_reactivity':0,
# 'agent_assistance':aas})
# print("GS:", gs, " ATT:", att, " AA", aas, " AF", af)
#
# df.head()
# DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif', CPD=False)
# bn.plot(DAG)
# DAG_update = bn.parameter_learning.fit(DAG, df)
# DAG_true = bn.import_DAG('bn_persona_model/persona_model_test.bif', CPD=True)
# q1 = bn.inference.fit(DAG_update, variables=['user_action'], evidence={
# 'game_state': 0,
# 'attempt':2,
# 'agent_feedback':0,
# })
# print("BEFORE")
# print(q1.values)
# df = bn.sampling(DAG_update, n=1000)
# DAG_update = bn.parameter_learning.fit(DAG_update, df)
# q1 = bn.inference.fit(DAG_update, variables=['user_action'], evidence={
# 'game_state': 0,
# 'attempt':2,
# 'agent_feedback':0,
# })
# print("AFTER")
# print(q1.values)
#df = bn.sampling(DAG, n=1000, verbose=2) #df = bn.sampling(DAG, n=1000, verbose=2)
#model = bn.structure_learning.fit(df) #model = bn.structure_learning.fit(df)
#G = bn.plot(model) #G = bn.plot(model)
#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': 0, # 'game_state': 0,
'attempt_t0':1, # 'attempt':1,
'game_state_t1': 0, # 'agent_feedback':1,
'attempt_t1':2, # })
'agent_assistance':0, # print(q1.values)
})
print(q1.values)
# robot_assistance = [0, 1, 2, 3, 4, 5] # robot_assistance = [0, 1, 2, 3, 4, 5]
# attempt_t0 = [0, 1, 2, 3] # attempt_t0 = [0, 1, 2, 3]
......
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