def estimate_distrib(skel, samples, query, evidence):
learner = PGMLearner()
bayesnet = learner.discrete_mle_estimateparams(skel, samples)
tablecpd = TableCPDFactorization(bayesnet)
fac = tablecpd.condprobve(query, evidence)
df2 = printdist(fac, bayesnet)
return df2
def learn_net(data):
'''learns Bayes net on raw data'''
data_dict = data.to_dict('records')
learner = PGMLearner()
skel = learner.discrete_constraint_estimatestruct(data=data_dict,indegree=1)
skel.toporder()
disc_bayes_net = learner.discrete_mle_estimateparams(graphskeleton=skel,data=data_dict)
return disc_bayes_net
def learn_net_discretize(data, vars_to_discretize, n_bins):
'''learn Bayes net after selected variables have been discretized'''
data_subset, bins = discretize(data, vars_to_discretize, n_bins=n_bins)
data_dict = data_subset.to_dict('records')
learner = PGMLearner()
skel = learner.discrete_constraint_estimatestruct(data=data_dict,indegree=1)
skel.toporder()
disc_bayes_net = learner.discrete_mle_estimateparams(graphskeleton=skel,data=data_dict)
return disc_bayes_net, bins
def test_libpgm(df1):
data = df1.T.to_dict().values()
#pprint(data)
skel = GraphSkeleton()
skel.load("bn_struct.txt")
learner = PGMLearner()
result = learner.discrete_mle_estimateparams(skel, data)
print json.dumps(result.Vdata, indent=2)
def learnBN(fdata_array, bn_file):
bn_path = os.path.join(experiment_dir, 'parameters', bn_file + '.txt')
skel = GraphSkeleton()
skel.load(bn_path)
skel.toporder()
learner = PGMLearner()
bn = learner.discrete_mle_estimateparams(skel, fdata_array)
return bn
def getBNparams(graph, ddata, n):
# Gets Disc. BN parameters given a graph skeleton
#skeleton should include t-1 and t nodes for each variable
nodes = range(1, (n * 2) + 1)
nodes = map(str, nodes)
edges = gk.edgelist(graph)
for i in range(len(edges)):
edges[i] = list([edges[i][0], str(n + int(edges[i][1]))])
skel = GraphSkeleton()
skel.V = nodes
skel.E = edges
learner = PGMLearner()
result = learner.discrete_mle_estimateparams(skel, ddata)
return result
def em(data, bn, skel):
lk_last = 100
times = 0
while 1:
d2 = data_with_hidden(data, bn)
learner = PGMLearner() #toolbox
bn = learner.discrete_mle_estimateparams(skel, d2) #toolbox
lk = likelihood(d2, bn)
print "LogLikelihood:", lk
times += 1
if abs((lk - lk_last) / lk_last) < 0.001:
break
lk_last = lk
print times
return bn
def em(data,bn,skel):
lk_last=100
times=0
while 1:
d2=data_with_hidden(data,bn)
learner = PGMLearner()#toolbox
bn=learner.discrete_mle_estimateparams(skel,d2)#toolbox
lk=likelihood(d2,bn)
print "LogLikelihood:", lk
times +=1
if abs((lk-lk_last)/lk_last)<0.001:
break
lk_last=lk
print times
return bn
def calc_accuracy(dff_train, dff_train_target, nb_iterations):
result = np.zeros(nb_iterations)
for itera in range(nb_iterations):
XX_train, XX_test, yy_train, yy_test = train_test_split(dff_train, dff_train_target, test_size=0.33)
data4bn = format_data(XX_train)
learner = PGMLearner()
# estimate parameters
result_bn = learner.discrete_mle_estimateparams(skel, data4bn)
#result_bn.Vdata
result_predict = calc_BNprob(XX_test)
BN_test_probs = pd.DataFrame()
BN_test_probs['ground_truth'] = yy_test
Test_prob = pd.concat([yy_test.reset_index().Surv, result_predict], axis = 1, ignore_index = True) .rename(columns = {0:'ground_truth' , 1:'class_resu'})
accuracy = Test_prob[Test_prob.ground_truth == Test_prob.class_resu].shape[0]/(1.0*Test_prob.shape[0])
#print("Accuracy is {}").format(accuracy)
result[itera] = accuracy
return result
# (8) --------------------------------------------------------------------------
# Learn the CPDs of a discrete Bayesian network, given data and a structure:
# say I have some data
data = bn.randomsample(200)
# and a graphskeleton
skel = GraphSkeleton()
skel.load("../tests/unittestdict.txt")
# instantiate my learner
learner = PGMLearner()
# estimate parameters
result = learner.discrete_mle_estimateparams(skel, data)
# output - toggle comment to see
#print json.dumps(result.Vdata, indent=2)
# (9) -------------------------------------------------------------------------
# Learn the structure of a discrete Bayesian network, given only data:
# say I have some data
data = bn.randomsample(2000)
# instantiate my learner
learner = PGMLearner()
# estimate parameters
result = learner.discrete_constraint_estimatestruct(data)
def fun(inputData):
#Defining formatting data method
def format_data(df):
result = []
for row in df.itertuples():
#print(row.Pclass)
result.append(
dict(great=row.great,
good=row.good,
clean=row.clean,
comfortable=row.comfortable,
bad=row.bad,
old=row.old,
Cleanliness=row.Cleanliness,
Location=row.Location,
Service=row.Service,
Rooms=row.Rooms,
Value=row.Value,
Overall=row.Overall))
return result
#load all preprocessed training data
df = pd.read_csv('features.csv', sep=',')
#format data to let them correctly processed by libpgm functions
node_data = format_data(df)
skel = GraphSkeleton()
#load structure of our net
skel.load("./our-skel.txt")
#setting the topologic order
skel.toporder()
#learner which will estimate parameters e if needed net structure
learner = PGMLearner()
#estismting parameters for our own model
res = learner.discrete_mle_estimateparams(skel, node_data)
# get CPT
a = TableCPDFactorization(res)
#compute the query and evidences as dicts
query = dict(Overall=1)
# prepare dictionary of values (dopo gli uguali devi mettere i valori che leggi dalla GUI)
evidence = dict(Value=inputData[0],
Location=inputData[1],
Cleanliness=inputData[2],
Service=inputData[3],
Rooms=inputData[4],
bad=inputData[5],
old=inputData[6],
good=inputData[7],
great=inputData[8],
comfortable=inputData[9],
clean=inputData[10])
print(query)
print(evidence)
#run the query given evidence
result = a.condprobve(query, evidence)
print json.dumps(result.vals, indent=2)
#res.Vdata["Overall"]["vals"][pos]
#arr=[]
dizionario = {}
for i in range(1, 6):
dizionario[res.Vdata["Overall"]["vals"][i - 1]] = result.vals[i - 1]
# arr.append(dizionario)
#print(str(arr))
return dizionario
nd = NodeData() skel = GraphSkeleton() #The structure is defined in the file titanic_skel jsonpath ="titanic_skel.json" skel.load(jsonpath) #instatiate the learner learner = PGMLearner() # The methos estimates the parameters for a discrete Bayesian network with # a structure given by graphskeleton in order to maximize the probability # of data given by data result_params = learner.discrete_mle_estimateparams(skel, training_data) result_params.Vdata['Class']# to inspect the network # Check the prediction accuracy # In[ ]: #results = calc_accuracy(dff_train, dff_train_target, 100) #plt.hist(results, bins='auto') calc_accuracy(df_train, df_train_target, 1) # ## Learning the structure
class TestPGMLearner(unittest.TestCase):
def setUp(self):
# instantiate learner
self.l = PGMLearner()
# generate graph skeleton
skel = GraphSkeleton()
skel.load("unittestdict.txt")
skel.toporder()
# generate sample sequence to try to learn from - discrete
nd = NodeData()
nd.load("unittestdict.txt")
self.samplediscbn = DiscreteBayesianNetwork(skel, nd)
self.samplediscseq = self.samplediscbn.randomsample(5000)
# generate sample sequence to try to learn from - discrete
nda = NodeData()
nda.load("unittestlgdict.txt")
self.samplelgbn = LGBayesianNetwork(skel, nda)
self.samplelgseq = self.samplelgbn.randomsample(10000)
self.skel = skel
def test_discrete_mle_estimateparams(self):
result = self.l.discrete_mle_estimateparams(self.skel, self.samplediscseq)
indexa = result.Vdata['SAT']['vals'].index('lowscore')
self.assertTrue(result.Vdata['SAT']['cprob']["['low']"][indexa] < 1 and result.Vdata['SAT']['cprob']["['low']"][indexa] > .9)
indexb = result.Vdata['Letter']['vals'].index('weak')
self.assertTrue(result.Vdata['Letter']['cprob']["['A']"][indexb] < .15 and result.Vdata['Letter']['cprob']["['A']"][indexb] > .05)
def test_lg_mle_estimateparams(self):
result = self.l.lg_mle_estimateparams(self.skel, self.samplelgseq)
self.assertTrue(result.Vdata['SAT']['mean_base'] < 15 and result.Vdata['SAT']['mean_base'] > 5)
self.assertTrue(result.Vdata['Letter']['variance'] < 15 and result.Vdata['Letter']['variance'] > 5)
def test_discrete_constraint_estimatestruct(self):
result = self.l.discrete_constraint_estimatestruct(self.samplediscseq)
self.assertTrue(["Difficulty", "Grade"] in result.E)
def test_lg_constraint_estimatestruct(self):
result = self.l.lg_constraint_estimatestruct(self.samplelgseq)
self.assertTrue(["Intelligence", "Grade"] in result.E)
def test_discrete_condind(self):
chi, pv, witness = self.l.discrete_condind(self.samplediscseq, "Difficulty", "Letter", ["Grade"])
self.assertTrue(pv > .05)
self.assertTrue(witness, ["Grade"])
chia, pva, witnessa = self.l.discrete_condind(self.samplediscseq, "Difficulty", "Intelligence", [])
self.assertTrue(pva < .05)
def test_discrete_estimatebn(self):
result = self.l.discrete_estimatebn(self.samplediscseq)
self.assertTrue(result.V)
self.assertTrue(result.E)
self.assertTrue(result.Vdata["Difficulty"]["cprob"][0])
def test_lg_estimatebn(self):
result = self.l.lg_estimatebn(self.samplelgseq)
self.assertTrue(result.V)
self.assertTrue(result.E)
self.assertTrue(result.Vdata["Intelligence"]["mean_base"])
def learnDiscreteBN_with_structure(df, continous_columns, features_column_names, label_column='cat',
draw_network=False):
features_df = df.copy()
features_df = features_df.drop(label_column, axis=1)
labels_df = DataFrame()
labels_df[label_column] = df[label_column].copy()
for i in continous_columns:
bins = np.arange((min(features_df[i])), (max(features_df[i])),
((max(features_df[i]) - min(features_df[i])) / 5.0))
features_df[i] = pandas.np.digitize(features_df[i], bins=bins)
data = []
for index, row in features_df.iterrows():
dict = {}
for i in features_column_names:
dict[i] = row[i]
dict[label_column] = labels_df[label_column][index]
data.append(dict)
print "Init done"
learner = PGMLearner()
graph = GraphSkeleton()
graph.V = []
graph.E = []
graph.V.append(label_column)
for vertice in features_column_names:
graph.V.append(vertice)
graph.E.append([vertice, label_column])
test = learner.discrete_mle_estimateparams(graphskeleton=graph, data=data)
print "done learning"
edges = test.E
vertices = test.V
probas = test.Vdata
# print probas
dot_string = 'digraph BN{\n'
dot_string += 'node[fontname="Arial"];\n'
dataframes = {}
print "save data"
for vertice in vertices:
print "New vertice: " + str(vertice)
dataframe = DataFrame()
pp = pprint.PrettyPrinter(indent=4)
# pp.pprint(probas[vertice])
dot_string += vertice.replace(" ", "_") + ' [label="' + vertice + '\n' + '" ]; \n'
if len(probas[vertice]['parents']) == 0:
dataframe['Outcome'] = None
dataframe['Probability'] = None
vertex_dict = {}
for index_outcome, outcome in enumerate(probas[vertice]['vals']):
vertex_dict[str(outcome)] = probas[vertice]["cprob"][index_outcome]
od = collections.OrderedDict(sorted(vertex_dict.items()))
# print "Vertice: " + str(vertice)
# print "%-7s|%-11s" % ("Outcome", "Probability")
# print "-------------------"
for k, v in od.iteritems():
# print "%-7s|%-11s" % (str(k), str(round(v, 3)))
dataframe.loc[len(dataframe)] = [k, v]
dataframes[vertice] = dataframe
else:
# pp.pprint(probas[vertice])
dataframe['Outcome'] = None
vertexen = {}
for index_outcome, outcome in enumerate(probas[vertice]['vals']):
temp = []
for parent_index, parent in enumerate(probas[vertice]["parents"]):
# print str([str(float(index_outcome))])
temp = probas[vertice]["cprob"]
dataframe[parent] = None
vertexen[str(outcome)] = temp
dataframe['Probability'] = None
od = collections.OrderedDict(sorted(vertexen.items()))
# [str(float(i)) for i in ast.literal_eval(key)]
# str(v[key][int(float(k))-1])
# print "Vertice: " + str(vertice) + " with parents: " + str(probas[vertice]['parents'])
# print "Outcome" + "\t\t" + '\t\t'.join(probas[vertice]['parents']) + "\t\tProbability"
# print "------------" * len(probas[vertice]['parents']) *3
# pp.pprint(od.values())
counter = 0
# print number_of_cols
for outcome, cprobs in od.iteritems():
for key in cprobs.keys():
array_frame = []
array_frame.append((outcome))
print_string = str(outcome) + "\t\t"
for parent_value, parent in enumerate([i for i in ast.literal_eval(key)]):
# print "parent-value:"+str(parent_value)
# print "parten:"+str(parent)
array_frame.append(int(float(parent)))
# print "lengte array_frame: "+str(len(array_frame))
print_string += parent + "\t\t"
array_frame.append(cprobs[key][counter])
# print "lengte array_frame (2): "+str(len(array_frame))
# print cprobs[key][counter]
print_string += str(cprobs[key][counter]) + "\t"
# for stront in [str(round(float(i), 3)) for i in ast.literal_eval(key)]:
# print_string += stront + "\t\t"
# print "print string: " + print_string
# print "array_frame:" + str(array_frame)
dataframe.loc[len(dataframe)] = array_frame
counter += 1
print "Vertice " + str(vertice) + " done"
dataframes[vertice] = dataframe
for edge in edges:
dot_string += edge[0].replace(" ", "_") + ' -> ' + edge[1].replace(" ", "_") + ';\n'
dot_string += '}'
# src = Source(dot_string)
# src.render('../data/BN', view=draw_network)
# src.render('../data/BN', view=False)
print "vizualisation done"
return dataframes
from libpgm.tablecpdfactorization import TableCPDFactorization
from libpgm.pgmlearner import PGMLearner
text = open("../unifiedMLData2.json")
data=text.read()
printable = set(string.printable)
asciiData=filter(lambda x: x in printable, data)
listofDicts=json.loads(asciiData)
skel = GraphSkeleton()
skel.load("../skeleton.json")
learner = PGMLearner()
result = learner.discrete_mle_estimateparams(skel, listofDicts)
tcf=TableCPDFactorization(result)
#Rating 1 Given Genre is Drama
myquery = dict(rating=[1])
myevidence = dict(genre='Drama')
result=tcf.specificquery(query=myquery,evidence=myevidence)
print result
tcf.refresh()
#Rating 2 Given Genre is Drama
myquery = dict(rating=[2])
myevidence = dict(genre='Drama')
class TestPGMLearner(unittest.TestCase):
def setUp(self):
# instantiate learner
self.l = PGMLearner()
# generate graph skeleton
skel = GraphSkeleton()
skel.load("unittestdict.txt")
skel.toporder()
# generate sample sequence to try to learn from - discrete
nd = NodeData()
nd.load("unittestdict.txt")
self.samplediscbn = DiscreteBayesianNetwork(skel, nd)
self.samplediscseq = self.samplediscbn.randomsample(5000)
# generate sample sequence to try to learn from - discrete
nda = NodeData()
nda.load("unittestlgdict.txt")
self.samplelgbn = LGBayesianNetwork(skel, nda)
self.samplelgseq = self.samplelgbn.randomsample(10000)
self.skel = skel
def test_discrete_mle_estimateparams(self):
result = self.l.discrete_mle_estimateparams(self.skel, self.samplediscseq)
indexa = result.Vdata['SAT']['vals'].index('lowscore')
self.assertTrue(result.Vdata['SAT']['cprob']["['low']"][indexa] < 1 and result.Vdata['SAT']['cprob']["['low']"][indexa] > .9)
indexb = result.Vdata['Letter']['vals'].index('weak')
self.assertTrue(result.Vdata['Letter']['cprob']["['A']"][indexb] < .15 and result.Vdata['Letter']['cprob']["['A']"][indexb] > .05)
def test_lg_mle_estimateparams(self):
result = self.l.lg_mle_estimateparams(self.skel, self.samplelgseq)
self.assertTrue(result.Vdata['SAT']['mean_base'] < 15 and result.Vdata['SAT']['mean_base'] > 5)
self.assertTrue(result.Vdata['Letter']['variance'] < 15 and result.Vdata['Letter']['variance'] > 5)
def test_discrete_constraint_estimatestruct(self):
result = self.l.discrete_constraint_estimatestruct(self.samplediscseq)
self.assertTrue(["Difficulty", "Grade"] in result.E)
def test_lg_constraint_estimatestruct(self):
result = self.l.lg_constraint_estimatestruct(self.samplelgseq)
self.assertTrue(["Intelligence", "Grade"] in result.E)
def test_discrete_condind(self):
chi, pv, witness = self.l.discrete_condind(self.samplediscseq, "Difficulty", "Letter", ["Grade"])
self.assertTrue(pv > .05)
self.assertTrue(witness, ["Grade"])
chia, pva, witnessa = self.l.discrete_condind(self.samplediscseq, "Difficulty", "Intelligence", [])
self.assertTrue(pva < .05)
def test_discrete_estimatebn(self):
result = self.l.discrete_estimatebn(self.samplediscseq)
self.assertTrue(result.V)
self.assertTrue(result.E)
self.assertTrue(result.Vdata["Difficulty"]["cprob"][0])
def test_lg_estimatebn(self):
result = self.l.lg_estimatebn(self.samplelgseq)
self.assertTrue(result.V)
self.assertTrue(result.E)
self.assertTrue(result.Vdata["Intelligence"]["mean_base"])
class PGMLearnerServer(object):
def __init__(self):
self.learner = PGMLearner()
rospy.Service("~discrete/parameter_estimation",
DiscreteParameterEstimation,
self.discrete_parameter_estimation_cb)
rospy.Service("~discrete/query", DiscreteQuery, self.discrete_query_cb)
rospy.Service("~discrete/structure_estimation",
DiscreteStructureEstimation,
self.discrete_structure_estimation_cb)
rospy.Service("~linear_gaussian/parameter_estimation",
LinearGaussianParameterEstimation,
self.lg_parameter_estimation_cb)
rospy.Service("~linear_gaussian/structure_estimation",
LinearGaussianStructureEstimation,
self.lg_structure_estimation_cb)
def discrete_parameter_estimation_cb(self, req):
skel = U.graph_skeleton_from_ros(req.graph)
skel.toporder()
data = U.graph_states_dict_from_ros(req.states)
res = self.learner.discrete_mle_estimateparams(skel, data)
return DiscreteParameterEstimationResponse(
U.discrete_nodes_to_ros(res.Vdata))
def discrete_query_cb(self, req):
nd = U.discrete_nodedata_from_ros(req.nodes)
skel = U.graph_skeleton_from_node_data(nd)
skel.toporder()
bn = DiscreteBayesianNetwork(skel, nd)
fn = TableCPDFactorization(bn)
q = {n: nd.Vdata[n]["vals"] for n in req.query}
ev = {ns.node: ns.state for ns in req.evidence}
rospy.loginfo("resolving query %s with evidence %s" % (q, ev))
ans = fn.condprobve(query=q, evidence=ev)
rospy.loginfo("%s -> %s" % (ans.scope, ans.vals))
res = DiscreteQueryResponse()
node = DiscreteNode()
node.name = ans.scope[0]
node.outcomes = q[node.name]
node.CPT.append(ConditionalProbability(node.outcomes, ans.vals))
res.nodes.append(node)
return res
def discrete_structure_estimation_cb(self, req):
states = [{ns.node: ns.state
for ns in s.node_states} for s in req.states]
pvalparam = 0.05 # default value
indegree = 1 # default value
if req.pvalparam != 0.0:
pvalparam = req.pvalparam
if req.indegree != 0:
indegree = req.indegree
res = self.learner.discrete_constraint_estimatestruct(
states, pvalparam=pvalparam, indegree=indegree)
return DiscreteStructureEstimationResponse(
U.graph_skeleton_to_ros(res))
def lg_parameter_estimation_cb(self, req):
skel = U.graph_skeleton_from_ros(req.graph)
skel.toporder()
data = U.graph_states_dict_from_ros(req.states)
res = self.learner.lg_mle_estimateparams(skel, data)
rospy.logdebug("parameter estimation: %s" % res.Vdata)
return LinearGaussianParameterEstimationResponse(
U.linear_gaussian_nodes_to_ros(res.Vdata))
def lg_structure_estimation_cb(self, req):
states = [{ns.node: ns.state
for ns in s.node_states} for s in req.states]
rospy.logdebug(states)
pvalparam = 0.05 # default value
bins = 10 # default value
indegree = 1 # default value
if req.pvalparam != 0.0:
pvalparam = req.pvalparam
if req.bins != 0:
bins = req.bins
if req.indegree != 0:
indegree = req.indegree
rospy.logdebug("bins: %d, pvalparam: %f, indegree: %d" %
(bins, pvalparam, indegree))
res = self.learner.lg_constraint_estimatestruct(states,
pvalparam=pvalparam,
bins=bins,
indegree=indegree)
rospy.logdebug("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
rospy.logdebug(res.V)
rospy.logdebug(res.E)
return LinearGaussianStructureEstimationResponse(
U.graph_skeleton_to_ros(res))
di['HandStrength'] = 'Medium'
elif (di['HandStrength'] == 'TwoPairs'
or (di['HandStrength'] == 'OnePair' and
(di['Rank'] == 'A' or di['Rank'] == 'K' or di['Rank'] == 'Q'
or di['Rank'] == 'J'))):
di['HandStrength'] = 'Weak'
else:
di['HandStrength'] = 'VeryWeak'
print('################### PART A ################################')
print('*************** Network Parameters for BN agent1*****************')
for skeleton in ["Poker_Network.txt"
]: # loading skeleton of Network from given-file
skel = GraphSkeleton()
skel.load(skeleton)
learner = PGMLearner()
result = learner.discrete_mle_estimateparams(skel, mdata)
print json.dumps(result.Vdata, indent=2)
print('*************** Network Parameters for BN agent2*****************')
for skeleton in ["Poker_Network.txt"]:
skel = GraphSkeleton()
skel.load(skeleton)
learner = PGMLearner()
result = learner.discrete_mle_estimateparams(skel, mdata2)
print json.dumps(result.Vdata, indent=2)
print('########################## PART B ################################')
print('************** NB1 parameters for agent 1 *************************')
for skeleton in ["Poker_Network1.txt"]:
skel = GraphSkeleton()
skel.load(skeleton)
#load all preprocessed training data
df = pd.read_csv('features.csv', sep=',')
#format data to let them correctly processed by libpgm functions
node_data = format_data(df)
skel = GraphSkeleton()
#load structure of our net
skel.load("./skel-learned2.txt")
#setting the topologic order
skel.toporder()
#learner which will estimate parameters e if needed net structure
learner = PGMLearner()
#estismting parameters for our own model
res = learner.discrete_mle_estimateparams(skel, node_data)
"""
#estimating net structure given training data and paramenters this is an alternative to create a new model on our data
net = learner.discrete_estimatebn(node_data)
with open("reteTestMeta.csv", "a") as gv:
gv.write(json.dumps(net.V, indent=2))
gv.write(json.dumps(net.E, indent=2))
res = learner.discrete_mle_estimateparams(net, node_data)
with open("modelloMeta.csv", "a") as gv:
gv.write(json.dumps(res.E, indent=2))
gv.write(json.dumps(res.Vdata, indent=2))
"""
#compute performances for each oveall score
for score in range(1, 6):
target = []
# (8) --------------------------------------------------------------------------
# Learn the CPDs of a discrete Bayesian network, given data and a structure:
# say I have some data
data = bn.randomsample(200)
# and a graphskeleton
skel = GraphSkeleton()
skel.load("../tests/unittestdict.txt")
# instantiate my learner
learner = PGMLearner()
# estimate parameters
result = learner.discrete_mle_estimateparams(skel, data)
# output - toggle comment to see
#print json.dumps(result.Vdata, indent=2)
# (9) -------------------------------------------------------------------------
# Learn the structure of a discrete Bayesian network, given only data:
# say I have some data
data = bn.randomsample(2000)
# instantiate my learner
learner = PGMLearner()
# estimate parameters
result = learner.discrete_constraint_estimatestruct(data)
from libpgm.pgmlearner import PGMLearner
text = open("../unifiedMLData2.json")
data = text.read()
printable = set(string.printable)
asciiData = filter(lambda x: x in printable, data)
#listofDicts=json.dumps(data)
listofDicts = json.loads(asciiData)
skel = GraphSkeleton()
skel.load("../skeleton.json")
learner = PGMLearner()
result = learner.discrete_mle_estimateparams(skel, listofDicts)
tcf = TableCPDFactorization(result)
#Rating 1 Given Occupation is student
myquery = dict(rating=[1])
myevidence = dict(occupation='student')
result = tcf.specificquery(query=myquery, evidence=myevidence)
print result
tcf.refresh()
#Rating 2 Given Occupation is student
myquery = dict(rating=[2])
myevidence = dict(occupation='student')
result = tcf.specificquery(query=myquery, evidence=myevidence)
def learnDiscreteBN_with_structure(df,
continous_columns,
features_column_names,
label_column='cat',
draw_network=False):
features_df = df.copy()
features_df = features_df.drop(label_column, axis=1)
labels_df = DataFrame()
labels_df[label_column] = df[label_column].copy()
for i in continous_columns:
bins = np.arange((min(features_df[i])), (max(features_df[i])),
((max(features_df[i]) - min(features_df[i])) / 5.0))
features_df[i] = pandas.np.digitize(features_df[i], bins=bins)
data = []
for index, row in features_df.iterrows():
dict = {}
for i in features_column_names:
dict[i] = row[i]
dict[label_column] = labels_df[label_column][index]
data.append(dict)
print "Init done"
learner = PGMLearner()
graph = GraphSkeleton()
graph.V = []
graph.E = []
graph.V.append(label_column)
for vertice in features_column_names:
graph.V.append(vertice)
graph.E.append([vertice, label_column])
test = learner.discrete_mle_estimateparams(graphskeleton=graph, data=data)
print "done learning"
edges = test.E
vertices = test.V
probas = test.Vdata
# print probas
dot_string = 'digraph BN{\n'
dot_string += 'node[fontname="Arial"];\n'
dataframes = {}
print "save data"
for vertice in vertices:
print "New vertice: " + str(vertice)
dataframe = DataFrame()
pp = pprint.PrettyPrinter(indent=4)
# pp.pprint(probas[vertice])
dot_string += vertice.replace(
" ", "_") + ' [label="' + vertice + '\n' + '" ]; \n'
if len(probas[vertice]['parents']) == 0:
dataframe['Outcome'] = None
dataframe['Probability'] = None
vertex_dict = {}
for index_outcome, outcome in enumerate(probas[vertice]['vals']):
vertex_dict[str(
outcome)] = probas[vertice]["cprob"][index_outcome]
od = collections.OrderedDict(sorted(vertex_dict.items()))
# print "Vertice: " + str(vertice)
# print "%-7s|%-11s" % ("Outcome", "Probability")
# print "-------------------"
for k, v in od.iteritems():
# print "%-7s|%-11s" % (str(k), str(round(v, 3)))
dataframe.loc[len(dataframe)] = [k, v]
dataframes[vertice] = dataframe
else:
# pp.pprint(probas[vertice])
dataframe['Outcome'] = None
vertexen = {}
for index_outcome, outcome in enumerate(probas[vertice]['vals']):
temp = []
for parent_index, parent in enumerate(
probas[vertice]["parents"]):
# print str([str(float(index_outcome))])
temp = probas[vertice]["cprob"]
dataframe[parent] = None
vertexen[str(outcome)] = temp
dataframe['Probability'] = None
od = collections.OrderedDict(sorted(vertexen.items()))
# [str(float(i)) for i in ast.literal_eval(key)]
# str(v[key][int(float(k))-1])
# print "Vertice: " + str(vertice) + " with parents: " + str(probas[vertice]['parents'])
# print "Outcome" + "\t\t" + '\t\t'.join(probas[vertice]['parents']) + "\t\tProbability"
# print "------------" * len(probas[vertice]['parents']) *3
# pp.pprint(od.values())
counter = 0
# print number_of_cols
for outcome, cprobs in od.iteritems():
for key in cprobs.keys():
array_frame = []
array_frame.append((outcome))
print_string = str(outcome) + "\t\t"
for parent_value, parent in enumerate(
[i for i in ast.literal_eval(key)]):
# print "parent-value:"+str(parent_value)
# print "parten:"+str(parent)
array_frame.append(int(float(parent)))
# print "lengte array_frame: "+str(len(array_frame))
print_string += parent + "\t\t"
array_frame.append(cprobs[key][counter])
# print "lengte array_frame (2): "+str(len(array_frame))
# print cprobs[key][counter]
print_string += str(cprobs[key][counter]) + "\t"
# for stront in [str(round(float(i), 3)) for i in ast.literal_eval(key)]:
# print_string += stront + "\t\t"
# print "print string: " + print_string
# print "array_frame:" + str(array_frame)
dataframe.loc[len(dataframe)] = array_frame
counter += 1
print "Vertice " + str(vertice) + " done"
dataframes[vertice] = dataframe
for edge in edges:
dot_string += edge[0].replace(" ", "_") + ' -> ' + edge[1].replace(
" ", "_") + ';\n'
dot_string += '}'
# src = Source(dot_string)
# src.render('../data/BN', view=draw_network)
# src.render('../data/BN', view=False)
print "vizualisation done"
return dataframes