def setUpClass(cls):
data = get_credit_data()
target = ps.BinaryTarget('class', b'bad')
searchSpace = ps.create_nominal_selectors(data, ignore=['class'])
cls.task = ps.SubgroupDiscoveryTask(data,
target,
searchSpace,
result_set_size=10,
depth=5,
qf=ps.StandardQF(1.0))
cls.result = ps.SimpleDFS().execute(cls.task)
import pandas as pd
import pysubgroup as ps
data = pd.read_table("../data/titanic.csv")
target = ps.BinaryTarget('Survived', 0)
search_space = ps.create_selectors(data, ignore=['Survived'])
task = ps.SubgroupDiscoveryTask(data, target, search_space,
result_set_size=5, depth=2,
qf=ps.CombinedInterestingnessMeasure([ps.StandardQF(1), ps.GeneralizationAware_StandardQF(1)]))
result = ps.SimpleDFS().execute(task, use_optimistic_estimates=False)
print(result.to_dataframe())
from scipy.io import arff
import pysubgroup as ps
import pandas as pd
data = pd.DataFrame(arff.loadarff("../data/credit-g.arff")[0])
target = ps.NominalTarget('class', b'bad')
searchSpace = ps.createNominalSelectors(data, ignore=['class'])
task = ps.SubgroupDiscoveryTask(data,
target,
searchSpace,
resultSetSize=10,
depth=3,
qf=ps.StandardQF(1.0))
result = ps.BeamSearch(beamWidth=10).execute(task)
for (q, sg) in result:
print(str(q) + ":\t" + str(sg.subgroupDescription))
print("******")
result = ps.SimpleDFS().execute(task)
for (q, sg) in result:
print(str(q) + ":\t" + str(sg.subgroupDescription))
# print WRAccQF().evaluateFromDataset(data, Subgroup(target, []))
def test_SimpleDFS(self):
self.runAlgorithm(ps.SimpleDFS(), "SimpleDFS", self.result,
self.qualities, self.task)
def test_StandardQF_GA_SimpleDFS(self):
self.task.qf = ps.GeneralizationAware_StandardQF(0.5)
self.runAlgorithm(ps.SimpleDFS(), "Standard_SimpleDFS", self.result,
self.qualities, self.task)
print(self.task.qf.cache)
def test_DFS(self):
self.runAlgorithm(ps.SimpleDFS(), "DFS", self.result[:-1],
self.qualities[:-1], self.task)
import pysubgroup as ps
import pandas as pd
data = pd.read_csv("~/datasets/titanic.csv")
target = ps.NominalTarget('survived', 0)
searchSpace = ps.createSelectors(data, ignore=['survived'])
task = ps.SubgroupDiscoveryTask(data,
target,
searchSpace,
resultSetSize=5,
depth=2,
qf=ps.CombinedInterestingnessMeasure(
[ps.StandardQF(1),
ps.GAStandardQF(1)]))
result = ps.SimpleDFS().execute(task, useOptimisticEstimates=False)
for (q, sg) in result:
print(str(q) + ":\t" + str(sg.subgroupDescription))
searchSpace,
result_set_size=200,
depth=4,
qf=QF)
GpGrowth(mode='b_u').to_file(task, 'E:/tmp/gp_credit.txt')
import time
start_time = time.time()
gp = GpGrowth(mode='b_u').execute(task)
print("--- %s seconds ---" % (time.time() - start_time))
#gp = [(qual, sg) for qual, sg in gp if sg.depth <= task.depth]
gp = sorted(gp)
quit()
start_time = time.time()
dfs1 = ps.SimpleDFS().execute(task)
print("--- %s seconds ---" % (time.time() - start_time))
dfs = [(qual, sg.subgroup_description) for qual, sg in dfs1]
dfs = sorted(dfs, reverse=True)
gp = sorted(gp, reverse=True)
def better_sorted(l):
the_dict = defaultdict(list)
prev_key = l[0][0]
for key, val in l:
if abs(prev_key - key) < 10**-11:
the_dict[prev_key].append(val)
else:
the_dict[key].append(val)
prev_key = key
