def train(self,sample_set):
cwmilibrary = CWMILibrary()
cwmilibrary.confounders = self.confounders
ardic = {}
nattributes = sample_set.get_number_of_features()
confounders = ["genus","family","order","class","phylum","superkingdom"]
confounders = ["order"]
cwmilibrary.P_Y = None
for confounder in confounders:
cwmilibrary.P_Y_Z = None
cwmilibrary.P_Z = None
for i in xrange(nattributes):
if i % 100 == 0:
print "Processed %d of %d"%(i,nattributes)
feature = i
feature_scores = {}
scores = cwmilibrary._calculate_information_scores(sample_set,feature,confounder)
feature_scores["%s_mi"%(confounder)] = scores["mi"]
feature_scores["%s_cmi"%(confounder)] = scores["cmi"]
cwmi = 0.0
if scores["hygivenz"] > 0:
cwmi = float(scores["cmi"]*scores["mi"])/scores["hygivenz"]
feature_scores["%s_cwmi"%(confounder)] = cwmi
if not ardic.has_key(feature):
ardic[feature] = {}
ardic[feature].update(feature_scores)
association_rule_set = AssociationRuleSet()
arlist = []
for key in ardic.keys():
class_label = self.get_best_class([key],sample_set)
arlist.append(AssociationRule([key],[class_label],ardic[key]))
association_rule_set.extend(arlist)
association_rule_set = association_rule_set.remap_index_to_feature(sample_set)
return association_rule_set
def train(self, sample_set):
cwmilibrary = CWMILibrary()
cwmilibrary.confounders = self.confounders
ardic = {}
nattributes = sample_set.get_number_of_features()
confounders = [
"genus", "family", "order", "class", "phylum", "superkingdom"
]
confounders = ["order"]
cwmilibrary.P_Y = None
for confounder in confounders:
cwmilibrary.P_Y_Z = None
cwmilibrary.P_Z = None
for i in xrange(nattributes):
if i % 100 == 0:
print "Processed %d of %d" % (i, nattributes)
feature = i
feature_scores = {}
scores = cwmilibrary._calculate_information_scores(
sample_set, feature, confounder)
feature_scores["%s_mi" % (confounder)] = scores["mi"]
feature_scores["%s_cmi" % (confounder)] = scores["cmi"]
cwmi = 0.0
if scores["hygivenz"] > 0:
cwmi = float(
scores["cmi"] * scores["mi"]) / scores["hygivenz"]
feature_scores["%s_cwmi" % (confounder)] = cwmi
if not ardic.has_key(feature):
ardic[feature] = {}
ardic[feature].update(feature_scores)
association_rule_set = AssociationRuleSet()
arlist = []
for key in ardic.keys():
class_label = self.get_best_class([key], sample_set)
arlist.append(AssociationRule([key], [class_label], ardic[key]))
association_rule_set.extend(arlist)
association_rule_set = association_rule_set.remap_index_to_feature(
sample_set)
return association_rule_set
def select(self,sample_set):
cwmilibrary = CWMILibrary()
cwmilibrary.confounders = self.confounders
ardic = {}
nattributes = sample_set.get_number_of_features()
confounder = self.confounder
cwmilibrary.P_Y = None
cwmilibrary.P_Y_Z = None
cwmilibrary.P_Z = None
for i in xrange(nattributes):
#if i % 100 == 0:
# print "Processed %d of %d"%(i,nattributes)
feature = i
feature_scores = {}
scores = cwmilibrary._calculate_information_scores(sample_set,feature,confounder)
feature_scores["mi"] = scores["mi"]
feature_scores["cmi"] = scores["cmi"]
cwmi = 0.0
if scores["hygivenz"] > 0:
cwmi = float(scores["cmi"]*scores["mi"])/scores["hygivenz"]
feature_scores["cwmi"] = cwmi
#association_rule = AssociationRule([feature],["NULL"],feature_scores)
if not ardic.has_key(feature):
ardic[feature] = {}
ardic[feature].update(feature_scores)
#arlist[feature] = feature_scores
association_rule_set = AssociationRuleSet()
arlist = []
for key in ardic.keys():
class_labels = self.get_best_classes([key],sample_set)
class_label = "NULL"
if len(class_labels) >0:
class_label = class_labels[0]
arlist.append(AssociationRule([key],[class_label],ardic[key]))
association_rule_set.extend(arlist)
class_labels = self._get_class_labels(sample_set)
association_rule_set = association_rule_set.remap_index_to_feature(sample_set)
"Make several copies for each score, then switch back and forth between each until filled..."
association_rule_sets = []
for score in self.scores:
aset = deepcopy(association_rule_set)
aset.set_target_accuracy(score)
association_rule_sets.append(aset)
used_features = {}
features = []
features_to_select = self.features_per_class*len(self.scores)*len(class_labels)
feature_class_counts = {}
for score in self.scores:
feature_class_counts[score] = {}
for class_label in class_labels:
feature_class_counts[score][class_label] = self.features_per_class
print "Processing features (%d)"%(features_to_select)
while features_to_select > 0:
for score_index in xrange(len(self.scores)):
score = self.scores[score_index]
association_rule_set = association_rule_sets[score_index]
"Pick the next rule for each class..."
for class_label in class_labels:
for rule_index in xrange(len(association_rule_set)):
rule = association_rule_set[rule_index]
if rule.rs[0] == class_label:
if not used_features.has_key(rule.ls[0]):
used_features[rule.ls[0]] = 1
feature_class_counts[score][rule.rs[0]] -= 1
features.append(rule.ls[0])
features_to_select-=1
break
print "Finished processing for %s, found %d features"%(str(self.scores),len(features))
if len(features) != self.features_per_class*len(scores)*len(class_labels):
print "ERROR! did not find enough features...%d insead of %d"%(len(features),self.features_per_class*len(scores)*len(class_labels))
return features