def fit(self,
stable_antecedents: List[str],
flexible_antecedents: List[str],
consequent: str,
conf: float,
supp: float,
desired_classes: List[str] = None,
desired_changes: List[list] = None,
is_nan: bool = False,
is_reduction: bool = True,
min_stable_antecedents: int = 1,
min_flexible_antecedents: int = 1):
"""
Get action rules.
Define antecedent and consequent.
- stable_antecedents - List of column names.
- flexible_antecedents - List of column names.
- consequent - Name of column.
Confidence and support.
- conf - value in % for confidence of classification rules.
- supp - value in % for support of classification rules.
Desired classes or desired changes must be entered.
- desired_classes - List of decision states. For example ["1"]. DEFAULT: None
- desired_changes - List of desired changes. For example [["0", "1"]]. DEFAULT: None
Should nan values be used.
- is_nan - True means nan values are used, False means nan values are not used. DEFAULT: FALSE
Should the reduction table be used.
- is_reduction - is reduction table used DEFAULT: TRUE
Minimal number of stable and flexible couples
- min_stable_antecedents - min. stable couples. DEFAULT: 1
- min_flexible_antecedents - min. flexible couples. DEFAULT: 1
"""
if bool(desired_classes) != bool(desired_changes):
desired_state = DesiredState(desired_classes=desired_classes,
desired_changes=desired_changes)
else:
raise Exception(
"Desired classes or desired changes must be entered")
antecedents = stable_antecedents + flexible_antecedents
self.decisions.prepare_data_fim(antecedents, consequent)
self.decisions.fit_fim_apriori(conf=conf, support=supp)
self.decisions.generate_decision_table()
stable = self.decisions.decision_table[stable_antecedents]
flex = self.decisions.decision_table[flexible_antecedents]
target = self.decisions.decision_table[[consequent]]
supp = self.decisions.support
conf = self.decisions.confidence
reduced_tables = Reduction(stable, flex, target, desired_state, supp,
conf, is_nan)
if is_reduction:
reduced_tables.reduce()
action_rules = ActionRules(
reduced_tables.stable_tables, reduced_tables.flexible_tables,
reduced_tables.decision_tables, desired_state, reduced_tables.supp,
reduced_tables.conf, is_nan, min_stable_antecedents,
min_flexible_antecedents)
action_rules.fit()
self.action_rules = action_rules.action_rules
def getSummary(): #actually calculate summary
opLabel.pack()
opEntry.pack()
opEntry.config(state='normal' , bg="white" , fg="black")
opEntry.delete(1.0,END)
opEntry.insert(INSERT,"Generating Summary...")
global endTime1,endTime2
startTime = time.time()
text = ipEntry.get("1.0", END)
ps = PorterStemmer()
red_ra=redRatio.get()/100.00
if var1.get():
words = text.split()
for w in words:
text += ps.stem(w)
text += " "
if var6.get()==1:
r = Reduction() #object of class Red..
reduced_text = r.reduce(text, red_ra)
op=' '.join(reduced_text)
global opTA
opTA=op
elif var6.get()==2:
m = modelVC.summary()
op = m.summarize(text,red_ra)
global opPA
opPA=op
if var2.get()==1:
op=shortForm.shortF(op)
opEntry.delete(1.0,END)
opEntry.insert(INSERT, op)
if var6.get()==1:
endTime1 = time.time() - startTime
print("Time taken: %f secs" %endTime1)
if var4.get():
tkMessageBox.showinfo("Statistics", "Time taken to complete: %f secs" %endTime)
if var6.get()==2:
endTime2 = time.time() - startTime
print("Time taken: %f secs" %endTime2)
if var4.get():
tkMessageBox.showinfo("Statistics", "Time taken to complete: %f secs" %endTime)
if var3.get() == 1:
file_f = open("log.txt", "a")
lenText=len(text)
file_f.write("\nInput length: %d" %lenText)
lenText = len(op)
file_f.write("\tSummary length: %d" % lenText)
file_f.write('\nStemming: %d' %var1.get())
file_f.write('\tShort Forms: %d' %var2.get())
file_f.write('\tReduction Ratio: %d percent' % redRatio.get())
file_f.write('\tModel: %d ' % var6.get())
if var6.get()==1:
file_f.write("\nTime taken: %f secs\n" %endTime1)
if var6.get()==2:
file_f.write("\nTime taken: %f secs\n" %endTime2)
file_f.close()
