Pipeline to Gama compatible string

pipeline_to_gama_string.py

p = Pipeline(steps=[('imputation', SimpleImputer(strategy='median')),('1', SMOTE(k_neighbors=21)),('0', EasyEnsembleClassifier(n_estimators=100))])
try:
  if p['imputation']:
    p = p[1:]
except:
  pass

print(p)
l = []
for i in range(len(p)):
  l.append(str(p[i].__class__()).replace('()',''))
#making string from pipeline
s = []
#For making list
for i in reversed(l):
  s.append(f"{i}(")
#for making data 
data_string ="data"
s.append(data_string)
#for making hyperparameters
for i in range(len(p)):
  keys = p[i].__dict__.keys() & clf_config[p[i].__class__].keys()
  for j in keys:
    # if j in clf_config[p[i].__class__].keys():
    if j == list(keys)[-1]:
      if type(p[i].__dict__[j])==str:
        s.append(f"{str(p[i].__class__()).replace('()','')}.{j}='{p[i].__dict__[j]}'")
      else:
        s.append(f"{str(p[i].__class__()).replace('()','')}.{j}={p[i].__dict__[j]}")
    else:
      if type(p[i].__dict__[j])==str:
        s.append(f"{str(p[i].__class__()).replace('()','')}.{j}='{p[i].__dict__[j]}', ")
      else:
        s.append(f"{str(p[i].__class__()).replace('()','')}.{j}={p[i].__dict__[j]}, ")
  s.append('), ')
s[-1] = ')'