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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Template for test
Step1:  Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Included is N Phosphorylation however no benchmarks are available, yet. 
Training data is from phospho.elm and benchmarks are from dbptm. 
Note
Step2:  Y Phosphorylation
Step3:  T Phosphorylation
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Python Code:
from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
Explanation: Template for test
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
benchmarks = ["Data/Training/phos_CDK1.csv", "Data/Training/phos_CK2.csv", "Data/Training/phos_MAPK1.csv", "Data/Training/phos_PKA.csv", "Data/Training/phos_PKC.csv"]
for j in benchmarks:
    for i in par:
        print("y", i, " ", j)
        y = Predictor()
        y.load_data(j)
        y.process_data(vector_function="sequence", amino_acid="S", imbalance_function=i, random_data=0)
        y.supervised_training("bagging")
        del y
        print("x", i, " ", j)
        x = Predictor()
        x.load_data(file=j)
        x.process_data(vector_function="sequence", amino_acid="S", imbalance_function=i, random_data=1)
        x.supervised_training("bagging")
        del x
Explanation: Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Included is N Phosphorylation however no benchmarks are available, yet. 
Training data is from phospho.elm and benchmarks are from dbptm. 
Note: SMOTEEN seems to preform best
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
benchmarks = ["Data/Training/phos_CDK1.csv", "Data/Training/phos_CK2.csv", "Data/Training/phos_MAPK1.csv", "Data/Training/phos_PKA.csv", "Data/Training/phos_PKC.csv"]
for j in benchmarks:
    for i in par:
        try:
            print("y", i, " ", j)
            y = Predictor()
            y.load_data(j)
            y.process_data(vector_function="sequence", amino_acid="Y", imbalance_function=i, random_data=0)
            y.supervised_training("bagging")
            del y
            print("x", i, " ", j)
            x = Predictor()
            x.load_data(file=j)
            x.process_data(vector_function="sequence", amino_acid="Y", imbalance_function=i, random_data=1)
            x.supervised_training("bagging")
            del x
        except:
            print("NotValidBenchmark")
Explanation: Y Phosphorylation
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
benchmarks = ["Data/Training/phos_CDK1.csv", "Data/Training/phos_CK2.csv", "Data/Training/phos_MAPK1.csv", "Data/Training/phos_PKA.csv", "Data/Training/phos_PKC.csv"]
for j in benchmarks:
    for i in par:
        print("y", i, " ", j)
        y = Predictor()
        y.load_data(j)
        y.process_data(vector_function="sequence", amino_acid="T", imbalance_function=i, random_data=0)
        y.supervised_training("bagging")
        del y
        print("x", i, " ", j)
        x = Predictor()
        x.load_data(file=j)
        x.process_data(vector_function="sequence", amino_acid="T", imbalance_function=i, random_data=1)
        x.supervised_training("bagging")
        del x
Explanation: T Phosphorylation
End of explanation
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