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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"]
for i in par:
    print("y", i)
    y = Predictor()
    y.load_data(file="Data/Training/clean_s_filtered.csv")
    y.process_data(vector_function="sequence", amino_acid="S", imbalance_function=i, random_data=0)
    y.supervised_training("bagging")
    y.benchmark("Data/Benchmarks/phos_stripped.csv", "S")
    del y
    print("x", i)
    x = Predictor()
    x.load_data(file="Data/Training/clean_s_filtered.csv")
    x.process_data(vector_function="sequence", amino_acid="S", imbalance_function=i, random_data=1)
    x.supervised_training("bagging")
    x.benchmark("Data/Benchmarks/phos_stripped.csv", "S")
    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"]
for i in par:
    print("y", i)
    y = Predictor()
    y.load_data(file="Data/Training/clean_Y_filtered.csv")
    y.process_data(vector_function="sequence", amino_acid="Y", imbalance_function=i, random_data=0)
    y.supervised_training("bagging")
    y.benchmark("Data/Benchmarks/phos_stripped.csv", "Y")
    del y
    print("x", i)
    x = Predictor()
    x.load_data(file="Data/Training/clean_Y_filtered.csv")
    x.process_data(vector_function="sequence", amino_acid="Y", imbalance_function=i, random_data=1)
    x.supervised_training("bagging")
    x.benchmark("Data/Benchmarks/phos_stripped.csv", "Y")
    del x
Explanation: Y Phosphorylation
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
    print("y", i)
    y = Predictor()
    y.load_data(file="Data/Training/clean_t_filtered.csv")
    y.process_data(vector_function="sequence", amino_acid="T", imbalance_function=i, random_data=0)
    y.supervised_training("bagging")
    y.benchmark("Data/Benchmarks/phos_stripped.csv", "T")
    del y
    print("x", i)
    x = Predictor()
    x.load_data(file="Data/Training/clean_t_filtered.csv")
    x.process_data(vector_function="sequence", amino_acid="T", imbalance_function=i, random_data=1)
    x.supervised_training("bagging")
    x.benchmark("Data/Benchmarks/phos_stripped.csv", "T")
    del x
Explanation: T Phosphorylation
End of explanation
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