human
I am working on the file "Label_Microservice/notebooks/Label_k8s_issues_with_MLP.ipynb".
The first task is:
Write it as a class
Can you write Python code for it?
assistant
#Here is the Python code you requested.

from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score
from collections import Counter
import dill as dpickle
import numpy as np
import pandas as pd

class MLP:
    def __init__(self,
                 counter, # for calculate auc
                 label_columns,
                 activation='relu',
                 alpha=0.0001,
                 early_stopping=True,
                 epsilon=1e-08,
                 hidden_layer_sizes=(100,),
                 learning_rate='constant',
                 learning_rate_init=0.001,
                 max_iter=500,
                 model_file="model.dpkl",
                 momentum=0.9,
                 n_iter_no_change=5,
                 precision_thre=0.7,
                 prob_thre=0.0,
                 random_state=1234,
                 recall_thre=0.5,
                 solver='adam',
                 validation_fraction=0.1):
        self.clf = MLPClassifier(activation=activation,
                                 alpha=alpha,
                                 early_stopping=early_stopping,
                                 epsilon=epsilon,
                                 hidden_layer_sizes=hidden_layer_sizes,
                                 learning_rate=learning_rate,
                                 learning_rate_init=learning_rate_init,
                                 max_iter=max_iter,
                                 momentum=momentum,
                                 n_iter_no_change=n_iter_no_change,
                                 random_state=random_state,
                                 solver=solver,
                                 validation_fraction=validation_fraction)
        self.model_file = model_file
        self.precision_thre = precision_thre
        self.prob_thre = prob_thre
        self.recall_thre = recall_thre
        self.counter = counter
        self.label_columns = label_columns
        self.precision = None
        self.recall = None
       ...