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optuna.visualization.plot_optimization_history(study)

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print(study.best_value)

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print(study.best_params)

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# pass the objective function to method optimize()

study.optimize(objective, n_trials=10)

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# create a study object 

study = optuna.create_study(study_name="randomForest_optimization",
                            direction="maximize",
                            sampler=TPESampler())

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# define the search space and the objecive function


def objective(trial):
    # Define the search space
    criterions = trial.suggest_categorical('criterion', ['gini', 'entropy'])
    max_depths = trial.suggest_int('max_depth', 1, 9, 1)
    n_estimators = trial.suggest_int('n_estimators', 100, 1000, 100)

    clf = RandomForestClassifier(n_estimators=n_estimators,

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def objective(trial):
    # Define the search space
    criterions = trial.suggest_categorical('criterion', ['gini', 'entropy'])
    max_depths = trial.suggest_int('max_depth', 1, 9, 1)
    n_estimators = trial.suggest_int('n_estimators', 100, 1000, 100)

    clf = sklearn.ensemble.RandomForestClassifier(n_estimators=n_estimators,
                                 criterion=criterions,
                                 max_depth=max_depths,

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# import packages 
import numpy as np 
import pandas as pd 
from sklearn.ensemble import RandomForestClassifier 
from sklearn import metrics 
from sklearn.model_selection import cross_val_score 
from sklearn.preprocessing import StandardScaler 
import joblib 

import optuna 

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# plot convergence 

from skopt.plots import plot_convergence

plot_convergence(result) 

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# summarizing finding:

print('Best Accuracy: %.3f' % (result.fun)) 
print('Best Parameters: %s' % (result.x))