from sklearn.svm import SVC

import pandas as pd

from sklearn.model_selection import train_test_split

import joblib

from sklearn.ensemble import RandomForestClassifier

import os

from sklearn.metrics import log_loss, ConfusionMatrixDisplay, average_precision_score, accuracy_score

from sklearn.metrics import roc_curve, precision_recall_curve, auc, f1_score, confusion_matrix

import matplotlib.pyplot as plt

import numpy as np

import pickle

#def read_data(path):

#    data = pd.read_csv(path)

    #data.set_index("ID_REF",inplace = True)

    #labels = data.pop("Result")

#    return data, labels

dataset = pd.read_csv('GenesExp1.csv')

dataset.set_index("ID_REF",inplace = True)

X = dataset.iloc[:, :20]

y = dataset.iloc[:, -1]

path_model="F:/GeneModel/"

#X,y=read_data("GenesExp1.csv")

# name="Model"

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40, random_state=1)

model=RandomForestClassifier(criterion='gini', max_depth=6, min_samples_leaf=1, min_samples_split=2,

                       n_estimators=100)

model.fit(X_train, y_train)

y_pred=model.predict(X_test)

pickle.dump(model, open('model.pkl','wb'))

model = pickle.load(open('model.pkl','rb'))

print(model.predict([[11.27285579,13.11888698,13.04983865,7.160173909,11.84600012,11.38408063,12.46225539,10.35803641,10.43634604,10.31537082,8.195574032,11.00985731, 9.804574801, 7.811523898,9.271842845,8.808279933,8.473070081,8.818380484,9.115116886, 9.315489635

]]))