import pandas as pd
import numpy as np
from tensorflow import keras
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, f1_score


learned_model = 'model'
model = keras.models.load_model(learned_model)

train=pd.read_csv('train.csv', header=None, skiprows=1)
indexNames = train[train[1] ==2].index
train.drop(indexNames, inplace=True)
cols=[0,2,3]
X=train[cols].to_numpy()
y=train[1].to_numpy()
X=np.asarray(X).astype('float32')

test=pd.read_csv('test.csv', header=None, skiprows=1)
cols=[0,2,3]
indexNames = test[test[1] ==2].index
test.drop(indexNames, inplace=True)
X_test=test[cols].to_numpy()
y_test=test[1].to_numpy()
X_test=np.asarray(X_test).astype('float32')

predictions = model.predict(X_test)


acc = accuracy_score(y_test, predictions)
print('Accuracy: ', acc)

f1=f1_score(y_test, predictions)
print('F1: ', f1)

with open('evaluation.txt', 'a') as f:
    f.write(str(acc) + "\n")

with open('evaluation.txt', 'r') as f:
    lines = f.readlines()


fig = plt.figure(figsize=(5,5))
chart = fig.add_subplot()
chart.set_ylabel("Accuracy")
chart.set_xlabel("Build")
x = np.arange(0, len(lines), 1)
y = [float(x) for x in lines]
plt.plot(x, y, "go")
plt.savefig("evaluation.png")