27 lines
665 B
Python
27 lines
665 B
Python
|
from sklearn.metrics import accuracy_score
|
||
|
|
import pandas as pd
|
||
|
|
from matplotlib import pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
import mlflow
|
||
|
|
mlflow.set_experiment("s444380")
|
||
|
|
|
||
|
|
test_data = pd.read_csv("out.csv")
|
||
|
|
|
||
|
|
y_true = test_data["OFFENSE_CODE_GROUP"]
|
||
|
|
y_pred = test_data["PREDICTED"]
|
||
|
|
|
||
|
|
|
||
|
|
accuracy = accuracy_score(y_true, y_pred)
|
||
|
|
|
||
|
|
mlflow.log_metric("accuracy", accuracy)
|
||
|
|
|
||
|
|
with open("eval_results.csv", "a", encoding="utf-8") as f:
|
||
|
|
f.write(f"{accuracy}\n")
|
||
|
|
|
||
|
|
eval_results = pd.read_csv("eval_results.csv", header=None).values
|
||
|
|
|
||
|
|
plt.plot(np.arange(len(eval_results)), eval_results)
|
||
|
|
plt.xlabel("Build")
|
||
|
|
plt.ylabel("Accuracy")
|
||
|
|
plt.savefig("plot.png")
|