import keras
import numpy as np
from sklearn.metrics import confusion_matrix

model = keras.saving.load_model("classifier.keras", compile=False)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
x_test = np.load("x-test.npy")
y_test = np.load("y-test.npy").astype("int32").reshape(-1)

metrics = model.evaluate(x_test, y_test, verbose=0, return_dict=True)
probabilities = model.predict(x_test, verbose=0).reshape(-1)
y_pred = (probabilities >= 0.5).astype("int32")

matrix = np.zeros((2, 2), dtype="int32")
np.add.at(matrix, (y_test, y_pred), 1)
tn, fp, fn, tp = matrix.ravel()
reference_matrix = confusion_matrix(y_test, y_pred, labels=[0, 1])
matches_reference = np.array_equal(matrix, reference_matrix)

print(f"loss: {metrics['loss']:.4f}")
print(f"accuracy: {metrics['accuracy']:.4f}")
print("confusion matrix rows=true columns=predicted labels=[0, 1]")
print(matrix)
print(f"tn={tn} fp={fp} fn={fn} tp={tp}")
print(f"matches sklearn reference: {matches_reference}")

if not matches_reference:
    raise RuntimeError("custom matrix differs from sklearn reference")