def setUp(self): self.model = wm.load() self.samples = load_samples()
import iris.model as model from iris.predictor import with_logging, make from iris.server import app mdl = model.load() app.predictor = with_logging(make(mdl), extra={'model_ctime': mdl.timestamp}) if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=8080)
def setUp(self): self.model = wm.load()
def plot_confusion_matrix(cm): target_names = ["0", "1", "2"] plt.imshow(cm, interpolation="nearest", cmap=plt.cm.Blues) plt.title("Confusion matrix") plt.colorbar() tick_marks = np.arange(len(target_names)) plt.xticks(tick_marks, target_names, rotation=45) plt.yticks(tick_marks, target_names) plt.tight_layout() plt.ylabel("True label") plt.xlabel("Predicted label") model = wm.load() samples = load_samples() label_test = [s["label"] for s in samples] data = [s["info"] for s in samples] label_pred = model.predict(data) cm = confusion_matrix(label_test, label_pred) print("Confusion matrix:\n", cm) plt.figure() plot_confusion_matrix(cm) plt.savefig("confusion_matrix.png") report = classification_report(label_test, label_pred) print("Classification report:\n", report)