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)
