class PredictionPipeline():
def __init__(self):
# load config file
with open("./config/predictionconfig.yml", "r") as ymlfile:
cfg = yaml.load(ymlfile, Loader=yaml.FullLoader)
self.interval = cfg['interval']
self.threshold = cfg['single_threshold']
# init DataPreprocessor
self.data_preprocessor = DataPreprocessor()
# init PredictionMaker
self.prediction_maker = PredictionMaker()
self.registry = CollectorRegistry()
self.pushgateway_url = os.getenv('PUSHGATEWAY_URL')
def run(self):
while True:
start_millis = int(round(time.time() * 1000))
print("Starting pipeline...")
# get data
df = self.data_preprocessor.get_data()
df = self.data_preprocessor.preprocess_data(df)
if df.empty == False:
# predict
result = self.prediction_maker.make_prediction(df)
end_millis = int(round(time.time() * 1000))
prediction_millis = end_millis - start_millis
prediction = Prediction(result)
# apply changes to K8s Cluster
prediction.apply(self.threshold)
# push to prometheus gateway
prediction.push_to_prometheus(self.registry,
self.pushgateway_url)
try:
g = Gauge('prediction_making_speed',
'Time in ms for making Prediction.',
registry=registry)
except:
pass
g.set(prediction_millis)
push_to_gateway('{}:9091'.format(self.pushgateway_url),
job='prediction-maker',
registry=registry)
# sleep until next interval
print("Prediction took {} ms.".format(prediction_millis))
print("Going back to sleep for {} sec...".format(self.interval))
time.sleep(self.interval)
class MechansimCreator():
''' Class responsible for creating the mechanism for the prediction
'''
def __init__(self, algorithm):
''' init data preprocessor and classifier
'''
self.data_preprocessor = DataPreprocessor()
if str(algorithm).lower() == 'decisiontree':
self.clf = DecisionTreeClassifier()
elif str(algorithm).lower() == 'randomforest':
self.clf = RandomForestClassifier()
def run(self):
''' create and store mechanism
'''
df = self.data_preprocessor.get_data()
df = self.data_preprocessor.preprocess_data(df)
# nextcluster as label for ML algorithm
y = df['nextcluster'].values
y = y.astype('int')
X_train, X_test, y_train, y_test = train_test_split(df[['currentclusternumber']].values, y)
# TODO: imbalanced classes?
print(df.nextcluster.value_counts())
# Classifer
self.clf = self.clf.fit(X_train, y_train)
y_pred = self.clf.predict(X_test)
print("Accuracy of Classifier:", metrics.accuracy_score(y_test, y_pred))
'''
dot_data = StringIO()
export_graphviz(clf, out_file=dot_data,
filled=True, rounded=True,
special_characters=True,feature_names = feature_cols)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png('./data/images/decisiontree/tree.png')
# Random Forest classifier
clf=RandomForestClassifier(n_estimators=32)
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)
print("Accuracy of RandomForest:",metrics.accuracy_score(y_test, y_pred))
i_tree = 0
for tree_in_forest in clf.estimators_:
if (i_tree < 10):
dot_data = StringIO()
export_graphviz(tree_in_forest, out_file=dot_data,
filled=True, rounded=True,
special_characters=True,feature_names = feature_cols)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png('./data/images/foresttrees/foresttree{}.png'.format(i_tree))
i_tree += 1
'''
# save the model to db
mechanism = Mechansim(self.clf, self.data_preprocessor.le, int(round(time.time() * 1000)))
mechanism.save()
