def test_ml(self):
model = ml.createModel("Classification", "KNN")
data = dict()
data["features"] = [[0], [1], [2], [3]]
data["label"] = [0, 0, 1, 1]
model.train(data)
self.assertEquals(model.predict([[1.1]]), [0])
def mlregressionop(action):
try:
if action == "create":
method = request.args['type']
model = createModel("Regression", method)
return jsonify(result="Success", model=model.getId())
elif action == "train":
modelId = request.args['id']
dataName = request.args['data']
label = request.args['target']
features = request.args['train'].split(",")
model = getModel(modelId)
datadf = datautil.load(dataName)
labelData = datautil.getColValues(datadf, label)
featureData = datautil.getColsValues(datadf, features)
data = dict()
data["train"] = featureData
data["target"] = labelData
model.train(data)
predit_data = model.predict(data['train'])
true_data = data['target']
rmse = np.sqrt(np.mean((predit_data - true_data)**2))
return jsonify(result="Success", model=modelId, rmse=rmse)
elif action == "predict":
modelId = request.args['id']
data = json.loads(request.args['data'])
model = getModel(modelId)
return jsonify(result="Success", predict=str(model.predict(data)))
elif action == "predictViz":
modelId = request.args['id']
scale = request.args['scale']
model = getModel(modelId)
return jsonify(result="Success",
predict=str(model.predictViz(int(scale))))
else:
return jsonify(result="Failed",
msg="Do not support this action {}".format(action))
except:
traceback.print_exc()
return jsonify(result="Failed", msg="Some Exception")
def mlaaop(action):
try:
if action == "create":
method = request.args['type']
model = createModel("association_analysis", method)
return jsonify(result="Success", model=model.getId())
elif action == "train":
modelId = request.args['id']
dataName = request.args['data']
label = request.args['label']
features = request.args['features'].split(",")
model = getModel(modelId)
datadf = datautil.load(dataName)
labelData = datautil.getColValues(datadf, label)
featureData = datautil.getColsValues(datadf, features)
data = dict()
data["train"] = featureData
data["target"] = labelData
model.train(data)
return jsonify(result="Success", model=modelId)
elif action == "predict":
'''
modelId = request.args['id']
data = json.loads(request.args['data'])
model = getModel(modelId)
return jsonify(result="Success", predict=str(model.predict(data)))
'''
print "pass"
pass
elif action == "predictViz":
modelId = request.args['id']
scale = request.args['scale']
model = getModel(modelId)
return jsonify(result="Success",
predict=str(model.predictViz(int(scale))))
else:
return jsonify(result="Failed",
msg="Do not support this action {}".format(action))
except:
traceback.print_exc()
return jsonify(result="Failed", msg="Some Exception")
def mlclsop(action):
try:
if action == "create":
method = request.args['type']
model = createModel("Classification", method)
return jsonify(result="Success", model=model.getId())
elif action == "train":
modelId = request.args['id']
dataName = request.args['data']
label = request.args['label']
features = request.args['features'].split(",")
model = getModel(modelId)
datadf = datautil.load(dataName)
labelData = datautil.getColValues(datadf, label)
featureData = datautil.getColsValues(datadf, features)
data = dict()
data["features"] = featureData
data["label"] = labelData
model.train(data)
return jsonify(result="Success", model=modelId)
elif action == "predict":
modelId = request.args['id']
data = json.loads(request.args['data'])
model = getModel(modelId)
return jsonify(result="Success", predict=str(model.predict(data)))
elif action == "predictViz":
modelId = request.args['id']
scale = request.args['scale']
model = getModel(modelId)
return jsonify(result="Success",
predict=str(model.predictViz(int(scale))))
else:
return jsonify(result="Failed",
msg="Do not support this action {}".format(action))
except:
traceback.print_exc()
return jsonify(result="Failed", msg="Some Exception")
