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 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")