-
Notifications
You must be signed in to change notification settings - Fork 19
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #57 from inception-project/feature/56-Add-simple-l…
…ogging-recommender-for-testing #56 - Add simple logging recommender for testing
- Loading branch information
Showing
1 changed file
with
29 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,29 @@ | ||
# Licensed to the Technische Universität Darmstadt under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The Technische Universität Darmstadt | ||
# licenses this file to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
from typing import List | ||
|
||
from cassis import Cas | ||
|
||
from ariadne.classifier import Classifier | ||
from ariadne.protocol import TrainingDocument | ||
|
||
|
||
class LogOnlyRecommender(Classifier): | ||
def fit(self, documents: List[TrainingDocument], layer: str, feature: str, project_id, user_id: str): | ||
print(f'Training triggered for [{feature}] on [{layer}] in [{len(documents)}] documents from project [{project_id}] for user [{user_id}]') | ||
|
||
def predict(self, cas: Cas, layer: str, feature: str, project_id: str, document_id: str, user_id: str): | ||
print(f'Prediction triggered on document [{document_id}] for [{feature}] on [{layer}] in project [{project_id}] for user [{user_id}]') |