blob: 23d5f798f9c24026b066f86a74085fbe98683bfb [file] [log] [blame]
# Licensed to Apache Software Foundation (ASF) under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Apache Software Foundation (ASF) 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.
# You may obtain a copy of the License at
#
# 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.
class Tool:
model_path = None
conda_env = {
"channels": ["defaults", "conda-forge"],
"dependencies": [
"python=3.8.10",
{
"pip": [
"mlflow",
"scikit-learn==0.24.2",
"boto3==1.22.2",
"pandas==1.3.5",
"setuptools<59.6.0",
],
},
],
"name": "mlflow-env",
}
@staticmethod
def train_automl(train_x, train_y, other_params=None, **kwargs):
raise NotImplementedError
@staticmethod
def eval_automl(automl, test_x, test_y):
score = automl.score(test_x, test_y)
return {"score": score}
@staticmethod
def save_automl(automl, save_path: str):
raise NotImplementedError
class BasePredictor:
def __init__(self, automl_path=None):
self.load_automl(automl_path)
def predict(self, inputs):
return {}
def load_automl(self, path):
...