c60bc8c4-3c84-48a0-8de6-9eddb48baa31

# coding=utf-8
# Copyright 2018-2023 EvaDB
#
# Licensed 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.


import pickle

import pandas as pd

from evadb.functions.abstract.abstract_function import AbstractFunction
from evadb.functions.decorators.decorators import setup


class ForecastModel(AbstractFunction):
    @property
    def name(self) -> str:
        return "ForecastModel"

    @setup(cacheable=False, function_type="Forecasting", batchable=True)
    def setup(
        self,
        model_name: str,
        model_path: str,
        predict_column_rename: str,
        time_column_rename: str,
        id_column_rename: str,
        horizon: int,
        library: str,
    ):
        self.library = library
        if "neuralforecast" in self.library:
            from neuralforecast import NeuralForecast

            loaded_model = NeuralForecast.load(path=model_path)
            self.model_name = model_name[4:] if "Auto" in model_name else model_name
        else:
            with open(model_path, "rb") as f:
                loaded_model = pickle.load(f)
            self.model_name = model_name
        self.model = loaded_model
        self.predict_column_rename = predict_column_rename
        self.time_column_rename = time_column_rename
        self.id_column_rename = id_column_rename
        self.horizon = int(horizon)

    def forward(self, data) -> pd.DataFrame:
        if self.library == "statsforecast":
            forecast_df = self.model.predict(h=self.horizon)
        else:
            forecast_df = self.model.predict()
        forecast_df.reset_index(inplace=True)
        forecast_df = forecast_df.rename(
            columns={
                "unique_id": self.id_column_rename,
                "ds": self.time_column_rename,
                self.model_name: self.predict_column_rename,
            }
        )[: self.horizon * forecast_df["unique_id"].nunique()]
        return forecast_df

1	# coding=utf-8
2	# Copyright 2018-2023 EvaDB
3	#
4	# Licensed under the Apache License, Version 2.0 (the "License");
5	# you may not use this file except in compliance with the License.
6	# You may obtain a copy of the License at
7	#
8	# http://www.apache.org/licenses/LICENSE-2.0
9	#
10	# Unless required by applicable law or agreed to in writing, software
11	# distributed under the License is distributed on an "AS IS" BASIS,
12	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13	# See the License for the specific language governing permissions and
14	# limitations under the License.
15
16
17	import pickle	×
18
19	import pandas as pd	×
20
21	from evadb.functions.abstract.abstract_function import AbstractFunction	×
22	from evadb.functions.decorators.decorators import setup	×
23
24
25	class ForecastModel(AbstractFunction):	×
26	@property	×
27	def name(self) -> str:	×
28	return "ForecastModel"	×
29
30	@setup(cacheable=False, function_type="Forecasting", batchable=True)	×
31	def setup(	×
32	self,
33	model_name: str,
34	model_path: str,
35	predict_column_rename: str,
36	time_column_rename: str,
37	id_column_rename: str,
38	horizon: int,
39	library: str,
40	):
41	self.library = library	×
42	if "neuralforecast" in self.library:	×
43	from neuralforecast import NeuralForecast	×
44
45	loaded_model = NeuralForecast.load(path=model_path)	×
46	self.model_name = model_name[4:] if "Auto" in model_name else model_name	×
47	else:
48	with open(model_path, "rb") as f:	×
49	loaded_model = pickle.load(f)	×
50	self.model_name = model_name	×
51	self.model = loaded_model	×
52	self.predict_column_rename = predict_column_rename	×
53	self.time_column_rename = time_column_rename	×
54	self.id_column_rename = id_column_rename	×
55	self.horizon = int(horizon)	×
56
57	def forward(self, data) -> pd.DataFrame:	×
58	if self.library == "statsforecast":	×
59	forecast_df = self.model.predict(h=self.horizon)	×
60	else:
61	forecast_df = self.model.predict()	×
62	forecast_df.reset_index(inplace=True)	×
63	forecast_df = forecast_df.rename(	×
64	columns={
65	"unique_id": self.id_column_rename,
66	"ds": self.time_column_rename,
67	self.model_name: self.predict_column_rename,
68	}
69	)[: self.horizon * forecast_df["unique_id"].nunique()]
70	return forecast_df	×

georgia-tech-db / eva / c60bc8c4-3c84-48a0-8de6-9eddb48baa31

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