This repository was archived by the owner on Apr 1, 2026. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 67
Expand file tree
/
Copy path__init__.py
More file actions
93 lines (81 loc) · 2.41 KB
/
__init__.py
File metadata and controls
93 lines (81 loc) · 2.41 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
# Copyright 2023 Google LLC
#
# 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.
"""BigQuery DataFrames ML provides a SKLearn-like API on the BigQuery engine.
.. code:: python
from bigframes.ml.linear_model import LinearRegression
model = LinearRegression()
model.fit(feature_columns, label_columns)
model.predict(feature_columns_from_test_data)
You can also save your fit parameters to BigQuery for later use.
.. code:: python
import bigframes.pandas as bpd
model.to_gbq(
your_model_id, # For example: "bqml_tutorial.penguins_model"
replace=True,
)
saved_model = bpd.read_gbq_model(your_model_id)
saved_model.predict(feature_columns_from_test_data)
See the `BigQuery ML linear regression tutorial
<https://docs.cloud.google.com/bigquery/docs/linear-regression-tutorial>`_ for a
detailed example.
See all, the references for ``bigframes.ml`` sub-modules:
* :mod:`bigframes.ml.cluster`
* :mod:`bigframes.ml.compose`
* :mod:`bigframes.ml.decomposition`
* :mod:`bigframes.ml.ensemble`
* :mod:`bigframes.ml.forecasting`
* :mod:`bigframes.ml.imported`
* :mod:`bigframes.ml.impute`
* :mod:`bigframes.ml.linear_model`
* :mod:`bigframes.ml.llm`
* :mod:`bigframes.ml.metrics`
* :mod:`bigframes.ml.model_selection`
* :mod:`bigframes.ml.pipeline`
* :mod:`bigframes.ml.preprocessing`
* :mod:`bigframes.ml.remote`
Alternatively, check out mod:`bigframes.bigquery.ml` for an interface that is
more similar to the BigQuery ML SQL syntax.
"""
from bigframes.ml import (
cluster,
compose,
decomposition,
ensemble,
forecasting,
imported,
impute,
linear_model,
llm,
metrics,
model_selection,
pipeline,
preprocessing,
remote,
)
__all__ = [
"cluster",
"compose",
"decomposition",
"ensemble",
"forecasting",
"imported",
"impute",
"linear_model",
"llm",
"metrics",
"model_selection",
"pipeline",
"preprocessing",
"remote",
]