tvalentyn commented on code in PR #22088:
URL: https://github.com/apache/beam/pull/22088#discussion_r940783481
##########
sdks/python/apache_beam/ml/inference/sklearn_inference_test.py:
##########
@@ -47,6 +47,12 @@
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import equal_to
+# pylint: disable=wrong-import-order, wrong-import-position,
ungrouped-imports, unused-import
+try:
+ from apache_beam.io.gcp.gcsfilesystem import GCSFileSystem
Review Comment:
why is this necessary? I am not seeing any usage of GCSFileSystem
##########
sdks/python/apache_beam/examples/inference/sklearn_japanese_housing_regression.py:
##########
@@ -32,50 +32,55 @@
import argparse
from typing import Iterable
+import pandas
+
import apache_beam as beam
from apache_beam.io.filesystems import FileSystems
from apache_beam.ml.inference.base import RunInference
from apache_beam.ml.inference.sklearn_inference import ModelFileType
from apache_beam.ml.inference.sklearn_inference import
SklearnModelHandlerPandas
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
-import pandas
-MODELS = [{
+# yapf: disable
+MODELS = [
+ {
'name': 'all_features',
- 'required_features': [
+ 'required_features': [
'Area',
'Year',
'MinTimeToNearestStation',
'MaxTimeToNearestStation',
'TotalFloorArea',
'Frontage',
'Breadth',
- 'BuildingYear'
- ]
-},
- {
- 'name': 'floor_area',
- 'required_features': ['Area', 'Year', 'TotalFloorArea']
- },
- {
- 'name': 'stations',
- 'required_features': [
- 'Area',
- 'Year',
- 'MinTimeToNearestStation',
- 'MaxTimeToNearestStation'
- ]
- }, {
- 'name': 'no_features', 'required_features': ['Area', 'Year']
- }]
+ 'BuildingYear']
+ }, {
+ 'name': 'floor_area',
+ 'required_features': ['Area', 'Year', 'TotalFloorArea']
+ }, {
+ 'name': 'stations',
+ 'required_features': [
+ 'Area',
+ 'Year',
+ 'MinTimeToNearestStation',
+ 'MaxTimeToNearestStation']
+ }, {
+ 'name': 'no_features',
+ 'required_features': ['Area', 'Year']
+ }
+]
+# yapf: enable
def sort_by_features(dataframe, max_size):
- """ Partitions the dataframe by what data it has available."""
+ """ Returns an index to a model, based on available data."""
for i, model in enumerate(MODELS):
required_features = dataframe[model['required_features']]
- if not required_features.isnull().any().any():
+ # A model can only make a prediction if all required features
+ # are present.
+ # required_features is 2D single row, so all must be called twice.
Review Comment:
```suggestion
# required_features is 2D single row, so all() must be called twice.
```
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