spark-instrumented-optimizer/python/pyspark/ml/tests/test_image.py
Josh Soref 13fd272cd3 Spelling r common dev mlib external project streaming resource managers python
### What changes were proposed in this pull request?

This PR intends to fix typos in the sub-modules:
* `R`
* `common`
* `dev`
* `mlib`
* `external`
* `project`
* `streaming`
* `resource-managers`
* `python`

Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618

NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)

### Why are the changes needed?

Misspelled words make it harder to read / understand content.

### Does this PR introduce _any_ user-facing change?

There are various fixes to documentation, etc...

### How was this patch tested?

No testing was performed

Closes #30402 from jsoref/spelling-R_common_dev_mlib_external_project_streaming_resource-managers_python.

Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
2020-11-27 10:22:45 -06:00

77 lines
3.2 KiB
Python

#
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# The 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
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#
import unittest
from pyspark.ml.image import ImageSchema
from pyspark.testing.mlutils import SparkSessionTestCase
from pyspark.sql import Row
from pyspark.testing.utils import QuietTest
class ImageFileFormatTest(SparkSessionTestCase):
def test_read_images(self):
data_path = 'data/mllib/images/origin/kittens'
df = self.spark.read.format("image") \
.option("dropInvalid", True) \
.option("recursiveFileLookup", True) \
.load(data_path)
self.assertEqual(df.count(), 4)
first_row = df.take(1)[0][0]
# compare `schema.simpleString()` instead of directly compare schema,
# because the df loaded from datasource may change schema column nullability.
self.assertEqual(df.schema.simpleString(), ImageSchema.imageSchema.simpleString())
self.assertEqual(df.schema["image"].dataType.simpleString(),
ImageSchema.columnSchema.simpleString())
array = ImageSchema.toNDArray(first_row)
self.assertEqual(len(array), first_row[1])
self.assertEqual(ImageSchema.toImage(array, origin=first_row[0]), first_row)
expected = {'CV_8UC3': 16, 'Undefined': -1, 'CV_8U': 0, 'CV_8UC1': 0, 'CV_8UC4': 24}
self.assertEqual(ImageSchema.ocvTypes, expected)
expected = ['origin', 'height', 'width', 'nChannels', 'mode', 'data']
self.assertEqual(ImageSchema.imageFields, expected)
self.assertEqual(ImageSchema.undefinedImageType, "Undefined")
with QuietTest(self.sc):
self.assertRaisesRegexp(
TypeError,
"image argument should be pyspark.sql.types.Row; however",
lambda: ImageSchema.toNDArray("a"))
with QuietTest(self.sc):
self.assertRaisesRegexp(
ValueError,
"image argument should have attributes specified in",
lambda: ImageSchema.toNDArray(Row(a=1)))
with QuietTest(self.sc):
self.assertRaisesRegexp(
TypeError,
"array argument should be numpy.ndarray; however, it got",
lambda: ImageSchema.toImage("a"))
if __name__ == "__main__":
from pyspark.ml.tests.test_image import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)