aeb3649fb9
### What changes were proposed in this pull request? This replaces deprecated API usage in PySpark tests with the preferred APIs. These have been deprecated for some time and usage is not consistent within tests. - https://docs.python.org/3/library/unittest.html#deprecated-aliases ### Why are the changes needed? For consistency and eventual removal of deprecated APIs. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existing tests Closes #30557 from BryanCutler/replace-deprecated-apis-in-tests. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
192 lines
8.5 KiB
Python
192 lines
8.5 KiB
Python
# -*- encoding: utf-8 -*-
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from pyspark.sql import Column, Row
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from pyspark.sql.types import StructType, StructField, LongType
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from pyspark.sql.utils import AnalysisException
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from pyspark.testing.sqlutils import ReusedSQLTestCase
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class ColumnTests(ReusedSQLTestCase):
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def test_column_name_encoding(self):
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"""Ensure that created columns has `str` type consistently."""
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columns = self.spark.createDataFrame([('Alice', 1)], ['name', u'age']).columns
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self.assertEqual(columns, ['name', 'age'])
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self.assertTrue(isinstance(columns[0], str))
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self.assertTrue(isinstance(columns[1], str))
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def test_and_in_expression(self):
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self.assertEqual(4, self.df.filter((self.df.key <= 10) & (self.df.value <= "2")).count())
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self.assertRaises(ValueError, lambda: (self.df.key <= 10) and (self.df.value <= "2"))
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self.assertEqual(14, self.df.filter((self.df.key <= 3) | (self.df.value < "2")).count())
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self.assertRaises(ValueError, lambda: self.df.key <= 3 or self.df.value < "2")
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self.assertEqual(99, self.df.filter(~(self.df.key == 1)).count())
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self.assertRaises(ValueError, lambda: not self.df.key == 1)
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def test_validate_column_types(self):
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from pyspark.sql.functions import udf, to_json
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from pyspark.sql.column import _to_java_column
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self.assertTrue("Column" in _to_java_column("a").getClass().toString())
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self.assertTrue("Column" in _to_java_column(u"a").getClass().toString())
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self.assertTrue("Column" in _to_java_column(self.spark.range(1).id).getClass().toString())
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self.assertRaisesRegex(
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TypeError,
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"Invalid argument, not a string or column",
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lambda: _to_java_column(1))
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class A():
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pass
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self.assertRaises(TypeError, lambda: _to_java_column(A()))
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self.assertRaises(TypeError, lambda: _to_java_column([]))
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self.assertRaisesRegex(
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TypeError,
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"Invalid argument, not a string or column",
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lambda: udf(lambda x: x)(None))
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self.assertRaises(TypeError, lambda: to_json(1))
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def test_column_operators(self):
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ci = self.df.key
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cs = self.df.value
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c = ci == cs
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self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
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rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci), (1 ** ci), (ci ** 1)
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self.assertTrue(all(isinstance(c, Column) for c in rcc))
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cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7]
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self.assertTrue(all(isinstance(c, Column) for c in cb))
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cbool = (ci & ci), (ci | ci), (~ci)
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self.assertTrue(all(isinstance(c, Column) for c in cbool))
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css = cs.contains('a'), cs.like('a'), cs.rlike('a'), cs.asc(), cs.desc(),\
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cs.startswith('a'), cs.endswith('a'), ci.eqNullSafe(cs)
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self.assertTrue(all(isinstance(c, Column) for c in css))
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self.assertTrue(isinstance(ci.cast(LongType()), Column))
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self.assertRaisesRegex(ValueError,
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"Cannot apply 'in' operator against a column",
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lambda: 1 in cs)
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def test_column_accessor(self):
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from pyspark.sql.functions import col
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self.assertIsInstance(col("foo")[1:3], Column)
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self.assertIsInstance(col("foo")[0], Column)
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self.assertIsInstance(col("foo")["bar"], Column)
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self.assertRaises(ValueError, lambda: col("foo")[0:10:2])
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def test_column_select(self):
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df = self.df
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self.assertEqual(self.testData, df.select("*").collect())
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self.assertEqual(self.testData, df.select(df.key, df.value).collect())
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self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect())
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def test_access_column(self):
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df = self.df
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self.assertTrue(isinstance(df.key, Column))
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self.assertTrue(isinstance(df['key'], Column))
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self.assertTrue(isinstance(df[0], Column))
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self.assertRaises(IndexError, lambda: df[2])
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self.assertRaises(AnalysisException, lambda: df["bad_key"])
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self.assertRaises(TypeError, lambda: df[{}])
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def test_column_name_with_non_ascii(self):
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columnName = "数量"
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self.assertTrue(isinstance(columnName, str))
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schema = StructType([StructField(columnName, LongType(), True)])
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df = self.spark.createDataFrame([(1,)], schema)
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self.assertEqual(schema, df.schema)
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self.assertEqual("DataFrame[数量: bigint]", str(df))
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self.assertEqual([("数量", 'bigint')], df.dtypes)
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self.assertEqual(1, df.select("数量").first()[0])
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self.assertEqual(1, df.select(df["数量"]).first()[0])
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self.assertTrue(columnName in repr(df[columnName]))
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def test_field_accessor(self):
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df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
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self.assertEqual(1, df.select(df.l[0]).first()[0])
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self.assertEqual(1, df.select(df.r["a"]).first()[0])
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self.assertEqual(1, df.select(df["r.a"]).first()[0])
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self.assertEqual("b", df.select(df.r["b"]).first()[0])
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self.assertEqual("b", df.select(df["r.b"]).first()[0])
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self.assertEqual("v", df.select(df.d["k"]).first()[0])
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def test_bitwise_operations(self):
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from pyspark.sql import functions
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row = Row(a=170, b=75)
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df = self.spark.createDataFrame([row])
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result = df.select(df.a.bitwiseAND(df.b)).collect()[0].asDict()
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self.assertEqual(170 & 75, result['(a & b)'])
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result = df.select(df.a.bitwiseOR(df.b)).collect()[0].asDict()
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self.assertEqual(170 | 75, result['(a | b)'])
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result = df.select(df.a.bitwiseXOR(df.b)).collect()[0].asDict()
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self.assertEqual(170 ^ 75, result['(a ^ b)'])
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result = df.select(functions.bitwiseNOT(df.b)).collect()[0].asDict()
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self.assertEqual(~75, result['~b'])
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def test_with_field(self):
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from pyspark.sql.functions import lit, col
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df = self.spark.createDataFrame([Row(a=Row(b=1, c=2))])
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self.assertIsInstance(df['a'].withField('b', lit(3)), Column)
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self.assertIsInstance(df['a'].withField('d', lit(3)), Column)
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result = df.withColumn('a', df['a'].withField('d', lit(3))).collect()[0].asDict()
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self.assertEqual(3, result['a']['d'])
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result = df.withColumn('a', df['a'].withField('b', lit(3))).collect()[0].asDict()
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self.assertEqual(3, result['a']['b'])
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self.assertRaisesRegex(TypeError,
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'col should be a Column',
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lambda: df['a'].withField('b', 3))
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self.assertRaisesRegex(TypeError,
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'fieldName should be a string',
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lambda: df['a'].withField(col('b'), lit(3)))
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def test_drop_fields(self):
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df = self.spark.createDataFrame([Row(a=Row(b=1, c=2, d=Row(e=3, f=4)))])
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self.assertIsInstance(df["a"].dropFields("b"), Column)
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self.assertIsInstance(df["a"].dropFields("b", "c"), Column)
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self.assertIsInstance(df["a"].dropFields("d.e"), Column)
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result = df.select(
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df["a"].dropFields("b").alias("a1"),
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df["a"].dropFields("d.e").alias("a2"),
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).first().asDict(True)
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self.assertTrue(
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"b" not in result["a1"] and
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"c" in result["a1"] and
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"d" in result["a1"]
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)
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self.assertTrue(
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"e" not in result["a2"]["d"] and
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"f" in result["a2"]["d"]
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)
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if __name__ == "__main__":
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import unittest
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from pyspark.sql.tests.test_column import * # noqa: F401
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try:
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import xmlrunner # type: ignore[import]
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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