本文整理汇总了Python中odps.df.DataFrame.exclude_fields方法的典型用法代码示例。如果您正苦于以下问题：Python DataFrame.exclude_fields方法的具体用法？Python DataFrame.exclude_fields怎么用？Python DataFrame.exclude_fields使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类odps.df.DataFrame的用法示例。
在下文中一共展示了DataFrame.exclude_fields方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_normalize
# 需要导入模块: from odps.df import DataFrame [as 别名]
# 或者: from odps.df.DataFrame import exclude_fields [as 别名]
def test_normalize(self):
self.delete_table(IONOSPHERE_NORMALIZED_TABLE)
self.delete_table(IONOSPHERE_TABLE_ONE_PART)
self.create_ionosphere_one_part(IONOSPHERE_TABLE_ONE_PART)
df = DataFrame(self.odps.get_table(IONOSPHERE_TABLE_ONE_PART)).filter_partition('part=0, part=1')
normalize(df.exclude_fields('class')).persist(IONOSPHERE_NORMALIZED_TABLE)示例2: test_kmeans
# 需要导入模块: from odps.df import DataFrame [as 别名]
# 或者: from odps.df.DataFrame import exclude_fields [as 别名]
def test_kmeans(self):
self.delete_table(IONOSPHERE_CLUSTER_LABEL_TABLE)
self.delete_offline_model(IONOSPHERE_CLUSTER_MODEL)
df = DataFrame(self.odps.get_table(IONOSPHERE_TABLE))
labeled, model = KMeans(center_count=3).transform(df.exclude_fields('class'))
model.persist(IONOSPHERE_CLUSTER_MODEL, delay=True)
pmml = model.load_pmml()
print(pmml)
eresult = calinhara_score(labeled, model)
print(eresult)示例3: test_mock_kmeans
# 需要导入模块: from odps.df import DataFrame [as 别名]
# 或者: from odps.df.DataFrame import exclude_fields [as 别名]
def test_mock_kmeans(self):
options.runner.dry_run = True
self.maxDiff = None
df = DataFrame(self.odps.get_table(IONOSPHERE_TABLE))
labeled, model = KMeans(center_count=3).transform(df.exclude_fields('class'))
labeled._add_case(self.gen_check_params_case(
{'inputTableName': IONOSPHERE_TABLE, 'centerCount': '3', 'distanceType': 'euclidean',
'idxTableName': IONOSPHERE_CLUSTER_LABEL_TABLE, 'initCentersMethod': 'sample',
'modelName': 'pm_k_means_0_2', 'appendColsIndex': ','.join('%d' % i for i in range(0, 35)),
'selectedColNames': ','.join('a%02d' % i for i in range(1, 35)), 'loop': '100', 'accuracy': '0.0'}))
labeled.persist(IONOSPHERE_CLUSTER_LABEL_TABLE)示例4: Test
# 需要导入模块: from odps.df import DataFrame [as 别名]
# 或者: from odps.df.DataFrame import exclude_fields [as 别名]
class Test(MLTestBase):
def setUp(self):
super(Test, self).setUp()
self.create_iris(IRIS_TABLE)
self.df = DataFrame(self.odps.get_table(IRIS_TABLE))
def test_coll_field_operations(self):
# select_features
self.assertRaises(ValueError, lambda: self.df.select_features())
df2 = self.df.select_features("sepal_length sepal_width petal_length")
self.assertEqual(
_df_roles(df2),
dict(category="", sepal_width="FEATURE", sepal_length="FEATURE", petal_length="FEATURE", petal_width=""),
)
df3 = df2.select_features("petal_width", add=True)
self.assertEqual(
_df_roles(df3),
dict(
category="",
sepal_width="FEATURE",
sepal_length="FEATURE",
petal_length="FEATURE",
petal_width="FEATURE",
),
)
# exclude_fields
self.assertRaises(ValueError, lambda: self.df.exclude_fields())
df4 = df3.exclude_fields("sepal_length sepal_width")
self.assertEqual(
_df_roles(df4),
dict(category="", sepal_width="", sepal_length="", petal_length="FEATURE", petal_width="FEATURE"),
)
# weight_field
self.assertRaises(ValueError, lambda: self.df.weight_field(None))
df5 = df3.weight_field("sepal_width")
self.assertEqual(
_df_roles(df5),
dict(
category="", sepal_width="WEIGHT", sepal_length="FEATURE", petal_length="FEATURE", petal_width="FEATURE"
),
)
# label_field
self.assertRaises(ValueError, lambda: self.df.label_field(None))
df6 = self.df.label_field("category")
self.assertEqual(
_df_roles(df6),
dict(
category="LABEL",
sepal_width="FEATURE",
sepal_length="FEATURE",
petal_length="FEATURE",
petal_width="FEATURE",
),
)
# roles
self.assertIs(self.df, self.df.roles())
df7 = self.df.roles(label="category", weight="sepal_width")
self.assertEqual(
_df_roles(df7),
dict(
category="LABEL",
petal_length="FEATURE",
petal_width="FEATURE",
sepal_width="WEIGHT",
sepal_length="FEATURE",
),
)
# discrete
df8 = self.df.discrete("sepal_width, sepal_length")
self.assertEqual(
_df_continuity(df8),
dict(
category="DISCRETE",
sepal_width="DISCRETE",
sepal_length="DISCRETE",
petal_length="CONTINUOUS",
petal_width="CONTINUOUS",
),
)
# continuous
df9 = df8.continuous("sepal_width")
self.assertEqual(
_df_continuity(df9),
dict(
category="DISCRETE",
sepal_width="CONTINUOUS",
sepal_length="DISCRETE",
petal_length="CONTINUOUS",
petal_width="CONTINUOUS",
),
)
# key_value
df10 = self.df.key_value("sepal_length sepal_width")
self.assertEqual(
_df_key_value(df10),
dict(
category="",
petal_length="",
petal_width="",
sepal_width="KVConfig(kv=:, item=,)",
#.........这里部分代码省略.........