本文整理汇总了Python中odps.df.DataFrame.from_vertex_label_field方法的典型用法代码示例。如果您正苦于以下问题：Python DataFrame.from_vertex_label_field方法的具体用法？Python DataFrame.from_vertex_label_field怎么用？Python DataFrame.from_vertex_label_field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.from_vertex_label_field方法的1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
# 需要导入模块: from odps.df import DataFrame [as 别名]
# 或者: from odps.df.DataFrame import from_vertex_label_field [as 别名]
#.........这里部分代码省略.........
self.edge_df = DataFrame(self.odps.get_table(WEIGHTED_GRAPH_EDGE_TABLE)) \
.roles(from_vertex='flow_out_id', to_vertex='flow_in_id', edge_weight='edge_weight')
options.runner.dry_run = True
def test_node_density(self):
df = DataFrame(self.odps.get_table(WEIGHTED_GRAPH_EDGE_TABLE))
output = NodeDensity(from_vertex_col='flow_out_id', to_vertex_col='flow_in_id') \
.transform(df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'workerMem': '4096', 'maxEdgeCnt': '500', 'fromVertexCol': 'flow_out_id',
'toVertexCol': 'flow_in_id',
'outputTableName': NODE_DENSITY_TABLE, 'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE}))
output.persist(NODE_DENSITY_TABLE)
def test_maximal_connected(self):
df = DataFrame(self.odps.get_table(WEIGHTED_GRAPH_EDGE_TABLE))
output = MaximalConnectedComponent(from_vertex_col='flow_out_id', to_vertex_col='flow_in_id') \
.transform(df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'workerMem': '4096', 'fromVertexCol': 'flow_out_id', 'toVertexCol': 'flow_in_id',
'outputTableName': MAXIMAL_CONNECTED_TABLE, 'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE}))
output.persist(MAXIMAL_CONNECTED_TABLE)
def test_triangle_count(self):
output = TriangleCount().transform(self.edge_df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'workerMem': '4096', 'maxEdgeCnt': '500', 'fromVertexCol': 'flow_out_id',
'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE, 'outputTableName': TRIANGLE_COUNT_TABLE,
'toVertexCol': 'flow_in_id'}))
output.persist(TRIANGLE_COUNT_TABLE)
def test_edge_density(self):
df = DataFrame(self.odps.get_table(WEIGHTED_GRAPH_EDGE_TABLE))
output = EdgeDensity(from_vertex_col='flow_out_id', to_vertex_col='flow_in_id') \
.transform(df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'workerMem': '4096', 'fromVertexCol': 'flow_out_id', 'toVertexCol': 'flow_in_id',
'outputTableName': EDGE_DENSITY_TABLE, 'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE}))
output.persist(EDGE_DENSITY_TABLE)
def test_page_rank(self):
output = PageRank().transform(self.edge_df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'hasEdgeWeight': 'true', 'fromVertexCol': 'flow_out_id',
'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE, 'edgeWeightCol': 'edge_weight',
'workerMem': '4096', 'toVertexCol': 'flow_in_id', 'outputTableName': PAGE_RANK_TABLE,
'maxIter': '30'}))
output.persist(PAGE_RANK_TABLE)
def test_label_prop_cluster(self):
output = LabelPropagationClustering() \
.transform(self.edge_df, self.vertex_df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'vertexWeightCol': 'node_weight', 'hasVertexWeight': 'true',
'hasEdgeWeight': 'true', 'fromVertexCol': 'flow_out_id',
'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE, 'edgeWeightCol': 'edge_weight',
'vertexCol': 'node', 'workerMem': '4096', 'toVertexCol': 'flow_in_id',
'outputTableName': LABEL_PROPAGATION_TABLE, 'maxIter': '30',
'inputVertexTableName': WEIGHTED_GRAPH_VERTEX_TABLE, 'randSelect': 'false'}))
output.persist(LABEL_PROPAGATION_TABLE)
def test_label_prop_cls(self):
edge_df = DataFrame(self.odps.get_table(WEIGHTED_GRAPH_EDGE_TABLE))
vertex_df = DataFrame(self.odps.get_table(WEIGHTED_GRAPH_VERTEX_TABLE))
output = LabelPropagationClassification(from_vertex_col='flow_out_id', to_vertex_col='flow_in_id',
vertex_col='node', vertex_label_col='label',
vertex_weight_col='node_weight', edge_weight_col='edge_weight') \
.transform(edge_df, vertex_df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'edgeWeightCol': 'edge_weight', 'vertexWeightCol': 'node_weight',
'hasVertexWeight': 'true', 'hasEdgeWeight': 'true', 'fromVertexCol': 'flow_out_id',
'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE, 'vertexCol': 'node',
'epsilon': '0.000001', 'workerMem': '4096', 'toVertexCol': 'flow_in_id',
'outputTableName': LABEL_PROPAGATION_TABLE, 'maxIter': '30', 'alpha': '0.8',
'vertexLabelCol': 'label', 'inputVertexTableName': WEIGHTED_GRAPH_VERTEX_TABLE}))
output.persist(LABEL_PROPAGATION_TABLE)
def test_k_core(self):
output = KCore(k=2).transform(self.edge_df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'fromVertexCol': 'flow_out_id', 'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE,
'workerMem': '4096', 'toVertexCol': 'flow_in_id', 'outputTableName': K_CORE_TABLE, 'k': '2'}))
output.persist(K_CORE_TABLE)
def test_sssp(self):
output = SSSP(start_vertex='1').transform(self.edge_df)._add_case(self.gen_check_params_case(
{'splitSize': '64', 'startVertex': '1', 'hasEdgeWeight': 'true', 'fromVertexCol': 'flow_out_id',
'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE, 'edgeWeightCol': 'edge_weight',
'workerMem': '4096', 'toVertexCol': 'flow_in_id', 'outputTableName': SSSP_TABLE}))
output.persist(SSSP_TABLE)
def test_tree_depth(self):
self.create_tree_graph(TREE_GRAPH_EDGE_TABLE)
tree_ds = DataFrame(self.odps.get_table(TREE_GRAPH_EDGE_TABLE)) \
.roles(from_vertex='flow_out_id', to_vertex='flow_in_id')
output = TreeDepth().transform(tree_ds)._add_case(self.gen_check_params_case(
{'outputTableName': tn('pyodps_test_ml_tree_depth'), 'fromVertexCol': 'flow_out_id', 'workerMem': '4096',
'inputEdgeTableName': tn('pyodps_test_ml_tree_graph_edge'), 'toVertexCol': 'flow_in_id',
'splitSize': '64'}))
output.persist(TREE_DEPTH_TABLE)
def test_modularity(self):
ds = self.edge_df.from_vertex_label_field('group_out_id').to_vertex_label_field('group_in_id')
logger.info('Modularity: ' + str(modularity(ds, _cases=self.gen_check_params_case(
{'splitSize': '64', 'fromVertexCol': 'flow_out_id', 'inputEdgeTableName': WEIGHTED_GRAPH_EDGE_TABLE,
'fromGroupCol': 'group_out_id', 'workerMem': '4096', 'toVertexCol': 'flow_in_id',
'outputTableName': TEMP_TABLE_PREFIX + 'modularity_0_3_res', 'toGroupCol': 'group_in_id'}))))