core/maxframe/learn/contrib/graph/connected

# Copyright 1999-2025 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import pandas as pd from maxframe import opcodes from ....core import OutputType from ....dataframe.operators import DataFrameOperator, DataFrameOperatorMixin from ....dataframe.utils import make_dtypes, parse_index from ....serialization.serializables import Int32Field, StringField class DataFrameConnectedComponentsOperator(DataFrameOperator, DataFrameOperatorMixin): _op_type_ = opcodes.CONNECTED_COMPONENTS vertex_col1 = StringField("vertex_col1", default=None) vertex_col2 = StringField("vertex_col2", default=None) max_iter = Int32Field("max_iter", default=6) def __call__(self, df): node_id_dtype = df.dtypes[self.vertex_col1] dtypes = make_dtypes({"id": node_id_dtype, "component": node_id_dtype}) # this will return a dataframe and a bool flag new_dataframe_tileable_kw = { "shape": (np.nan, 2), "index_value": parse_index(pd.RangeIndex(0)), "columns_value": parse_index(dtypes.index, store_data=True), "dtypes": dtypes, } new_scalar_tileable_kw = {"dtype": np.dtype(np.bool_), "shape": ()} return self.new_tileables( [df], kws=[new_dataframe_tileable_kw, new_scalar_tileable_kw], ) @property def output_limit(self): return 2 def connected_components( dataframe, vertex_col1: str, vertex_col2: str, max_iter: int = 6 ): """ The connected components algorithm labels each node as belonging to a specific connected component with the ID of its lowest-numbered vertex. Parameters ---------- dataframe : DataFrame A DataFrame containing the edges of the graph. vertex_col1 : str The name of the column in `dataframe` that contains the one of edge vertices. The column value must be an integer. vertex_col2 : str The name of the column in `dataframe` that contains the other one of edge vertices. The column value must be an integer. max_iter : int The algorithm use large and small star transformation to find all connected components, `max_iter` controls the max round of the iterations before finds all edges. Default is 6. Returns ------- DataFrame Return dataFrame contains all connected component edges by two columns `id` and `component`. `component` is the lowest-numbered vertex in the connected components. Notes ------- After `execute()`, the dataframe has a bool member `flag` to indicate if the `connected_components` already converged in `max_iter` rounds. `True` means the dataframe already contains all edges of the connected components. If `False` you can run `connected_components` more times to reach the converged state. Examples -------- >>> import numpy as np >>> import maxframe.dataframe as md >>> import maxframe.learn.contrib.graph.connected_components >>> df = md.DataFrame({'x': [4, 1], 'y': [0, 4]}) >>> df.execute() x y 0 4 1 1 0 4 Get connected components with 1 round iteration. >>> components, converged = connected_components(df, "x", "y", 1) >>> session.execute(components, converged) >>> components A B 0 1 0 1 4 0 >>> converged True Sometimes, a single iteration may not be sufficient to propagate the connectivity of all edges. By default, `connected_components` performs 6 iterations of calculations. If you are unsure whether the connected components have converged, you can check the `flag` variable in the output DataFrame after calling `execute()`. >>> df = md.DataFrame({'x': [4, 1, 7, 5, 8, 11, 11], 'y': [0, 4, 4, 7, 7, 9, 13]}) >>> df.execute() x y 0 4 0 1 1 4 2 7 4 3 5 7 4 8 7 5 11 9 6 11 13 >>> components, converged = connected_components(df, "x", "y", 1) >>> session.execute(components, converged) >>> components id component 0 4 0 1 7 0 2 8 4 3 13 9 4 1 0 5 5 0 6 11 9 If `flag` is True, it means convergence has been achieved. >>> converged False You can determine whether to continue iterating or to use a larger number of iterations (but not too large, which would result in wasted computational overhead). >>> components, converged = connected_components(components, "id", "component", 1) >>> session.execute(components, converged) >>> components id component 0 4 0 1 7 0 2 13 9 3 1 0 4 5 0 5 11 9 6 8 0 >>> components, converged = connected_components(df, "x", "y") >>> session.execute(components, converged) >>> components id component 0 4 0 1 7 0 2 13 9 3 1 0 4 5 0 5 11 9 6 8 0 """ # Check if vertex columns are provided if not vertex_col1 or not vertex_col2: raise ValueError("Both vertex_col1 and vertex_col2 must be provided.") # Check if max_iter is provided and within the valid range if max_iter is None: raise ValueError("max_iter must be provided.") if not (1 <= max_iter <= 50): raise ValueError("max_iter must be an integer between 1 and 50.") # Verify that the vertex columns exist in the dataframe missing_cols = [ col for col in (vertex_col1, vertex_col2) if col not in dataframe.dtypes ] if missing_cols: raise ValueError( f"The following required columns {missing_cols} are not in {list(dataframe.dtypes.index)}" ) # Ensure that the vertex columns are of integer type # TODO support string dtype incorrect_dtypes = [ col for col in (vertex_col1, vertex_col2) if dataframe[col].dtype != np.dtype("int") ] if incorrect_dtypes: dtypes_str = ", ".join(str(dataframe[col].dtype) for col in incorrect_dtypes) raise ValueError( f"Columns {incorrect_dtypes} should be of integer type, but found {dtypes_str}." ) op = DataFrameConnectedComponentsOperator( vertex_col1=vertex_col1, vertex_col2=vertex_col2, _output_types=[OutputType.dataframe, OutputType.scalar], max_iter=max_iter, ) return op( dataframe, )

core/maxframe/learn/contrib/graph/connected_components.py (64 lines of code) (raw):