verify Cyto API conn, add docs
This commit is contained in:
Florian Förster
parent
d2165afbfd
commit
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@ -10,6 +10,7 @@ LOGGING_LEVEL_GRAPHS: Final[LoggingLevels] = LoggingLevels.INFO
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LOGGING_LEVEL_TIMELINE: Final[LoggingLevels] = LoggingLevels.DEBUG
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LOGGING_LEVEL_TOKEN_ANALYSIS: Final[LoggingLevels] = LoggingLevels.INFO
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LOGGING_LEVEL_SHARED_HELPERS: Final[LoggingLevels] = LoggingLevels.INFO
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LOGGING_LEVEL_RENDERING: Final[LoggingLevels] = LoggingLevels.INFO
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logger_shared_helpers = logging.getLogger('lang_main.shared')
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logger_shared_helpers.setLevel(LOGGING_LEVEL_SHARED_HELPERS)
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@ -23,3 +24,5 @@ logger_token_analysis.setLevel(LOGGING_LEVEL_TOKEN_ANALYSIS)
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logger_preprocess.setLevel(LOGGING_LEVEL_PREPROCESS)
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logger_pipelines = logging.getLogger('lang_main.pipelines')
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logger_pipelines.setLevel(LOGGING_LEVEL_PIPELINES)
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logger_rendering = logging.getLogger('lang_main.render')
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logger_rendering.setLevel(LOGGING_LEVEL_RENDERING)
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@ -295,14 +295,15 @@ class Pipeline(BasePipeline):
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else:
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args = ret
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if args is not None and action_kwargs:
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if args is not None:
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ret = action(*args, **action_kwargs)
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elif args is not None:
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ret = action(*args)
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elif args is None and action_kwargs:
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ret = action(**action_kwargs)
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# elif args is not None:
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# ret = action(*args)
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# elif args is None and action_kwargs:
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# ret = action(**action_kwargs)
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else:
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ret = action()
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# ret = action()
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ret = action(**action_kwargs)
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if ret is not None and not isinstance(ret, tuple):
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ret = (ret,)
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@ -182,6 +182,7 @@ def build_tk_graph_rescaling_pipe() -> Pipeline:
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return pipe_graph_rescaling
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# ** token analysis: rendering
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def build_tk_graph_rendering_pipe() -> Pipeline:
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pipe_graph_rendering = Pipeline(
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name='Graph_Static-Rendering',
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@ -5,6 +5,8 @@ from typing import cast
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import py4cytoscape as p4c
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from networkx import DiGraph, Graph
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from py4cytoscape.exceptions import CyError
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from requests.exceptions import RequestException
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from lang_main.constants import (
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CYTO_BASE_NETWORK_NAME,
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@ -19,6 +21,7 @@ from lang_main.constants import (
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PROPERTY_NAME_DEGREE_WEIGHTED,
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SAVE_PATH_FOLDER,
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)
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from lang_main.loggers import logger_rendering as logger
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from lang_main.types import (
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CytoExportFileTypes,
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CytoExportPageSizes,
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@ -29,9 +32,40 @@ from lang_main.types import (
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# ** Cytoscape API related, using py4cytoscape
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def verify_connection():
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"""Cytoscape: checks if CyREST and Cytoscape versions are compatible nad
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if Cytoscape API endpoint is reachable
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Raises
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------
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CyError
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incompatible CyREST or Cytoscape versions
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RequestException
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API endpoint not reachable
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"""
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try:
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p4c.cytoscape_ping()
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except CyError as error:
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logger.error('[CyError] CyREST or Cytoscape version not supported.')
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raise error
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except RequestException as error:
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logger.error('[CytoAPIConnection] Connection to CyREST API failed.')
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raise error
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def import_to_cytoscape(
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graph: DiGraph | Graph,
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) -> None:
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"""Cytoscape: import NetworkX graph as new network collection
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Parameters
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----------
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graph : DiGraph | Graph
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NetworkX graph object
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"""
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logger.debug('Checking Cytoscape connection...')
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verify_connection()
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logger.debug('Importing network to Cytoscape...')
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p4c.delete_all_networks()
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p4c.create_network_from_networkx(
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graph,
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@ -39,9 +73,11 @@ def import_to_cytoscape(
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collection=CYTO_COLLECTION_NAME,
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)
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p4c.analyze_network(directed=False)
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logger.debug('Importing network to Cytoscape successful.')
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def reset_current_network_to_base() -> None:
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"""resets to currently selected network in Cytoscape back to the base one"""
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p4c.set_current_network(CYTO_BASE_NETWORK_NAME)
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@ -51,7 +87,21 @@ def export_network_to_image(
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network_name: str = CYTO_BASE_NETWORK_NAME,
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pdf_export_page_size: CytoExportPageSizes = 'A4',
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) -> None:
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# target_folder = Path.cwd() / 'results'
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"""Cytoscape: export current selected view as image
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Parameters
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----------
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filename : str
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export filename
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filetype : CytoExportFileTypes, optional
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export filetype supported by Cytoscape, by default 'SVG'
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network_name : str, optional
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network to export, by default CYTO_BASE_NETWORK_NAME
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pdf_export_page_size : CytoExportPageSizes, optional
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page size which should be used for PDF exports supported by Cytoscape,
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by default 'A4'
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"""
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logger.debug('Exporting image to file...')
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target_folder = SAVE_PATH_FOLDER
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if not target_folder.exists():
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target_folder.mkdir(parents=True)
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@ -70,6 +120,7 @@ def export_network_to_image(
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export_text_as_font=text_as_font,
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page_size=pdf_export_page_size,
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)
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logger.debug('Exporting image to file successful.')
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def layout_network(
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@ -77,15 +128,52 @@ def layout_network(
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layout_properties: CytoLayoutProperties = CYTO_LAYOUT_PROPERTIES,
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network_name: str = CYTO_BASE_NETWORK_NAME,
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) -> None:
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"""Cytoscape: apply a supported layout algorithm to currently selected
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network
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Parameters
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----------
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layout_name : CytoLayouts, optional
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layout algorithm supported by Cytoscape (name of the CyREST API, does not
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necessarily match the name in the Cytoscape UI),
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by default CYTO_LAYOUT_NAME
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layout_properties : CytoLayoutProperties, optional
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configuration of parameters for the given layout algorithm, by default CYTO_LAYOUT_PROPERTIES
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network_name : str, optional
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network to apply the layout algorithm on, by default CYTO_BASE_NETWORK_NAME
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"""
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logger.debug('Applying layout to network...')
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p4c.set_layout_properties(layout_name, layout_properties)
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p4c.layout_network(layout_name=layout_name, network=network_name)
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p4c.fit_content(selected_only=False, network=network_name)
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logger.debug('Layout application to network successful.')
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def apply_style_to_network(
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style_name: str = CYTO_STYLESHEET_NAME,
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pth_to_stylesheet: Path = CYTO_PATH_STYLESHEET,
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network_name: str = CYTO_BASE_NETWORK_NAME,
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) -> None:
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"""Cytoscape: apply a chosen Cytoscape style to the defined network
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Parameters
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----------
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style_name : str, optional
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Cytoscape name of the style which should be applied,
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by default CYTO_STYLESHEET_NAME
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pth_to_stylesheet : Path, optional
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path where the stylesheet definition in Cytoscape's XML format can
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be found,
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by default CYTO_PATH_STYLESHEET
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network_name : str, optional
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network to apply the style on, by default CYTO_BASE_NETWORK_NAME
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Raises
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------
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FileNotFoundError
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if provided stylesheet can not be found under the provided path
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"""
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logger.debug('Applying style to network...')
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styles_avail = cast(list[str], p4c.get_visual_style_names())
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if CYTO_STYLESHEET_NAME not in styles_avail:
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if not pth_to_stylesheet.exists():
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@ -96,15 +184,36 @@ def apply_style_to_network(
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)
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p4c.import_visual_styles(str(pth_to_stylesheet))
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p4c.set_visual_style(CYTO_STYLESHEET_NAME, network=network_name)
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p4c.set_visual_style(style_name, network=network_name)
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time.sleep(1) # if not waited image export could be without applied style
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p4c.fit_content(selected_only=False, network=network_name)
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logger.debug('Style application to network successful.')
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def get_subgraph_node_selection(
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network_name: str = CYTO_BASE_NETWORK_NAME,
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property_degree_weighted: str = PROPERTY_NAME_DEGREE_WEIGHTED,
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num_subgraphs: int = CYTO_NUMBER_SUBGRAPHS,
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) -> list[CytoNodeID]:
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"""Cytoscape: obtain the relevant nodes for iterative subgraph generation
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Parameters
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----------
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network_name : str, optional
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network to retrieve the nodes from, by default CYTO_BASE_NETWORK_NAME
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property_degree_weighted : str, optional
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property name which contains the weighted degree,
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by default PROPERTY_NAME_DEGREE_WEIGHTED
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num_subgraphs : int, optional
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number of relevant nodes which form the basis to generate subgraphs from,
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by default CYTO_NUMBER_SUBGRAPHS
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Returns
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-------
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list[CytoNodeID]
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list containing all relevant Cytoscape nodes
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"""
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logger.debug('Selecting nodes for subgraph generation...')
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node_table = p4c.get_table_columns(network=network_name)
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node_table['stress_norm'] = node_table['Stress'] / node_table['Stress'].max()
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node_table[CYTO_SELECTION_PROPERTY] = (
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@ -113,22 +222,42 @@ def get_subgraph_node_selection(
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* node_table['stress_norm']
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)
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node_table = node_table.sort_values(by=CYTO_SELECTION_PROPERTY, ascending=False)
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node_table_choice = node_table.iloc[:CYTO_NUMBER_SUBGRAPHS, :]
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node_table_choice = node_table.iloc[:num_subgraphs, :]
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logger.debug('Selection of nodes for subgraph generation successful.')
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return node_table_choice['SUID'].to_list()
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def select_neighbours_of_node(
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node: CytoNodeID,
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neighbour_iter_depth: int = CYTO_ITER_NEIGHBOUR_DEPTH,
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network_name: str = CYTO_BASE_NETWORK_NAME,
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) -> None:
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"""Cytoscape: iterative selection of a node's neighbouring nodes and
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their connecting edges
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Parameters
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----------
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node : CytoNodeID
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node which neighbours should be selected
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neighbour_iter_depth : int, optional
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indicates how many levels of neighbours should be choosen, e.g. 1 --> only
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first-level neighbours are considered which are directly connected to the node,
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2 --> all nodes with iteration depth of 1 are chosen and additionally their
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direct neighbours,
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by default CYTO_ITER_NEIGHBOUR_DEPTH
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network_name : str, optional
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network to perform action on, by default CYTO_BASE_NETWORK_NAME
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"""
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logger.debug('Selecting node neighbours for %s...', node)
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p4c.clear_selection(network=network_name)
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p4c.select_nodes(node, network=network_name)
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for _ in range(CYTO_ITER_NEIGHBOUR_DEPTH):
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for _ in range(neighbour_iter_depth):
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_ = p4c.select_first_neighbors(network=network_name)
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_ = p4c.select_edges_connecting_selected_nodes()
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logger.debug('Selection of node neighbours for %s successful.', node)
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def make_subnetwork(
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@ -136,6 +265,19 @@ def make_subnetwork(
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network_name: str = CYTO_BASE_NETWORK_NAME,
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export_image: bool = True,
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) -> None:
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"""Cytoscape: generate a new subnetwork based on the currently
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selected nodes and edges
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Parameters
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----------
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index : int
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id-like property to identify the subnetwork relative to its parent
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network_name : str, optional
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network to generate subnetwork from, by default CYTO_BASE_NETWORK_NAME
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export_image : bool, optional
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trigger image export of newly generated subnetwork, by default True
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"""
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logger.debug('Generating subnetwork with index %d...', index)
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subnetwork_name = network_name + f'_sub_{index+1}'
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p4c.create_subnetwork(
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nodes='selected',
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@ -146,14 +288,33 @@ def make_subnetwork(
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p4c.set_current_network(subnetwork_name)
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p4c.fit_content(selected_only=False, network=subnetwork_name)
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if export_image:
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time.sleep(1)
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export_network_to_image(filename=subnetwork_name, network_name=subnetwork_name)
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logger.debug('Generation of subnetwork with index %d successful.', index)
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def build_subnetworks(
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nodes_to_analyse: Iterable[CytoNodeID],
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network_name: str = CYTO_BASE_NETWORK_NAME,
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export_image: bool = True,
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) -> None:
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"""Cytoscape: iteratively build subnetworks from a collection of nodes
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and their respective neighbouring nodes
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Parameters
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----------
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nodes_to_analyse : Iterable[CytoNodeID]
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collection of nodes to make subnetworks from, for each node a dedicated
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subnetwork will be generated
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network_name : str, optional
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network which contains the provided nodes,
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by default CYTO_BASE_NETWORK_NAME
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export_image : bool, optional
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trigger image export of newly generated subnetworks, by default True
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"""
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logger.debug('Generating all subnetworks for node selection...')
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for idx, node in enumerate(nodes_to_analyse):
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select_neighbours_of_node(node=node, network_name=network_name)
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make_subnetwork(index=idx, network_name=network_name, export_image=export_image)
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logger.debug('Generation of all subnetworks for node selection successful.')
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@ -13,7 +13,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 2,
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"id": "af118d77-d87a-4687-be5b-e810a24c403e",
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"metadata": {},
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"outputs": [
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@ -21,7 +21,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2024-07-17 16:02:39 +0000 | io:INFO | Loaded TOML config file successfully.\n"
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"2024-07-19 05:54:50 +0000 | io:INFO | Loaded TOML config file successfully.\n"
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]
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},
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{
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@ -66,7 +66,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 3,
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"id": "4256081a-6364-4e8f-8cfd-799912ca6b94",
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"metadata": {},
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"outputs": [],
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@ -77,7 +77,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 4,
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"id": "e9a92ad6-5e63-49c4-b9e7-9f81da8549fe",
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"metadata": {},
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"outputs": [],
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@ -90,7 +90,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 5,
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"id": "c2421d89-ed8c-41dd-b363-ad5b5b716704",
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"metadata": {},
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"outputs": [
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@ -98,7 +98,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2024-07-17 16:02:46 +0000 | io:INFO | Loaded file successfully.\n"
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"2024-07-19 05:54:58 +0000 | io:INFO | Loaded file successfully.\n"
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]
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}
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],
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@ -167,7 +167,7 @@
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{
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"data": {
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"text/plain": [
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"{}"
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"{'degree_weighted': 186350}"
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]
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},
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"execution_count": 9,
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@ -181,15 +181,28 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 10,
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"id": "8d36d22e-73fd-44fe-ab08-98f8186bc6b2",
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"metadata": {},
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"outputs": [],
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"source": []
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'degree_weighted': 186350}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tkg.undirected.nodes['Wartungstätigkeit']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"id": "1e61aca3-efea-4e38-8174-5ca4b2585256",
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"metadata": {},
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"outputs": [],
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@ -202,7 +215,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"id": "5d83c04c-03ab-4086-a4e9-ae430e4c6090",
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"metadata": {},
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"outputs": [
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@ -210,7 +223,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2024-07-17 06:04:17 +0000 | io:INFO | Loaded file successfully.\n"
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"2024-07-19 05:56:10 +0000 | io:INFO | Loaded file successfully.\n"
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]
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}
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],
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@ -220,7 +233,7 @@
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},
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{
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"cell_type": "code",
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||||
"execution_count": 12,
|
||||
"execution_count": 13,
|
||||
"id": "4718b54e-0891-4f70-8c67-90c439bc8bfd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -230,7 +243,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 14,
|
||||
"id": "ddcb4ff0-eac4-45ba-9c6e-83ada4b0276c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -240,7 +253,7 @@
|
||||
"TokenGraph(name: TokenGraph, number of nodes: 6859, number of edges: 25499)"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -251,17 +264,28 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "97c46ca7-ca9f-4d11-8d86-cfbd819b7573",
|
||||
"execution_count": 15,
|
||||
"id": "3d514552-3b55-41d1-af80-a1b559711608",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"False"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tkg.rescaled_weights = False"
|
||||
"tkg.rescaled_weights"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 16,
|
||||
"id": "b73844e0-4242-4a8c-b552-48f10df34cc0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -271,23 +295,23 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 17,
|
||||
"id": "593b9f87-4e9f-45e4-9367-55347924357b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'num_nodes': 6028,\n",
|
||||
" 'num_edges': 17950,\n",
|
||||
"{'num_nodes': 6859,\n",
|
||||
" 'num_edges': 25499,\n",
|
||||
" 'min_edge_weight': 0.0952,\n",
|
||||
" 'max_edge_weight': 1.0,\n",
|
||||
" 'node_memory': 382321,\n",
|
||||
" 'edge_memory': 1005200,\n",
|
||||
" 'total_memory': 1387521}"
|
||||
" 'node_memory': 433996,\n",
|
||||
" 'edge_memory': 1427944,\n",
|
||||
" 'total_memory': 1861940}"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -298,23 +322,23 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 18,
|
||||
"id": "aed4354a-69e4-4215-bd4b-a6c7c37c3ac5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'num_nodes': 6028,\n",
|
||||
" 'num_edges': 17554,\n",
|
||||
" 'min_edge_weight': 0.09520000219345093,\n",
|
||||
" 'max_edge_weight': 1.7527999877929688,\n",
|
||||
" 'node_memory': 382321,\n",
|
||||
" 'edge_memory': 983024,\n",
|
||||
" 'total_memory': 1365345}"
|
||||
"{'num_nodes': 6859,\n",
|
||||
" 'num_edges': 24796,\n",
|
||||
" 'min_edge_weight': 1,\n",
|
||||
" 'max_edge_weight': 92690,\n",
|
||||
" 'node_memory': 433996,\n",
|
||||
" 'edge_memory': 1388576,\n",
|
||||
" 'total_memory': 1822572}"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -325,23 +349,23 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 19,
|
||||
"id": "587de2ae-26ed-42f5-a8bd-104f9cbf1490",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'num_nodes': 6028,\n",
|
||||
" 'num_edges': 17554,\n",
|
||||
"{'num_nodes': 6859,\n",
|
||||
" 'num_edges': 24796,\n",
|
||||
" 'min_edge_weight': 0.0952,\n",
|
||||
" 'max_edge_weight': 1.0,\n",
|
||||
" 'node_memory': 382321,\n",
|
||||
" 'edge_memory': 983024,\n",
|
||||
" 'total_memory': 1365345}"
|
||||
" 'node_memory': 433996,\n",
|
||||
" 'edge_memory': 1388576,\n",
|
||||
" 'total_memory': 1822572}"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -350,16 +374,6 @@
|
||||
"get_graph_metadata(undirected)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "f6fc2c6a-2171-411d-92f1-4ab694adda7b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Gtest = rescale_edge_weights(tkg)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "859be6a3-a919-433a-a0a7-f4d74cdc6bf7",
|
||||
@ -377,7 +391,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 20,
|
||||
"id": "f381b25a-6149-4a2a-876c-4cbd8bb9bd04",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -401,7 +415,7 @@
|
||||
"source": [
|
||||
"break_early = True\n",
|
||||
"i = 0\n",
|
||||
"for n1, n2, w in Gtest.edges.data('weight'):\n",
|
||||
"for n1, n2, w in directed.edges.data('weight'):\n",
|
||||
" if break_early and i == 10:\n",
|
||||
" break\n",
|
||||
" print(n1, n2, w)\n",
|
||||
@ -411,38 +425,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "c45a8136-2a81-4ede-a087-cf86ce85d939",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "'TokenGraph' object has no attribute 'rescaled_weights'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mGtest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_undirected\u001b[49m\u001b[43m(\u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32mA:\\Arbeitsaufgaben\\lang-main\\src\\lang_main\\analysis\\graphs.py:481\u001b[0m, in \u001b[0;36mTokenGraph.to_undirected\u001b[1;34m(self, inplace, logging)\u001b[0m\n\u001b[0;32m 479\u001b[0m \u001b[38;5;66;03m# cast to integer edge weights only if edges were not rescaled previously\u001b[39;00m\n\u001b[0;32m 480\u001b[0m cast_int: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 481\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrescaled_weights\u001b[49m:\n\u001b[0;32m 482\u001b[0m cast_int \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 484\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_undirected \u001b[38;5;241m=\u001b[39m convert_graph_to_undirected(\n\u001b[0;32m 485\u001b[0m graph\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 486\u001b[0m logging\u001b[38;5;241m=\u001b[39mlogging,\n\u001b[0;32m 487\u001b[0m cast_int\u001b[38;5;241m=\u001b[39mcast_int,\n\u001b[0;32m 488\u001b[0m )\n",
|
||||
"\u001b[1;31mAttributeError\u001b[0m: 'TokenGraph' object has no attribute 'rescaled_weights'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Gtest.to_undirected(inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "303d8d0c-9320-4739-adf6-ac9ba82731de",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 21,
|
||||
"id": "a7929935-3bd2-4eb8-907c-4d37251f11ea",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -452,21 +435,21 @@
|
||||
"text": [
|
||||
"Wartungstätigkeit Vorgabe 1.0\n",
|
||||
"Wartungstätigkeit Maschinenhersteller 1.0\n",
|
||||
"Wartungstätigkeit sehen 0.2533999979496002\n",
|
||||
"Wartungstätigkeit Maschinenbediener 0.8215000033378601\n",
|
||||
"Wartungstätigkeit Laserabteilung 0.8215000033378601\n",
|
||||
"Wartungstätigkeit Arbeitsplan 0.8219000101089478\n",
|
||||
"Wartungstätigkeit abarbeiten 0.8215000033378601\n",
|
||||
"Wartungstätigkeit Webmaschinenkontrollliste 0.2533999979496002\n",
|
||||
"Wartungstätigkeit sehen 0.2534\n",
|
||||
"Wartungstätigkeit Maschinenbediener 0.8215\n",
|
||||
"Wartungstätigkeit Laserabteilung 0.8215\n",
|
||||
"Wartungstätigkeit Arbeitsplan 0.8219\n",
|
||||
"Wartungstätigkeit abarbeiten 0.8215\n",
|
||||
"Wartungstätigkeit Webmaschinenkontrollliste 0.2534\n",
|
||||
"Vorgabe Maschinenhersteller 1.0\n",
|
||||
"Vorgabe Wartungsplan 0.9180999994277954\n"
|
||||
"Vorgabe Wartungsplan 0.9181\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"break_early = True\n",
|
||||
"i = 0\n",
|
||||
"for n1, n2, w in Gtest.undirected.edges.data('weight'):\n",
|
||||
"for n1, n2, w in undirected.edges.data('weight'):\n",
|
||||
" if break_early and i == 10:\n",
|
||||
" break\n",
|
||||
" print(n1, n2, w)\n",
|
||||
@ -474,298 +457,6 @@
|
||||
" i += 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "4df89d59-3832-4b6d-9eae-b3220b6f7e5b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'num_nodes': 6028,\n",
|
||||
" 'num_edges': 17554,\n",
|
||||
" 'min_edge_weight': 0.09520000219345093,\n",
|
||||
" 'max_edge_weight': 1.7527999877929688,\n",
|
||||
" 'node_memory': 382321,\n",
|
||||
" 'edge_memory': 983024,\n",
|
||||
" 'total_memory': 1365345}"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Gtest.metadata_undirected"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "11b9a5b4-8319-44e8-b0eb-0434f952a28e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"id": "c63770ed-5748-4484-816f-22d0e327af73",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"False"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"'weight' not in Gtest['Wartungstätigkeit']['Vorgabe']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "62e5d9f4-69c8-4990-9a64-6e2f40031d6f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ca7c4578-c9bb-4b0b-b261-e0208d6cb970",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "adf02de4-f22c-499f-91bf-ac1be1b3186f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'num_nodes': 6028,\n",
|
||||
" 'num_edges': 17950,\n",
|
||||
" 'min_edge_weight': 0.0952,\n",
|
||||
" 'max_edge_weight': 1.0,\n",
|
||||
" 'node_memory': 382321,\n",
|
||||
" 'edge_memory': 1005200,\n",
|
||||
" 'total_memory': 1387521}"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Gtest.metadata_directed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9e939c9c-ba9e-4576-9d85-afba8481af93",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"id": "bfafdc8b-418d-4dd8-b986-d36082ba870e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"c_comps = nx.connected_components(Gtest.undirected)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "abf5603d-dab9-49a0-8c86-d2429a36d24d",
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"5714\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"4\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"3\n",
|
||||
"4\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"4\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"4\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"4\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"3\n",
|
||||
"2\n",
|
||||
"2\n",
|
||||
"2\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for comp in c_comps:\n",
|
||||
" print(len(comp))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "80db8d16-fa48-417d-91b0-37e5b32f3b74",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user