diff --git a/pdm.lock b/pdm.lock
index 8053296..cd59a8f 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -5,7 +5,7 @@
groups = ["default", "notebooks", "trials"]
strategy = ["cross_platform", "inherit_metadata"]
lock_version = "4.4.1"
-content_hash = "sha256:8781981bde2786c60273cd73599f4ab6a388d0b435484d5ba0afa0656723dd98"
+content_hash = "sha256:e00f157f833ee7615d96375c352e2caa6b4f6b50e5615ccbefa79446189594c7"
[[package]]
name = "annotated-types"
@@ -2938,13 +2938,13 @@ files = [
[[package]]
name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.2"
requires_python = ">=3.8"
summary = "Backported and Experimental Type Hints for Python 3.8+"
groups = ["default", "notebooks", "trials"]
files = [
- {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
- {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
+ {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+ {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
]
[[package]]
diff --git a/pyproject.toml b/pyproject.toml
index c59bde7..05b6632 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,6 +12,7 @@ dependencies = [
"sentence-transformers>=2.7.0",
"numpy>=1.26.4",
"pip>=24.0",
+ "typing-extensions>=4.12.2",
]
requires-python = ">=3.11"
readme = "README.md"
@@ -47,4 +48,7 @@ quote-style = "single"
skip-magic-trailing-comma = false
[tool.ruff.lint]
-select = ["E", "F", "I"]
\ No newline at end of file
+select = ["E", "F", "I"]
+
+[tool.ruff.lint.isort]
+extra-standard-library = ["typing_extensions"]
\ No newline at end of file
diff --git a/scripts/analyse_dataset.py b/scripts/analyse_dataset.py
index 17dfaf6..677172c 100644
--- a/scripts/analyse_dataset.py
+++ b/scripts/analyse_dataset.py
@@ -1,42 +1,44 @@
import typing
-import warnings
-from pathlib import Path
from typing import cast
+from pandas import DataFrame, Series
+
from lang_main.analysis.graphs import TokenGraph
from lang_main.constants import (
- DO_GRAPH_POSTPROCESSING,
- DO_PREPROCESSING,
- DO_TIME_ANALYSIS,
- DO_TOKEN_ANALYSIS,
- INPUT_PATH_FOLDER,
PATH_TO_DATASET,
SAVE_PATH_FOLDER,
SKIP_GRAPH_POSTPROCESSING,
SKIP_PREPROCESSING,
SKIP_TIME_ANALYSIS,
SKIP_TOKEN_ANALYSIS,
- THRESHOLD_AMOUNT_CHARACTERS,
- THRESHOLD_EDGE_WEIGHT,
)
-from lang_main.io import create_saving_folder, load_pickle
+from lang_main.io import create_saving_folder, get_entry_point, load_pickle
+from lang_main.pipelines.base import PipelineContainer
from lang_main.pipelines.predefined import (
- pipe_merge,
- pipe_target_feat,
- pipe_timeline,
- pipe_token_analysis,
+ build_base_target_feature_pipe,
+ build_merge_duplicates_pipe,
+ build_timeline_pipe,
+ build_tk_graph_pipe,
+ build_tk_graph_post_pipe,
)
from lang_main.types import (
+ EntryPoints,
ObjectID,
PandasIndex,
SpacyDoc,
TimelineCandidates,
)
-from pandas import DataFrame, Series
+
+# ** build pipelines
+pipe_merge = build_merge_duplicates_pipe()
+pipe_target_feat = build_base_target_feature_pipe()
+pipe_timeline = build_timeline_pipe()
+pipe_token_analysis = build_tk_graph_pipe()
+pipe_graph_postprocessing = build_tk_graph_post_pipe()
-# ** processing pipeline
-def run_preprocessing() -> DataFrame:
+# ** preprocessing pipeline
+def run_preprocessing() -> None:
create_saving_folder(
saving_path_folder=SAVE_PATH_FOLDER,
overwrite_existing=False,
@@ -46,134 +48,69 @@ def run_preprocessing() -> DataFrame:
tuple[DataFrame], pipe_target_feat.run(starting_values=(PATH_TO_DATASET,))
)
target_feat_data = ret[0]
- # only entries with more than threshold amount of characters
- data_filter = typing.cast(Series, (target_feat_data['len'] > THRESHOLD_AMOUNT_CHARACTERS))
- subset_data = target_feat_data.loc[data_filter].copy()
- # merge duplicates, results saved separately
- ret = typing.cast(tuple[DataFrame], pipe_merge.run(starting_values=(subset_data,)))
- preprocessed_data = ret[0]
-
- return preprocessed_data
+ _ = typing.cast(tuple[DataFrame], pipe_merge.run(starting_values=(target_feat_data,)))
-def run_token_analysis(
- preprocessed_data: DataFrame,
-) -> tuple[TokenGraph, dict[PandasIndex, SpacyDoc]]:
+# ** token analysis
+def run_token_analysis() -> None:
+ # load entry point
+ entry_point_path = get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TOKEN_ANALYSIS)
+ loaded_results = cast(tuple[DataFrame], load_pickle(entry_point_path))
+ preprocessed_data = loaded_results[0]
# build token graph
(tk_graph, docs_mapping) = typing.cast(
- tuple[TokenGraph, dict[PandasIndex, SpacyDoc]],
+ tuple[TokenGraph, dict[PandasIndex, SpacyDoc] | None],
pipe_token_analysis.run(starting_values=(preprocessed_data,)),
)
- tk_graph.save_graph(SAVE_PATH_FOLDER, directed=False)
- tk_graph.to_pickle(SAVE_PATH_FOLDER, filename=f'{pipe_token_analysis.name}-TokenGraph')
-
- return tk_graph, docs_mapping
+ tk_graph.to_GraphML(SAVE_PATH_FOLDER, filename='TokenGraph', directed=False)
-def run_graph_postprocessing(
- tk_graph: TokenGraph,
-) -> TokenGraph:
+def run_graph_postprocessing() -> None:
+ # load entry point
+ entry_point_path = get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TK_GRAPH_POST)
+ loaded_results = cast(
+ tuple[TokenGraph, dict[PandasIndex, SpacyDoc] | None],
+ load_pickle(entry_point_path),
+ )
+ tk_graph = loaded_results[0]
# filter graph by edge weight and remove single nodes (no connection)
- tk_graph_filtered = tk_graph.filter_by_edge_weight(THRESHOLD_EDGE_WEIGHT)
- tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1)
- tk_graph_filtered.save_graph(
+ ret = cast(tuple[TokenGraph], pipe_graph_postprocessing.run(starting_values=(tk_graph,)))
+ tk_graph_filtered = ret[0]
+ # tk_graph_filtered = tk_graph.filter_by_edge_weight(THRESHOLD_EDGE_WEIGHT, None)
+ # tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
+ tk_graph_filtered.to_GraphML(
SAVE_PATH_FOLDER, filename='TokenGraph-filtered', directed=False
)
- tk_graph_filtered.to_pickle(
- SAVE_PATH_FOLDER, filename=f'{pipe_token_analysis.name}-TokenGraph-filtered'
- )
-
- return tk_graph_filtered
-def run_time_analysis() -> tuple[TimelineCandidates, dict[ObjectID, str]]:
- filename = 'without_nan'
- loading_path = INPUT_PATH_FOLDER.joinpath(filename).with_suffix('.pkl')
- verify_path(loading_path)
- ret = load_pickle(loading_path)
- preprocessed_data = ret[0]
+# ** time analysis
+def run_time_analysis() -> None:
+ # load entry point
+ entry_point_path = get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE)
+ loaded_results = cast(tuple[DataFrame], load_pickle(entry_point_path))
+ preprocessed_data = loaded_results[0]
- ret = cast(
+ _ = cast(
tuple[TimelineCandidates, dict[ObjectID, str]],
pipe_timeline.run(starting_values=(preprocessed_data,)),
)
- return ret
-def verify_path(
- loading_path: Path,
-) -> None:
- if not loading_path.exists():
- raise FileNotFoundError(f'Could not load results. File not found: {loading_path}')
+def build_pipeline_container() -> PipelineContainer:
+ container = PipelineContainer(
+ name='Pipeline-Container-Base', working_dir=SAVE_PATH_FOLDER
+ )
+ container.add(run_preprocessing, skip=SKIP_PREPROCESSING)
+ container.add(run_token_analysis, skip=SKIP_TOKEN_ANALYSIS)
+ container.add(run_graph_postprocessing, skip=SKIP_GRAPH_POSTPROCESSING)
+ container.add(run_time_analysis, skip=SKIP_TIME_ANALYSIS)
+
+ return container
def main() -> None:
- pre_step_skipped: bool = False
- # ** preprocess
- if DO_PREPROCESSING and not SKIP_PREPROCESSING:
- preprocessed_data = run_preprocessing()
- elif not SKIP_PREPROCESSING:
- # !! hardcoded result filenames
- target_pattern: str = r'*Pipe-Merge_Duplicates_Step-1*'
- loading_path = list(SAVE_PATH_FOLDER.glob(target_pattern))[0]
- verify_path(loading_path)
- ret = typing.cast(tuple[DataFrame], load_pickle(loading_path))
- preprocessed_data = ret[0]
- else:
- pre_step_skipped = True
- warnings.warn('No preprocessing action selected. Skipped.')
- # sys.exit(0)
- # ** token analysis
- if DO_TOKEN_ANALYSIS and not SKIP_TOKEN_ANALYSIS:
- if pre_step_skipped:
- raise RuntimeError(
- 'Preprocessing step skipped. Token analysis cannot be performed.'
- )
- preprocessed_data_trunc = typing.cast(
- DataFrame, preprocessed_data[['batched_idxs', 'entry', 'num_occur']].copy()
- ) # type: ignore
- tk_graph, docs_mapping = run_token_analysis(preprocessed_data_trunc)
- elif not SKIP_TOKEN_ANALYSIS:
- # !! hardcoded result filenames
- # whole graph
- filename: str = f'{pipe_token_analysis.name}-TokenGraph'
- loading_path = SAVE_PATH_FOLDER.joinpath(filename).with_suffix('.pkl')
- verify_path(loading_path)
- # tk_graph = typing.cast(TokenGraph, load_pickle(loading_path))
- tk_graph = TokenGraph.from_pickle(loading_path)
- pre_step_skipped = False
- else:
- pre_step_skipped = True
- warnings.warn('No token analysis action selected. Skipped.')
- # ** graph postprocessing
- if DO_GRAPH_POSTPROCESSING and not SKIP_GRAPH_POSTPROCESSING:
- if pre_step_skipped:
- raise RuntimeError(
- (
- 'Preprocessing or token analysis step skipped. '
- 'Graph postprocessing cannot be performed.'
- )
- )
- tk_graph_filtered = run_graph_postprocessing(tk_graph)
- elif not SKIP_GRAPH_POSTPROCESSING:
- # !! hardcoded result filenames
- # filtered graph
- filename: str = f'{pipe_token_analysis.name}-TokenGraph-filtered'
- loading_path = SAVE_PATH_FOLDER.joinpath(filename).with_suffix('.pkl')
- verify_path(loading_path)
- # tk_graph_filtered = typing.cast(TokenGraph, load_pickle(loading_path))
- tk_graph_filtered = TokenGraph.from_pickle(loading_path)
- pre_step_skipped = False
- else:
- warnings.warn('No graph postprocessing action selected. Skipped.')
- # ** time analysis
- if DO_TIME_ANALYSIS and not SKIP_TIME_ANALYSIS:
- # no check for fails, runs separately
- ret = run_time_analysis()
- elif not SKIP_TIME_ANALYSIS:
- ...
- else:
- warnings.warn('No time analysis action selected. Skipped.')
+ procedure = build_pipeline_container()
+ procedure.run()
if __name__ == '__main__':
diff --git a/scripts/dashboard/Pipe-TargetFeature_Step-3_remove_NA.pkl b/scripts/dashboard/Pipe-TargetFeature_Step-3_remove_NA.pkl
deleted file mode 100644
index bba1d89..0000000
Binary files a/scripts/dashboard/Pipe-TargetFeature_Step-3_remove_NA.pkl and /dev/null differ
diff --git a/scripts/dashboard/Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl b/scripts/dashboard/Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl
deleted file mode 100644
index 5565194..0000000
Binary files a/scripts/dashboard/Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl and /dev/null differ
diff --git a/scripts/dashboard/app.py b/scripts/dashboard/app.py
deleted file mode 100644
index 4a977b6..0000000
--- a/scripts/dashboard/app.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import time
-import webbrowser
-from pathlib import Path
-from threading import Thread
-from typing import cast
-
-import pandas as pd
-import plotly.express as px
-from dash import (
- Dash,
- Input,
- Output,
- State,
- callback,
- dash_table,
- dcc,
- html,
-)
-from lang_main.io import load_pickle
-from lang_main.types import ObjectID, TimelineCandidates
-from pandas import DataFrame
-
-# df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
-
-# ** data
-p_df = Path(r'./Pipe-TargetFeature_Step-3_remove_NA.pkl').resolve()
-p_tl = Path(r'/Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl').resolve()
-ret = cast(DataFrame, load_pickle(p_df))
-data = ret[0]
-ret = cast(tuple[TimelineCandidates, dict[ObjectID, str]], load_pickle(p_tl))
-cands = ret[0]
-texts = ret[1]
-
-# p_df = Path(r'.\test-notebooks\dashboard\data.pkl')
-# p_cands = Path(r'.\test-notebooks\dashboard\map_candidates.pkl')
-# p_map = Path(r'.\test-notebooks\dashboard\map_texts.pkl')
-# data = cast(DataFrame, load_pickle(p_df))
-# cands = cast(TimelineCandidates, load_pickle(p_cands))
-# texts = cast(dict[ObjectID, str], load_pickle(p_map))
-
-table_feats = [
- 'ErstellungsDatum',
- 'ErledigungsDatum',
- 'VorgangsTypName',
- 'VorgangsBeschreibung',
-]
-table_feats_dates = [
- 'ErstellungsDatum',
- 'ErledigungsDatum',
-]
-
-# ** graph config
-markers = {
- 'size': 12,
- 'color': 'yellow',
- 'line': {
- 'width': 2,
- 'color': 'red',
- },
-}
-hover_data = {
- 'ErstellungsDatum': '|%d.%m.%Y',
- 'VorgangsBeschreibung': True,
-}
-
-
-app = Dash(prevent_initial_callbacks=True)
-
-app.layout = [
- html.H1(children='Demo Zeitreihenanalyse', style={'textAlign': 'center'}),
- html.Div(
- children=[
- html.H2('Wählen Sie ein Objekt aus (ObjektID):'),
- dcc.Dropdown(
- list(cands.keys()),
- id='dropdown-selection',
- placeholder='ObjektID auswählen...',
- ),
- ]
- ),
- html.Div(
- children=[
- html.H3(id='object_text'),
- dcc.Dropdown(id='choice-candidates'),
- dcc.Graph(id='graph-output'),
- ]
- ),
- html.Div(children=[dash_table.DataTable(id='table-candidates')]),
-]
-
-
-@callback(
- Output('object_text', 'children'),
- Input('dropdown-selection', 'value'),
- prevent_initial_call=True,
-)
-def update_obj_text(obj_id):
- obj_id = int(obj_id)
- obj_text = texts[obj_id]
- headline = f'HObjektText: {obj_text}'
- return headline
-
-
-@callback(
- Output('choice-candidates', 'options'),
- Input('dropdown-selection', 'value'),
- prevent_initial_call=True,
-)
-def update_choice_candidates(obj_id):
- obj_id = int(obj_id)
- cands_obj_id = cands[obj_id]
- choices = list(range(1, len(cands_obj_id) + 1))
- return choices
-
-
-@callback(
- Output('graph-output', 'figure'),
- Input('choice-candidates', 'value'),
- State('dropdown-selection', 'value'),
- prevent_initial_call=True,
-)
-def update_timeline(index, obj_id):
- obj_id = int(obj_id)
- # title
- obj_text = texts[obj_id]
- title = f'HObjektText: {obj_text}'
- # cands
- cands_obj_id = cands[obj_id]
- cands_choice = cands_obj_id[int(index) - 1]
- # data
- df = data.loc[list(cands_choice)].sort_index() # type: ignore
- # figure
- fig = px.line(
- data_frame=df,
- x='ErstellungsDatum',
- y='ObjektID',
- title=title,
- hover_data=hover_data,
- )
- fig.update_traces(mode='markers+lines', marker=markers, marker_symbol='diamond')
- fig.update_xaxes(
- tickformat='%B\n%Y',
- rangeslider_visible=True,
- )
- fig.update_yaxes(type='category')
- fig.update_layout(hovermode='x unified')
- return fig
-
-
-@callback(
- [Output('table-candidates', 'data'), Output('table-candidates', 'columns')],
- Input('choice-candidates', 'value'),
- State('dropdown-selection', 'value'),
- prevent_initial_call=True,
-)
-def update_table_candidates(index, obj_id):
- obj_id = int(obj_id)
- # cands
- cands_obj_id = cands[obj_id]
- cands_choice = cands_obj_id[int(index) - 1]
- # data
- df = data.loc[list(cands_choice)].sort_index() # type: ignore
- df = df.filter(items=table_feats, axis=1).sort_values(
- by='ErstellungsDatum', ascending=True
- )
- cols = [{'name': i, 'id': i} for i in df.columns]
- # convert dates to strings
- for col in table_feats_dates:
- df[col] = df[col].dt.strftime(r'%Y-%m-%d')
-
- table_data = df.to_dict('records')
- return table_data, cols
-
-
-def _start_webbrowser():
- host = '127.0.0.1'
- port = '8050'
- adress = f'http://{host}:{port}/'
- time.sleep(2)
- webbrowser.open_new(adress)
-
-
-def main():
- webbrowser_thread = Thread(target=_start_webbrowser, daemon=True)
- webbrowser_thread.start()
- app.run(debug=True)
-
-
-if __name__ == '__main__':
- main()
diff --git a/scripts/dashboard/cyto.py b/scripts/dashboard/cyto.py
index b70270e..349980d 100644
--- a/scripts/dashboard/cyto.py
+++ b/scripts/dashboard/cyto.py
@@ -1,9 +1,9 @@
+import copy
import time
import webbrowser
from pathlib import Path
from threading import Thread
from typing import cast
-import copy
import dash_cytoscape as cyto
from dash import Dash, Input, Output, State, dcc, html
@@ -30,20 +30,20 @@ app = Dash(__name__, external_stylesheets=external_stylesheets)
cose_layout = {
'name': 'cose',
- 'nodeOverlap': 20,
+ 'nodeOverlap': 500,
'refresh': 20,
'fit': True,
- 'padding': 30,
- 'randomize': True,
- 'componentSpacing': 40,
- 'nodeRepulsion': 2000,
+ 'padding': 20,
+ 'randomize': False,
+ 'componentSpacing': 1.2,
+ 'nodeRepulsion': 1000,
'edgeElasticity': 1000,
'idealEdgeLength': 100,
'nestingFactor': 1.2,
'gravity': 50,
- 'numIter': 2000,
- 'initialTemp': 1000,
- 'coolingFactor': 0.95,
+ 'numIter': 3000,
+ 'initialTemp': 2000,
+ 'coolingFactor': 0.7,
'minTemp': 1.0,
'nodeDimensionsIncludeLabels': True,
}
@@ -108,9 +108,8 @@ my_stylesheet = [
# {'selector': '.triangle', 'style': {'shape': 'triangle'}},
]
-app.layout = html.Div(
+layout = html.Div(
[
- html.Button('Trigger JS Layout', id='test_js'),
html.Button('Trigger JS Weight', id='test_js_weight'),
html.Div(id='output'),
html.Div(
@@ -166,11 +165,13 @@ app.layout = html.Div(
style={'width': '40%'},
),
html.H3('Graph'),
+ html.Button('Re-Layout', id='trigger_relayout'),
html.Div(
[
cyto.Cytoscape(
id='cytoscape-graph',
style={'width': '100%', 'height': '600px'},
+ layout=cose_layout,
stylesheet=my_stylesheet,
elements=cyto_data_base,
zoom=1,
@@ -192,6 +193,9 @@ app.layout = html.Div(
)
+app.layout = layout
+
+
@app.callback(
Output('cytoscape-graph', 'layout', allow_duplicate=True),
Input('layout_choice', 'value'),
@@ -266,17 +270,17 @@ app.clientside_callback(
"""
function(n_clicks, layout) {
layout.edgeElasticity = function(edge) {
- return edge.data().weight * 4;
+ return edge.data().weight * 0.05;
};
layout.idealEdgeLength = function(edge) {
- return edge.data().weight * 0.8;
+ return edge.data().weight * 0.4;
};
cy.layout(layout).run();
return layout;
}
""",
Output('cytoscape-graph', 'layout', allow_duplicate=True),
- Input('test_js', 'n_clicks'),
+ Input('trigger_relayout', 'n_clicks'),
State('cytoscape-graph', 'layout'),
prevent_initial_call=True,
)
diff --git a/scripts/dashboard/cyto_2.py b/scripts/dashboard/cyto_2.py
deleted file mode 100644
index 7276328..0000000
--- a/scripts/dashboard/cyto_2.py
+++ /dev/null
@@ -1,368 +0,0 @@
-import json
-import os
-
-import dash
-import dash_cytoscape as cyto
-from dash import Input, Output, State, callback, dcc, html
-
-# Load extra layouts
-cyto.load_extra_layouts()
-
-
-# Display utility functions
-def _merge(a, b):
- return dict(a, **b)
-
-
-def _omit(omitted_keys, d):
- return {k: v for k, v in d.items() if k not in omitted_keys}
-
-
-# Custom Display Components
-def Card(children, **kwargs):
- return html.Section(
- children,
- style=_merge(
- {
- 'padding': 20,
- 'margin': 5,
- 'borderRadius': 5,
- 'border': 'thin lightgrey solid',
- 'background-color': 'white',
- # Remove possibility to select the text for better UX
- 'user-select': 'none',
- '-moz-user-select': 'none',
- '-webkit-user-select': 'none',
- '-ms-user-select': 'none',
- },
- kwargs.get('style', {}),
- ),
- **_omit(['style'], kwargs),
- )
-
-
-def SectionTitle(title, size, align='center', color='#222'):
- return html.Div(
- style={'text-align': align, 'color': color},
- children=dcc.Markdown('#' * size + ' ' + title),
- )
-
-
-def NamedCard(title, size, children, **kwargs):
- size = min(size, 6)
- size = max(size, 1)
-
- return html.Div([Card([SectionTitle(title, size, align='left')] + children, **kwargs)])
-
-
-def NamedSlider(name, **kwargs):
- return html.Div(
- style={'padding': '20px 10px 25px 4px'},
- children=[
- html.P(f'{name}:'),
- html.Div(style={'margin-left': '6px'}, children=dcc.Slider(**kwargs)),
- ],
- )
-
-
-def NamedDropdown(name, **kwargs):
- return html.Div(
- style={'margin': '10px 0px'},
- children=[
- html.P(children=f'{name}:', style={'margin-left': '3px'}),
- dcc.Dropdown(**kwargs),
- ],
- )
-
-
-def NamedRadioItems(name, **kwargs):
- return html.Div(
- style={'padding': '20px 10px 25px 4px'},
- children=[html.P(children=f'{name}:'), dcc.RadioItems(**kwargs)],
- )
-
-
-def NamedInput(name, **kwargs):
- return html.Div(children=[html.P(children=f'{name}:'), dcc.Input(**kwargs)])
-
-
-# Utils
-def DropdownOptionsList(*args):
- return [{'label': val.capitalize(), 'value': val} for val in args]
-
-
-asset_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'assets')
-
-app = dash.Dash(__name__, assets_folder=asset_path)
-server = app.server
-
-
-# ###################### DATA PREPROCESSING ######################
-# Load data
-with open('sample_network.txt', 'r', encoding='utf-8') as f:
- network_data = f.read().split('\n')
-
-# We select the first 750 edges and associated nodes for an easier visualization
-edges = network_data[:750]
-nodes = set()
-
-following_node_di = {} # user id -> list of users they are following
-following_edges_di = {} # user id -> list of cy edges starting from user id
-
-followers_node_di = {} # user id -> list of followers (cy_node format)
-followers_edges_di = {} # user id -> list of cy edges ending at user id
-
-cy_edges = []
-cy_nodes = []
-
-for edge in edges:
- if ' ' not in edge:
- continue
-
- source, target = edge.split(' ')
-
- cy_edge = {'data': {'id': source + target, 'source': source, 'target': target}}
- cy_target = {'data': {'id': target, 'label': 'User #' + str(target[-5:])}}
- cy_source = {'data': {'id': source, 'label': 'User #' + str(source[-5:])}}
-
- if source not in nodes:
- nodes.add(source)
- cy_nodes.append(cy_source)
- if target not in nodes:
- nodes.add(target)
- cy_nodes.append(cy_target)
-
- # Process dictionary of following
- if not following_node_di.get(source):
- following_node_di[source] = []
- if not following_edges_di.get(source):
- following_edges_di[source] = []
-
- following_node_di[source].append(cy_target)
- following_edges_di[source].append(cy_edge)
-
- # Process dictionary of followers
- if not followers_node_di.get(target):
- followers_node_di[target] = []
- if not followers_edges_di.get(target):
- followers_edges_di[target] = []
-
- followers_node_di[target].append(cy_source)
- followers_edges_di[target].append(cy_edge)
-
-genesis_node = cy_nodes[0]
-genesis_node['classes'] = 'genesis'
-default_elements = [genesis_node]
-
-default_stylesheet = [
- {'selector': 'node', 'style': {'opacity': 0.65, 'z-index': 9999}},
- {
- 'selector': 'edge',
- 'style': {'curve-style': 'bezier', 'opacity': 0.45, 'z-index': 5000},
- },
- {'selector': '.followerNode', 'style': {'background-color': '#0074D9'}},
- {
- 'selector': '.followerEdge',
- 'style': {
- 'mid-target-arrow-color': 'blue',
- 'mid-target-arrow-shape': 'vee',
- 'line-color': '#0074D9',
- },
- },
- {'selector': '.followingNode', 'style': {'background-color': '#FF4136'}},
- {
- 'selector': '.followingEdge',
- 'style': {
- 'mid-target-arrow-color': 'red',
- 'mid-target-arrow-shape': 'vee',
- 'line-color': '#FF4136',
- },
- },
- {
- 'selector': '.genesis',
- 'style': {
- 'background-color': '#B10DC9',
- 'border-width': 2,
- 'border-color': 'purple',
- 'border-opacity': 1,
- 'opacity': 1,
- 'label': 'data(label)',
- 'color': '#B10DC9',
- 'text-opacity': 1,
- 'font-size': 12,
- 'z-index': 9999,
- },
- },
- {
- 'selector': ':selected',
- 'style': {
- 'border-width': 2,
- 'border-color': 'black',
- 'border-opacity': 1,
- 'opacity': 1,
- 'label': 'data(label)',
- 'color': 'black',
- 'font-size': 12,
- 'z-index': 9999,
- },
- },
-]
-
-# ################################# APP LAYOUT ################################
-styles = {
- 'json-output': {
- 'overflow-y': 'scroll',
- 'height': 'calc(50% - 25px)',
- 'border': 'thin lightgrey solid',
- },
- 'tab': {'height': 'calc(98vh - 80px)'},
-}
-
-app.layout = html.Div(
- [
- html.Div(
- className='eight columns',
- children=[
- cyto.Cytoscape(
- id='cytoscape',
- elements=default_elements,
- stylesheet=default_stylesheet,
- style={'height': '95vh', 'width': '100%'},
- )
- ],
- ),
- html.Div(
- className='four columns',
- children=[
- dcc.Tabs(
- id='tabs',
- children=[
- dcc.Tab(
- label='Control Panel',
- children=[
- NamedDropdown(
- name='Layout',
- id='dropdown-layout',
- options=DropdownOptionsList(
- 'random',
- 'grid',
- 'circle',
- 'concentric',
- 'breadthfirst',
- 'cose',
- 'cose-bilkent',
- 'dagre',
- 'cola',
- 'klay',
- 'spread',
- 'euler',
- ),
- value='grid',
- clearable=False,
- ),
- NamedRadioItems(
- name='Expand',
- id='radio-expand',
- options=DropdownOptionsList('followers', 'following'),
- value='followers',
- ),
- ],
- ),
- dcc.Tab(
- label='JSON',
- children=[
- html.Div(
- style=styles['tab'],
- children=[
- html.P('Node Object JSON:'),
- html.Pre(
- id='tap-node-json-output',
- style=styles['json-output'],
- ),
- html.P('Edge Object JSON:'),
- html.Pre(
- id='tap-edge-json-output',
- style=styles['json-output'],
- ),
- ],
- )
- ],
- ),
- ],
- ),
- ],
- ),
- ]
-)
-
-
-# ############################## CALLBACKS ####################################
-@callback(Output('tap-node-json-output', 'children'), Input('cytoscape', 'tapNode'))
-def display_tap_node(data):
- return json.dumps(data, indent=2)
-
-
-@callback(Output('tap-edge-json-output', 'children'), Input('cytoscape', 'tapEdge'))
-def display_tap_edge(data):
- return json.dumps(data, indent=2)
-
-
-@callback(Output('cytoscape', 'layout'), Input('dropdown-layout', 'value'))
-def update_cytoscape_layout(layout):
- return {'name': layout}
-
-
-@callback(
- Output('cytoscape', 'elements'),
- Input('cytoscape', 'tapNodeData'),
- State('cytoscape', 'elements'),
- State('radio-expand', 'value'),
-)
-def generate_elements(nodeData, elements, expansion_mode):
- if not nodeData:
- return default_elements
-
- # If the node has already been expanded, we don't expand it again
- if nodeData.get('expanded'):
- return elements
-
- # This retrieves the currently selected element, and tag it as expanded
- for element in elements:
- if nodeData['id'] == element.get('data').get('id'):
- element['data']['expanded'] = True
- break
-
- if expansion_mode == 'followers':
- followers_nodes = followers_node_di.get(nodeData['id'])
- followers_edges = followers_edges_di.get(nodeData['id'])
-
- if followers_nodes:
- for node in followers_nodes:
- node['classes'] = 'followerNode'
- elements.extend(followers_nodes)
-
- if followers_edges:
- for follower_edge in followers_edges:
- follower_edge['classes'] = 'followerEdge'
- elements.extend(followers_edges)
-
- elif expansion_mode == 'following':
- following_nodes = following_node_di.get(nodeData['id'])
- following_edges = following_edges_di.get(nodeData['id'])
-
- if following_nodes:
- for node in following_nodes:
- if node['data']['id'] != genesis_node['data']['id']:
- node['classes'] = 'followingNode'
- elements.append(node)
-
- if following_edges:
- for follower_edge in following_edges:
- follower_edge['classes'] = 'followingEdge'
- elements.extend(following_edges)
-
- return elements
-
-
-if __name__ == '__main__':
- app.run_server(debug=True)
diff --git a/scripts/dashboard/sample_network.txt b/scripts/dashboard/sample_network.txt
deleted file mode 100644
index 7af6dcd..0000000
--- a/scripts/dashboard/sample_network.txt
+++ /dev/null
@@ -1,10297 +0,0 @@
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diff --git a/scripts/dashboard/timeline.py b/scripts/dashboard/timeline.py
new file mode 100644
index 0000000..021d96d
--- /dev/null
+++ b/scripts/dashboard/timeline.py
@@ -0,0 +1,507 @@
+import time
+import webbrowser
+from pathlib import Path
+from threading import Thread
+from typing import cast
+
+import dash_cytoscape as cyto
+import pandas as pd
+import plotly.express as px
+from dash import (
+ Dash,
+ Input,
+ Output,
+ State,
+ callback,
+ dash_table,
+ dcc,
+ html,
+)
+from pandas import DataFrame
+
+from lang_main.analysis import graphs
+from lang_main.io import load_pickle
+from lang_main.types import ObjectID, TimelineCandidates
+from lang_main.analysis import tokens
+from lang_main.constants import SPCY_MODEL
+
+# df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
+
+# ** data
+# p_df = Path(r'../Pipe-TargetFeature_Step-3_remove_NA.pkl').resolve()
+p_df = Path(r'../results/test_20240619/TIMELINE.pkl').resolve()
+# p_tl = Path(r'/Pipe-Timeline_Analysis_Step-4_get_timeline_candidates.pkl').resolve()
+p_tl = Path(r'../results/test_20240619/TIMELINE_POSTPROCESSING.pkl').resolve()
+ret = cast(tuple[DataFrame], load_pickle(p_df))
+data = ret[0]
+ret = cast(tuple[TimelineCandidates, dict[ObjectID, str]], load_pickle(p_tl))
+cands = ret[0]
+texts = ret[1]
+
+# p_df = Path(r'.\test-notebooks\dashboard\data.pkl')
+# p_cands = Path(r'.\test-notebooks\dashboard\map_candidates.pkl')
+# p_map = Path(r'.\test-notebooks\dashboard\map_texts.pkl')
+# data = cast(DataFrame, load_pickle(p_df))
+# cands = cast(TimelineCandidates, load_pickle(p_cands))
+# texts = cast(dict[ObjectID, str], load_pickle(p_map))
+
+table_feats = [
+ 'ErstellungsDatum',
+ 'ErledigungsDatum',
+ 'VorgangsTypName',
+ 'VorgangsBeschreibung',
+]
+table_feats_dates = [
+ 'ErstellungsDatum',
+ 'ErledigungsDatum',
+]
+
+# ** figure config
+markers = {
+ 'size': 12,
+ 'color': 'yellow',
+ 'line': {
+ 'width': 2,
+ 'color': 'red',
+ },
+}
+hover_data = {
+ 'ErstellungsDatum': '|%d.%m.%Y',
+ 'VorgangsBeschreibung': True,
+}
+
+# ** graphs
+target = '../results/test_20240529/Pipe-Token_Analysis_Step-1_build_token_graph.pkl'
+p = Path(target).resolve()
+ret = load_pickle(p)
+tk_graph = cast(graphs.TokenGraph, ret[0])
+tk_graph_filtered = graphs.filter_graph_by_edge_weight(tk_graph, 150, None)
+tk_graph_filtered = graphs.filter_graph_by_node_degree(tk_graph_filtered, 1, None)
+# tk_graph_filtered = tk_graph.filter_by_edge_weight(150, None)
+# tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
+cyto_data_base, weight_data = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
+
+MIN_WEIGHT = weight_data['min']
+MAX_WEIGHT = weight_data['max']
+
+cyto.load_extra_layouts()
+
+cose_layout = {
+ 'name': 'cose',
+ 'nodeOverlap': 500,
+ 'refresh': 20,
+ 'fit': True,
+ 'padding': 20,
+ 'randomize': False,
+ 'componentSpacing': 1.2,
+ 'nodeRepulsion': 1000,
+ 'edgeElasticity': 1000,
+ 'idealEdgeLength': 100,
+ 'nestingFactor': 1.2,
+ 'gravity': 50,
+ 'numIter': 3000,
+ 'initialTemp': 2000,
+ 'coolingFactor': 0.7,
+ 'minTemp': 1.0,
+ 'nodeDimensionsIncludeLabels': True,
+}
+
+my_stylesheet = [
+ # Group selectors
+ {
+ 'selector': 'node',
+ 'style': {
+ 'shape': 'circle',
+ 'content': 'data(label)',
+ 'background-color': '#B10DC9',
+ 'border-width': 2,
+ 'border-color': 'black',
+ 'border-opacity': 1,
+ 'opacity': 1,
+ 'color': 'black',
+ 'text-opacity': 1,
+ 'font-size': 12,
+ 'z-index': 9999,
+ },
+ },
+ {
+ 'selector': 'edge',
+ 'style': {
+ #'width': f'mapData(weight, {MIN_WEIGHT}, {MAX_WEIGHT}, 1, 10)',
+ # 'width': """function(ele) {
+ # return ele.data('weight');
+ # """,
+ 'curve-style': 'bezier',
+ 'line-color': 'grey',
+ 'line-style': 'solid',
+ 'line-opacity': 1,
+ },
+ },
+ # Class selectors
+ # {'selector': '.red', 'style': {'background-color': 'red', 'line-color': 'red'}},
+ # {'selector': '.triangle', 'style': {'shape': 'triangle'}},
+]
+
+# ** app
+external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
+app = Dash(__name__, external_stylesheets=external_stylesheets)
+
+graph_layout = html.Div(
+ [
+ html.Button('Trigger JS Weight', id='test_js_weight'),
+ html.Button('Trigger Candidate Graph', id='cand_graph'),
+ html.Div(id='output'),
+ html.Div(
+ [
+ html.H2('Token Graph', style={'margin': 0}),
+ html.Button(
+ 'Reset Default',
+ id='bt-reset',
+ style={
+ 'marginLeft': 'auto',
+ 'width': '300px',
+ },
+ ),
+ ],
+ style={
+ 'display': 'flex',
+ 'marginBottom': '1em',
+ },
+ ),
+ html.H3('Layout'),
+ dcc.Dropdown(
+ id='layout_choice',
+ options=[
+ 'cose',
+ 'cola',
+ 'euler',
+ 'random',
+ ],
+ value='cose',
+ clearable=False,
+ ),
+ html.Div(
+ [
+ html.H3('Graph Filter'),
+ dcc.Input(
+ id='weight_min',
+ type='number',
+ min=MIN_WEIGHT,
+ max=MAX_WEIGHT,
+ step=1,
+ placeholder=f'Minimum edge weight: {MIN_WEIGHT} - {MAX_WEIGHT}',
+ debounce=True,
+ style={'width': '40%'},
+ ),
+ dcc.Input(
+ id='weight_max',
+ type='number',
+ min=MIN_WEIGHT,
+ max=MAX_WEIGHT,
+ step=1,
+ placeholder=f'Maximum edge weight: {MIN_WEIGHT} - {MAX_WEIGHT}',
+ debounce=True,
+ style={'width': '40%'},
+ ),
+ html.H3('Graph'),
+ html.Button('Re-Layout', id='trigger_relayout'),
+ html.Div(
+ [
+ cyto.Cytoscape(
+ id='cytoscape-graph',
+ style={'width': '100%', 'height': '600px'},
+ layout=cose_layout,
+ stylesheet=my_stylesheet,
+ elements=cyto_data_base,
+ zoom=1,
+ ),
+ ],
+ style={
+ 'border': '3px solid black',
+ 'borderRadius': '25px',
+ 'marginTop': '1em',
+ 'marginBottom': '2em',
+ 'padding': '7px',
+ },
+ ),
+ ],
+ style={'marginTop': '1em'},
+ ),
+ ],
+)
+
+app.layout = html.Div(
+ [
+ html.H1(children='Demo Zeitreihenanalyse', style={'textAlign': 'center'}),
+ html.Div(
+ children=[
+ html.H2('Wählen Sie ein Objekt aus (ObjektID):'),
+ dcc.Dropdown(
+ list(cands.keys()),
+ id='dropdown-selection',
+ placeholder='ObjektID auswählen...',
+ ),
+ ]
+ ),
+ html.Div(
+ children=[
+ html.H3(id='object_text'),
+ dcc.Dropdown(id='choice-candidates'),
+ dcc.Graph(id='graph-output'),
+ ]
+ ),
+ html.Div(
+ [dash_table.DataTable(id='table-candidates')], style={'marginBottom': '2em'}
+ ),
+ graph_layout,
+ ],
+ style={'margin': '2em'},
+)
+
+
+@callback(
+ Output('object_text', 'children'),
+ Input('dropdown-selection', 'value'),
+ prevent_initial_call=True,
+)
+def update_obj_text(obj_id):
+ obj_id = int(obj_id)
+ obj_text = texts[obj_id]
+ headline = f'HObjektText: {obj_text}'
+ return headline
+
+
+@callback(
+ Output('choice-candidates', 'options'),
+ Input('dropdown-selection', 'value'),
+ prevent_initial_call=True,
+)
+def update_choice_candidates(obj_id):
+ obj_id = int(obj_id)
+ cands_obj_id = cands[obj_id]
+ choices = list(range(1, len(cands_obj_id) + 1))
+ return choices
+
+
+@callback(
+ Output('graph-output', 'figure'),
+ Input('choice-candidates', 'value'),
+ State('dropdown-selection', 'value'),
+ prevent_initial_call=True,
+)
+def update_timeline(index, obj_id):
+ obj_id = int(obj_id)
+ # title
+ obj_text = texts[obj_id]
+ title = f'HObjektText: {obj_text}'
+ # cands
+ cands_obj_id = cands[obj_id]
+ cands_choice = cands_obj_id[int(index) - 1]
+ # data
+ df = data.loc[list(cands_choice)].sort_index() # type: ignore
+ # figure
+ fig = px.line(
+ data_frame=df,
+ x='ErstellungsDatum',
+ y='ObjektID',
+ title=title,
+ hover_data=hover_data,
+ )
+ fig.update_traces(mode='markers+lines', marker=markers, marker_symbol='diamond')
+ fig.update_xaxes(
+ tickformat='%B\n%Y',
+ rangeslider_visible=True,
+ )
+ fig.update_yaxes(type='category')
+ fig.update_layout(hovermode='x unified')
+ return fig
+
+
+@callback(
+ [Output('table-candidates', 'data'), Output('table-candidates', 'columns')],
+ Input('choice-candidates', 'value'),
+ State('dropdown-selection', 'value'),
+ prevent_initial_call=True,
+)
+def update_table_candidates(index, obj_id):
+ # obj_id = int(obj_id)
+ # # cands
+ # cands_obj_id = cands[obj_id]
+ # cands_choice = cands_obj_id[int(index) - 1]
+ # # data
+ # df = data.loc[list(cands_choice)].sort_index() # type: ignore
+ df = pre_filter_data(data, idx=index, obj_id=obj_id)
+ df = df.filter(items=table_feats, axis=1).sort_values(
+ by='ErstellungsDatum', ascending=True
+ )
+ cols = [{'name': i, 'id': i} for i in df.columns]
+ # convert dates to strings
+ for col in table_feats_dates:
+ df[col] = df[col].dt.strftime(r'%Y-%m-%d')
+
+ table_data = df.to_dict('records')
+ return table_data, cols
+
+
+def pre_filter_data(
+ data: DataFrame,
+ idx: int,
+ obj_id: ObjectID,
+) -> DataFrame:
+ obj_id = int(obj_id)
+ data = data.copy()
+ # cands
+ cands_obj_id = cands[obj_id]
+ cands_choice = cands_obj_id[int(idx) - 1]
+ # data
+ data = data.loc[list(cands_choice)].sort_index() # type: ignore
+
+ return data
+
+
+# ** graph
+@app.callback(
+ Output('cytoscape-graph', 'elements', allow_duplicate=True),
+ Output('weight_min', 'min', allow_duplicate=True),
+ Output('weight_min', 'max', allow_duplicate=True),
+ Output('weight_min', 'placeholder', allow_duplicate=True),
+ Output('weight_max', 'min', allow_duplicate=True),
+ Output('weight_max', 'max', allow_duplicate=True),
+ Output('weight_max', 'placeholder', allow_duplicate=True),
+ Input('cand_graph', 'n_clicks'),
+ State('choice-candidates', 'value'),
+ State('dropdown-selection', 'value'),
+ prevent_initial_call=True,
+)
+def update_graph_candidates(_, index, obj_id):
+ df = pre_filter_data(data, idx=index, obj_id=obj_id)
+ tk_graph_cands, _ = tokens.build_token_graph(
+ data=df,
+ model=SPCY_MODEL,
+ target_feature='VorgangsBeschreibung',
+ build_map=False,
+ )
+ cyto_data, weight_info = graphs.convert_graph_to_cytoscape(tk_graph_cands)
+ weight_min = weight_info['min']
+ weight_max = weight_info['max']
+ placeholder_min = f'Minimum edge weight: {weight_min} - {weight_max}'
+ placeholder_max = f'Minimum edge weight: {weight_min} - {weight_max}'
+ return (
+ cyto_data,
+ weight_min,
+ weight_max,
+ placeholder_min,
+ weight_min,
+ weight_max,
+ placeholder_max,
+ )
+
+
+@app.callback(
+ Output('cytoscape-graph', 'layout', allow_duplicate=True),
+ Input('layout_choice', 'value'),
+ prevent_initial_call=True,
+)
+def update_layout_internal(layout_choice):
+ # return {'name': layout_choice}
+ return cose_layout
+ # return cose_bilkent_layout
+ # return cola_layout
+
+
+@app.callback(
+ Output('cytoscape-graph', 'zoom'),
+ Output('cytoscape-graph', 'elements', allow_duplicate=True),
+ Output('weight_min', 'value'),
+ Output('weight_max', 'value'),
+ Input('bt-reset', 'n_clicks'),
+ prevent_initial_call=True,
+)
+def reset_layout(n_clicks):
+ return (1, cyto_data_base, None, None)
+
+
+# update edge weight
+@app.callback(
+ Output('cytoscape-graph', 'elements', allow_duplicate=True),
+ Input('weight_min', 'value'),
+ Input('weight_max', 'value'),
+ prevent_initial_call=True,
+)
+def update_edge_weight(weight_min, weight_max):
+ if not any([weight_min, weight_max]):
+ return cyto_data_base
+
+ if weight_min is None:
+ weight_min = MIN_WEIGHT
+ if weight_max is None:
+ weight_max = MAX_WEIGHT
+ tk_graph_filtered = graphs.filter_graph_by_edge_weight(tk_graph, weight_min, weight_max)
+ # tk_graph_filtered = tk_graph.filter_by_edge_weight(weight_min, weight_max)
+ tk_graph_filtered = graphs.filter_graph_by_node_degree(tk_graph_filtered, 1, None)
+ # tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
+ cyto_data, _ = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
+ return cyto_data
+
+
+app.clientside_callback(
+ """
+ function(n_clicks, layout) {
+ layout.edgeElasticity = function(edge) {
+ return edge.data().weight * 0.05;
+ };
+ layout.idealEdgeLength = function(edge) {
+ return edge.data().weight * 0.4;
+ };
+ cy.layout(layout).run();
+ return layout;
+ }
+ """,
+ Output('cytoscape-graph', 'layout', allow_duplicate=True),
+ Input('trigger_relayout', 'n_clicks'),
+ State('cytoscape-graph', 'layout'),
+ prevent_initial_call=True,
+)
+
+app.clientside_callback(
+ """
+ function(n_clicks, stylesheet) {
+ function edge_weight(ele) {
+ let threshold = 1000;
+ let weight = ele.data('weight');
+ if (weight > threshold) {
+ weight = 12;
+ } else {
+ weight = weight / threshold * 10;
+ weight = Math.max(1, weight);
+ }
+ return weight;
+ }
+ stylesheet[1].style.width = edge_weight;
+ cy.style(stylesheet).update();
+ return stylesheet;
+ }
+ """,
+ Output('cytoscape-graph', 'stylesheet'),
+ Input('test_js_weight', 'n_clicks'),
+ State('cytoscape-graph', 'stylesheet'),
+ prevent_initial_call=False,
+)
+
+
+def _start_webbrowser():
+ host = '127.0.0.1'
+ port = '8050'
+ adress = f'http://{host}:{port}/'
+ time.sleep(2)
+ webbrowser.open_new(adress)
+
+
+def main():
+ webbrowser_thread = Thread(target=_start_webbrowser, daemon=True)
+ webbrowser_thread.start()
+ app.run(debug=True)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/scripts/lang_main_config copy.toml b/scripts/lang_main_config copy.toml
deleted file mode 100644
index 8cf2829..0000000
--- a/scripts/lang_main_config copy.toml
+++ /dev/null
@@ -1,38 +0,0 @@
-# lang_main: Config file
-
-[paths]
-inputs = 'A:/Arbeitsaufgaben/lang-main/scripts'
-results = 'A:/Arbeitsaufgaben/lang-main/scripts/results/test_20240529/'
-dataset = 'A:/Arbeitsaufgaben/lang-main/data/02_202307/Export4.csv'
-#results = './results/Export7/'
-#dataset = './01_03_Rohdaten_202403/Export7_59499_Zeilen.csv'
-#results = './results/Export7_trunc/'
-#dataset = './01_03_Rohdaten_202403/Export7_trunc.csv'
-
-[control]
-preprocessing = true
-preprocessing_skip = false
-token_analysis = false
-token_analysis_skip = true
-graph_postprocessing = false
-graph_postprocessing_skip = true
-
-#[export_filenames]
-#filename_cossim_filter_candidates = 'CosSim-FilterCandidates'
-
-[preprocess]
-filename_cossim_filter_candidates = 'CosSim-FilterCandidates'
-date_cols = [
- "VorgangsDatum",
- "ErledigungsDatum",
- "Arbeitsbeginn",
- "ErstellungsDatum",
-]
-threshold_amount_characters = 5
-threshold_similarity = 0.8
-
-[graph_postprocessing]
-threshold_edge_weight = 150
-
-[time_analysis]
-threshold_unique_texts = 5
\ No newline at end of file
diff --git a/scripts/lang_main_config.toml b/scripts/lang_main_config.toml
index a0b65d9..f5c2788 100644
--- a/scripts/lang_main_config.toml
+++ b/scripts/lang_main_config.toml
@@ -2,22 +2,20 @@
[paths]
inputs = './inputs/'
-results = './results/test_20240529/'
+results = './results/test_20240619/'
dataset = '../data/02_202307/Export4.csv'
#results = './results/Export7/'
#dataset = './01_03_Rohdaten_202403/Export7_59499_Zeilen.csv'
#results = './results/Export7_trunc/'
#dataset = './01_03_Rohdaten_202403/Export7_trunc.csv'
+# only debugging features, production-ready pipelines should always
+# be fully executed
[control]
-preprocessing = false
-preprocessing_skip = false
-token_analysis = true
-token_analysis_skip = false
-graph_postprocessing = false
+preprocessing_skip = true
+token_analysis_skip = true
graph_postprocessing_skip = true
-time_analysis = false
-time_analysis_skip = true
+time_analysis_skip = false
#[export_filenames]
#filename_cossim_filter_candidates = 'CosSim-FilterCandidates'
diff --git a/src/lang_main/analysis/graphs.py b/src/lang_main/analysis/graphs.py
index b282495..dde0248 100644
--- a/src/lang_main/analysis/graphs.py
+++ b/src/lang_main/analysis/graphs.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
import copy
import sys
import typing
@@ -169,6 +171,90 @@ def convert_graph_to_cytoscape(
return cyto_data, weight_metadata
+def filter_graph_by_edge_weight(
+ graph: TokenGraph,
+ bound_lower: int | None,
+ bound_upper: int | None,
+) -> TokenGraph:
+ """filters all edges which are within the provided bounds
+
+ Parameters
+ ----------
+ bound_lower : int | None
+ lower bound for edge weights, edges with weight equal to this value are retained
+ bound_upper : int | None
+ upper bound for edge weights, edges with weight equal to this value are retained
+
+ Returns
+ -------
+ TokenGraph
+ a copy of the graph with filtered edges
+ """
+ original_graph_edges = copy.deepcopy(graph.edges)
+ filtered_graph = graph.copy()
+
+ if not any([bound_lower, bound_upper]):
+ logger.warning('No bounds provided, returning original graph.')
+ return filtered_graph
+
+ for edge in original_graph_edges:
+ weight = typing.cast(int, filtered_graph[edge[0]][edge[1]]['weight'])
+ if bound_lower is not None and weight < bound_lower:
+ filtered_graph.remove_edge(edge[0], edge[1])
+ if bound_upper is not None and weight > bound_upper:
+ filtered_graph.remove_edge(edge[0], edge[1])
+
+ if filtered_graph._undirected is not None:
+ filtered_graph.to_undirected(inplace=True, logging=False)
+
+ filtered_graph.update_metadata(logging=False)
+
+ return filtered_graph
+
+
+def filter_graph_by_node_degree(
+ graph: TokenGraph,
+ bound_lower: int | None,
+ bound_upper: int | None,
+) -> TokenGraph:
+ """filters all nodes which are within the provided bounds by their degree
+
+ Parameters
+ ----------
+ bound_lower : int | None
+ lower bound for node degree, nodes with degree equal to this value are retained
+ bound_upper : int | None
+ upper bound for node degree, nodes with degree equal to this value are retained
+
+ Returns
+ -------
+ TokenGraph
+ a copy of the graph with filtered nodes
+ """
+ # filter nodes by degree
+ original_graph_nodes = copy.deepcopy(graph.nodes)
+ filtered_graph = graph.copy()
+
+ if not any([bound_lower, bound_upper]):
+ logger.warning('No bounds provided, returning original graph.')
+ return filtered_graph
+
+ for node in original_graph_nodes:
+ degree = filtered_graph.degree[node] # type: ignore
+ if bound_lower is not None and degree < bound_lower:
+ filtered_graph.remove_node(node)
+ if bound_upper is not None and degree > bound_upper:
+ filtered_graph.remove_node(node)
+
+ if filtered_graph._undirected is not None:
+ filtered_graph.to_undirected(inplace=True, logging=False)
+
+ filtered_graph.update_metadata(logging=False)
+
+ return filtered_graph
+
+
+# ** ---------------------------------------
class TokenGraph(DiGraph):
def __init__(
self,
@@ -286,87 +372,6 @@ class TokenGraph(DiGraph):
graph=self._undirected, logging=logging
)
- def filter_by_edge_weight(
- self,
- bound_lower: int | None,
- bound_upper: int | None,
- ) -> Self:
- """filters all edges which are within the provided bounds
-
- Parameters
- ----------
- bound_lower : int | None
- lower bound for edge weights, edges with weight equal to this value are retained
- bound_upper : int | None
- upper bound for edge weights, edges with weight equal to this value are retained
-
- Returns
- -------
- Self
- a copy of the graph with filtered edges
- """
- original_graph_edges = copy.deepcopy(self.edges)
- filtered_graph = self.copy()
-
- if not any([bound_lower, bound_upper]):
- logger.warning('No bounds provided, returning original graph.')
- return filtered_graph
-
- for edge in original_graph_edges:
- weight = typing.cast(int, filtered_graph[edge[0]][edge[1]]['weight'])
- if bound_lower is not None and weight < bound_lower:
- filtered_graph.remove_edge(edge[0], edge[1])
- if bound_upper is not None and weight > bound_upper:
- filtered_graph.remove_edge(edge[0], edge[1])
-
- if filtered_graph._undirected is not None:
- filtered_graph.to_undirected(inplace=True, logging=False)
-
- filtered_graph.update_metadata(logging=False)
-
- return filtered_graph
-
- def filter_by_node_degree(
- self,
- bound_lower: int | None,
- bound_upper: int | None,
- ) -> Self:
- """filters all nodes which are within the provided bounds by their degree
-
- Parameters
- ----------
- bound_lower : int | None
- lower bound for node degree, nodes with degree equal to this value are retained
- bound_upper : int | None
- upper bound for node degree, nodes with degree equal to this value are retained
-
- Returns
- -------
- Self
- a copy of the graph with filtered nodes
- """
- # filter nodes by degree
- original_graph_nodes = copy.deepcopy(self.nodes)
- filtered_graph = self.copy()
-
- if not any([bound_lower, bound_upper]):
- logger.warning('No bounds provided, returning original graph.')
- return filtered_graph
-
- for node in original_graph_nodes:
- degree = filtered_graph.degree[node] # type: ignore
- if bound_lower is not None and degree < bound_lower:
- filtered_graph.remove_node(node)
- if bound_upper is not None and degree > bound_upper:
- filtered_graph.remove_node(node)
-
- if filtered_graph._undirected is not None:
- filtered_graph.to_undirected(inplace=True, logging=False)
-
- filtered_graph.update_metadata(logging=False)
-
- return filtered_graph
-
def _save_prepare(
self,
path: Path,
@@ -379,14 +384,13 @@ class TokenGraph(DiGraph):
return saving_path
- def save_graph(
+ def to_GraphML(
self,
path: Path,
filename: str | None = None,
directed: bool = False,
) -> None:
- """save one of the stored graphs to disk file,
- currently only GraphML format is supported
+ """save one of the stored graphs to GraphML format on disk,
Parameters
----------
diff --git a/src/lang_main/analysis/preprocessing.py b/src/lang_main/analysis/preprocessing.py
index 65ce4dc..54a9f93 100644
--- a/src/lang_main/analysis/preprocessing.py
+++ b/src/lang_main/analysis/preprocessing.py
@@ -22,7 +22,7 @@ from lang_main.analysis.shared import (
similar_index_groups,
)
from lang_main.loggers import logger_preprocess as logger
-from lang_main.pipelines.base import BasePipeline
+from lang_main.pipelines.base import Pipeline
from lang_main.types import Embedding, PandasIndex
# ** RE patterns
@@ -119,10 +119,9 @@ def remove_duplicates(
).copy()
logger.info('Removed all duplicates from dataset successfully.')
logger.info(
- (
- f'New Dataset properties: number of entries: {len(wo_duplicates)}, '
- f'number of features {len(wo_duplicates.columns)}'
- )
+ 'New Dataset properties: number of entries: %d, number of features %d',
+ len(wo_duplicates),
+ len(wo_duplicates.columns),
)
return (wo_duplicates,)
@@ -176,6 +175,7 @@ def clean_string_slim(string: str) -> str:
string = pattern_special_chars.sub(' ', string)
string = pattern_repeated_chars.sub(r'\1', string)
# string = pattern_dates.sub('', string)
+ # dates are used for context, should not be removed at this stage
string = pattern_whitespace.sub(' ', string)
# remove whitespaces at the beginning and the end
string = string.strip()
@@ -241,11 +241,84 @@ def analyse_feature(
return (result_df,)
+# ** pre-filter
+def numeric_pre_filter_feature(
+ data: DataFrame,
+ feature: str,
+ bound_lower: int | None,
+ bound_upper: int | None,
+) -> tuple[DataFrame]:
+ if not any([bound_lower, bound_upper]):
+ raise ValueError('No bounds for filtering provided')
+
+ data = data.copy()
+ if bound_lower is None:
+ bound_lower = cast(int, data[feature].min())
+ if bound_upper is None:
+ bound_upper = cast(int, data[feature].max())
+
+ filter_lower = data[feature] >= bound_lower
+ filter_upper = data[feature] <= bound_upper
+ filter = filter_lower & filter_upper
+
+ data = data.loc[filter]
+
+ return (data,)
+
+
# ** embedding based similarity
# following functions used to identify similar entries to have
# a more robust identification of duplicates negating negative side effects
# of several disturbances like typos, escape characters, etc.
# build mapping of embeddings for given model
+def merge_similarity_dupl(
+ data: DataFrame,
+ model: SentenceTransformer,
+ cos_sim_threshold: float,
+) -> tuple[DataFrame]:
+ logger.info('Start merging of similarity candidates...')
+
+ # data
+ merged_data = data.copy()
+ model_input = merged_data['entry']
+ candidates_idx = candidates_by_index(
+ data_model_input=model_input,
+ model=model,
+ cos_sim_threshold=cos_sim_threshold,
+ )
+ # graph of similar ids
+ similar_id_graph, _ = similar_index_connection_graph(candidates_idx)
+
+ for similar_id_group in similar_index_groups(similar_id_graph):
+ similar_id_group = list(similar_id_group)
+ similar_data = merged_data.loc[similar_id_group, :]
+ # keep first entry with max number occurrences, then number of
+ # associated objects, then length of entry
+ similar_data = similar_data.sort_values(
+ by=['num_occur', 'num_assoc_obj_ids', 'len'],
+ ascending=[False, False, False],
+ )
+ # merge information to first entry
+ data_idx = cast(PandasIndex, similar_data.index[0])
+ similar_data.at[data_idx, 'num_occur'] = similar_data['num_occur'].sum()
+ assoc_obj_ids = similar_data['assoc_obj_ids'].to_numpy()
+ assoc_obj_ids = np.concatenate(assoc_obj_ids)
+ assoc_obj_ids = np.unique(assoc_obj_ids)
+ similar_data.at[data_idx, 'assoc_obj_ids'] = assoc_obj_ids
+ similar_data.at[data_idx, 'num_assoc_obj_ids'] = len(assoc_obj_ids)
+ # remaining indices, should be removed
+ similar_id_group.remove(data_idx)
+ merged_similar_data = similar_data.drop(index=similar_id_group)
+ # update entry in main dataset, drop remaining entries
+ merged_data.update(merged_similar_data)
+ merged_data = merged_data.drop(index=similar_id_group)
+
+ logger.info('Similarity candidates merged successfully.')
+
+ return (merged_data,)
+
+
+#####################################################################
def build_embedding_map(
data: Series,
model: GermanSpacyModel | SentenceTransformer,
@@ -373,7 +446,7 @@ def list_cosSim_dupl_candidates(
save_candidates: bool = False,
saving_path: Path | None = None,
filename: str = 'CosSim-FilterCandidates',
- pipeline: BasePipeline | None = None,
+ pipeline: Pipeline | None = None,
) -> tuple[list[tuple[PandasIndex, PandasIndex]], dict[int, tuple[Embedding, str]]]:
"""providing an overview of candidates with a similarity score greater than
given threshold; more suitable for debugging purposes
@@ -465,53 +538,6 @@ def similar_ids_groups(
"""
-def merge_similarity_dupl(
- data: DataFrame,
- model: SentenceTransformer,
- cos_sim_threshold: float,
-) -> tuple[DataFrame]:
- logger.info('Start merging of similarity candidates...')
-
- # data
- merged_data = data.copy()
- model_input = merged_data['entry']
- candidates_idx = candidates_by_index(
- data_model_input=model_input,
- model=model,
- cos_sim_threshold=cos_sim_threshold,
- )
- # graph of similar ids
- similar_id_graph, _ = similar_index_connection_graph(candidates_idx)
-
- for similar_id_group in similar_index_groups(similar_id_graph):
- similar_id_group = list(similar_id_group)
- similar_data = merged_data.loc[similar_id_group, :]
- # keep first entry with max number occurrences, then number of
- # associated objects, then length of entry
- similar_data = similar_data.sort_values(
- by=['num_occur', 'num_assoc_obj_ids', 'len'],
- ascending=[False, False, False],
- )
- # merge information to first entry
- data_idx = cast(PandasIndex, similar_data.index[0])
- similar_data.at[data_idx, 'num_occur'] = similar_data['num_occur'].sum()
- assoc_obj_ids = similar_data['assoc_obj_ids'].to_numpy()
- assoc_obj_ids = np.concatenate(assoc_obj_ids)
- assoc_obj_ids = np.unique(assoc_obj_ids)
- similar_data.at[data_idx, 'assoc_obj_ids'] = assoc_obj_ids
- similar_data.at[data_idx, 'num_assoc_obj_ids'] = len(assoc_obj_ids)
- # remaining indices, should be removed
- similar_id_group.remove(data_idx)
- merged_similar_data = similar_data.drop(index=similar_id_group)
- # update entry in main dataset, drop remaining entries
- merged_data.update(merged_similar_data)
- merged_data = merged_data.drop(index=similar_id_group)
-
- logger.info('Similarity candidates merged successfully.')
-
- return (merged_data.copy(),)
-
-
# merge duplicates
def merge_similarity_dupl_old(
data: DataFrame,
diff --git a/src/lang_main/constants.py b/src/lang_main/constants.py
index 62e3b0d..40e37f4 100644
--- a/src/lang_main/constants.py
+++ b/src/lang_main/constants.py
@@ -24,13 +24,13 @@ PATH_TO_DATASET: Final[Path] = path_dataset_conf.resolve()
# if not PATH_TO_DATASET.exists():
# raise FileNotFoundError(f'Dataset path >>{PATH_TO_DATASET}<< does not exist.')
# ** control
-DO_PREPROCESSING: Final[bool] = CONFIG['control']['preprocessing']
+# DO_PREPROCESSING: Final[bool] = CONFIG['control']['preprocessing']
SKIP_PREPROCESSING: Final[bool] = CONFIG['control']['preprocessing_skip']
-DO_TOKEN_ANALYSIS: Final[bool] = CONFIG['control']['token_analysis']
+# DO_TOKEN_ANALYSIS: Final[bool] = CONFIG['control']['token_analysis']
SKIP_TOKEN_ANALYSIS: Final[bool] = CONFIG['control']['token_analysis_skip']
-DO_GRAPH_POSTPROCESSING: Final[bool] = CONFIG['control']['graph_postprocessing']
+# DO_GRAPH_POSTPROCESSING: Final[bool] = CONFIG['control']['graph_postprocessing']
SKIP_GRAPH_POSTPROCESSING: Final[bool] = CONFIG['control']['graph_postprocessing_skip']
-DO_TIME_ANALYSIS: Final[bool] = CONFIG['control']['time_analysis']
+# DO_TIME_ANALYSIS: Final[bool] = CONFIG['control']['time_analysis']
SKIP_TIME_ANALYSIS: Final[bool] = CONFIG['control']['time_analysis_skip']
# ** models
@@ -66,11 +66,11 @@ UNIQUE_CRITERION_FEATURE: Final[str] = CONFIG['time_analysis']['uniqueness'][
]
FEATURE_NAME_OBJ_ID: Final[str] = CONFIG['time_analysis']['uniqueness']['feature_name_obj_id']
# ** time_analysis.model_input
-MODEL_INPUT_FEATURES: Final[tuple[str]] = tuple(
+MODEL_INPUT_FEATURES: Final[tuple[str, ...]] = tuple(
CONFIG['time_analysis']['model_input']['input_features']
)
ACTIVITY_FEATURE: Final[str] = CONFIG['time_analysis']['model_input']['activity_feature']
-ACTIVITY_TYPES: Final[tuple[str]] = tuple(
+ACTIVITY_TYPES: Final[tuple[str, ...]] = tuple(
CONFIG['time_analysis']['model_input']['activity_types']
)
THRESHOLD_NUM_ACTIVITIES: Final[int] = CONFIG['time_analysis']['model_input'][
diff --git a/src/lang_main/io.py b/src/lang_main/io.py
index bc19a1a..9107d72 100644
--- a/src/lang_main/io.py
+++ b/src/lang_main/io.py
@@ -1,4 +1,3 @@
-import os
import pickle
import shutil
import tomllib
@@ -22,7 +21,7 @@ def create_saving_folder(
if overwrite_existing:
# overwrite if desired (deletes whole path and re-creates it)
shutil.rmtree(saving_path_folder)
- os.makedirs(saving_path_folder)
+ saving_path_folder.mkdir(parents=True)
else:
logger.info(
(
@@ -62,56 +61,14 @@ def load_pickle(
return obj
-# TODO: remove, too specialised for common application
-"""
-def filter_candidates_idx(
- data_model_input: Series,
- model: SentenceTransformer,
- cos_sim_threshold: float,
-) -> Iterator[tuple[PandasIndex, PandasIndex]]:
- common function to filter candidate indices based on cosine similarity
- using SentenceTransformer model in batch mode,
- feed of data as Series to retain information about indices of entries
-
- Parameters
- ----------
- data_model_input : Series
- containing indices and text entries to process
- model : SentenceTransformer
- necessary SentenceTransformer model to encode text entries
- cos_sim_threshold : float
- threshold for cosine similarity to filter candidates
-
- Yields
- ------
- Iterator[tuple[PandasIndex, PandasIndex]]
- index pairs which meet the cosine similarity threshold
-
-
- # embeddings
- batch = typing.cast(list[str],
- data_model_input.to_list())
- embds = typing.cast(Tensor,
- model.encode(
- batch,
- convert_to_numpy=False,
- convert_to_tensor=True,
- show_progress_bar=False,
- ))
-
- # cosine similarity
- cos_sim = typing.cast(
- npt.NDArray,
- sentence_transformers.util.cos_sim(embds, embds).numpy()
- )
- np.fill_diagonal(cos_sim, 0.)
- cos_sim = np.triu(cos_sim)
- cos_sim_idx = np.argwhere(cos_sim >= cos_sim_threshold)
-
- for idx_array in cos_sim_idx:
- idx_pair = typing.cast(
- tuple[np.int64, np.int64],
- tuple(data_model_input.index[idx] for idx in idx_array)
+def get_entry_point(
+ saving_path: Path,
+ filename: str,
+) -> Path:
+ entry_point_path = (saving_path / filename).with_suffix('.pkl')
+ if not entry_point_path.exists():
+ raise FileNotFoundError(
+ f'Could not find provided entry data under path: >>{entry_point_path}<<'
)
- yield idx_pair
-"""
+
+ return entry_point_path
diff --git a/src/lang_main/lang_main_config.toml b/src/lang_main/lang_main_config.toml
index c694e25..ef7dbbc 100644
--- a/src/lang_main/lang_main_config.toml
+++ b/src/lang_main/lang_main_config.toml
@@ -9,14 +9,12 @@ dataset = './01_2_Rohdaten_neu/Export4.csv'
#results = './results/Export7_trunc/'
#dataset = './01_03_Rohdaten_202403/Export7_trunc.csv'
+# only debugging features, production-ready pipelines should always
+# be fully executed
[control]
-preprocessing = true
preprocessing_skip = false
-token_analysis = false
token_analysis_skip = false
-graph_postprocessing = false
graph_postprocessing_skip = false
-time_analysis = false
time_analysis_skip = false
#[export_filenames]
@@ -42,9 +40,12 @@ criterion_feature = 'HObjektText'
feature_name_obj_id = 'ObjektID'
[time_analysis.model_input]
+# input_features = [
+# 'VorgangsTypName',
+# 'VorgangsArtText',
+# 'VorgangsBeschreibung',
+# ]
input_features = [
- 'VorgangsTypName',
- 'VorgangsArtText',
'VorgangsBeschreibung',
]
activity_feature = 'VorgangsTypName'
diff --git a/src/lang_main/pipelines/base.py b/src/lang_main/pipelines/base.py
index d1da557..2cc25a7 100644
--- a/src/lang_main/pipelines/base.py
+++ b/src/lang_main/pipelines/base.py
@@ -1,9 +1,14 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
from collections.abc import Callable
from pathlib import Path
-from typing import Any
+from typing import Any, Never, cast
+from typing_extensions import override
-from lang_main.loggers import logger_pipelines as logger
from lang_main.io import load_pickle, save_pickle
+from lang_main.loggers import logger_pipelines as logger
+from lang_main.types import ResultHandling
# ** pipelines to perform given actions on dataset in a customisable manner
@@ -12,7 +17,18 @@ class NoPerformableActionError(Exception):
"""Error describing that no action is available in the current pipeline"""
-class BasePipeline:
+class WrongActionTypeError(Exception):
+ """Error raised if added action type is not supported by corresponding pipeline"""
+
+
+class OutputInPipelineContainerError(Exception):
+ """Error raised if an output was detected by one of the performed
+ actions in a PipelineContainer. Each action in a PipelineContainer is itself a
+ procedure which does not have any parameters or return values and should therefore not
+ return any values."""
+
+
+class BasePipeline(ABC):
def __init__(
self,
name: str,
@@ -25,18 +41,12 @@ class BasePipeline:
self.name = name
# working directory for pipeline == output path
self.working_dir = working_dir
- # if not self.working_dir.exists():
- # self.working_dir.mkdir(parents=True)
# container for actions to perform during pass
self.actions: list[Callable] = []
self.action_names: list[str] = []
- self.actions_kwargs: list[dict[str, Any]] = []
- self.is_save_result: list[bool] = []
# progress tracking, start at 1
self.curr_proc_idx: int = 1
- # intermediate result
- self._intermediate_result: Any | None = None
def __repr__(self) -> str:
return (
@@ -44,15 +54,132 @@ class BasePipeline:
f'working dir: {self.working_dir}, contents: {self.action_names})'
)
- @property
- def intermediate_result(self) -> Any:
- return self._intermediate_result
+ def panic_wrong_action_type(
+ self,
+ action: Any,
+ compatible_type: str,
+ ) -> Never:
+ raise WrongActionTypeError(
+ (
+ f'Action must be of type {compatible_type}, '
+ f'but is of type >>{type(action)}<<.'
+ )
+ )
+ def prep_run(self) -> None:
+ logger.info('Starting pipeline >>%s<<...', self.name)
+ # progress tracking
+ self.curr_proc_idx = 1
+ # check if performable actions available
+ if len(self.actions) == 0:
+ raise NoPerformableActionError(
+ 'The pipeline does not contain any performable actions.'
+ )
+
+ def post_run(self) -> None:
+ logger.info(
+ 'Processing pipeline >>%s<< successfully ended after %d steps.',
+ self.name,
+ (self.curr_proc_idx - 1),
+ )
+
+ @abstractmethod
+ def add(self) -> None: ...
+
+ @abstractmethod
+ def logic(self) -> None: ...
+
+ def run(self, *args, **kwargs) -> Any:
+ self.prep_run()
+ ret = self.logic(*args, **kwargs)
+ self.post_run()
+ return ret
+
+
+class PipelineContainer(BasePipeline):
+ def __init__(
+ self,
+ name: str,
+ working_dir: Path,
+ ) -> None:
+ super().__init__(name=name, working_dir=working_dir)
+
+ self.action_skip: list[bool] = []
+
+ @override
+ def add(
+ self,
+ action: Callable,
+ skip: bool = False,
+ ) -> None:
+ if isinstance(action, Callable):
+ self.actions.append(action)
+ self.action_names.append(action.__name__)
+ self.action_skip.append(skip)
+ else:
+ self.panic_wrong_action_type(action=action, compatible_type=Callable.__name__)
+
+ @override
+ def logic(self) -> None:
+ for idx, (action, action_name) in enumerate(zip(self.actions, self.action_names)):
+ # loading
+ if self.action_skip[idx]:
+ logger.info('[No Calculation] Skipping >>%s<<...', action_name)
+ self.curr_proc_idx += 1
+ continue
+ # calculation
+ ret = action()
+ if ret is not None:
+ raise OutputInPipelineContainerError(
+ (
+ f'Output in PipelineContainers not allowed. Action {action_name} '
+ f'returned values in Container {self.name}.'
+ )
+ )
+ # processing tracking
+ self.curr_proc_idx += 1
+
+
+class Pipeline(BasePipeline):
+ def __init__(
+ self,
+ name: str,
+ working_dir: Path,
+ ) -> None:
+ # init base class
+ super().__init__(name=name, working_dir=working_dir)
+
+ # name of pipeline
+ self.name = name
+ # working directory for pipeline == output path
+ self.working_dir = working_dir
+ # if not self.working_dir.exists():
+ # self.working_dir.mkdir(parents=True)
+
+ # container for actions to perform during pass
+ self.actions_kwargs: list[dict[str, Any]] = []
+ self.save_results: ResultHandling = []
+ self.load_results: ResultHandling = []
+ # intermediate result
+ self._intermediate_result: tuple[Any, ...] | None = None
+
+ def __repr__(self) -> str:
+ return (
+ f'{self.__class__.__name__}(name: {self.name}, '
+ f'working dir: {self.working_dir}, contents: {self.action_names})'
+ )
+
+ # @property
+ # def intermediate_result(self) -> tuple[Any, ...] | None:
+ # return self._intermediate_result
+ @override
def add(
self,
action: Callable,
action_kwargs: dict[str, Any] = {},
save_result: bool = False,
+ load_result: bool = False,
+ filename: str | None = None,
) -> None:
# check explicitly for function type
# if isinstance(action, FunctionType):
@@ -60,11 +187,10 @@ class BasePipeline:
self.actions.append(action)
self.action_names.append(action.__name__)
self.actions_kwargs.append(action_kwargs.copy())
- self.is_save_result.append(save_result)
+ self.save_results.append((save_result, filename))
+ self.load_results.append((load_result, filename))
else:
- raise TypeError(
- f'Action must be custom function, but is of type >>{type(action)}<<.'
- )
+ self.panic_wrong_action_type(action=action, compatible_type=Callable.__name__)
# TODO: add multiple entries by utilising simple add method
"""
@@ -88,57 +214,84 @@ class BasePipeline:
f"but is of type >>{type(action)}<<."))
"""
- def save_curr_result(
+ def get_result_path(
self,
- filename: str,
+ action_idx: int,
+ filename: str | None,
+ ) -> tuple[Path, str]:
+ action_name = self.action_names[action_idx]
+ if filename is None:
+ target_filename = f'Pipe-{self.name}_Step-{self.curr_proc_idx}_{action_name}'
+ else:
+ target_filename = filename
+ target_path = self.working_dir.joinpath(target_filename).with_suffix('.pkl')
+ return target_path, action_name
+
+ def load_step(
+ self,
+ action_idx: int,
+ filename: str | None,
+ ) -> tuple[Any, ...]:
+ target_path, action_name = self.get_result_path(action_idx, filename)
+
+ if not target_path.exists():
+ raise FileNotFoundError(
+ (
+ f'No intermediate results for action >>{action_name}<< '
+ f'under >>{target_path}<< found'
+ )
+ )
+ # results should be tuple, but that is not guaranteed
+ result_loaded = cast(tuple[Any, ...], load_pickle(target_path))
+ if not isinstance(result_loaded, tuple):
+ raise TypeError(f'Loaded results must be tuple, not {type(result_loaded)}')
+
+ return result_loaded
+
+ def save_step(
+ self,
+ action_idx: int,
+ filename: str | None,
) -> None:
- target_filename = f'Pipe-{self.name}_Step-{self.curr_proc_idx}_' + filename
- target_path = self.working_dir.joinpath(target_filename)
- target_path = target_path.with_suffix('.pkl')
+ # target_filename = f'Pipe-{self.name}_Step-{self.curr_proc_idx}_' + filename
+ # target_path = self.working_dir.joinpath(target_filename)
+ # target_path = target_path.with_suffix('.pkl')
+ target_path, _ = self.get_result_path(action_idx, filename)
# saving file locally
save_pickle(obj=self._intermediate_result, path=target_path)
- def load_intermediate_result(
- self,
- saving_path: str,
- filename: str,
- ) -> tuple[Any, ...]:
- target_path = Path(saving_path + filename).with_suffix('.pkl')
- # loading DataFrame or Series from pickle
- data = load_pickle(target_path)
-
- return data
-
- def prep_run(self) -> None:
- logger.info('Starting processing pipeline >>%s<<...', self.name)
- # progress tracking
- self.curr_proc_idx = 1
- # check if performable actions available
- if len(self.actions) == 0:
- raise NoPerformableActionError(
- 'The pipeline does not contain any performable actions.'
- )
-
- def run(
+ @override
+ def logic(
self,
starting_values: tuple[Any, ...],
) -> tuple[Any, ...]:
- # prepare start
- self.prep_run()
-
for idx, (action, action_kwargs) in enumerate(zip(self.actions, self.actions_kwargs)):
+ # loading
+ if self.load_results[idx][0]:
+ filename = self.load_results[idx][1]
+ ret = self.load_step(action_idx=idx, filename=filename)
+ logger.info(
+ '[No Calculation] Loaded result for action >>%s<< successfully',
+ self.action_names[idx],
+ )
+ self.curr_proc_idx += 1
+ continue
+ # calculation
if idx == 0:
ret = action(*starting_values, **action_kwargs)
else:
ret = action(*ret, **action_kwargs)
+
+ if not isinstance(ret, tuple):
+ ret = (ret,)
+ ret = cast(tuple[Any, ...], ret)
# save intermediate result
self._intermediate_result = ret
- # check if result should be saved locally
- if self.is_save_result[idx]:
- self.save_curr_result(filename=self.action_names[idx])
+ # saving result locally, always save last action
+ if self.save_results[idx][0] or idx == (len(self.actions) - 1):
+ filename = self.save_results[idx][1]
+ self.save_step(action_idx=idx, filename=filename)
# processing tracking
self.curr_proc_idx += 1
- logger.info('Processing pipeline >>%s<< successfully ended.', self.name)
-
return ret
diff --git a/src/lang_main/pipelines/predefined.py b/src/lang_main/pipelines/predefined.py
index b40a456..a130532 100644
--- a/src/lang_main/pipelines/predefined.py
+++ b/src/lang_main/pipelines/predefined.py
@@ -1,9 +1,11 @@
+from lang_main.analysis import graphs
from lang_main.analysis.preprocessing import (
analyse_feature,
clean_string_slim,
entry_wise_cleansing,
load_raw_data,
merge_similarity_dupl,
+ numeric_pre_filter_feature,
remove_duplicates,
remove_NA,
)
@@ -23,40 +25,50 @@ from lang_main.constants import (
SAVE_PATH_FOLDER,
SPCY_MODEL,
STFR_MODEL,
+ THRESHOLD_AMOUNT_CHARACTERS,
+ THRESHOLD_EDGE_WEIGHT,
THRESHOLD_NUM_ACTIVITIES,
THRESHOLD_SIMILARITY,
THRESHOLD_TIMELINE_SIMILARITY,
THRESHOLD_UNIQUE_TEXTS,
UNIQUE_CRITERION_FEATURE,
)
-from lang_main.pipelines.base import BasePipeline
+from lang_main.pipelines.base import Pipeline
+from lang_main.types import EntryPoints
+
# ** pipeline configuration
# ** target feature preparation
-pipe_target_feat = BasePipeline(name='TargetFeature', working_dir=SAVE_PATH_FOLDER)
-pipe_target_feat.add(
- load_raw_data,
- {
- 'date_cols': DATE_COLS,
- },
-)
-pipe_target_feat.add(remove_duplicates)
-pipe_target_feat.add(remove_NA, save_result=True)
-pipe_target_feat.add(
- entry_wise_cleansing,
- {
- 'target_feature': 'VorgangsBeschreibung',
- 'cleansing_func': clean_string_slim,
- },
- save_result=True,
-)
-pipe_target_feat.add(
- analyse_feature,
- {
- 'target_feature': 'VorgangsBeschreibung',
- },
- save_result=True,
-)
+def build_base_target_feature_pipe() -> Pipeline:
+ pipe_target_feat = Pipeline(name='TargetFeature', working_dir=SAVE_PATH_FOLDER)
+ pipe_target_feat.add(
+ load_raw_data,
+ {
+ 'date_cols': DATE_COLS,
+ },
+ )
+ pipe_target_feat.add(remove_duplicates)
+ pipe_target_feat.add(remove_NA, save_result=True)
+ pipe_target_feat.add(
+ entry_wise_cleansing,
+ {
+ 'target_feature': 'VorgangsBeschreibung',
+ 'cleansing_func': clean_string_slim,
+ },
+ save_result=True,
+ filename=EntryPoints.TIMELINE,
+ )
+ pipe_target_feat.add(
+ analyse_feature,
+ {
+ 'target_feature': 'VorgangsBeschreibung',
+ },
+ save_result=True,
+ )
+
+ return pipe_target_feat
+
+
# output: DataFrame containing target feature with
# number of occurrences and associated ObjectIDs
@@ -81,68 +93,114 @@ pipe_target_feat.add(
# save_result=True,
# )
+
# ** Merge duplicates
-pipe_merge = BasePipeline(name='Merge_Duplicates', working_dir=SAVE_PATH_FOLDER)
-# pipe_merge.add(merge_similarity_dupl, save_result=True)
-pipe_merge.add(
- merge_similarity_dupl,
- {
- 'model': STFR_MODEL,
- 'cos_sim_threshold': THRESHOLD_SIMILARITY,
- },
- save_result=True,
-)
+def build_merge_duplicates_pipe() -> Pipeline:
+ pipe_merge = Pipeline(name='Merge_Duplicates', working_dir=SAVE_PATH_FOLDER)
+ # pipe_merge.add(merge_similarity_dupl, save_result=True)
+ pipe_merge.add(
+ numeric_pre_filter_feature,
+ {
+ 'feature': 'len',
+ 'bound_lower': THRESHOLD_AMOUNT_CHARACTERS,
+ 'bound_upper': None,
+ },
+ )
+ pipe_merge.add(
+ merge_similarity_dupl,
+ {
+ 'model': STFR_MODEL,
+ 'cos_sim_threshold': THRESHOLD_SIMILARITY,
+ },
+ save_result=True,
+ filename=EntryPoints.TOKEN_ANALYSIS,
+ )
+
+ return pipe_merge
+
# ** token analysis
-pipe_token_analysis = BasePipeline(name='Token_Analysis', working_dir=SAVE_PATH_FOLDER)
-pipe_token_analysis.add(
- build_token_graph,
- {
- 'model': SPCY_MODEL,
- 'target_feature': 'entry',
- 'weights_feature': 'num_occur',
- 'batch_idx_feature': 'batched_idxs',
- 'build_map': True,
- 'batch_size_model': 50,
- },
- save_result=True,
-)
+def build_tk_graph_pipe() -> Pipeline:
+ pipe_token_analysis = Pipeline(name='Token_Analysis', working_dir=SAVE_PATH_FOLDER)
+ pipe_token_analysis.add(
+ build_token_graph,
+ {
+ 'model': SPCY_MODEL,
+ 'target_feature': 'entry',
+ 'weights_feature': 'num_occur',
+ 'batch_idx_feature': 'batched_idxs',
+ 'build_map': False,
+ 'batch_size_model': 50,
+ },
+ save_result=True,
+ filename=EntryPoints.TK_GRAPH_POST,
+ )
+
+ return pipe_token_analysis
+
+
+def build_tk_graph_post_pipe() -> Pipeline:
+ pipe_graph_postprocessing = Pipeline(
+ name='Graph_Postprocessing', working_dir=SAVE_PATH_FOLDER
+ )
+ pipe_graph_postprocessing.add(
+ graphs.filter_graph_by_edge_weight,
+ {
+ 'bound_lower': THRESHOLD_EDGE_WEIGHT,
+ 'bound_upper': None,
+ },
+ )
+ pipe_graph_postprocessing.add(
+ graphs.filter_graph_by_node_degree,
+ {
+ 'bound_lower': 1,
+ 'bound_upper': None,
+ },
+ save_result=True,
+ filename=EntryPoints.TK_GRAPH_ANALYSIS,
+ )
+
+ return pipe_graph_postprocessing
# ** timeline analysis
-pipe_timeline = BasePipeline(name='Timeline_Analysis', working_dir=SAVE_PATH_FOLDER)
-pipe_timeline.add(
- remove_non_relevant_obj_ids,
- {
- 'thresh_unique_feat_per_id': THRESHOLD_UNIQUE_TEXTS,
- 'feature_uniqueness': UNIQUE_CRITERION_FEATURE,
- 'feature_obj_id': FEATURE_NAME_OBJ_ID,
- },
- save_result=True,
-)
-pipe_timeline.add(
- generate_model_input,
- {
- 'target_feature_name': 'nlp_model_input',
- 'model_input_features': MODEL_INPUT_FEATURES,
- },
-)
-pipe_timeline.add(
- filter_activities_per_obj_id,
- {
- 'activity_feature': ACTIVITY_FEATURE,
- 'relevant_activity_types': ACTIVITY_TYPES,
- 'feature_obj_id': FEATURE_NAME_OBJ_ID,
- 'threshold_num_activities': THRESHOLD_NUM_ACTIVITIES,
- },
-)
-pipe_timeline.add(
- get_timeline_candidates,
- {
- 'model': STFR_MODEL,
- 'cos_sim_threshold': THRESHOLD_TIMELINE_SIMILARITY,
- 'feature_obj_id': FEATURE_NAME_OBJ_ID,
- 'model_input_feature': 'nlp_model_input',
- },
- save_result=True,
-)
+def build_timeline_pipe() -> Pipeline:
+ pipe_timeline = Pipeline(name='Timeline_Analysis', working_dir=SAVE_PATH_FOLDER)
+ pipe_timeline.add(
+ remove_non_relevant_obj_ids,
+ {
+ 'thresh_unique_feat_per_id': THRESHOLD_UNIQUE_TEXTS,
+ 'feature_uniqueness': UNIQUE_CRITERION_FEATURE,
+ 'feature_obj_id': FEATURE_NAME_OBJ_ID,
+ },
+ save_result=True,
+ )
+ pipe_timeline.add(
+ generate_model_input,
+ {
+ 'target_feature_name': 'nlp_model_input',
+ 'model_input_features': MODEL_INPUT_FEATURES,
+ },
+ )
+ pipe_timeline.add(
+ filter_activities_per_obj_id,
+ {
+ 'activity_feature': ACTIVITY_FEATURE,
+ 'relevant_activity_types': ACTIVITY_TYPES,
+ 'feature_obj_id': FEATURE_NAME_OBJ_ID,
+ 'threshold_num_activities': THRESHOLD_NUM_ACTIVITIES,
+ },
+ )
+ pipe_timeline.add(
+ get_timeline_candidates,
+ {
+ 'model': STFR_MODEL,
+ 'cos_sim_threshold': THRESHOLD_TIMELINE_SIMILARITY,
+ 'feature_obj_id': FEATURE_NAME_OBJ_ID,
+ 'model_input_feature': 'nlp_model_input',
+ },
+ save_result=True,
+ filename=EntryPoints.TIMELINE_POST,
+ )
+
+ return pipe_timeline
diff --git a/src/lang_main/types.py b/src/lang_main/types.py
index 731a630..baf2dc1 100644
--- a/src/lang_main/types.py
+++ b/src/lang_main/types.py
@@ -17,8 +17,20 @@ class LoggingLevels(enum.IntEnum):
# ** devices
class STFRDeviceTypes(enum.StrEnum):
- CPU = 'cpu'
- GPU = 'cuda'
+ CPU = enum.auto()
+ GPU = enum.auto()
+
+
+# ** pipelines
+ResultHandling: TypeAlias = list[tuple[bool, str | None]]
+
+
+class EntryPoints(enum.StrEnum):
+ TIMELINE = 'TIMELINE'
+ TIMELINE_POST = 'TIMELINE_POSTPROCESSING'
+ TK_GRAPH_POST = 'TK-GRAPH_POSTPROCESSING'
+ TK_GRAPH_ANALYSIS = 'TK-GRAPH_ANALYSIS'
+ TOKEN_ANALYSIS = 'TOKEN_ANALYSIS'
# ** datasets
diff --git a/test-notebooks/misc.ipynb b/test-notebooks/misc.ipynb
new file mode 100644
index 0000000..920cde4
--- /dev/null
+++ b/test-notebooks/misc.ipynb
@@ -0,0 +1,1687 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b9ec018c-dffc-420f-85b4-b792e3aebb17",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2024-06-19 11:10:00 +0000 | io:INFO | Loaded TOML config file successfully.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+ " _torch_pytree._register_pytree_node(\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "from lang_main.analysis.preprocessing import numeric_pre_filter_feature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "492f6106-b60b-47f3-bb82-7f831028a8d9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = {\n",
+ " 'col1': range(10),\n",
+ " 'col2': range(10),\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "966a6e04-6e4c-4962-a3b1-51370c1e1571",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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