improved dashboard, fixed language tags, tests graph plotting
This commit is contained in:
Florian Förster
parent
fb4437a3a2
commit
2656780907
176
pdm.lock
generated
176
pdm.lock
generated
@ -2,10 +2,62 @@
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# It is not intended for manual editing.
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"pandas>=0.25",
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"toolz",
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@ -136,7 +188,7 @@ name = "attrs"
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@ -187,7 +239,7 @@ name = "blinker"
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@ -231,7 +283,7 @@ name = "certifi"
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@ -276,7 +328,7 @@ name = "charset-normalizer"
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@ -317,7 +369,7 @@ name = "click"
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@ -342,7 +394,7 @@ name = "colorama"
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@ -402,7 +454,7 @@ name = "dash"
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@ -426,7 +478,7 @@ files = [
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@ -437,7 +489,7 @@ name = "dash-cytoscape"
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@ -449,7 +501,7 @@ files = [
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@ -459,7 +511,7 @@ files = [
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@ -647,7 +699,7 @@ name = "idna"
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@ -658,7 +710,7 @@ name = "importlib-metadata"
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@ -767,7 +819,7 @@ name = "itsdangerous"
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@ -792,7 +844,7 @@ name = "jinja2"
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@ -839,7 +891,7 @@ name = "jsonschema"
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@ -856,7 +908,7 @@ name = "jsonschema-specifications"
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@ -1160,7 +1212,7 @@ name = "markupsafe"
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@ -1325,7 +1377,7 @@ name = "nest-asyncio"
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@ -1336,7 +1388,7 @@ name = "networkx"
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@ -1361,7 +1413,7 @@ name = "numpy"
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@ -1566,7 +1618,7 @@ name = "pandas"
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||||
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||||
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||||
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@ -1701,7 +1753,7 @@ name = "plotly"
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||||
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||||
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||||
@ -1890,12 +1942,22 @@ files = [
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||||
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||||
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||||
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||||
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|
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@ -1919,7 +1981,7 @@ files = [
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||||
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||||
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||||
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|
||||
{file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
|
||||
{file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
|
||||
@ -2041,7 +2103,7 @@ name = "referencing"
|
||||
version = "0.35.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "JSON Referencing + Python"
|
||||
groups = ["notebooks"]
|
||||
groups = ["notebooks", "trails", "trials"]
|
||||
dependencies = [
|
||||
"attrs>=22.2.0",
|
||||
"rpds-py>=0.7.0",
|
||||
@ -2096,7 +2158,7 @@ name = "requests"
|
||||
version = "2.31.0"
|
||||
requires_python = ">=3.7"
|
||||
summary = "Python HTTP for Humans."
|
||||
groups = ["default", "notebooks", "trials"]
|
||||
groups = ["default", "notebooks"]
|
||||
dependencies = [
|
||||
"certifi>=2017.4.17",
|
||||
"charset-normalizer<4,>=2",
|
||||
@ -2112,7 +2174,7 @@ files = [
|
||||
name = "retrying"
|
||||
version = "1.3.4"
|
||||
summary = "Retrying"
|
||||
groups = ["trials"]
|
||||
groups = ["default"]
|
||||
dependencies = [
|
||||
"six>=1.7.0",
|
||||
]
|
||||
@ -2151,7 +2213,7 @@ name = "rpds-py"
|
||||
version = "0.18.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Python bindings to Rust's persistent data structures (rpds)"
|
||||
groups = ["notebooks"]
|
||||
groups = ["notebooks", "trails", "trials"]
|
||||
files = [
|
||||
{file = "rpds_py-0.18.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:6b5ff7e1d63a8281654b5e2896d7f08799378e594f09cf3674e832ecaf396ce8"},
|
||||
{file = "rpds_py-0.18.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8927638a4d4137a289e41d0fd631551e89fa346d6dbcfc31ad627557d03ceb6d"},
|
||||
@ -2302,12 +2364,29 @@ files = [
|
||||
{file = "scikit_learn-1.4.2-cp312-cp312-win_amd64.whl", hash = "sha256:d762070980c17ba3e9a4a1e043ba0518ce4c55152032f1af0ca6f39b376b5928"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "scikit-network"
|
||||
version = "0.32.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Graph algorithms"
|
||||
groups = ["trails", "trials"]
|
||||
dependencies = [
|
||||
"numpy>=1.22.4",
|
||||
"scipy>=1.7.3",
|
||||
]
|
||||
files = [
|
||||
{file = "scikit_network-0.32.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6228326cc5a813f4a7c031b64f1276d9cc1d5ccff082e62fb176f1e53ae2cc46"},
|
||||
{file = "scikit_network-0.32.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d634aab1e379702134c877c8c4a6803b7d7ba4efc103198474b7e896ec37e242"},
|
||||
{file = "scikit_network-0.32.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e32e247728815750b12b950279cae6b12041a0c4bf95d1accdd273b0c1a6ea1"},
|
||||
{file = "scikit_network-0.32.1-cp311-cp311-win_amd64.whl", hash = "sha256:c7016dce37120aa527fcff4254985360f164306ed060c496a6cf296a6d0fd2c3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.13.0"
|
||||
requires_python = ">=3.9"
|
||||
summary = "Fundamental algorithms for scientific computing in Python"
|
||||
groups = ["default"]
|
||||
groups = ["default", "trails", "trials"]
|
||||
dependencies = [
|
||||
"numpy<2.3,>=1.22.4",
|
||||
]
|
||||
@ -2364,7 +2443,7 @@ name = "setuptools"
|
||||
version = "69.5.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Easily download, build, install, upgrade, and uninstall Python packages"
|
||||
groups = ["default", "trials"]
|
||||
groups = ["default"]
|
||||
files = [
|
||||
{file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
|
||||
{file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
|
||||
@ -2375,7 +2454,7 @@ name = "six"
|
||||
version = "1.16.0"
|
||||
requires_python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||
summary = "Python 2 and 3 compatibility utilities"
|
||||
groups = ["default", "notebooks", "trials"]
|
||||
groups = ["default", "notebooks", "trails", "trials"]
|
||||
files = [
|
||||
{file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
|
||||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||
@ -2635,7 +2714,7 @@ name = "tenacity"
|
||||
version = "8.3.0"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Retry code until it succeeds"
|
||||
groups = ["trials"]
|
||||
groups = ["default"]
|
||||
files = [
|
||||
{file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
|
||||
{file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
|
||||
@ -2790,6 +2869,17 @@ files = [
|
||||
{file = "tokenizers-0.15.2.tar.gz", hash = "sha256:e6e9c6e019dd5484be5beafc775ae6c925f4c69a3487040ed09b45e13df2cb91"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "toolz"
|
||||
version = "0.12.1"
|
||||
requires_python = ">=3.7"
|
||||
summary = "List processing tools and functional utilities"
|
||||
groups = ["trails", "trials"]
|
||||
files = [
|
||||
{file = "toolz-0.12.1-py3-none-any.whl", hash = "sha256:d22731364c07d72eea0a0ad45bafb2c2937ab6fd38a3507bf55eae8744aa7d85"},
|
||||
{file = "toolz-0.12.1.tar.gz", hash = "sha256:ecca342664893f177a13dac0e6b41cbd8ac25a358e5f215316d43e2100224f4d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.3.0"
|
||||
@ -2941,7 +3031,7 @@ name = "typing-extensions"
|
||||
version = "4.12.2"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
groups = ["default", "notebooks", "trials"]
|
||||
groups = ["default", "notebooks"]
|
||||
files = [
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
@ -2952,7 +3042,7 @@ name = "tzdata"
|
||||
version = "2024.1"
|
||||
requires_python = ">=2"
|
||||
summary = "Provider of IANA time zone data"
|
||||
groups = ["default"]
|
||||
groups = ["default", "trails", "trials"]
|
||||
files = [
|
||||
{file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
|
||||
{file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
|
||||
@ -2974,7 +3064,7 @@ name = "urllib3"
|
||||
version = "2.2.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
groups = ["default", "notebooks", "trials"]
|
||||
groups = ["default", "notebooks"]
|
||||
files = [
|
||||
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||
@ -3063,7 +3153,7 @@ name = "werkzeug"
|
||||
version = "3.0.3"
|
||||
requires_python = ">=3.8"
|
||||
summary = "The comprehensive WSGI web application library."
|
||||
groups = ["trials"]
|
||||
groups = ["default"]
|
||||
dependencies = [
|
||||
"MarkupSafe>=2.1.1",
|
||||
]
|
||||
@ -3088,7 +3178,7 @@ name = "zipp"
|
||||
version = "3.18.2"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Backport of pathlib-compatible object wrapper for zip files"
|
||||
groups = ["trials"]
|
||||
groups = ["default"]
|
||||
files = [
|
||||
{file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
|
||||
{file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
|
||||
|
||||
@ -13,6 +13,9 @@ dependencies = [
|
||||
"numpy>=1.26.4",
|
||||
"pip>=24.0",
|
||||
"typing-extensions>=4.12.2",
|
||||
"plotly>=5.22.0",
|
||||
"dash>=2.17.0",
|
||||
"dash-cytoscape>=1.0.1",
|
||||
]
|
||||
requires-python = ">=3.11"
|
||||
readme = "README.md"
|
||||
@ -31,10 +34,13 @@ notebooks = [
|
||||
"jupyterlab>=4.2.0",
|
||||
"ipywidgets>=8.1.2",
|
||||
]
|
||||
trials = [
|
||||
"plotly>=5.22.0",
|
||||
"dash>=2.17.0",
|
||||
"dash-cytoscape>=1.0.1",
|
||||
# Alph relies on Pygraphviz which again relies on Graphviz, to sucessfully build
|
||||
# Pygraphviz the MSVC toolchain should be installed on the target system if it is Windows
|
||||
# (Linux not tested yet). The compiler needs additional information about the location of Graphviz
|
||||
# files. This information is provided by extra options.
|
||||
# --config-setting="--global-option=build_ext" --config-setting="--global-option=-IC:\Program Files\Graphviz\include" --config-setting="--global-option=-LC:\Program Files\Graphviz\lib"
|
||||
trails = [
|
||||
"alph[graphviz]>=0.4.10",
|
||||
]
|
||||
|
||||
[tool.ruff]
|
||||
|
||||
@ -30,11 +30,11 @@ from lang_main.types import (
|
||||
)
|
||||
|
||||
# ** build pipelines
|
||||
pipe_merge = build_merge_duplicates_pipe()
|
||||
pipe_target_feat = build_base_target_feature_pipe()
|
||||
pipe_timeline = build_timeline_pipe()
|
||||
pipe_merge = build_merge_duplicates_pipe()
|
||||
pipe_token_analysis = build_tk_graph_pipe()
|
||||
pipe_graph_postprocessing = build_tk_graph_post_pipe()
|
||||
pipe_timeline = build_timeline_pipe()
|
||||
|
||||
|
||||
# ** preprocessing pipeline
|
||||
@ -76,8 +76,6 @@ def run_graph_postprocessing() -> None:
|
||||
# filter graph by edge weight and remove single nodes (no connection)
|
||||
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
|
||||
)
|
||||
|
||||
@ -16,8 +16,8 @@ target = '../results/test_20240529/Pipe-Token_Analysis_Step-1_build_token_graph.
|
||||
p = Path(target).resolve()
|
||||
ret = lang_main.io.load_pickle(p)
|
||||
tk_graph = cast(graphs.TokenGraph, ret[0])
|
||||
tk_graph_filtered = tk_graph.filter_by_edge_weight(150, None)
|
||||
tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
|
||||
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)
|
||||
cyto_data_base, weight_data = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
|
||||
|
||||
MIN_WEIGHT = weight_data['min']
|
||||
@ -235,8 +235,16 @@ def update_edge_weight(weight_min, weight_max):
|
||||
weight_min = MIN_WEIGHT
|
||||
if weight_max is None:
|
||||
weight_max = MAX_WEIGHT
|
||||
tk_graph_filtered = tk_graph.filter_by_edge_weight(weight_min, weight_max)
|
||||
tk_graph_filtered = tk_graph_filtered.filter_by_node_degree(1, None)
|
||||
tk_graph_filtered = graphs.filter_graph_by_edge_weight(
|
||||
tk_graph,
|
||||
weight_min,
|
||||
weight_max,
|
||||
)
|
||||
tk_graph_filtered = graphs.filter_graph_by_node_degree(
|
||||
tk_graph_filtered,
|
||||
1,
|
||||
None,
|
||||
)
|
||||
cyto_data, _ = graphs.convert_graph_to_cytoscape(tk_graph_filtered)
|
||||
return cyto_data
|
||||
|
||||
|
||||
@ -2,10 +2,9 @@ import time
|
||||
import webbrowser
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
from typing import cast
|
||||
from typing import Any, Final, cast
|
||||
|
||||
import dash_cytoscape as cyto
|
||||
import pandas as pd
|
||||
import plotly.express as px
|
||||
from dash import (
|
||||
Dash,
|
||||
@ -19,45 +18,33 @@ from dash import (
|
||||
)
|
||||
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
|
||||
import lang_main.io
|
||||
from lang_main.analysis import graphs, tokens
|
||||
from lang_main.constants import SPCY_MODEL
|
||||
|
||||
# df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
|
||||
from lang_main.types import ObjectID, TimelineCandidates
|
||||
|
||||
# ** 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()
|
||||
(data,) = cast(tuple[DataFrame], lang_main.io.load_pickle(p_df))
|
||||
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]
|
||||
cands, texts = cast(
|
||||
tuple[TimelineCandidates, dict[ObjectID, str]], lang_main.io.load_pickle(p_tl)
|
||||
)
|
||||
|
||||
# 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 = [
|
||||
TABLE_FEATS: Final[list[str]] = [
|
||||
'ErstellungsDatum',
|
||||
'ErledigungsDatum',
|
||||
'VorgangsTypName',
|
||||
'VorgangsBeschreibung',
|
||||
]
|
||||
table_feats_dates = [
|
||||
TABLE_FEATS_DATES: Final[list[str]] = [
|
||||
'ErstellungsDatum',
|
||||
'ErledigungsDatum',
|
||||
]
|
||||
|
||||
# ** figure config
|
||||
markers = {
|
||||
MARKERS: Final[dict[str, Any]] = {
|
||||
'size': 12,
|
||||
'color': 'yellow',
|
||||
'line': {
|
||||
@ -65,15 +52,15 @@ markers = {
|
||||
'color': 'red',
|
||||
},
|
||||
}
|
||||
hover_data = {
|
||||
HOVER_DATA: Final[dict[str, Any]] = {
|
||||
'ErstellungsDatum': '|%d.%m.%Y',
|
||||
'VorgangsBeschreibung': True,
|
||||
}
|
||||
|
||||
# ** graphs
|
||||
# ** graph
|
||||
target = '../results/test_20240529/Pipe-Token_Analysis_Step-1_build_token_graph.pkl'
|
||||
p = Path(target).resolve()
|
||||
ret = load_pickle(p)
|
||||
ret = lang_main.io.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)
|
||||
@ -142,14 +129,12 @@ my_stylesheet = [
|
||||
# {'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.Button('Trigger Candidate Graph', id='graph-build-btn'),
|
||||
dcc.Store(id='graph-store', storage_type='memory'),
|
||||
dcc.Store(id='graph-store-cyto-curr_cands', storage_type='memory'),
|
||||
html.Div(id='output'),
|
||||
html.Div(
|
||||
[
|
||||
@ -184,7 +169,7 @@ graph_layout = html.Div(
|
||||
[
|
||||
html.H3('Graph Filter'),
|
||||
dcc.Input(
|
||||
id='weight_min',
|
||||
id='graph-weight_min',
|
||||
type='number',
|
||||
min=MIN_WEIGHT,
|
||||
max=MAX_WEIGHT,
|
||||
@ -194,7 +179,7 @@ graph_layout = html.Div(
|
||||
style={'width': '40%'},
|
||||
),
|
||||
dcc.Input(
|
||||
id='weight_max',
|
||||
id='graph-weight_max',
|
||||
type='number',
|
||||
min=MIN_WEIGHT,
|
||||
max=MAX_WEIGHT,
|
||||
@ -204,7 +189,7 @@ graph_layout = html.Div(
|
||||
style={'width': '40%'},
|
||||
),
|
||||
html.H3('Graph'),
|
||||
html.Button('Re-Layout', id='trigger_relayout'),
|
||||
html.Button('Re-Layout', id='graph-trigger_relayout'),
|
||||
html.Div(
|
||||
[
|
||||
cyto.Cytoscape(
|
||||
@ -230,6 +215,12 @@ graph_layout = html.Div(
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
# ** app
|
||||
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
|
||||
app = Dash(__name__, external_stylesheets=external_stylesheets)
|
||||
|
||||
|
||||
app.layout = html.Div(
|
||||
[
|
||||
html.H1(children='Demo Zeitreihenanalyse', style={'textAlign': 'center'}),
|
||||
@ -238,16 +229,16 @@ app.layout = html.Div(
|
||||
html.H2('Wählen Sie ein Objekt aus (ObjektID):'),
|
||||
dcc.Dropdown(
|
||||
list(cands.keys()),
|
||||
id='dropdown-selection',
|
||||
id='selector-obj_id',
|
||||
placeholder='ObjektID auswählen...',
|
||||
),
|
||||
]
|
||||
),
|
||||
html.Div(
|
||||
children=[
|
||||
html.H3(id='object_text'),
|
||||
dcc.Dropdown(id='choice-candidates'),
|
||||
dcc.Graph(id='graph-output'),
|
||||
html.H3(id='object-text'),
|
||||
dcc.Dropdown(id='selector-candidates'),
|
||||
dcc.Graph(id='graph-candidates'),
|
||||
]
|
||||
),
|
||||
html.Div(
|
||||
@ -260,8 +251,8 @@ app.layout = html.Div(
|
||||
|
||||
|
||||
@callback(
|
||||
Output('object_text', 'children'),
|
||||
Input('dropdown-selection', 'value'),
|
||||
Output('object-text', 'children'),
|
||||
Input('selector-obj_id', 'value'),
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_obj_text(obj_id):
|
||||
@ -272,21 +263,24 @@ def update_obj_text(obj_id):
|
||||
|
||||
|
||||
@callback(
|
||||
Output('choice-candidates', 'options'),
|
||||
Input('dropdown-selection', 'value'),
|
||||
[Output('selector-candidates', 'options'), Output('selector-candidates', 'value')],
|
||||
Input('selector-obj_id', '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
|
||||
return choices, choices[0]
|
||||
|
||||
|
||||
# TODO check possible storage of pre-filtered result
|
||||
# TODO change input of ``update_table_candidates`` and ``display_candidates_as_graph``
|
||||
# TODO to storage component
|
||||
@callback(
|
||||
Output('graph-output', 'figure'),
|
||||
Input('choice-candidates', 'value'),
|
||||
State('dropdown-selection', 'value'),
|
||||
Output('graph-candidates', 'figure'),
|
||||
Input('selector-candidates', 'value'),
|
||||
State('selector-obj_id', 'value'),
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_timeline(index, obj_id):
|
||||
@ -295,19 +289,20 @@ def update_timeline(index, obj_id):
|
||||
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]
|
||||
# cands_per_obj_id = cands[obj_id]
|
||||
# cands_similar = cands_per_obj_id[int(index) - 1]
|
||||
# data
|
||||
df = data.loc[list(cands_choice)].sort_index() # type: ignore
|
||||
# df = data.loc[list(cands_similar)].sort_index() # type: ignore
|
||||
df = pre_filter_data(data, idx=index, obj_id=obj_id)
|
||||
# figure
|
||||
fig = px.line(
|
||||
data_frame=df,
|
||||
x='ErstellungsDatum',
|
||||
y='ObjektID',
|
||||
title=title,
|
||||
hover_data=hover_data,
|
||||
hover_data=HOVER_DATA,
|
||||
)
|
||||
fig.update_traces(mode='markers+lines', marker=markers, marker_symbol='diamond')
|
||||
fig.update_traces(mode='markers+lines', marker=MARKERS, marker_symbol='diamond')
|
||||
fig.update_xaxes(
|
||||
tickformat='%B\n%Y',
|
||||
rangeslider_visible=True,
|
||||
@ -319,24 +314,18 @@ def update_timeline(index, obj_id):
|
||||
|
||||
@callback(
|
||||
[Output('table-candidates', 'data'), Output('table-candidates', 'columns')],
|
||||
Input('choice-candidates', 'value'),
|
||||
State('dropdown-selection', 'value'),
|
||||
Input('selector-candidates', 'value'),
|
||||
State('selector-obj_id', '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(
|
||||
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:
|
||||
for col in TABLE_FEATS_DATES:
|
||||
df[col] = df[col].dt.strftime(r'%Y-%m-%d')
|
||||
|
||||
table_data = df.to_dict('records')
|
||||
@ -348,6 +337,7 @@ def pre_filter_data(
|
||||
idx: int,
|
||||
obj_id: ObjectID,
|
||||
) -> DataFrame:
|
||||
idx = int(idx)
|
||||
obj_id = int(obj_id)
|
||||
data = data.copy()
|
||||
# cands
|
||||
@ -359,33 +349,53 @@ def pre_filter_data(
|
||||
return data
|
||||
|
||||
|
||||
# ** graph
|
||||
# ** graph callbacks
|
||||
# TODO store pre-calculated 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'),
|
||||
Output('graph-weight_min', 'min', allow_duplicate=True),
|
||||
Output('graph-weight_min', 'max', allow_duplicate=True),
|
||||
Output('graph-weight_min', 'placeholder', allow_duplicate=True),
|
||||
Output('graph-weight_max', 'min', allow_duplicate=True),
|
||||
Output('graph-weight_max', 'max', allow_duplicate=True),
|
||||
Output('graph-weight_max', 'placeholder', allow_duplicate=True),
|
||||
Output('graph-store', 'data'),
|
||||
Output('graph-store-cyto-curr_cands', 'data'),
|
||||
# Input('graph-build-btn', 'n_clicks'),
|
||||
Input('selector-candidates', 'value'),
|
||||
State('selector-obj_id', 'value'),
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_graph_candidates(_, index, obj_id):
|
||||
def display_candidates_as_graph(index, obj_id):
|
||||
t1 = time.perf_counter()
|
||||
df = pre_filter_data(data, idx=index, obj_id=obj_id)
|
||||
t2 = time.perf_counter()
|
||||
print(f'Time for filtering: {t2 - t1} s')
|
||||
|
||||
t1 = time.perf_counter()
|
||||
tk_graph_cands, _ = tokens.build_token_graph(
|
||||
data=df,
|
||||
model=SPCY_MODEL,
|
||||
target_feature='VorgangsBeschreibung',
|
||||
build_map=False,
|
||||
logging_graph=False,
|
||||
)
|
||||
t2 = time.perf_counter()
|
||||
print(f'Time for graph building: {t2 - t1} s')
|
||||
|
||||
t1 = time.perf_counter()
|
||||
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}'
|
||||
placeholder_max = f'Maximum edge weight: {weight_min} - {weight_max}'
|
||||
t2 = time.perf_counter()
|
||||
print(f'Time for graph metadata and conversion: {t2 - t1} s')
|
||||
|
||||
t1 = time.perf_counter()
|
||||
graph_to_store = lang_main.io.encode_to_base64_str(tk_graph_cands)
|
||||
t2 = time.perf_counter()
|
||||
print(f'Time for encoding: {t2 - t1} s')
|
||||
return (
|
||||
cyto_data,
|
||||
weight_min,
|
||||
@ -394,6 +404,8 @@ def update_graph_candidates(_, index, obj_id):
|
||||
weight_min,
|
||||
weight_max,
|
||||
placeholder_max,
|
||||
graph_to_store,
|
||||
cyto_data,
|
||||
)
|
||||
|
||||
|
||||
@ -412,30 +424,44 @@ def update_layout_internal(layout_choice):
|
||||
@app.callback(
|
||||
Output('cytoscape-graph', 'zoom'),
|
||||
Output('cytoscape-graph', 'elements', allow_duplicate=True),
|
||||
Output('weight_min', 'value'),
|
||||
Output('weight_max', 'value'),
|
||||
Output('graph-weight_min', 'value'),
|
||||
Output('graph-weight_max', 'value'),
|
||||
Input('bt-reset', 'n_clicks'),
|
||||
State('graph-store-cyto-curr_cands', 'data'),
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def reset_layout(n_clicks):
|
||||
return (1, cyto_data_base, None, None)
|
||||
def reset_layout(_, current_cands_cyto_elements):
|
||||
return (1, current_cands_cyto_elements, None, None)
|
||||
|
||||
|
||||
# update edge weight
|
||||
@app.callback(
|
||||
Output('cytoscape-graph', 'elements', allow_duplicate=True),
|
||||
Input('weight_min', 'value'),
|
||||
Input('weight_max', 'value'),
|
||||
Input('graph-weight_min', 'value'),
|
||||
Input('graph-weight_max', 'value'),
|
||||
State('graph-store', 'data'),
|
||||
State('graph-store-cyto-curr_cands', 'data'),
|
||||
State('graph-weight_min', 'min'),
|
||||
State('graph-weight_min', 'max'),
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
def update_edge_weight(weight_min, weight_max):
|
||||
if not any([weight_min, weight_max]):
|
||||
return cyto_data_base
|
||||
def update_edge_weight(
|
||||
weight_min,
|
||||
weight_max,
|
||||
current_graph,
|
||||
current_cands_cyto_elements,
|
||||
current_min,
|
||||
current_max,
|
||||
):
|
||||
if not any((weight_min, weight_max)):
|
||||
return current_cands_cyto_elements
|
||||
|
||||
if weight_min is None:
|
||||
weight_min = MIN_WEIGHT
|
||||
weight_min = current_min
|
||||
if weight_max is None:
|
||||
weight_max = MAX_WEIGHT
|
||||
weight_max = current_max
|
||||
|
||||
tk_graph = cast(graphs.TokenGraph, lang_main.io.decode_from_base64_str(current_graph))
|
||||
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)
|
||||
@ -444,6 +470,7 @@ def update_edge_weight(weight_min, weight_max):
|
||||
return cyto_data
|
||||
|
||||
|
||||
# ** graph: layout with edge weight
|
||||
app.clientside_callback(
|
||||
"""
|
||||
function(n_clicks, layout) {
|
||||
@ -458,11 +485,12 @@ app.clientside_callback(
|
||||
}
|
||||
""",
|
||||
Output('cytoscape-graph', 'layout', allow_duplicate=True),
|
||||
Input('trigger_relayout', 'n_clicks'),
|
||||
Input('graph-trigger_relayout', 'n_clicks'),
|
||||
State('cytoscape-graph', 'layout'),
|
||||
prevent_initial_call=True,
|
||||
)
|
||||
|
||||
# ** graph: display edge weight (line thickness)
|
||||
app.clientside_callback(
|
||||
"""
|
||||
function(n_clicks, stylesheet) {
|
||||
@ -1,4 +1,5 @@
|
||||
import logging
|
||||
import os
|
||||
import shutil
|
||||
import sys
|
||||
from pathlib import Path
|
||||
@ -41,3 +42,9 @@ else:
|
||||
loaded_cfg = load_toml_config(path_to_toml=cfg_path_external)
|
||||
|
||||
CONFIG: Final[dict[str, Any]] = loaded_cfg.copy()
|
||||
|
||||
# append Graphviz binary folder to system path if not already contained
|
||||
if sys.platform == 'win32':
|
||||
path = Path(r'C:\Program Files\Graphviz\bin')
|
||||
if path.is_dir() and str(path).lower() not in os.environ['PATH'].lower():
|
||||
os.environ['PATH'] += f';{path}'
|
||||
|
||||
@ -164,6 +164,9 @@ def convert_graph_to_cytoscape(
|
||||
}
|
||||
cyto_data.append(edge_data)
|
||||
|
||||
min_weight: int = 0
|
||||
max_weight: int = 0
|
||||
if weights:
|
||||
min_weight = min(weights)
|
||||
max_weight = max(weights)
|
||||
weight_metadata: WeightData = {'min': min_weight, 'max': max_weight}
|
||||
@ -284,6 +287,9 @@ class TokenGraph(DiGraph):
|
||||
f'{len(self.edges)})'
|
||||
)
|
||||
|
||||
def disable_logging(self) -> None:
|
||||
self.logging = False
|
||||
|
||||
# !! only used to verify that saving was done correctly
|
||||
"""
|
||||
def __key(self) -> tuple[Hashable, ...]:
|
||||
@ -337,16 +343,9 @@ class TokenGraph(DiGraph):
|
||||
logging: bool | None = ...,
|
||||
) -> Graph: ...
|
||||
|
||||
@overload
|
||||
def to_undirected(
|
||||
self,
|
||||
inplace: bool = ...,
|
||||
logging: bool | None = ...,
|
||||
) -> Graph | None: ...
|
||||
|
||||
def to_undirected(
|
||||
self,
|
||||
inplace=True,
|
||||
inplace: bool = True,
|
||||
logging: bool | None = None,
|
||||
) -> Graph | None:
|
||||
if logging is None:
|
||||
|
||||
@ -21,7 +21,8 @@ from lang_main.types import PandasIndex
|
||||
# POS_OF_INTEREST: frozenset[str] = frozenset(['NOUN', 'PROPN', 'ADJ', 'VERB', 'AUX'])
|
||||
# POS_OF_INTEREST: frozenset[str] = frozenset(['NOUN', 'ADJ', 'VERB', 'AUX'])
|
||||
# POS_OF_INTEREST: frozenset[str] = frozenset(['NOUN', 'PROPN'])
|
||||
POS_OF_INTEREST: frozenset[str] = frozenset(['NOUN', 'PROPN', 'VERB', 'AUX'])
|
||||
# POS_OF_INTEREST: frozenset[str] = frozenset(['NOUN', 'PROPN', 'VERB', 'AUX'])
|
||||
POS_OF_INTEREST: frozenset[str] = frozenset(['NOUN', 'PROPN', 'VERB', 'AUX', 'ADV'])
|
||||
|
||||
# POS_INDIRECT: frozenset[str] = frozenset(['AUX', 'VERB'])
|
||||
POS_INDIRECT: frozenset[str] = frozenset(['AUX'])
|
||||
@ -153,6 +154,7 @@ def build_token_graph(
|
||||
batch_idx_feature: str = ...,
|
||||
build_map: Literal[False],
|
||||
batch_size_model: int = ...,
|
||||
logging_graph: bool = ...,
|
||||
) -> tuple[TokenGraph, None]: ...
|
||||
|
||||
|
||||
@ -166,6 +168,7 @@ def build_token_graph(
|
||||
batch_idx_feature: str = ...,
|
||||
build_map: Literal[True] = ...,
|
||||
batch_size_model: int = ...,
|
||||
logging_graph: bool = ...,
|
||||
) -> tuple[TokenGraph, dict[PandasIndex, SpacyDoc]]: ...
|
||||
|
||||
|
||||
@ -175,11 +178,12 @@ def build_token_graph(
|
||||
*,
|
||||
target_feature: str = 'entry',
|
||||
weights_feature: str | None = None,
|
||||
batch_idx_feature: str = 'batched_idxs',
|
||||
batch_idx_feature: str | None = 'batched_idxs',
|
||||
build_map: bool = True,
|
||||
batch_size_model: int = 50,
|
||||
logging_graph: bool = True,
|
||||
) -> tuple[TokenGraph, dict[PandasIndex, SpacyDoc] | None]:
|
||||
graph = TokenGraph()
|
||||
graph = TokenGraph(enable_logging=logging_graph)
|
||||
model_input = cast(tuple[str], tuple(data[target_feature].to_list()))
|
||||
if weights_feature is not None:
|
||||
weights = cast(tuple[int], tuple(data[weights_feature].to_list()))
|
||||
@ -187,7 +191,9 @@ def build_token_graph(
|
||||
weights = None
|
||||
|
||||
docs_mapping: dict[PandasIndex, SpacyDoc] | None
|
||||
if build_map:
|
||||
if build_map and batch_idx_feature is None:
|
||||
raise ValueError('Can not build mapping if batched indices are unknown.')
|
||||
elif build_map:
|
||||
indices = cast(tuple[list[PandasIndex]], tuple(data[batch_idx_feature].to_list()))
|
||||
docs_mapping = {}
|
||||
else:
|
||||
@ -199,10 +205,10 @@ def build_token_graph(
|
||||
for doc in tqdm(
|
||||
model.pipe(model_input, batch_size=batch_size_model), total=len(model_input)
|
||||
):
|
||||
weight: int | None = None
|
||||
if weights is not None:
|
||||
weight = weights[index]
|
||||
else:
|
||||
weight = None
|
||||
|
||||
add_doc_info_to_graph(
|
||||
graph=graph,
|
||||
doc=doc,
|
||||
@ -219,7 +225,7 @@ def build_token_graph(
|
||||
# metadata
|
||||
graph.update_metadata()
|
||||
# convert to undirected
|
||||
graph.to_undirected()
|
||||
graph.to_undirected(logging=False)
|
||||
|
||||
return graph, docs_mapping
|
||||
|
||||
@ -250,7 +256,7 @@ def build_token_graph_simple(
|
||||
# metadata
|
||||
graph.update_metadata()
|
||||
# convert to undirected
|
||||
graph.to_undirected()
|
||||
graph.to_undirected(logging=False)
|
||||
|
||||
return graph, docs_mapping
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import pickle
|
||||
import base64
|
||||
import shutil
|
||||
import tomllib
|
||||
from pathlib import Path
|
||||
@ -61,6 +62,24 @@ def load_pickle(
|
||||
return obj
|
||||
|
||||
|
||||
def encode_to_base64_str(
|
||||
obj: Any,
|
||||
encoding: str = 'utf-8',
|
||||
) -> str:
|
||||
serialised = pickle.dumps(obj, protocol=pickle.HIGHEST_PROTOCOL)
|
||||
b64_bytes = base64.b64encode(serialised)
|
||||
return b64_bytes.decode(encoding=encoding)
|
||||
|
||||
|
||||
def decode_from_base64_str(
|
||||
b64_str: str,
|
||||
encoding: str = 'utf-8',
|
||||
) -> Any:
|
||||
b64_bytes = b64_str.encode(encoding=encoding)
|
||||
decoded = base64.b64decode(b64_bytes)
|
||||
return pickle.loads(decoded)
|
||||
|
||||
|
||||
def get_entry_point(
|
||||
saving_path: Path,
|
||||
filename: str,
|
||||
|
||||
@ -97,6 +97,18 @@ class BasePipeline(ABC):
|
||||
|
||||
|
||||
class PipelineContainer(BasePipeline):
|
||||
"""Container class for basic actions.
|
||||
Basic actions are usually functions, which do not take any parameters
|
||||
and return nothing. Indeed, if an action returns any values after its
|
||||
procedure is finished, an error is raised. Therefore, PipelineContainers
|
||||
can be seen as a concatenation of many (independent) simple procedures
|
||||
which are executed in the order in which they were added to the pipe.
|
||||
With a simple call of the ``run`` method the actions are performed.
|
||||
Additionally, there is an option to skip actions which can be set in
|
||||
the ``add`` method. This allows for easily configurable pipelines,
|
||||
e.g., via a user configuration.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
@ -169,9 +181,6 @@ class Pipeline(BasePipeline):
|
||||
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,
|
||||
|
||||
File diff suppressed because one or more lines are too long
Loading…
x
Reference in New Issue
Block a user