+FCA

c4ace07c · Prof. Dr. Robert Jäschke · 6ca894ac · c4ace07c
Commit c4ace07c authored 2 years ago by Prof. Dr. Robert Jäschke
--- a/FCA.ipynb
+++ b/FCA.ipynb
@@ -7,7 +7,7 @@
   "source": [
    "# FCA & DraCor\n",
    "\n",
-    "We want to analyse which characters speak in scenes of different plays. Therefore, we extract the corresponding information from [DraCor](https://dracor.org/) and analyse it using [Formal Concept Analysis](https://www.upriss.org.uk/fca/fca.html).\n",
+    "We want to analyse which characters speak in scenes of different plays. Therefore, we extract the corresponding information from [DraCor](https://dracor.org/) and analyse it using [Formal Concept Analysis](https://www.upriss.org.uk/fca/fca.html) (FCA).\n",
    "\n",
    "## DraCor\n",
    "\n",
@@ -112,7 +112,7 @@
  },
  {
   "cell_type": "markdown",
-   "id": "every-capture",
+   "id": "durable-multimedia",
   "metadata": {},
   "source": [
    "We use the [concepts](https://github.com/xflr6/concepts) Python library for FCA:"
@@ -121,7 +121,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "younger-playback",
+   "id": "ranking-island",
   "metadata": {},
   "outputs": [],
   "source": [

 %% Cell type:markdown id:economic-poker tags:
 # FCA & DraCor
-We want to analyse which characters speak in scenes of different plays. Therefore, we extract the corresponding information from [DraCor](https://dracor.org/) and analyse it using [Formal Concept Analysis](https://www.upriss.org.uk/fca/fca.html).
+We want to analyse which characters speak in scenes of different plays. Therefore, we extract the corresponding information from [DraCor](https://dracor.org/) and analyse it using [Formal Concept Analysis](https://www.upriss.org.uk/fca/fca.html) (FCA).
 ## DraCor
 [This simple Python library](https://pypi.org/project/dracor/) allows us to download and process plays from [DraCor](https://dracor.org/). Specifically, it allows us to extract information about which character speaks in which scene.
 %% Cell type:code id:legislative-disclaimer tags:
 ``` python
 !pip install dracor
 ```
 %% Cell type:markdown id:finite-introduction tags:
 Let us now download a play and extract the speaker information from the scenes:
 %% Cell type:code id:regular-arthur tags:
 ``` python
 import dracor
 play = dracor.play("ger", "goethe-iphigenie-auf-tauris")
 scenes = dracor.scenes(play)
 scenes
 ```
 %% Output
    {'a0_s0': {'#iphigenie'},
     'a0_s1': {'#arkas', '#iphigenie'},
     'a0_s2': {'#iphigenie', '#thoas'},
     'a0_s3': {'#iphigenie'},
     'a1_s0': {'#orest', '#pylades'},
     'a1_s1': {'#iphigenie', '#pylades'},
     'a2_s0': {'#iphigenie', '#orest'},
     'a2_s1': {'#orest'},
     'a2_s2': {'#iphigenie', '#orest', '#pylades'},
     'a3_s0': {'#iphigenie'},
     'a3_s1': {'#arkas', '#iphigenie'},
     'a3_s2': {'#iphigenie'},
     'a3_s3': {'#iphigenie', '#pylades'},
     'a3_s4': {'#iphigenie'},
     'a4_s0': {'#arkas', '#thoas'},
     'a4_s1': {'#thoas'},
     'a4_s2': {'#iphigenie', '#thoas'},
     'a4_s3': {'#iphigenie', '#orest', '#thoas'},
     'a4_s4': {'#arkas', '#orest', '#pylades', '#thoas'},
     'a4_s5': {'#iphigenie', '#orest', '#thoas'}}
 %% Cell type:markdown id:protecting-spare tags:
 ([more Shakespeare plays](https://dracor.org/shake) or [German plays](https://dracor.org/ger))
 %% Cell type:markdown id:virgin-administration tags:
 ## Formal Concept Analysis (FCA)
 To create a [context](https://concepts.readthedocs.io/en/latest/api.html#context), we need a list of *objects* (we use the scenes of the play), a list of *attributes* (we use the characters of the play), and an *incidence relation* between them (in our example whether a character appears in a scene):
 %% Cell type:code id:urban-alloy tags:
 ``` python
 objects = scenes.keys()
 attributes = set()
 for scene, speakers in scenes.items():
    attributes.update(speakers)
 attributes = sorted(attributes)
 incidence = [tuple([a in scenes[o] for a in attributes]) for o in objects]
 ```
-%% Cell type:markdown id:every-capture tags:
+%% Cell type:markdown id:durable-multimedia tags:
 We use the [concepts](https://github.com/xflr6/concepts) Python library for FCA:
-%% Cell type:code id:younger-playback tags:
+%% Cell type:code id:ranking-island tags:
 ``` python
 !pip install concepts
 ```
 %% Cell type:markdown id:communist-element tags:
 Now we can create a formal context and compute the number of formal concepts:
 %% Cell type:code id:grave-hungary tags:
 ``` python
 import concepts
 ctx = concepts.Context(objects, attributes, incidence)
 len(ctx.lattice)                                       # the number of formal concepts
 ```
 %% Output
    17
 %% Cell type:markdown id:hungry-vulnerability tags:
 Finally, we can (try to) visualize the concept lattice:
 %% Cell type:code id:piano-practice tags:
 ``` python
 ctx.lattice.graphviz()
 ```
 %% Output
    <graphviz.graphs.Digraph at 0x7f3682f591f0>
 %% Cell type:markdown id:announced-lawsuit tags:
 ... and save the context:
 %% Cell type:code id:organic-cable tags:
 ``` python
 ctx.tofile("play.cxt", frmat="cxt")
 ```
 %% Cell type:markdown id:muslim-charity tags:
 Let's test some other examples. Choose a play from the list below (recommendations: [ger/goethe-iphigenie-auf-tauris](https://dracor.org/ger/goethe-iphigenie-auf-tauris), [shake/a-midsummer-night-s-dream](https://dracor.org/shake/a-midsummer-night-s-dream)):
 %% Cell type:code id:interior-steam tags:
 ``` python
 from ipywidgets import interact, Dropdown
 import dracor
 corpora = dracor.corpora()
 corpus_widget = Dropdown(options=corpora, value='shake', description='Corpus:')
 play_widget = Dropdown(options=dracor.plays("shake"), value="a-midsummer-night-s-dream", description='Play:')
 def on_update_corpus_widget(*args):
    play_widget.options = dracor.plays(corpus_widget.value)
 corpus_widget.observe(on_update_corpus_widget, 'value')
 @interact(corpus=corpus_widget, play=play_widget)
 def visualize_play_lattice(corpus, play):
    """Compute the lattice for the given play, show it, and save it in a file."""
    scenes = dracor.scenes(dracor.play(corpus, play))
    # extract formal context (G, M, I) = (objects, attributes, incidence)
    objects = scenes.keys()
    attributes = set()
    for scene, speakers in scenes.items():
        attributes.update(speakers)
    attributes = sorted(attributes)
    incidence = [tuple([a in scenes[o] for a in attributes]) for o in objects]
    ctx = concepts.Context(objects, attributes, incidence)
    print(len(ctx.lattice), "formal concepts")
    ctx.tofile(play + ".cxt", frmat="cxt")
    return ctx.lattice.graphviz()
 ```
 %% Output
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