feat: eli5 explanations; cleanups

app.py

@@ -2,7 +2,9 @@
 # requires-python = ">=3.12"
 # dependencies = [
 #     "altair==5.5.0",
+#     "eli5",
 #     "fugashi-plus",
+#     "ipython",
 #     "marimo",
 #     "numpy==2.2.6",
 #     "pandas==2.3.0",
@@ -15,18 +17,20 @@
 
 import marimo
 
-__generated_with = "0.14.10"
+__generated_with = "0.14.13"
 app = marimo.App(width="full", app_title="Scattertext on Japanese novels")
 
 with app.setup:
-    import marimo as mo
+    import io
+    import random
+
+    import eli5
     import fugashi
-    import pandas as pd
-    import scipy
+    import marimo as mo
     import numpy as np
-    import random
-    import io
+    import pandas as pd
     import scattertext as st
+    import scipy
     from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
 
     RANDOM_SEED = 42
@@ -85,7 +89,9 @@ def function_export():
         chunked_labels = []
         for text, cat, fname in zip(texts, categories, filenames):
             tokens = text.split()
-            for block_idx, start in enumerate(range(0, len(tokens), chunk_size), start=1):
+            for block_idx, start in enumerate(
+                range(0, len(tokens), chunk_size), start=1
+            ):
                 chunk = " ".join(tokens[start : start + chunk_size])
                 chunked_texts.append(chunk)
                 chunked_cats.append(cat)
@@ -221,8 +227,8 @@ def data_settings():
 def data_check(data_form, parse_texts):
     mo.stop(data_form.value is None)
 
-    from pathlib import Path
     import itertools
+    from pathlib import Path
 
     validation_messages: list[str] = []
 
@@ -265,15 +271,19 @@ def data_check(data_form, parse_texts):
 
         nA = len(data_form.value["files_a"]) or 2
         nB = len(data_form.value["files_b"]) or 2
-        categories = [data_form.value["label_a"]] * nA + [data_form.value["label_b"]] * nB
+        categories = [data_form.value["label_a"]] * nA + [
+            data_form.value["label_b"]
+        ] * nB
         filenames = itertools.chain(category_a_names, category_b_names)
         texts = itertools.chain(category_a_texts, category_b_texts)
 
-        data = pd.DataFrame({
-            "category": categories,
-            "filename": filenames,
-            "text": texts,
-        })
+        data = pd.DataFrame(
+            {
+                "category": categories,
+                "filename": filenames,
+                "text": texts,
+            }
+        )
 
         with mo.status.spinner("コーパスを形態素解析中..."):
             data["text"] = parse_texts(list(data["text"]))
@@ -305,9 +315,10 @@ def sampling_controls_setup():
         start=50,
         stop=50_000,
         value=2000,
-        step=500,
+        step=50,
         label="1チャンクあたり最大トークン数",
         full_width=True,
+        include_input=True,
     )
     sample_frac = mo.ui.slider(
         start=0.1,
@@ -316,6 +327,7 @@ def sampling_controls_setup():
         step=0.05,
         label="使用割合（1.0で全データ）",
         full_width=True,
+        include_input=True,
     )
     sampling_form = (
         mo.md("{chunk_size}\n{sample_frac}")
@@ -432,7 +444,7 @@ def _(cats, chunked_texts, fnames, run_model, train_scikit_cached):
         chunked_texts, cats, fnames
     )
     # also send the raw text chunks onward for download
-    return chunk_cats, chunk_fnames, scikit_corpus, vectorizer
+    return chunk_cats, vectorizer
 
 
 @app.cell
@@ -517,13 +529,15 @@ def _(
 
     # still need feature‐matrices for training the classifier
     X_train = vectorizer.transform(texts_train)
-    X_test  = vectorizer.transform(texts_test)
+    X_test = vectorizer.transform(texts_test)
 
     def get_train_data():
-        df = pd.DataFrame({
-            "text": texts_train,
-            "label": y_train,
-        })
+        df = pd.DataFrame(
+            {
+                "text": texts_train,
+                "label": y_train,
+            }
+        )
         print(df)
         buf = io.BytesIO()
         df.to_csv(buf, index=False)
@@ -537,10 +551,12 @@ def _(
     )
 
     def get_test_data():
-        df = pd.DataFrame({
-            "text": texts_test,
-            "label": y_test,
-        })
+        df = pd.DataFrame(
+            {
+                "text": texts_test,
+                "label": y_test,
+            }
+        )
         buf = io.BytesIO()
         df.to_csv(buf, index=False)
         return buf.getvalue()
@@ -554,10 +570,12 @@ def _(
 
     def get_all_data():
         # concatenate train and test text+labels
-        df_all = pd.DataFrame({
-            "text": texts_train + texts_test,
-            "label": list(y_train) + list(y_test),
-        })
+        df_all = pd.DataFrame(
+            {
+                "text": texts_train + texts_test,
+                "label": list(y_train) + list(y_test),
+            }
+        )
         buf = io.BytesIO()
         df_all.to_csv(buf, index=False)
         return buf.getvalue()
@@ -581,7 +599,7 @@ def _(
             max_depth=int(model_form.value["widgets"]["rf_max_depth"]),
             random_state=RANDOM_SEED,
         )
-    else:  # GradientBoostingClassifier
+    else:
         clf = GradientBoostingClassifier(
             n_estimators=int(model_form.value["widgets"]["gb_n"]),
             learning_rate=float(model_form.value["widgets"]["gb_lr"]),
@@ -620,22 +638,22 @@ def _(
     #     roc_auc = auc(fpr, tpr)
     #     roc_df = pd.DataFrame({"fpr": fpr, "tpr": tpr})
 
-    feature_names = vectorizer.get_feature_names_out()
-    importances = (
-        pd.DataFrame({"単語": feature_names, "重要度": term_scores})
-        .sort_values("重要度", ascending=False)
-        .head(20)
-    )
+    # feature_names = vectorizer.get_feature_names_out()
+    # importances = (
+    #     pd.DataFrame({"単語": feature_names, "重要度": term_scores})
+    #     .sort_values("重要度", ascending=False)
+    #     .head(20)
+    # )
 
-    imp_chart = (
-        alt.Chart(importances)
-        .mark_bar()
-        .encode(
-            x=alt.X("重要度:Q", title="重要度"),
-            y=alt.Y("単語:N", sort="-x"),
-        )
-        .properties(title="Top‐20 重要特徴語", width=600, height=400)
-    )
+    # imp_chart = (
+    #     alt.Chart(importances)
+    #     .mark_bar()
+    #     .encode(
+    #         x=alt.X("重要度:Q", title="重要度"),
+    #         y=alt.Y("単語:N", sort="-x"),
+    #     )
+    #     .properties(title="Top‐20 重要特徴語", width=600, height=400)
+    # )
     cm_chart = (
         alt.Chart(cm_df)
         .mark_rect()
@@ -664,10 +682,15 @@ def _(
     mo.vstack(
         [
             mo.hstack(
-                [download_training_dataset, download_test_dataset, download_all_dataset],
+                [
+                    download_training_dataset,
+                    download_test_dataset,
+                    download_all_dataset,
+                ],
                 align="start",
             ),
-            mo.ui.altair_chart(imp_chart),
+            eli5.show_weights(clf, vec=vectorizer, target_names=clf.classes_, top=20),
+            # mo.ui.altair_chart(imp_chart),
             mo.ui.altair_chart(cm_chart),
             # mo.ui.altair_chart(roc_chart), # Turned out to not be too informative as task is too easy?
             mo.md(f"""
@@ -678,36 +701,29 @@ def _(
         """),
         ]
     )
-    return (term_scores,)
+    return
 
 
 @app.cell
-def _(
-    chunk_fnames,
-    data_form,
-    model_form,
-    run_model,
-    scikit_corpus,
-    term_scores,
-):
-    mo.stop(not run_model.value or not model_form.value)
-
-    with mo.status.spinner("分類モデルのScatterplotを作成中…"):
-        scikit_html = st.produce_scattertext_explorer(
-            corpus=scikit_corpus,
-            category=data_form.value["label_a"],
-            category_name=data_form.value["label_a"],
-            not_category_name=data_form.value["label_b"],
-            scores=term_scores,
-            terms_to_include=st.AutoTermSelector.get_selected_terms(
-                scikit_corpus, term_scores, 4000
-            ),
-            metadata=chunk_fnames,
-            transform=lambda freqs, _index, total: freqs / total.sum(),
-            rescale_x=lambda arr: arr,  # identity
-            rescale_y=lambda arr: arr,  # identity
-        )
-    mo.iframe(scikit_html)
+def _():
+    # mo.stop(not run_model.value or not model_form.value)
+
+    # with mo.status.spinner("分類モデルのScatterplotを作成中…"):
+    #     scikit_html = st.produce_scattertext_explorer(
+    #         corpus=scikit_corpus,
+    #         category=data_form.value["label_a"],
+    #         category_name=data_form.value["label_a"],
+    #         not_category_name=data_form.value["label_b"],
+    #         scores=term_scores,
+    #         terms_to_include=st.AutoTermSelector.get_selected_terms(
+    #             scikit_corpus, term_scores, 4000
+    #         ),
+    #         metadata=chunk_fnames,
+    #         transform=lambda freqs, _index, total: freqs / total.sum(),
+    #         rescale_x=lambda arr: arr,  # identity
+    #         rescale_y=lambda arr: arr,  # identity
+    #     )
+    # mo.iframe(scikit_html)
     return
 
 

pyproject.toml

@@ -6,7 +6,9 @@ readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "altair>=5.5.0",
+    "eli5>=0.16.0",
     "fugashi-plus>=1.4.0.post1",
+    "ipython>=9.4.0",
     "marimo>=0.13.15",
     "numpy>=2.2.6",
     "pandas>=2.3.0",

uv.lock

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 ]
 
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978, upload-time = "2024-11-30T04:30:14.439Z" }
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+]
+
 [[package]]
 name = "attrs"
 version = "25.3.0"
@@ -197,6 +206,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "decorator"
+version = "5.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" }
+wheels = [
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+]
+
 [[package]]
 name = "docutils"
 version = "0.21.2"
@@ -206,6 +224,33 @@ wheels = [
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 ]
 
+[[package]]
+name = "eli5"
+version = "0.16.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "graphviz" },
+    { name = "jinja2" },
+    { name = "numpy" },
+    { name = "scikit-learn" },
+    { name = "scipy" },
+    { name = "tabulate" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/dc/1f/f504e0b730e08d4ef89b1628dc83951a93677660bb8d8738d012f6f0da7f/eli5-0.16.0.tar.gz", hash = "sha256:6d8ab55b3535f8feecf4f98dbaadd6876121e5a2549735635cf71707d34bd47c", size = 271026, upload-time = "2025-04-20T16:38:00.92Z" }
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+]
+
+[[package]]
+name = "executing"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
 [[package]]
 name = "flashtext"
 version = "2.7"
@@ -243,6 +288,15 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/77/68/074333a52f6fa82402332054ca0dfa721f7dcfa7eace313f64cdb44bacde/gensim-4.3.2.tar.gz", hash = "sha256:99ac6af6ffd40682e70155ed9f92ecbf4384d59fb50af120d343ea5ee1b308ab", size = 23263982, upload-time = "2023-08-24T08:51:27.492Z" }
 
+[[package]]
+name = "graphviz"
+version = "0.21"
+source = { registry = "https://pypi.org/simple" }
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+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -261,6 +315,39 @@ wheels = [
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 ]
 
+[[package]]
+name = "ipython"
+version = "9.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "decorator" },
+    { name = "ipython-pygments-lexers" },
+    { name = "jedi" },
+    { name = "matplotlib-inline" },
+    { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "prompt-toolkit" },
+    { name = "pygments" },
+    { name = "stack-data" },
+    { name = "traitlets" },
+]
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+]
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+
 [[package]]
 name = "itsdangerous"
 version = "2.2.0"
@@ -305,7 +392,7 @@ wheels = [
 
 [[package]]
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-version = "4.24.0"
+version = "4.25.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -313,9 +400,9 @@ dependencies = [
     { name = "referencing" },
     { name = "rpds-py" },
 ]
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