From 01308e9c68ba3ee05b2050989dbd893cf6c16ca7 Mon Sep 17 00:00:00 2001
From: vincent d warmerdam <vincentwarmerdam@gmail.com>
Date: Mon, 22 Jul 2024 14:03:05 +0000
Subject: [PATCH] release

---
 Untitled.ipynb   | 52 +++++++++++++++++++++++++++++++++++++++-
 bulk/__init__.py | 62 ++++++++++++++++++++++++++++++++++++++++++++++++
 setup.py         |  4 ++--
 3 files changed, 115 insertions(+), 3 deletions(-)

diff --git a/Untitled.ipynb b/Untitled.ipynb
index cf80a59..d3f9663 100644
--- a/Untitled.ipynb
+++ b/Untitled.ipynb
@@ -53,7 +53,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b2f8a2aa96d84f7992698bc529823d47",
+       "model_id": "85a84c8facf74b79887c29265bfad96e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -134,6 +134,56 @@
     "explorer.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "55668ebd-6887-4352-a967-63ad26759bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "text_emb_pipeline = make_pipeline(\n",
+    "  enc, pca\n",
+    ")\n",
+    "\n",
+    "# Calculate embeddings \n",
+    "X_tfm_pca = pca.fit_transform(X)\n",
+    "\n",
+    "# Write to disk. Note! Text column must be named \"text\"\n",
+    "df = pd.DataFrame({\"text\": sentences})\n",
+    "df['x'] = X_tfm_pca[:, 0]\n",
+    "df['y'] = X_tfm_pca[:, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "14626a98-3bc7-48c1-af09-ca90cd1a1add",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "938d1ff742114fb6b0d1754458bfa784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(Text(value='', description='String:', placeholder='Type something'), HBox(childr…"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "explorer = BaseTextExplorer(df, encoder=enc, X=X)\n",
+    "explorer.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 29,
diff --git a/bulk/__init__.py b/bulk/__init__.py
index e69de29..c63d54b 100644
--- a/bulk/__init__.py
+++ b/bulk/__init__.py
@@ -0,0 +1,62 @@
+import jscatter
+from ipywidgets import HBox, VBox, HTML, Layout, Button, Text
+from IPython.display import display
+from sklearn.metrics.pairwise import cosine_similarity
+
+class BaseTextExplorer:
+    """
+    Early preview of Jupyter Widget explorer.
+    """
+    def __init__(self, dataf, X=None, encoder=None):
+        self.dataf = dataf
+        self.scatter = jscatter.Scatter(data=self.dataf, x="x", y="y", width=500, height=500)
+        self.html = HTML(layout=Layout(width='600px', overflow_y='scroll', height='400px'))
+        self.sample_btn = Button(description='resample')
+        self.elem = HBox([self.scatter.show(), VBox([self.sample_btn, self.html])])
+        self.X = X
+        self.encoder = encoder
+        
+        if self.encoder and (self.X is not None):
+            self.text_input = Text(value='', placeholder='Type something', description='String:')
+            self.elem = HBox([VBox([self.text_input, self.scatter.show()]), VBox([self.sample_btn, self.html])])
+
+            def update_text(change):
+                X_tfm = encoder.transform([self.text_input.value])
+                dists = cosine_similarity(X, X_tfm).reshape(1, -1)
+                self.dists = dists
+                norm_dists = 0.01 + (dists - dists.min())/(0.1 + dists.max() - dists.min())
+                self.scatter.color(by=norm_dists[0])
+                self.scatter.size(by=norm_dists[0])
+
+            self.text_input.observe(update_text)
+        
+        self.scatter.widget.observe(lambda d: self.update(), ['selection'])
+        self.sample_btn.on_click(lambda d: self.update())
+
+    def show(self):
+        return self.elem
+
+    def update(self):
+        if len(self.scatter.selection()) > 10:
+            texts = self.dataf.iloc[self.scatter.selection()].sample(10)["text"]
+        else:
+            texts = self.dataf.iloc[self.scatter.selection()]["text"]
+        self.html.value = ''.join([f'<p style="margin: 0px">{t}</p>' for t in texts])
+
+    def observe(self, func):
+        self.scatter.widget.observe(func, ['selection'])
+        
+    @property
+    def selected_idx(self):
+        return self.scatter.selection()
+
+    @property
+    def selected_texts(self):
+        return list(self.dataf.iloc[self.selection_idx]["text"])
+
+    @property
+    def selected_dataframe(self):
+        return self.dataf.iloc[self.selection_idx]
+
+    def _repr_html_(self):
+        return display(self.elem)
diff --git a/setup.py b/setup.py
index d448036..a256648 100644
--- a/setup.py
+++ b/setup.py
@@ -2,9 +2,9 @@
 
 setup(
     name="bulk",
-    version="0.3.1",
+    version="0.3.2",
     packages=find_packages(),
-    install_requires=["radicli>=0.0.8,<0.1.0", "bokeh>=2.4.3,<3.0.0", "pandas>=1.0.0", "wasabi>=0.9.1", "numpy<2", "jupyter-scatter"],
+    install_requires=["radicli>=0.0.8,<0.1.0", "bokeh>=2.4.3,<3.0.0", "pandas>=1.0.0", "wasabi>=0.9.1", "numpy<2", "jupyter-scatter", "scikit-learn"],
     extras_require={
         "dev": ["pytest-playwright==0.3.0"],
     },