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Wordclouds
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| Extracted from: https://arxiv.org/abs/2204.07547 licensed CC-BY 4.0 by: | ||
| Robert Haase, Elnaz Fazeli, David Legland, Michael Doube, Siân Culley, Ilya Belevich, Eija Jokitalo, Martin Schorb, Anna Klemm, Christian Tischer | ||
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| Used embedding: https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1 |
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| @@ -0,0 +1,194 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "20a04b0c-06cb-4f29-8381-a6a0d4a20ccd", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Wordcloud plots\n", | ||
| "For text exploration, it might make sense to visualize texts as data points and interact with them." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "8d301701-368f-4365-b555-dae6f06d8bea", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import stackview\n", | ||
| "import pandas as pd" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "a991df41-86ab-4188-af47-e6e0cf6d7b32", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Here we reuse a list of sentences and a [UMAP](https://umap-learn.readthedocs.io/en/latest/) produced from their text-embeddings. The sentences are taken from [Haase et al. 2022](https://arxiv.org/abs/2204.07547) which is licensed [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "bb75ed7e-aa83-4015-a74b-8dfb9405ecf1", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<div>\n", | ||
| "<style scoped>\n", | ||
| " .dataframe tbody tr th:only-of-type {\n", | ||
| " vertical-align: middle;\n", | ||
| " }\n", | ||
| "\n", | ||
| " .dataframe tbody tr th {\n", | ||
| " vertical-align: top;\n", | ||
| " }\n", | ||
| "\n", | ||
| " .dataframe thead th {\n", | ||
| " text-align: right;\n", | ||
| " }\n", | ||
| "</style>\n", | ||
| "<table border=\"1\" class=\"dataframe\">\n", | ||
| " <thead>\n", | ||
| " <tr style=\"text-align: right;\">\n", | ||
| " <th></th>\n", | ||
| " <th>Unnamed: 0</th>\n", | ||
| " <th>sentence</th>\n", | ||
| " <th>UMAP0</th>\n", | ||
| " <th>UMAP1</th>\n", | ||
| " </tr>\n", | ||
| " </thead>\n", | ||
| " <tbody>\n", | ||
| " <tr>\n", | ||
| " <th>0</th>\n", | ||
| " <td>0</td>\n", | ||
| " <td>A Hitchhiker’s Guide through the Bio-image Ana...</td>\n", | ||
| " <td>-2.863276</td>\n", | ||
| " <td>8.680281</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>1</th>\n", | ||
| " <td>1</td>\n", | ||
| " <td>Modern research in the life sciences is unthin...</td>\n", | ||
| " <td>-3.731295</td>\n", | ||
| " <td>7.875060</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>2</th>\n", | ||
| " <td>2</td>\n", | ||
| " <td>In the past decade, we observed a dramatic inc...</td>\n", | ||
| " <td>-4.748690</td>\n", | ||
| " <td>6.128065</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>3</th>\n", | ||
| " <td>3</td>\n", | ||
| " <td>As it is increasingly difficult to keep track ...</td>\n", | ||
| " <td>-4.183692</td>\n", | ||
| " <td>6.847530</td>\n", | ||
| " </tr>\n", | ||
| " <tr>\n", | ||
| " <th>4</th>\n", | ||
| " <td>4</td>\n", | ||
| " <td>We give guidance on which aspects to consider ...</td>\n", | ||
| " <td>-4.912832</td>\n", | ||
| " <td>6.691180</td>\n", | ||
| " </tr>\n", | ||
| " </tbody>\n", | ||
| "</table>\n", | ||
| "</div>" | ||
| ], | ||
| "text/plain": [ | ||
| " Unnamed: 0 sentence UMAP0 \\\n", | ||
| "0 0 A Hitchhiker’s Guide through the Bio-image Ana... -2.863276 \n", | ||
| "1 1 Modern research in the life sciences is unthin... -3.731295 \n", | ||
| "2 2 In the past decade, we observed a dramatic inc... -4.748690 \n", | ||
| "3 3 As it is increasingly difficult to keep track ... -4.183692 \n", | ||
| "4 4 We give guidance on which aspects to consider ... -4.912832 \n", | ||
| "\n", | ||
| " UMAP1 \n", | ||
| "0 8.680281 \n", | ||
| "1 7.875060 \n", | ||
| "2 6.128065 \n", | ||
| "3 6.847530 \n", | ||
| "4 6.691180 " | ||
| ] | ||
| }, | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "df = pd.read_csv(\"data/sentence_embeddings.csv\")\n", | ||
| "df.head()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "b5a69daa-2282-47d6-a020-cf3f8a5539fe", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "A word cloud plot is an interactive plot where you can select texts and from your selection, a wordcloud is generated." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "99720165-0a6c-4c0a-8922-e6350b5a70f3", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "application/vnd.jupyter.widget-view+json": { | ||
| "model_id": "b30463a9357f4846827c31acb06fc0bc", | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| }, | ||
| "text/plain": [ | ||
| "VBox(children=(HBox(children=(HBox(children=(VBox(children=(VBox(children=(HBox(children=(VBox(children=(Image…" | ||
| ] | ||
| }, | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "stackview.wordcloudplot(df, column_text=\"sentence\", column_x=\"UMAP0\", column_y=\"UMAP1\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "39c71ab5-43f3-4768-9e99-973905082950", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.10" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,82 @@ | ||
| def wordcloudplot(df, column_x: str = "x", column_y: str = "y", column_text: str = "text", | ||
| column_selection: str = "selection", | ||
| figsize=(4, 4), markersize=4, width=400, height=400): | ||
| """ | ||
| Visualizes a scatter plot of columns in a given dataframe next to a word cloud. | ||
| Per default, the dataframe should contain a column "text". | ||
| Parameters | ||
| ---------- | ||
| df: pandas.DataFrame | ||
| The dataframe to plot | ||
| column_x: str, optional | ||
| The column to use for the x-axis | ||
| column_y: str, optional | ||
| The column to use for the y-axis | ||
| column_text: str, optional | ||
| The column to use for the text that make the word cloud | ||
| column_selection: str, optional | ||
| The column to use for the selection | ||
| figsize: tuple, optional | ||
| The size of the scatter plot figure | ||
| markersize: int | ||
| The size of the markers | ||
| width: int | ||
| The width of the word cloud | ||
| height: int | ||
| The height of the word cloud | ||
| Returns | ||
| ------- | ||
| An ipywidgets widget | ||
| """ | ||
| import numpy as np | ||
| from ._grid import grid | ||
| from ._curtain import curtain | ||
| from ._slice import slice | ||
| from ._scatterplot import scatterplot | ||
| import functools | ||
| from wordcloud import WordCloud | ||
|
|
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| if column_selection in df.columns: | ||
| selected_texts = df[df['selection'] == 1][column_text] | ||
| text = "\n".join(selected_texts) | ||
| else: | ||
| selected_texts = df[column_text] | ||
| text = "\n".join(selected_texts) | ||
|
|
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| wordcloud = WordCloud(colormap="twilight", background_color="white", width=width, height=height).generate(text) | ||
| image = wordcloud.to_image() | ||
| selected_image = np.array(image) | ||
|
|
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| image_display = slice(selected_image) | ||
|
|
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| def update(selection, df, column_text, selected_image, widget): | ||
| selected_texts = df[column_text][list(selection)] | ||
| text = "\n".join(selected_texts) | ||
|
|
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| if len(text) == 0: | ||
| text = "empty wordcloud" | ||
|
|
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| wordcloud = WordCloud(colormap="twilight", background_color="white", width=width, height=height).generate(text) | ||
| image = wordcloud.to_image() | ||
| temp = np.array(image) | ||
|
|
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| # overwrite the pixels in the given image | ||
| np.copyto(selected_image, temp.astype(selected_image.dtype)) | ||
|
|
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| # redraw the visualization | ||
| widget.update() | ||
|
|
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| update_selection = functools.partial(update, df=df, column_text=column_text, selected_image=selected_image, | ||
| widget=image_display) | ||
|
|
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| scatterplot = scatterplot(df, column_x, column_y, column_selection, figsize=figsize, | ||
| selection_changed_callback=update_selection, markersize=markersize) | ||
|
|
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| return grid([[ | ||
| image_display, | ||
| scatterplot, | ||
|
|
||
| ]]) | ||
|
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