{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# run this cell to install pycaret in Google Colab\n", "# !pip install pycaret" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# If you are using Jupyter notebook, you can pip install pycaret using jupyter notebook or command line\n", "# pip install pycaret" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0.0\n" ] } ], "source": [ "from pycaret.utils import version\n", "version()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# only run this cell if you are using google colab\n", "# from pycaret.utils import enable_colab\n", "# enable_colab()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Importing Dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countryengenderloan_amountnonpaymentsectorstatus
0Dominican Republic\"Banco Esperanza\" is a group of 10 women looki...F1225partnerRetail0
1Dominican Republic\"Caminemos Hacia Adelante\" or \"Walking Forward...F1975lenderClothing0
2Dominican Republic\"Creciendo Por La Union\" is a group of 10 peop...F2175partnerClothing0
3Dominican Republic\"Cristo Vive\" (\"Christ lives\" is a group of 10...F1425partnerClothing0
4Dominican Republic\"Cristo Vive\" is a large group of 35 people, 2...F4025partnerFood0
\n", "
" ], "text/plain": [ " country en \\\n", "0 Dominican Republic \"Banco Esperanza\" is a group of 10 women looki... \n", "1 Dominican Republic \"Caminemos Hacia Adelante\" or \"Walking Forward... \n", "2 Dominican Republic \"Creciendo Por La Union\" is a group of 10 peop... \n", "3 Dominican Republic \"Cristo Vive\" (\"Christ lives\" is a group of 10... \n", "4 Dominican Republic \"Cristo Vive\" is a large group of 35 people, 2... \n", "\n", " gender loan_amount nonpayment sector status \n", "0 F 1225 partner Retail 0 \n", "1 F 1975 lender Clothing 0 \n", "2 F 2175 partner Clothing 0 \n", "3 F 1425 partner Clothing 0 \n", "4 F 4025 partner Food 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.datasets import get_data\n", "data = get_data('kiva')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\"Banco Esperanza\" is a group of 10 women looking to receive a small loan. Each of them has taken out a very small loan already, so this would be their second. With this loan the group is going to try and expand their small businesses and start generating more income.

\\r\\n\\r\\nEduviges is the group representative and leader of the group. Eduviges has a lot on the line because she has 6 children that she has to take care of. She told me that those children are the reason she wants to be successful. She wants to be able to provide a different life for them and show them that they can be successful as well.

\\r\\n\\r\\nEduviges has a very small business selling shoes and Avon products. She plans to expand using this loan and dreams of success. The whole group is ready for this new challenge and are on the road to bettering their lives. On behalf of Eduviges, the group, and Esperanza International--------- Thank you for your support!!!!'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.en[0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = data.head(1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Setting up Environment" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Description Value
session_id786
# Documents1000
Vocab Size3865
Custom StopwordsFalse
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycaret.nlp import *\n", "nlp1 = setup(data, target='en', session_id=786)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Train Topic Model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "lda = create_model('lda')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LdaModel(num_terms=3865, num_topics=4, decay=0.5, chunksize=100)\n" ] } ], "source": [ "print(lda)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "nmf = create_model('nmf')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NMF(alpha=0.0, beta_loss='frobenius', init='nndsvd', l1_ratio=0.0, max_iter=200,\n", " n_components=4, random_state=786, shuffle=False, solver='cd', tol=0.0001,\n", " verbose=0)\n" ] } ], "source": [ "print(nmf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Analyze Trained Model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "rgba(255, 153, 51, 0.6)", "line": { "color": "rgba(255, 153, 51, 1.0)", "width": 1 } }, "name": "count", "orientation": "v", "text": "", "type": "bar", "x": [ "loan", "business", "sell", "child", "year", "small", "group", "buy", "community", "live", "woman", "family", "old", "store", "entrepreneur", "hope", "start", "support", "work", "good", "clothing", "product", "make", "help", "able", "member", "use", "go", "new", "say", "school", "purchase", "people", "also", "mother", "well", "want", "husband", "life", "customer", "order", "hard", "increase", "income", "provide", "request", "run", "money", "take", "food", "house", "need", "grow", "operate", "keep", "age", "receive", "continue", "first", "plan", "home", "time", "thank", "explain", "large", "improve", "day", "expand", "future", "dream", "allow", "clothe", "currently", "education", "daughter", "profit", "esperanza", "young", "offer", "together", "shoe", "son", "get", "ago", "attend", "inventory", "enough", "pay", "item", "sale", "neighborhood", "lot", "invest", "grocery", "many", "santo_domingo", "give", "boy", "capital", "single" ], "y": [ 1951, 1905, 1146, 1107, 878, 748, 739, 693, 639, 628, 568, 522, 515, 512, 491, 477, 458, 458, 454, 441, 440, 433, 431, 418, 388, 367, 361, 360, 357, 355, 340, 333, 330, 327, 327, 327, 323, 322, 300, 299, 280, 275, 271, 269, 262, 261, 257, 242, 240, 237, 222, 220, 219, 218, 217, 215, 211, 209, 205, 204, 203, 203, 199, 187, 183, 179, 178, 173, 173, 173, 168, 167, 163, 160, 159, 155, 154, 153, 151, 143, 142, 141, 139, 139, 137, 134, 131, 130, 130, 128, 127, 126, 124, 123, 122, 120, 116, 115, 114, 112 ] } 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "rgba(255, 153, 51, 0.6)", "line": { "color": "rgba(255, 153, 51, 1.0)", "width": 1 } }, "name": "count", "orientation": "v", "text": "", "type": "bar", "x": [ "loan", "business", "group", "child", "sell", "small", "live", "member", "able", "hope", "woman", "community", "family", "support", "buy", "start", "help", "year", "life", "make", "store", "clothing", "go", "well", "work", "use", "want", "provide", "first", "thank", "old", "take", "also", "age", "dream", "future", "grow", "mother", "esperanza", "receive", "continue", "product", "food", "home", 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countryengenderloan_amountnonpaymentsectorstatusTopic_0Topic_1Topic_2Topic_3Dominant_TopicPerc_Dominant_Topic
0Dominican Republicwoman look receive small loan take small loan ...F1225partnerRetail00.8437240.0734550.0790650.003756Topic 00.84
1Dominican Republicwalk forward group entrepreneur seek second lo...F1975lenderClothing00.6750550.3057830.0160160.003147Topic 00.68
2Dominican Republicgroup people hope start business group look re...F2175partnerClothing00.9481050.0457130.0016550.004527Topic 00.95
3Dominican Republiclive group woman look receive first loan young...F1425partnerClothing00.9498890.0453050.0012860.003519Topic 00.95
4Dominican Republicvive large group people hope take loan many se...F4025partnerFood00.9207510.0744020.0012860.003560Topic 00.92
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" ], "text/plain": [ " country en \\\n", "0 Dominican Republic woman look receive small loan take small loan ... \n", "1 Dominican Republic walk forward group entrepreneur seek second lo... \n", "2 Dominican Republic group people hope start business group look re... \n", "3 Dominican Republic live group woman look receive first loan young... \n", "4 Dominican Republic vive large group people hope take loan many se... \n", "\n", " gender loan_amount nonpayment sector status Topic_0 Topic_1 \\\n", "0 F 1225 partner Retail 0 0.843724 0.073455 \n", "1 F 1975 lender Clothing 0 0.675055 0.305783 \n", "2 F 2175 partner Clothing 0 0.948105 0.045713 \n", "3 F 1425 partner Clothing 0 0.949889 0.045305 \n", "4 F 4025 partner Food 0 0.920751 0.074402 \n", "\n", " Topic_2 Topic_3 Dominant_Topic Perc_Dominant_Topic \n", "0 0.079065 0.003756 Topic 0 0.84 \n", "1 0.016016 0.003147 Topic 0 0.68 \n", "2 0.001655 0.004527 Topic 0 0.95 \n", "3 0.001286 0.003519 Topic 0 0.95 \n", "4 0.001286 0.003560 Topic 0 0.92 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = assign_model(lda)\n", "results.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning Resources:\n", "- PyCaret NLP Module : https://www.pycaret.org/nlp\n", "- NLP Tutorial (Level Beginner) : https://www.pycaret.org/nlp101\n", "- NLP Tutorial (Level Intermediate) : https://www.pycaret.org/nlp102\n", "- Topic Modeling in PyCaret (Video Tutorial) : https://www.youtube.com/watch?v=G6ShuoM3T1M" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }