From 55d0508a65e27f1579f558156c4f212e5209ab0b Mon Sep 17 00:00:00 2001 From: ahmaddynugroho Date: Fri, 30 Jun 2023 21:40:53 +0700 Subject: [PATCH] feat: topic tren analysis --- notebook/tren.ipynb | 350 ++++++++++++++++++++++++++++++++++++++++++++ notebook/tren.py | 21 +++ 2 files changed, 371 insertions(+) create mode 100644 notebook/tren.ipynb create mode 100644 notebook/tren.py diff --git a/notebook/tren.ipynb b/notebook/tren.ipynb new file mode 100644 index 0000000..ff8f622 --- /dev/null +++ b/notebook/tren.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.read_parquet('../dist/hpct.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "quarter = [\n", + " ['2022-01', '2022-02', '2022-03'],\n", + " ['2022-04', '2022-05', '2022-06'],\n", + " ['2022-07', '2022-08', '2022-09'],\n", + " ['2022-10', '2022-11', '2022-12'],\n", + "]\n", + "masks = [\n", + " [\n", + " df['date'].apply(lambda d: m in d)\n", + " for m in q\n", + " ]\n", + " for q in quarter\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_q1 = pd.concat([\n", + " df[masks[0][0]]['topics'],\n", + " df[masks[0][1]]['topics'],\n", + " df[masks[0][2]]['topics'],\n", + "])\n", + "df_q2 = pd.concat([\n", + " df[masks[1][0]]['topics'],\n", + " df[masks[1][1]]['topics'],\n", + " df[masks[1][2]]['topics'],\n", + "])\n", + "df_q3 = pd.concat([\n", + " df[masks[2][0]]['topics'],\n", + " df[masks[2][1]]['topics'],\n", + " df[masks[2][2]]['topics'],\n", + "])\n", + "df_q4 = pd.concat([\n", + " df[masks[3][0]]['topics'],\n", + " df[masks[3][1]]['topics'],\n", + " df[masks[3][2]]['topics'],\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "q1_mean = {}\n", + "q2_mean = {}\n", + "q3_mean = {}\n", + "q4_mean = {}\n", + "\n", + "def calculate_mean(t, qn_mean):\n", + " for tnp in t:\n", + " tn, tp = tnp\n", + " tn = int(tn)\n", + " if tn in qn_mean:\n", + " _, t_sum, ti = qn_mean[tn]\n", + " t_sum = t_sum + tp\n", + " ti = ti + 1\n", + " qn_mean[tn] = (t_sum / ti, t_sum, ti)\n", + " else:\n", + " qn_mean[tn] = (tp, tp, 1)\n", + "def calculate_q1(t):\n", + " calculate_mean(t, q1_mean)\n", + "def calculate_q2(t):\n", + " calculate_mean(t, q2_mean)\n", + "def calculate_q3(t):\n", + " calculate_mean(t, q3_mean)\n", + "def calculate_q4(t):\n", + " calculate_mean(t, q4_mean)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "85553 None\n", + "85554 None\n", + "85555 None\n", + "85556 None\n", + "85557 None\n", + " ... \n", + "1198330 None\n", + "1198331 None\n", + "1198332 None\n", + "1198333 None\n", + "1198334 None\n", + "Name: topics, Length: 307541, dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_q1.apply(calculate_q1)\n", + "df_q2.apply(calculate_q2)\n", + "df_q3.apply(calculate_q3)\n", + "df_q4.apply(calculate_q4)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_quarterly_topics = {}\n", + "\n", + "def port_to_df(qn_mean):\n", + " for n in qn_mean:\n", + " if n in df_quarterly_topics:\n", + " df_quarterly_topics[n].append(qn_mean[n][0])\n", + " else:\n", + " df_quarterly_topics[n] = [qn_mean[n][0]]\n", + "\n", + "port_to_df(q1_mean)\n", + "port_to_df(q2_mean)\n", + "port_to_df(q3_mean)\n", + "port_to_df(q4_mean)\n", + "\n", + "df_quarterly_topics = pd.DataFrame(df_quarterly_topics)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "figs, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "colors = [\n", + " 'tab:brown',\n", + " 'tab:pink',\n", + " 'tab:gray',\n", + " 'tab:olive',\n", + " 'tab:cyan',\n", + "]\n", + "\n", + "for i in range(5):\n", + " ax1.plot(df_quarterly_topics[i], label=f'{i+1}')\n", + "for i in range(5, 10):\n", + " ax2.plot(df_quarterly_topics[i],\n", + " f'{colors[i-5]}', label=f'{i+1}')\n", + "\n", + "ax1.set(xlabel='Kuarter', ylabel='Rata-rata persentase topik')\n", + "ax2.set(xlabel='Kuarter', ylabel='Rata-rata persentase topik')\n", + "\n", + "ax1.legend()\n", + "ax1.axis([0, 3, 0.35, 0.53])\n", + "ax2.legend()\n", + "ax2.axis([0, 3, 0.35, 0.53])\n", + "\n", + "plt.subplots_adjust(wspace=0.4)\n", + "plt.show()\n", + "\n", + "figs.savefig('../dist/quarterly_topics.svg')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/tren.py b/notebook/tren.py new file mode 100644 index 0000000..8179c47 --- /dev/null +++ b/notebook/tren.py @@ -0,0 +1,21 @@ +from pickle import load +import pandas as pd + +print('[INFO] preparing data') +df = pd.read_parquet('../dist/hp.parquet') + +with open('../dist/dictionary.pickle', 'rb') as f: + dictionary = load(f) +with open('../dist/corpus.pickle', 'rb') as f: + corpus = load(f) +with open('../dist/model.pickle', 'rb') as f: + model = load(f) + +print('[INFO] building corpus') +df['corpus'] = corpus + +print('[INFO] building topics') +df['topics'] = model.get_document_topics(df['corpus']) + +print('[INFO] saving df') +df.to_parquet('../dist/hpct.parquet') \ No newline at end of file