Added comments and made a start on infer.py
This commit is contained in:
261
notebooks/getting_csv_for_inference.ipynb
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261
notebooks/getting_csv_for_inference.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 33,
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"id": "79ac71dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "aa9117f0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>source</th>\n",
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" <th>review_id</th>\n",
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" <th>user_name</th>\n",
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" <th>review_title</th>\n",
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" <th>review_description</th>\n",
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" <th>rating</th>\n",
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" <th>thumbs_up</th>\n",
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" <th>review_date</th>\n",
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" <th>developer_response</th>\n",
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" <th>developer_response_date</th>\n",
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" <th>appVersion</th>\n",
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" <th>laguage_code</th>\n",
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" <th>country_code</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Google Play</td>\n",
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" <td>18d6584c-d0e9-4833-a744-f607058aee97</td>\n",
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" <td>Milky Way</td>\n",
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" <td>NaN</td>\n",
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" <td>Suddenly, the driver can't have my location an...</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>2023-08-10 17:48:51</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>en</td>\n",
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" <td>in</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Google Play</td>\n",
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" <td>50a08f18-cece-4ddf-b617-028844c8aa28</td>\n",
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" <td>Bradlee Severa</td>\n",
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" <td>NaN</td>\n",
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" <td>Very cordial.. And helped with a quick turnaro...</td>\n",
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" <td>5</td>\n",
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" <td>0.0</td>\n",
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" <td>2023-08-10 17:38:35</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>4.485.10000</td>\n",
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" <td>en</td>\n",
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" <td>in</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Google Play</td>\n",
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" <td>b0d8e75a-80a7-4dcd-abaf-72b046dbeeb7</td>\n",
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" <td>Amit Aggarwal</td>\n",
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" <td>NaN</td>\n",
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" <td>Very good experience</td>\n",
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" <td>5</td>\n",
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" <td>0.0</td>\n",
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" <td>2023-08-10 17:38:17</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>4.486.10002</td>\n",
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" <td>en</td>\n",
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" <td>in</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Google Play</td>\n",
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" <td>502702a9-25ed-4373-a96c-7fa1f06caacd</td>\n",
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" <td>Bryant Inman</td>\n",
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" <td>NaN</td>\n",
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" <td>All I use</td>\n",
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" <td>5</td>\n",
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" <td>0.0</td>\n",
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" <td>2023-08-10 17:37:45</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>4.467.10008</td>\n",
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" <td>en</td>\n",
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" <td>in</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Google Play</td>\n",
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" <td>f47a3fb6-23db-49bd-9e63-f33c8d724d07</td>\n",
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" <td>Addie Whittaker</td>\n",
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" <td>NaN</td>\n",
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" <td>I have enjoyed traveling by Uber my drivers ha...</td>\n",
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" <td>5</td>\n",
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" <td>0.0</td>\n",
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" <td>2023-08-10 17:36:56</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>4.486.10002</td>\n",
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" <td>en</td>\n",
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" <td>in</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" source review_id user_name \\\n",
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"0 Google Play 18d6584c-d0e9-4833-a744-f607058aee97 Milky Way \n",
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"1 Google Play 50a08f18-cece-4ddf-b617-028844c8aa28 Bradlee Severa \n",
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"2 Google Play b0d8e75a-80a7-4dcd-abaf-72b046dbeeb7 Amit Aggarwal \n",
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"3 Google Play 502702a9-25ed-4373-a96c-7fa1f06caacd Bryant Inman \n",
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"4 Google Play f47a3fb6-23db-49bd-9e63-f33c8d724d07 Addie Whittaker \n",
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"\n",
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" review_title review_description rating \\\n",
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"0 NaN Suddenly, the driver can't have my location an... 1 \n",
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"1 NaN Very cordial.. And helped with a quick turnaro... 5 \n",
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"2 NaN Very good experience 5 \n",
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"3 NaN All I use 5 \n",
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"4 NaN I have enjoyed traveling by Uber my drivers ha... 5 \n",
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"\n",
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" thumbs_up review_date developer_response developer_response_date \\\n",
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"0 0.0 2023-08-10 17:48:51 NaN NaN \n",
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"1 0.0 2023-08-10 17:38:35 NaN NaN \n",
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"2 0.0 2023-08-10 17:38:17 NaN NaN \n",
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"3 0.0 2023-08-10 17:37:45 NaN NaN \n",
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"4 0.0 2023-08-10 17:36:56 NaN NaN \n",
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"\n",
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" appVersion laguage_code country_code \n",
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"0 NaN en in \n",
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"1 4.485.10000 en in \n",
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"2 4.486.10002 en in \n",
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"3 4.467.10008 en in \n",
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"4 4.486.10002 en in "
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv(\"../data/raw/uber_reviews.csv\", low_memory=False)\n",
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"df.head(5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "36683790",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.DataFrame'>\n",
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"RangeIndex: 1069616 entries, 0 to 1069615\n",
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"Data columns (total 13 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 source 1069616 non-null str \n",
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" 1 review_id 1069616 non-null str \n",
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" 2 user_name 1069615 non-null str \n",
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" 3 review_title 2180 non-null str \n",
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" 4 review_description 1069447 non-null str \n",
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" 5 rating 1069616 non-null int64 \n",
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" 6 thumbs_up 1067436 non-null float64\n",
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" 7 review_date 1069616 non-null str \n",
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" 8 developer_response 198264 non-null str \n",
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" 9 developer_response_date 197278 non-null str \n",
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" 10 appVersion 828068 non-null str \n",
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" 11 laguage_code 1069616 non-null str \n",
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" 12 country_code 1069616 non-null str \n",
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"dtypes: float64(1), int64(1), str(11)\n",
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"memory usage: 106.1 MB\n"
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]
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}
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],
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"source": [
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "0b1c7f73",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df[['review_description']].reset_index(drop=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "90e6d653",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.head(5)\n",
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"df.to_csv(\"../data/raw/review_description.csv\", index=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "multitag",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.15"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@@ -5,19 +5,7 @@
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"execution_count": 1,
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"id": "2b7cfa1a",
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'sklearn'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodel_selection\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
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]
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}
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],
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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@@ -1209,7 +1197,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.11"
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"version": "3.11.15"
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}
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},
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"nbformat": 4,
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37
src/infer.py
37
src/infer.py
@@ -2,14 +2,21 @@ import pandas as pd
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import numpy as np
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import torch
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import argparse
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from transformers import AutoTokenizer
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from torch.utils.tensorboard import SummaryWriter
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from transformers import AutoTokenizer
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from transformers import AutoModelForSequenceClassification
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# mappings
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binary_map = {1:'Yes', 0:'No'}
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aspect_map = {0:'App', 1:'Driver', 2:'General', 3:'Payment', 4:'Pricing', 5:'Service'}
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sentiment_map = {0:'Positive', 1:'Neutral', 2:'Negative'}
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label_names = {
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'bug_report': ['No', 'Yes'],
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'feature_request': ['No', 'Yes'],
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'aspect': ['App', 'Driver', 'General', 'Payment', 'Pricing', 'Service'],
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'aspect_sentiment': ['Positive', 'Neutral', 'Negative']
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}
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SEED = 4321
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torch.manual_seed(SEED)
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@@ -17,9 +24,31 @@ np.random.seed(SEED)
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def parse_args():
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parser = argparse.ArgumentParser(description="RECLASS, Multitask learning for review classification.")
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parser.add_argument("--model_path", type=str, help="Enter the models path / the desired .pt file")
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parser.add_argument("--task", type=str, default="all", choices=["all", "bug_report", "feature_request", "aspect", "aspect_sentiment"], help="Specific task to train for stl usage only" )
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parser.add_argument("--interactive", help="Loops reading input until exit")
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parser.add_argument("--model_path", type=str, required=True, help=".pt file path")
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parser.add_argument("--task", type=str, default="all", choices=["all", "bug_report", "feature_request", "aspect", "aspect_sentiment"])
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parser.add_argument("--interactive", help="Loops reading input until exit()")
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parser.add_argument("--text", help="Use command line text for input")
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parser.add_argument("--dataset", type=str, required=True, help="Enter a file for inference")
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return parser.parse_args()
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def main():
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args = parse_args()
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print(f'='*50)
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print(f' '*15 + "Starting inference")
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if torch.cuda.is_available():
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print(f' '*15 + "GPU:", torch.cuda.get_device_name(0))
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torch.cuda.manual_seed_all(SEED)
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torch.cuda.manual_seed(SEED)
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else:
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print(f' '*15 + "No GPUs available")
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print(f'='*50 + "\n")
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print(f"Running inference on: {args.model_path.upper()} using {args.dataset}")
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tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
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infer_data = f"data/processed/{args.dataset}_infer.csv"
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if __name__ == main():
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main()
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@@ -52,7 +52,7 @@ class Model(nn.Module):
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# Applied across shared cls token, before all task heads
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self.dropout = nn.Dropout(dropout_rate)
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# get logits for each head
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self.bug_head = nn.Linear(hidden_size, 2)
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self.feature_head = nn.Linear(hidden_size, 2)
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self.aspect_head = nn.Linear(hidden_size, 6)
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@@ -160,8 +160,8 @@ def preprocess_uber_reviews(input_path, output_path):
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return df_clean
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if __name__ == "__main__":
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input_file = "multitag/data/uber_reviews.csv"
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output_file = "multitag/data/uber_reviews_cleaned.csv"
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input_file = "data/raw/uber_reviews.csv"
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output_file = "data/raw/uber_reviews_cleaned.csv"
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df_clean = preprocess_uber_reviews(input_file, output_file)
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print("\nPreprocessing complete!")
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@@ -190,7 +190,6 @@ class Sampler:
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mini_sample = self.data.sample(200) # reading some samples manually
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return mini_sample
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def save_sample(self, sample_df,output_path):
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"""Save sample and display statistics"""
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@@ -126,6 +126,7 @@ def main():
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print("Aspect sentiment class weights:", aspect_sentiment_weights.cpu().numpy())
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# equal weighted task losses. unequal was considered but equal weights performed well without adding complexity
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# CrossEntropyLoss = LogSoftmax + NLLLoss (negative log likelihood)
|
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criterions = {
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||||
'bug_report': nn.CrossEntropyLoss(weight=bug_weights),
|
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'feature_request': nn.CrossEntropyLoss(weight=feature_weights),
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@@ -134,6 +135,7 @@ def main():
|
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}
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# -------------------- Optimizer and scheduler -------------------
|
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# adaptive momentum and weight decay keeps track of previous weight adaptions and ensures they dont get too large (weight also shrinks towards 0 each pass)
|
||||
optimizer = torch.optim.AdamW(
|
||||
model.parameters(),
|
||||
lr=args.lr,
|
||||
|
||||
Reference in New Issue
Block a user