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Updated deep learning sample notebooks - 2 #1621

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<insert pull request description here>


Checklist

Please go through each entry in the below checklist and mark an 'X' if that condition has been met. Every entry should be marked with an 'X' to be get the Pull Request approved.

  • All imports are in the first cell?
    • First block of imports are standard libraries
    • Second block are 3rd party libraries
    • Third block are all arcgis imports? Note that in some cases, for samples, it is a good idea to keep the imports next to where they are used, particularly for uncommonly used features that we want to highlight.
  • All GIS object instantiations are one of the following?
    • gis = GIS()
    • gis = GIS('home') or gis = GIS('pro')
    • gis = GIS(profile="your_online_portal")
    • gis = GIS(profile="your_enterprise_portal")
  • If this notebook requires setup or teardown, did you add the appropriate code to ./misc/setup.py and/or ./misc/teardown.py?
  • If this notebook references any portal items that need to be staged on AGOL/Python API playground, did you coordinate with a Python API team member to stage the item the correct way with the api_data_owner user?
  • If the notebook requires working with local data (such as CSV, FGDB, SHP, Raster files), upload the files as items to the Geosaurus Online Org using api_data_owner account and change the notebook to first download and unpack the files.
  • Code simplified & split out across multiple cells, useful comments?
  • Consistent voice/tense/narrative style? Thoroughly checked for typos?
  • All images used like <img src="base64str_here"> instead of <img src="https://some.url">? All map widgets contain a static image preview? (Call mapview_inst.take_screenshot() to do so)
  • All file paths are constructed in an OS-agnostic fashion with os.path.join()? (Instead of r"\foo\bar", os.path.join(os.path.sep, "foo", "bar"), etc.)
  • Is your code formatted using Jupyter Black? You can use Jupyter Black to format your code in the notebook.
  • IF YOU WANT THIS SAMPLE TO BE DISPLAYED ON THE DEVELOPERS.ARCGIS.COM WEBSITE, ping @ mohi9282 so he can add it to the list for the next deploy

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{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Identifying country names from incomplete house addresses"]}, {"cell_type": "markdown", "metadata": {}, "source": ["<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\">\n", "<ul class=\"toc-item\">\n", "<li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\">Introduction</a></span></li>\n", "<li><span><a href=\"#Prerequisites\" data-toc-modified-id=\"Prerequisites-2\">Prerequisites</a></span></li>\n", "<li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-3\">Imports</a></span></li>\n", "<li><span><a href=\"#Data-preparation\" data-toc-modified-id=\"Data-preparation-4\">Data preparation</a></span></li>\n", "<li><span><a href=\"#TextClassifier-model\" data-toc-modified-id=\"TextClassifier-model-5\">TextClassifier model</a></span></li>\n", "<ul class=\"toc-item\">\n", "<li><span><a href=\"#Load-model-architecture\" data-toc-modified-id=\"Load-model-architecture-5.1\">Load model architecture</a></span></li>\n", "<li><span><a href=\"#Model-training\" data-toc-modified-id=\"Model-training-5.2\">Model training</a></span></li> \n", "<li><span><a href=\"#Validate-results\" data-toc-modified-id=\"Validate-results-5.3\">Validate results</a></span></li>\n", "<li><span><a href=\"#Model-metrics\" data-toc-modified-id=\"Model-metrics-5.4\">Model metrics</a></span></li> \n", "<li><span><a href=\"#Get-misclassified-records\" data-toc-modified-id=\"Get-misclassified-records-5.5\">Get misclassified records</a></span></li>\n", "<li><span><a href=\"#Saving-the-trained-model\" data-toc-modified-id=\"Saving-the-trained-model-5.6\">Saving the trained model</a></span></li>\n", "</ul>\n", "<li><span><a href=\"#Model-inference\" data-toc-modified-id=\"Model-inference-6\">Model inference</a></span></li>\n", "<li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-7\">Conclusion</a></span></li>\n", "<li><span><a href=\"#References\" data-toc-modified-id=\"References-8\">References</a></span></li>\n", "</ul></div>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Introduction"]}, {"cell_type": "markdown", "metadata": {}, "source": ["[Geocoding](https://en.wikipedia.org/wiki/Geocoding) is the process of taking input text, such as an **address** or the name of a place, and returning a **latitude/longitude** location for that place. In this notebook, we will be picking up a dataset consisting of incomplete house addresses from 10 countries. We will build a classifier using `TextClassifier` class of `arcgis.learn.text` module to predict the country for these incomplete house addresses. \n", "\n", "The house addresses in the dataset consist of text in multiple languages like English, Japanese, French, Spanish, etc. The dataset is a small subset of the house addresses taken from [OpenAddresses data](http://results.openaddresses.io/) \n", "\n", "**A note on the dataset**\n", "- The data is collected around 2020-05-27 by [OpenAddresses](http://openaddresses.io).\n", "- The data licenses can be found in `data/country-classifier/LICENSE.txt`."]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Prerequisites"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- Data preparation and model training workflows using arcgis.learn have a dependency on [transformers](https://huggingface.co/transformers/v3.0.2/index.html). Refer to the section **\"Install deep learning dependencies of arcgis.learn module\"** [on this page](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-deep-learning-dependencies) for detailed documentation on the installation of the dependencies.\n", "\n", "- **Labeled data**: For `TextClassifier` to learn, it needs to see documents/texts that have been assigned a label. Labeled data for this sample notebook is located at `data/country-classifier/house-addresses.csv`\n", "\n", "- To learn more about how `TextClassifier` works, please see the guide on [Text Classification with arcgis.learn](https://developers.arcgis.com/python/guide/text-classification)."]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Imports"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import os\n", "import zipfile\n", "import pandas as pd\n", "from pathlib import Path\n", "from arcgis.gis import GIS\n", "from arcgis.learn import prepare_textdata\n", "from arcgis.learn.text import TextClassifier"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["gis = GIS('home')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Data preparation\n", "\n", "Data preparation involves splitting the data into training and validation sets, creating the necessary data structures for loading data into the model and so on. The `prepare_data()` function can directly read the training samples and automate the entire process."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/html": ["<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n", " <div class=\"item_left\" style=\"width: 210px; float: left;\">\n", " <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=ab36969cfe814c89ba3b659cf734492a' target='_blank'>\n", " <img 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' width='200' height='133' class=\"itemThumbnail\">\n", " </a>\n", " </div>\n", "\n", " <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n", " <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=ab36969cfe814c89ba3b659cf734492a' target='_blank'><b>country_classifier</b>\n", " </a>\n", " <br/>Training data for TextClassifier class of arcgis.learn.text module<img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/layers16.png' style=\"vertical-align:middle;\">Image Collection by api_data_owner\n", " <br/>Last Modified: December 01, 2020\n", " <br/>0 comments, 0 views\n", " </div>\n", " </div>\n", " "], "text/plain": ["<Item title:\"country_classifier\" type:Image Collection owner:api_data_owner>"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["training_data = gis.content.get('ab36969cfe814c89ba3b659cf734492a')\n", "training_data"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": ["filepath = training_data.download(file_name=training_data.name)"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": ["with zipfile.ZipFile(filepath, 'r') as zip_ref:\n", " zip_ref.extractall(Path(filepath).parent)"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["DATA_ROOT = Path(os.path.join(os.path.splitext(filepath)[0]))"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": ["data = prepare_textdata(DATA_ROOT, \"classification\", train_file=\"house-addresses.csv\", \n", " text_columns=\"Address\", label_columns=\"Country\", batch_size=64)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The `show_batch()` method can be used to see the training samples, along with labels."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070 th {\n", " text-align: left;\n", " }#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col1{\n", " text-align: left;\n", " }</style><table id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Address</th> <th class=\"col_heading level0 col1\" >Country</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >S/N, LG CASARES, 32170</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >SN, CALLE E. NABARRETE, PLAN DE AYALA (CAMPO CINCO), Ahome, Sinaloa</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >152, RUA SANTA RITA DURAO, Belo Horizonte, MG, 30140-110</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >BR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >133, Warande, 201, 9660</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >BE</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >4000, 13 Avenue SE, 133, MEDICINE HAT</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >12, Avenue de la R\u00e9publique, Beauvais, 60000</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >FR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >1487-6, \u6709\u99ac\u753a</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >JP</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >4, Rue d'Houat, Saint-Gilles, 35590</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >FR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >32, Hartjie My Liefie Avenue, Bloemfontein, Mangaung</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >ZA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >Street, Centurion, City of Tshwane</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >ZA</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20eb7a87cc8>"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["data.show_batch(10)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# TextClassifier model"]}, {"cell_type": "markdown", "metadata": {}, "source": ["`TextClassifier` model in `arcgis.learn.text` is built on top of [Hugging Face Transformers](https://huggingface.co/transformers/v3.0.2/index.html) library. The model training and inferencing workflow are similar to computer vision models in `arcgis.learn`. \n", "\n", "Run the command below to see what backbones are supported for the text classification task."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['BERT', 'RoBERTa', 'DistilBERT', 'ALBERT', 'FlauBERT', 'CamemBERT', 'XLNet', 'XLM', 'XLM-RoBERTa', 'Bart', 'ELECTRA', 'Longformer', 'MobileBERT']\n"]}], "source": ["print(TextClassifier.supported_backbones)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Call the model's `available_backbone_models()` method with the backbone name to get the available models for that backbone. The call to **available_backbone_models** method will list out only few of the available models for each backbone. Visit [this](https://huggingface.co/transformers/pretrained_models.html) link to get a complete list of models for each backbone."]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["('xlm-roberta-base', 'xlm-roberta-large')\n"]}], "source": ["print(TextClassifier.available_backbone_models(\"xlm-roberta\"))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Load model architecture"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Invoke the `TextClassifier` class by passing the data and the backbone you have chosen. The dataset consists of house addresses in multiple languages like Japanese, English, French, Spanish, etc., hence we will use a [multi-lingual transformer backbone](https://huggingface.co/transformers/v3.0.2/multilingual.html) to train our model."]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/html": [], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/html": [], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model = TextClassifier(data, backbone=\"xlm-roberta-base\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Model training"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The `learning rate`[[1]](#References) is a **tuning parameter** that determines the step size at each iteration while moving toward a minimum of a loss function, it represents the speed at which a machine learning model **\"learns\"**. `arcgis.learn` includes a learning rate finder, and is accessible through the model's `lr_find()` method, that can automatically select an **optimum learning rate**, without requiring repeated experiments."]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"image/png": 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"text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}, {"data": {"text/plain": ["0.001202264434617413"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["model.lr_find()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Training the model is an iterative process. We can train the model using its `fit()` method till the validation loss (or error rate) continues to go down with each training pass also known as an epoch. This is indicative of the model learning the task."]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"data": {"text/html": ["<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: left;\">\n", " <th>epoch</th>\n", " <th>train_loss</th>\n", " <th>valid_loss</th>\n", " <th>accuracy</th>\n", " <th>error_rate</th>\n", " <th>time</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>0.308638</td>\n", " <td>0.182150</td>\n", " <td>0.929600</td>\n", " <td>0.070400</td>\n", " <td>05:28</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>0.103615</td>\n", " <td>0.068711</td>\n", " <td>0.970600</td>\n", " <td>0.029400</td>\n", " <td>05:46</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>0.076326</td>\n", " <td>0.041269</td>\n", " <td>0.981600</td>\n", " <td>0.018400</td>\n", " <td>05:30</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>0.055707</td>\n", " <td>0.034307</td>\n", " <td>0.986300</td>\n", " <td>0.013700</td>\n", " <td>05:33</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>0.041812</td>\n", " <td>0.032772</td>\n", " <td>0.986400</td>\n", " <td>0.013600</td>\n", " <td>05:27</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>0.049993</td>\n", " <td>0.032165</td>\n", " <td>0.986600</td>\n", " <td>0.013400</td>\n", " <td>05:26</td>\n", " </tr>\n", " </tbody>\n", "</table>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model.fit(epochs=6, lr=0.001)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Validate results"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Once we have the trained model, we can see the results to see how it performs."]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070 th {\n", " text-align: left;\n", " 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" text-align: left;\n", " }</style><table id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >text</th> <th class=\"col_heading level0 col1\" >target</th> <th class=\"col_heading level0 col2\" >prediction</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >SN, AVENIDA JOSE MARIA MORELOS Y PAVON OTE., APATZING\u00c1N DE LA CONSTITUCI\u00d3N, Apatzing\u00e1n, Michoac\u00e1n de Ocampo</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row0_col2\" class=\"data row0 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >906, AVENIDA JOSEFA ORT\u00cdZ DE DOM\u00cdNGUEZ, CIUDAD MENDOZA, Camerino Z. Mendoza, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row1_col2\" class=\"data row1 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >32, CIRCUITO JOS\u00c9 MAR\u00cdA URIARTE, FRACCIONAMIENTO RANCHO ALEGRE, Tlajomulco de Z\u00fa\u00f1iga, Jalisco</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row2_col2\" class=\"data row2 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >SN, ESTRADA SP 250 SENTIDO GRAMADAO, LADO DIREITO FAZENDA SAO RAFAEL CASA 4, S\u00e3o Miguel Arcanjo, SP, 18230-000</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >BR</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row3_col2\" class=\"data row3 col2\" >BR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >SN, CALLE JOSEFA ORT\u00cdZ DE DOM\u00cdNGUEZ, RINC\u00d3N DE BUENA VISTA, Omealca, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row4_col2\" class=\"data row4 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >SN, CALLE MICHOACAN, DOLORES HIDALGO CUNA DE LA INDEPENDENCIA NACIONAL, Dolores Hidalgo Cuna de la Independencia Nacional, Guanajuato</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row5_col2\" class=\"data row5 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >SN, CALLE VERDUZCO, COALCOM\u00c1N DE V\u00c1ZQUEZ PALLARES, Coalcom\u00e1n de V\u00e1zquez Pallares, Michoac\u00e1n de Ocampo</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row6_col2\" class=\"data row6 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >1712, CALLE M\u00c1RTIRES DEL 7 DE ENERO, CIUDAD MENDOZA, Camerino Z. Mendoza, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row7_col2\" class=\"data row7 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >SN, AVENIDA JACOBO G\u00c1LVEZ, FRACCIONAMIENTO RANCHO ALEGRE, Tlajomulco de Z\u00fa\u00f1iga, Jalisco</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row8_col2\" class=\"data row8 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >SN, ANDADOR MZNA 6 AMP. LOS ROBLES, EL PUEBLITO (CRUCERO NACIONAL), C\u00f3rdoba, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row9_col2\" class=\"data row9 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row10_col0\" class=\"data row10 col0\" >SN, CALLE S\u00c9PTIMA PONIENTE SUR (EJE VIAL), COMIT\u00c1N DE DOM\u00cdNGUEZ, Comit\u00e1n de Dom\u00ednguez, Chiapas</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row10_col1\" class=\"data row10 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row10_col2\" class=\"data row10 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row11_col0\" class=\"data row11 col0\" >18, CALLE FELIPE GORRITI / FELIPE GORRITI KALEA, Pamplona / Iru\u00f1a, Pamplona / Iru\u00f1a, Navarra, 31004</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row11_col1\" class=\"data row11 col1\" >ES</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row11_col2\" class=\"data row11 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row12_col0\" class=\"data row12 col0\" >SN, RUA X VINTE E SEIS, QUADRA 14 LOTE 35 SALA 3, Aparecida de Goi\u00e2nia, GO, 74922-680</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row12_col1\" class=\"data row12 col1\" >BR</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row12_col2\" class=\"data row12 col2\" >BR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row13_col0\" class=\"data row13 col0\" >SN, CALLE NINGUNO, HEROICA CIUDAD DE JUCHIT\u00c1N DE ZARAGOZA, Heroica Ciudad de Juchit\u00e1n de Zaragoza, Oaxaca</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row13_col1\" class=\"data row13 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row13_col2\" class=\"data row13 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row14_col0\" class=\"data row14 col0\" >1169, RUA DOUTOR ALBUQUERQUE LINS, BLOCO B ANDAR 11 APARTAMENTO 112B, S\u00e3o Paulo, SP, 01203-001</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row14_col1\" class=\"data row14 col1\" >BR</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row14_col2\" class=\"data row14 col2\" >BR</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20ebbd53bc8>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model.show_results(15)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Test the model prediction on an input text"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["('1016, 8A, CL RICARDO LEON - SANTA ANA (CARTAGENA), 30319', 'ES', 1.0)\n"]}], "source": ["text = \"\"\"1016, 8A, CL RICARDO LEON - SANTA ANA (CARTAGENA), 30319\"\"\"\n", "print(model.predict(text))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Model metrics\n", "\n", "To get a sense of how well the model is trained, we will calculate some important metrics for our `text-classifier` model. First, to find how accurate[[2]](#References) the model is in correctly predicting the classes in the dataset, we will call the model's `accuracy()` method."]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": {"text/plain": ["0.9866"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["model.accuracy()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Other important metrics to look at are Precision, Recall & F1-measures [[3]](#References). To find `precision`, `recall` & `f1` scores per label/class we will call the model's `metrics_per_label()` method."]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", " <div>\n", " <style>\n", " /* Turns off some styling */\n", " progress {\n", " /* gets rid of default border in Firefox and Opera. */\n", " border: none;\n", " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", " background-size: auto;\n", " }\n", " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", " background: #F44336;\n", " }\n", " </style>\n", " <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " 100.00% [10000/10000 05:05<00:00]\n", " </div>\n", " "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}, {"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>Precision_score</th>\n", " <th>Recall_score</th>\n", " <th>F1_score</th>\n", " <th>Support</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>AU</th>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>929.0</td>\n", " </tr>\n", " <tr>\n", " <th>BE</th>\n", " <td>0.9990</td>\n", " <td>0.9990</td>\n", " <td>0.9990</td>\n", " <td>1043.0</td>\n", " </tr>\n", " <tr>\n", " <th>BR</th>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>950.0</td>\n", " </tr>\n", " <tr>\n", " <th>CA</th>\n", " <td>0.9088</td>\n", " <td>0.9709</td>\n", " <td>0.9388</td>\n", " <td>996.0</td>\n", " </tr>\n", " <tr>\n", " <th>ES</th>\n", " <td>0.9969</td>\n", " <td>0.9980</td>\n", " <td>0.9975</td>\n", " <td>982.0</td>\n", " </tr>\n", " <tr>\n", " <th>FR</th>\n", " <td>1.0000</td>\n", " <td>0.9990</td>\n", " <td>0.9995</td>\n", " <td>1009.0</td>\n", " </tr>\n", " <tr>\n", " <th>JP</th>\n", " <td>1.0000</td>\n", " <td>0.9990</td>\n", " <td>0.9995</td>\n", " <td>989.0</td>\n", " </tr>\n", " <tr>\n", " <th>MX</th>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1024.0</td>\n", " </tr>\n", " <tr>\n", " <th>US</th>\n", " <td>0.9691</td>\n", " <td>0.9093</td>\n", " <td>0.9383</td>\n", " <td>1070.0</td>\n", " </tr>\n", " <tr>\n", " <th>ZA</th>\n", " <td>0.9990</td>\n", " <td>0.9980</td>\n", " <td>0.9985</td>\n", " <td>1008.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>"], "text/plain": [" Precision_score Recall_score F1_score Support\n", "AU 1.0000 1.0000 1.0000 929.0\n", "BE 0.9990 0.9990 0.9990 1043.0\n", "BR 1.0000 1.0000 1.0000 950.0\n", "CA 0.9088 0.9709 0.9388 996.0\n", "ES 0.9969 0.9980 0.9975 982.0\n", "FR 1.0000 0.9990 0.9995 1009.0\n", "JP 1.0000 0.9990 0.9995 989.0\n", "MX 1.0000 1.0000 1.0000 1024.0\n", "US 0.9691 0.9093 0.9383 1070.0\n", "ZA 0.9990 0.9980 0.9985 1008.0"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["model.metrics_per_label()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Get misclassified records\n", "\n", "Its always a good idea to see the cases where your model is not performing well. This step will help us to:\n", "- Identify if there is a problem in the dataset.\n", "- Identify if there is a problem with text/documents belonging to a specific label/class. \n", "- Identify if there is a class imbalance in your dataset, due to which the model didn't see much of the labeled data for a particular class, hence not able to learn properly about that class.\n", "\n", "To get the **misclassified records** we will call the model's `get_misclassified_records` method."]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", " <div>\n", " <style>\n", " /* Turns off some styling */\n", " progress {\n", " /* gets rid of default border in Firefox and Opera. */\n", " border: none;\n", " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", " background-size: auto;\n", " }\n", " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", " background: #F44336;\n", " }\n", " </style>\n", " <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " 100.00% [10000/10000 05:07<00:00]\n", " </div>\n", " "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["misclassified_records = model.get_misclassified_records()"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [{"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_7224c262_33ad_11eb_b039_a4bb6dafa070 th {\n", " text-align: left;\n", " 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" text-align: left;\n", " }</style><table id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Address</th> <th class=\"col_heading level0 col1\" >Target</th> <th class=\"col_heading level0 col2\" >Prediction</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >107, HAMILTON CT, EASLEY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row0_col2\" class=\"data row0 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >40443, CHEAKAMUS WAY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row1_col2\" class=\"data row1 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >309, SOUTH STREET, BARABOO</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row2_col2\" class=\"data row2 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >19109, DUTY ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row3_col2\" class=\"data row3 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >8171, CR 29, 43357</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row4_col2\" class=\"data row4 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >6565, WISCONSIN AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row5_col2\" class=\"data row5 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >7332, 25TH AVENUE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row6_col2\" class=\"data row6 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >14778, CAMINITO PUNTA ARENAS, Del Mar, 92014</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row7_col2\" class=\"data row7 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >916, PINE ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row8_col2\" class=\"data row8 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >168, BROAD SOUND PL, Iredell</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row9_col2\" class=\"data row9 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row10_col0\" class=\"data row10 col0\" >316, BEAUMIER LANE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row10_col1\" class=\"data row10 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row10_col2\" class=\"data row10 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row11_col0\" class=\"data row11 col0\" >1518, BARCLAY ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row11_col1\" class=\"data row11 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row11_col2\" class=\"data row11 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row12_col0\" class=\"data row12 col0\" >235, GLADEFIELD DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row12_col1\" class=\"data row12 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row12_col2\" class=\"data row12 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row13_col0\" class=\"data row13 col0\" >2701, CURRANT CV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row13_col1\" class=\"data row13 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row13_col2\" class=\"data row13 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row14_col0\" class=\"data row14 col0\" >94, ASPETUCK VILLAGE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row14_col1\" class=\"data row14 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row14_col2\" class=\"data row14 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row15_col0\" class=\"data row15 col0\" >27, South 10Th Avenue</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row15_col1\" class=\"data row15 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row15_col2\" class=\"data row15 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row16_col0\" class=\"data row16 col0\" >254, GREEN HILLS DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row16_col1\" class=\"data row16 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row16_col2\" class=\"data row16 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row17_col0\" class=\"data row17 col0\" >1025, BROOKFORD RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row17_col1\" class=\"data row17 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row17_col2\" class=\"data row17 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row18_col0\" class=\"data row18 col0\" >8981, FAIRMOUNT RD SE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row18_col1\" class=\"data row18 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row18_col2\" class=\"data row18 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row19_col0\" class=\"data row19 col0\" >5, PICKWICK LA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row19_col1\" class=\"data row19 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row19_col2\" class=\"data row19 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row20_col0\" class=\"data row20 col0\" >540, CHARLESTON HWY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row20_col1\" class=\"data row20 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row20_col2\" class=\"data row20 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row21_col0\" class=\"data row21 col0\" >1763, RD, McDowell</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row21_col1\" class=\"data row21 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row21_col2\" class=\"data row21 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row22_col0\" class=\"data row22 col0\" >40022, GOVERNMENT RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row22_col1\" class=\"data row22 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row22_col2\" class=\"data row22 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row23_col0\" class=\"data row23 col0\" >435, EMORY RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row23_col1\" class=\"data row23 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row23_col2\" class=\"data row23 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row24_col0\" class=\"data row24 col0\" >1, Bokomo Road, Malmesbury, Swartland</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row24_col1\" class=\"data row24 col1\" >ZA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row24_col2\" class=\"data row24 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row25_col0\" class=\"data row25 col0\" >3529, BRADLEY AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row25_col1\" class=\"data row25 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row25_col2\" class=\"data row25 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row26_col0\" class=\"data row26 col0\" >710, 9TH ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row26_col1\" class=\"data row26 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row26_col2\" class=\"data row26 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row27_col0\" class=\"data row27 col0\" >1421, PINOT NOIR DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row27_col1\" class=\"data row27 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row27_col2\" class=\"data row27 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row28_col0\" class=\"data row28 col0\" >1224, ST LUKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row28_col1\" class=\"data row28 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row28_col2\" class=\"data row28 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row29_col0\" class=\"data row29 col0\" >1822, RT 6</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row29_col1\" class=\"data row29 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row29_col2\" class=\"data row29 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row30_col0\" class=\"data row30 col0\" >140</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row30_col1\" class=\"data row30 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row30_col2\" class=\"data row30 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row31_col0\" class=\"data row31 col0\" >2302, RIVER MIST RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row31_col1\" class=\"data row31 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row31_col2\" class=\"data row31 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row32_col0\" class=\"data row32 col0\" >4159, Maher St</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row32_col1\" class=\"data row32 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row32_col2\" class=\"data row32 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row33_col0\" class=\"data row33 col0\" >24, DEARBORN STREET, Franklin</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row33_col1\" class=\"data row33 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row33_col2\" class=\"data row33 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row34_col0\" class=\"data row34 col0\" >2109, MALDON PL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row34_col1\" class=\"data row34 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row34_col2\" class=\"data row34 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row35_col0\" class=\"data row35 col0\" >Flora Road, Moquini Coastal Estate, Mossel Bay</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row35_col1\" class=\"data row35 col1\" >ZA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row35_col2\" class=\"data row35 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row36_col0\" class=\"data row36 col0\" >5990, THIROS CIR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row36_col1\" class=\"data row36 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row36_col2\" class=\"data row36 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row37_col0\" class=\"data row37 col0\" >167, CARLSBAD CAVERNS ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row37_col1\" class=\"data row37 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row37_col2\" class=\"data row37 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row38_col0\" class=\"data row38 col0\" >2119, E 3RD AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row38_col1\" class=\"data row38 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row38_col2\" class=\"data row38 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row39_col0\" class=\"data row39 col0\" >505, HARLEY WAY, SHARON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row39_col1\" class=\"data row39 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row39_col2\" class=\"data row39 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row40_col0\" class=\"data row40 col0\" >1354, ST LUKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row40_col1\" class=\"data row40 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row40_col2\" class=\"data row40 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row41_col0\" class=\"data row41 col0\" >3140, SOUTHWOOD RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row41_col1\" class=\"data row41 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row41_col2\" class=\"data row41 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row42_col0\" class=\"data row42 col0\" >4205, Glenn</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row42_col1\" class=\"data row42 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row42_col2\" class=\"data row42 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row43_col0\" class=\"data row43 col0\" >103, BILLETS BRIDGE RD, Courthouse, Camden</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row43_col1\" class=\"data row43 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row43_col2\" class=\"data row43 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row44_col0\" class=\"data row44 col0\" >838087, 4TH LINE EAST, TOWNSHIP OF MULMUR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row44_col1\" class=\"data row44 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row44_col2\" class=\"data row44 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row45_col0\" class=\"data row45 col0\" >3317, Doncaster DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row45_col1\" class=\"data row45 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row45_col2\" class=\"data row45 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row46_col0\" class=\"data row46 col0\" >2726, E TRUESDALE DRIVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row46_col1\" class=\"data row46 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row46_col2\" class=\"data row46 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row47_col0\" class=\"data row47 col0\" >2131, SHAMROCK DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row47_col1\" class=\"data row47 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row47_col2\" class=\"data row47 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row48_col0\" class=\"data row48 col0\" >1185, ST ANNES RD, Unit 99</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row48_col1\" class=\"data row48 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row48_col2\" class=\"data row48 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row49_col0\" class=\"data row49 col0\" >9109, CONTESSA CT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row49_col1\" class=\"data row49 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row49_col2\" class=\"data row49 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row50_col0\" class=\"data row50 col0\" >408, RUBY RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row50_col1\" class=\"data row50 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row50_col2\" class=\"data row50 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row51_col0\" class=\"data row51 col0\" >2101, FONTAINE RD, 10</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row51_col1\" class=\"data row51 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row51_col2\" class=\"data row51 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row52_col0\" class=\"data row52 col0\" >52, OLD HWY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row52_col1\" class=\"data row52 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row52_col2\" class=\"data row52 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row53_col0\" class=\"data row53 col0\" >200, EAGLE SHORE DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row53_col1\" class=\"data row53 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row53_col2\" class=\"data row53 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row54_col0\" class=\"data row54 col0\" >1450, MEADOW AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row54_col1\" class=\"data row54 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row54_col2\" class=\"data row54 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row55_col0\" class=\"data row55 col0\" >0, BEECH ST, Rockingham</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row55_col1\" class=\"data row55 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row55_col2\" class=\"data row55 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row56_col0\" class=\"data row56 col0\" >291, SPRY POINT RD, LITTLE POND, KNS</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row56_col1\" class=\"data row56 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row56_col2\" class=\"data row56 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row57_col0\" class=\"data row57 col0\" >10905, YORKTOWN CV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row57_col1\" class=\"data row57 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row57_col2\" class=\"data row57 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row58_col0\" class=\"data row58 col0\" >3903, TATTLE BRANCH RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row58_col1\" class=\"data row58 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row58_col2\" class=\"data row58 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row59_col0\" class=\"data row59 col0\" >682, ISLAND 90 SIX MILE LAKE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row59_col1\" class=\"data row59 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row59_col2\" class=\"data row59 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row60_col0\" class=\"data row60 col0\" >1887, LITITZ PIKE, UNIT 4, MANHEIM TOWNSHIP</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row60_col1\" class=\"data row60 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row60_col2\" class=\"data row60 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row61_col0\" class=\"data row61 col0\" >2821, E 18TH AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row61_col1\" class=\"data row61 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row61_col2\" class=\"data row61 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row62_col0\" class=\"data row62 col0\" >2106, MARK ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row62_col1\" class=\"data row62 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row62_col2\" class=\"data row62 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row63_col0\" class=\"data row63 col0\" >25890, 119TH STREET</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row63_col1\" class=\"data row63 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row63_col2\" class=\"data row63 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row64_col0\" class=\"data row64 col0\" >1222, VAN STEFFY AV, WYOMISSING</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row64_col1\" class=\"data row64 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row64_col2\" class=\"data row64 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row65_col0\" class=\"data row65 col0\" >16772, Heritage Ln</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row65_col1\" class=\"data row65 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row65_col2\" class=\"data row65 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row66_col0\" class=\"data row66 col0\" >450, LINCOLN AVENUE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row66_col1\" class=\"data row66 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row66_col2\" class=\"data row66 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row67_col0\" class=\"data row67 col0\" >27, GRANTHAM GLEN</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row67_col1\" class=\"data row67 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row67_col2\" class=\"data row67 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row68_col0\" class=\"data row68 col0\" >14972, GREENBRAE ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row68_col1\" class=\"data row68 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row68_col2\" class=\"data row68 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row69_col0\" class=\"data row69 col0\" >35, CR 1322</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row69_col1\" class=\"data row69 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row69_col2\" class=\"data row69 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row70_col0\" class=\"data row70 col0\" >2438, DOUBLETREE DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row70_col1\" class=\"data row70 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row70_col2\" class=\"data row70 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row71_col0\" class=\"data row71 col0\" >6999, SHIELDS DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row71_col1\" class=\"data row71 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row71_col2\" class=\"data row71 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row72_col0\" class=\"data row72 col0\" >232, COUNTY RD 5, JACKSON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row72_col1\" class=\"data row72 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row72_col2\" class=\"data row72 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row73_col0\" class=\"data row73 col0\" >2265, Coronado Parkway North, Unit B</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row73_col1\" class=\"data row73 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row73_col2\" class=\"data row73 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row74_col0\" class=\"data row74 col0\" >1026, E 18TH AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row74_col1\" class=\"data row74 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row74_col2\" class=\"data row74 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row75_col0\" class=\"data row75 col0\" >224, PINE CREST PL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row75_col1\" class=\"data row75 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row75_col2\" class=\"data row75 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row76_col0\" class=\"data row76 col0\" >6259, ROGERS RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row76_col1\" class=\"data row76 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row76_col2\" class=\"data row76 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row77_col0\" class=\"data row77 col0\" >576, WYCHE ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row77_col1\" class=\"data row77 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row77_col2\" class=\"data row77 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row78_col0\" class=\"data row78 col0\" >1109, GLENN AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row78_col1\" class=\"data row78 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row78_col2\" class=\"data row78 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row79_col0\" class=\"data row79 col0\" >4821, POSTON DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row79_col1\" class=\"data row79 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row79_col2\" class=\"data row79 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row80_col0\" class=\"data row80 col0\" >1610, WALNUT AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row80_col1\" class=\"data row80 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row80_col2\" class=\"data row80 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row81_col0\" class=\"data row81 col0\" >4134, TN SUNP.A-3 T.JARAL SEC1 UE1, 29749</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row81_col1\" class=\"data row81 col1\" >ES</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row81_col2\" class=\"data row81 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row82_col0\" class=\"data row82 col0\" >6, HUQUENIN CT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row82_col1\" class=\"data row82 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row82_col2\" class=\"data row82 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row83_col0\" class=\"data row83 col0\" >3761, OLD CLAYBURN RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row83_col1\" class=\"data row83 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row83_col2\" class=\"data row83 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row84_col0\" class=\"data row84 col0\" >4832-8</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row84_col1\" class=\"data row84 col1\" >JP</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row84_col2\" class=\"data row84 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row85_col0\" class=\"data row85 col0\" >1209, ALSON MILLS WAY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row85_col1\" class=\"data row85 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row85_col2\" class=\"data row85 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row86_col0\" class=\"data row86 col0\" >262, BASSETT ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row86_col1\" class=\"data row86 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row86_col2\" class=\"data row86 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row87_col0\" class=\"data row87 col0\" >216, 3RD ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row87_col1\" class=\"data row87 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row87_col2\" class=\"data row87 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row88_col0\" class=\"data row88 col0\" >3749, CLARITY RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row88_col1\" class=\"data row88 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row88_col2\" class=\"data row88 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row89_col0\" class=\"data row89 col0\" >2619, SQUIRE PL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row89_col1\" class=\"data row89 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row89_col2\" class=\"data row89 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row90_col0\" class=\"data row90 col0\" >1950, PITTMAN CENTER RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row90_col1\" class=\"data row90 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row90_col2\" class=\"data row90 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row91_col0\" class=\"data row91 col0\" >WILDCAT TR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row91_col1\" class=\"data row91 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row91_col2\" class=\"data row91 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row92_col0\" class=\"data row92 col0\" >28, SUNKIST VALLEY RD, Caledon</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row92_col1\" class=\"data row92 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row92_col2\" class=\"data row92 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row93_col0\" class=\"data row93 col0\" >31, 4780</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row93_col1\" class=\"data row93 col1\" >BE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row93_col2\" class=\"data row93 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row94_col0\" class=\"data row94 col0\" >Cabarrus</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row94_col1\" class=\"data row94 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row94_col2\" class=\"data row94 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row95_col0\" class=\"data row95 col0\" >494, Oxbow Creek</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row95_col1\" class=\"data row95 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row95_col2\" class=\"data row95 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row96_col0\" class=\"data row96 col0\" >0, HIGH ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row96_col1\" class=\"data row96 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row96_col2\" class=\"data row96 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row97_col0\" class=\"data row97 col0\" >121, WHITETAIL ARCHERY AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row97_col1\" class=\"data row97 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row97_col2\" class=\"data row97 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row98_col0\" class=\"data row98 col0\" >676, STATE ROUTE 179</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row98_col1\" class=\"data row98 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row98_col2\" class=\"data row98 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row99_col0\" class=\"data row99 col0\" >8455, BACARDI AVENUE, INVER GROVE HEIGHTS</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row99_col1\" class=\"data row99 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row99_col2\" class=\"data row99 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row100_col0\" class=\"data row100 col0\" >41, WILLIAM BLAYDES ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row100_col1\" class=\"data row100 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row100_col2\" class=\"data row100 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row101_col0\" class=\"data row101 col0\" >1539, 29 AV N</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row101_col1\" class=\"data row101 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row101_col2\" class=\"data row101 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row102_col0\" class=\"data row102 col0\" >4250, OREGON AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row102_col1\" class=\"data row102 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row102_col2\" class=\"data row102 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row103_col0\" class=\"data row103 col0\" >845, NORTH MARY LAKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row103_col1\" class=\"data row103 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row103_col2\" class=\"data row103 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row104_col0\" class=\"data row104 col0\" >3338, FALLS DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row104_col1\" class=\"data row104 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row104_col2\" class=\"data row104 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row105_col0\" class=\"data row105 col0\" >3301, CONFLANS RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row105_col1\" class=\"data row105 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row105_col2\" class=\"data row105 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row106_col0\" class=\"data row106 col0\" >3750, WEINBRENNER RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row106_col1\" class=\"data row106 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row106_col2\" class=\"data row106 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row107_col0\" class=\"data row107 col0\" >9, BROOK SIDE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row107_col1\" class=\"data row107 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row107_col2\" class=\"data row107 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row108_col0\" class=\"data row108 col0\" >312, WHEATON STREET</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row108_col1\" class=\"data row108 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row108_col2\" class=\"data row108 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row109_col0\" class=\"data row109 col0\" >RAILROAD RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row109_col1\" class=\"data row109 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row109_col2\" class=\"data row109 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row110_col0\" class=\"data row110 col0\" >1001, Steinerwaeldel, Volksberg, 67290</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row110_col1\" class=\"data row110 col1\" >FR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row110_col2\" class=\"data row110 col2\" >BE</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row111_col0\" class=\"data row111 col0\" >1919, POCO FARM RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row111_col1\" class=\"data row111 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row111_col2\" class=\"data row111 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row112_col0\" class=\"data row112 col0\" >166, OLGA DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row112_col1\" class=\"data row112 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row112_col2\" class=\"data row112 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row113_col0\" class=\"data row113 col0\" >CALEDONIA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row113_col1\" class=\"data row113 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row113_col2\" class=\"data row113 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row114_col0\" class=\"data row114 col0\" >708, FAIRMEADOW DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row114_col1\" class=\"data row114 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row114_col2\" class=\"data row114 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row115_col0\" class=\"data row115 col0\" >126, POAS CL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row115_col1\" class=\"data row115 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row115_col2\" class=\"data row115 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row116_col0\" class=\"data row116 col0\" >1136, CLARENDON CIR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row116_col1\" class=\"data row116 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row116_col2\" class=\"data row116 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row117_col0\" class=\"data row117 col0\" >CREEK RD, DOUGLASS</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row117_col1\" class=\"data row117 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row117_col2\" class=\"data row117 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row118_col0\" class=\"data row118 col0\" >625505, 15TH SIDEROAD, TOWNSHIP OF MELANCTHON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row118_col1\" class=\"data row118 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row118_col2\" class=\"data row118 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row119_col0\" class=\"data row119 col0\" >529, WENGLER AVE, SHARON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row119_col1\" class=\"data row119 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row119_col2\" class=\"data row119 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row120_col0\" class=\"data row120 col0\" >40114, TN SECTOR 8, 45646</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row120_col1\" class=\"data row120 col1\" >ES</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row120_col2\" class=\"data row120 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row121_col0\" class=\"data row121 col0\" >9955, East 138Th Place</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row121_col1\" class=\"data row121 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row121_col2\" class=\"data row121 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row122_col0\" class=\"data row122 col0\" >1032, HENEY LAKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row122_col1\" class=\"data row122 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row122_col2\" class=\"data row122 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row123_col0\" class=\"data row123 col0\" >1021, WOODCREEK OAKS BLVD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row123_col1\" class=\"data row123 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row123_col2\" class=\"data row123 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row124_col0\" class=\"data row124 col0\" >514, CLEARFIELD ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row124_col1\" class=\"data row124 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row124_col2\" class=\"data row124 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row125_col0\" class=\"data row125 col0\" >1991, Braeburn Circle SE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row125_col1\" class=\"data row125 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row125_col2\" class=\"data row125 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row126_col0\" class=\"data row126 col0\" >Boiling Spring Lakes, Brunswick</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row126_col1\" class=\"data row126 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row126_col2\" class=\"data row126 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row127_col0\" class=\"data row127 col0\" >3965, SAGE DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row127_col1\" class=\"data row127 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row127_col2\" class=\"data row127 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row128_col0\" class=\"data row128 col0\" >3175, W 34TH AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row128_col1\" class=\"data row128 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row128_col2\" class=\"data row128 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row129_col0\" class=\"data row129 col0\" >83, ST ANDREWS CRESCENT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row129_col1\" class=\"data row129 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row129_col2\" class=\"data row129 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row130_col0\" class=\"data row130 col0\" >10990, 1ST STREET, HEWITT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row130_col1\" class=\"data row130 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row130_col2\" class=\"data row130 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row131_col0\" class=\"data row131 col0\" >22, POLLETT LN, DIEPPE, NB</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row131_col1\" class=\"data row131 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row131_col2\" class=\"data row131 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row132_col0\" class=\"data row132 col0\" >Kent Street, Richibucto, Kent</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row132_col1\" class=\"data row132 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row132_col2\" class=\"data row132 col2\" >ZA</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20ebbe63108>"]}, "execution_count": 24, "metadata": {}, "output_type": "execute_result"}], "source": ["misclassified_records.style.set_table_styles([dict(selector='th', props=[('text-align', 'left')])])\\\n", " .set_properties(**{'text-align': \"left\"}).hide_index()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Saving the trained model\n", "\n", "Once you are satisfied with the model, you can save it using the save() method. This creates an Esri Model Definition (EMD file) that can be used for inferencing on unseen data."]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Computing model metrics...\n"]}, {"data": {"text/plain": ["WindowsPath('models/country-classifier')"]}, "execution_count": 25, "metadata": {}, "output_type": "execute_result"}], "source": ["model.save(\"country-classifier\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Model inference\n", "\n", "The trained model can be used to classify new text documents using the predict method. This method accepts a string or a list of strings to predict the labels of these new documents/text."]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", " <div>\n", " <style>\n", " /* Turns off some styling */\n", " progress {\n", " /* gets rid of default border in Firefox and Opera. */\n", " border: none;\n", " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", " background-size: auto;\n", " }\n", " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", " background: #F44336;\n", " }\n", " </style>\n", " <progress value='15' class='' max='15' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " 100.00% [15/15 00:00<00:00]\n", " </div>\n", " "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070 th 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" text-align: left;\n", " }</style><table id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Address</th> <th class=\"col_heading level0 col1\" >CountryCode</th> <th class=\"col_heading level0 col2\" >Confidence</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >179, RUA JOSE BARBALHO FILHO, APARTAMENTO 103 BLOCO G, Jo\u00e3o Pessoa, PB, 58027-000</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >BR</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col2\" class=\"data row0 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >2531, PARTRIDGE CRES</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col2\" class=\"data row1 col2\" >0.834484</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >SN, CALLE ESCUINAPA, URUAPAN, Uruapan, Michoac\u00e1n de Ocampo</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >MX</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col2\" class=\"data row2 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >44, WOODFORD DR, FREDERICKSBURG, Stafford County, VA, 22405</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >US</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col2\" class=\"data row3 col2\" >0.999997</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >587, CALLE CABO SAN LUCAS, ENSENADA, Ensenada, Baja California</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >MX</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col2\" class=\"data row4 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >80009, Street, Fernie, Chief Albert Luthuli</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >ZA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col2\" class=\"data row5 col2\" >0.999997</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >1906, Pelton Mountain Rd, Chipman Brook, Kings County</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col2\" class=\"data row6 col2\" >0.999895</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >1, Chemin de Promelles, 1472</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >BE</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col2\" class=\"data row7 col2\" >0.999912</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >1408, Cedarglen Court, Oakville, ON</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col2\" class=\"data row8 col2\" >0.998583</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >70, POPLAR ST N</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col2\" class=\"data row9 col2\" >0.942083</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col0\" class=\"data row10 col0\" >48, CL RAMON TURRO, 8389</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col1\" class=\"data row10 col1\" >ES</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col2\" class=\"data row10 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col0\" class=\"data row11 col0\" >454, NORTH MANNHEIM ROAD, Hillside, 60162</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col1\" class=\"data row11 col1\" >US</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col2\" class=\"data row11 col2\" >0.999981</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col0\" class=\"data row12 col0\" >43, Qoqonga Street, Mfuleni, City of Cape Town</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col1\" class=\"data row12 col1\" >ZA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col2\" class=\"data row12 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col0\" class=\"data row13 col0\" >1 B, TRAVESSA GENESIO SILVEIRA, Mossor\u00f3, RN, 59600-000</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col1\" class=\"data row13 col1\" >BR</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col2\" class=\"data row13 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col0\" class=\"data row14 col0\" >GaMaphale, Greater Letaba</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col1\" class=\"data row14 col1\" >ZA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col2\" class=\"data row14 col2\" >0.999998</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20ebbe53848>"]}, "execution_count": 26, "metadata": {}, "output_type": "execute_result"}], "source": ["text_list = data._train_df.sample(15).Address.values\n", "result = model.predict(text_list)\n", "\n", "df = pd.DataFrame(result, columns=[\"Address\", \"CountryCode\", \"Confidence\"])\n", "\n", "df.style.set_table_styles([dict(selector='th', props=[('text-align', 'left')])])\\\n", " .set_properties(**{'text-align': \"left\"}).hide_index()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Conclusion"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In this notebook, we have built a text classifier using `TextClassifier` class of `arcgis.learn.text` module. The dataset consisted of house addresses of 10 countries written in languages like English, Japanese, French, Spanish, etc. To achieve this we used a [multi-lingual transformer backbone](https://huggingface.co/transformers/v3.0.2/multilingual.html) like `XLM-RoBERTa` to build a classifier to predict the country for an input house address. "]}, {"cell_type": "markdown", "metadata": {}, "source": ["# References"]}, {"cell_type": "markdown", "metadata": {}, "source": ["[1] [Learning Rate](https://en.wikipedia.org/wiki/Learning_rate)\n", "\n", "[2] [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision)\n", "\n", "[3] [Precision, recall and F1-measures](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures)"]}], "metadata": {"esriNotebookRuntime": {"notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced with GPU support", "notebookRuntimeVersion": "5.0"}, "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": "3.8.2"}, "toc": {"base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false}}, "nbformat": 4, "nbformat_minor": 4}
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AttributeError                            Traceback (most recent call last)
Cell In[39], line 1
----> 1 misclassified_records.style.set_table_styles([dict(selector='th', props=[('text-align', 'left')])])\
      2         .set_properties(**{'text-align': "left"}).hide_index()

AttributeError: 'Styler' object has no attribute 'style'


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{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Identifying country names from incomplete house addresses"]}, {"cell_type": "markdown", "metadata": {}, "source": ["<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\">\n", "<ul class=\"toc-item\">\n", "<li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\">Introduction</a></span></li>\n", "<li><span><a href=\"#Prerequisites\" data-toc-modified-id=\"Prerequisites-2\">Prerequisites</a></span></li>\n", "<li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-3\">Imports</a></span></li>\n", "<li><span><a href=\"#Data-preparation\" data-toc-modified-id=\"Data-preparation-4\">Data preparation</a></span></li>\n", "<li><span><a href=\"#TextClassifier-model\" data-toc-modified-id=\"TextClassifier-model-5\">TextClassifier model</a></span></li>\n", "<ul class=\"toc-item\">\n", "<li><span><a href=\"#Load-model-architecture\" data-toc-modified-id=\"Load-model-architecture-5.1\">Load model architecture</a></span></li>\n", "<li><span><a href=\"#Model-training\" data-toc-modified-id=\"Model-training-5.2\">Model training</a></span></li> \n", "<li><span><a href=\"#Validate-results\" data-toc-modified-id=\"Validate-results-5.3\">Validate results</a></span></li>\n", "<li><span><a href=\"#Model-metrics\" data-toc-modified-id=\"Model-metrics-5.4\">Model metrics</a></span></li> \n", "<li><span><a href=\"#Get-misclassified-records\" data-toc-modified-id=\"Get-misclassified-records-5.5\">Get misclassified records</a></span></li>\n", "<li><span><a href=\"#Saving-the-trained-model\" data-toc-modified-id=\"Saving-the-trained-model-5.6\">Saving the trained model</a></span></li>\n", "</ul>\n", "<li><span><a href=\"#Model-inference\" data-toc-modified-id=\"Model-inference-6\">Model inference</a></span></li>\n", "<li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-7\">Conclusion</a></span></li>\n", "<li><span><a href=\"#References\" data-toc-modified-id=\"References-8\">References</a></span></li>\n", "</ul></div>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Introduction"]}, {"cell_type": "markdown", "metadata": {}, "source": ["[Geocoding](https://en.wikipedia.org/wiki/Geocoding) is the process of taking input text, such as an **address** or the name of a place, and returning a **latitude/longitude** location for that place. In this notebook, we will be picking up a dataset consisting of incomplete house addresses from 10 countries. We will build a classifier using `TextClassifier` class of `arcgis.learn.text` module to predict the country for these incomplete house addresses. \n", "\n", "The house addresses in the dataset consist of text in multiple languages like English, Japanese, French, Spanish, etc. The dataset is a small subset of the house addresses taken from [OpenAddresses data](http://results.openaddresses.io/) \n", "\n", "**A note on the dataset**\n", "- The data is collected around 2020-05-27 by [OpenAddresses](http://openaddresses.io).\n", "- The data licenses can be found in `data/country-classifier/LICENSE.txt`."]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Prerequisites"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- Data preparation and model training workflows using arcgis.learn have a dependency on [transformers](https://huggingface.co/transformers/v3.0.2/index.html). Refer to the section **\"Install deep learning dependencies of arcgis.learn module\"** [on this page](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-deep-learning-dependencies) for detailed documentation on the installation of the dependencies.\n", "\n", "- **Labeled data**: For `TextClassifier` to learn, it needs to see documents/texts that have been assigned a label. Labeled data for this sample notebook is located at `data/country-classifier/house-addresses.csv`\n", "\n", "- To learn more about how `TextClassifier` works, please see the guide on [Text Classification with arcgis.learn](https://developers.arcgis.com/python/guide/text-classification)."]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Imports"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import os\n", "import zipfile\n", "import pandas as pd\n", "from pathlib import Path\n", "from arcgis.gis import GIS\n", "from arcgis.learn import prepare_textdata\n", "from arcgis.learn.text import TextClassifier"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["gis = GIS('home')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Data preparation\n", "\n", "Data preparation involves splitting the data into training and validation sets, creating the necessary data structures for loading data into the model and so on. The `prepare_data()` function can directly read the training samples and automate the entire process."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/html": ["<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n", " <div class=\"item_left\" style=\"width: 210px; float: left;\">\n", " <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=ab36969cfe814c89ba3b659cf734492a' target='_blank'>\n", " <img 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' width='200' height='133' class=\"itemThumbnail\">\n", " </a>\n", " </div>\n", "\n", " <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n", " <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=ab36969cfe814c89ba3b659cf734492a' target='_blank'><b>country_classifier</b>\n", " </a>\n", " <br/>Training data for TextClassifier class of arcgis.learn.text module<img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/layers16.png' style=\"vertical-align:middle;\">Image Collection by api_data_owner\n", " <br/>Last Modified: December 01, 2020\n", " <br/>0 comments, 0 views\n", " </div>\n", " </div>\n", " "], "text/plain": ["<Item title:\"country_classifier\" type:Image Collection owner:api_data_owner>"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["training_data = gis.content.get('ab36969cfe814c89ba3b659cf734492a')\n", "training_data"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": ["filepath = training_data.download(file_name=training_data.name)"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": ["with zipfile.ZipFile(filepath, 'r') as zip_ref:\n", " zip_ref.extractall(Path(filepath).parent)"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["DATA_ROOT = Path(os.path.join(os.path.splitext(filepath)[0]))"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": ["data = prepare_textdata(DATA_ROOT, \"classification\", train_file=\"house-addresses.csv\", \n", " text_columns=\"Address\", label_columns=\"Country\", batch_size=64)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The `show_batch()` method can be used to see the training samples, along with labels."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070 th {\n", " text-align: left;\n", " }#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col1,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col0,#T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col1{\n", " text-align: left;\n", " }</style><table id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Address</th> <th class=\"col_heading level0 col1\" >Country</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >S/N, LG CASARES, 32170</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >SN, CALLE E. NABARRETE, PLAN DE AYALA (CAMPO CINCO), Ahome, Sinaloa</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >152, RUA SANTA RITA DURAO, Belo Horizonte, MG, 30140-110</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >BR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >133, Warande, 201, 9660</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >BE</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >4000, 13 Avenue SE, 133, MEDICINE HAT</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >12, Avenue de la R\u00e9publique, Beauvais, 60000</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >FR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >1487-6, \u6709\u99ac\u753a</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >JP</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >4, Rue d'Houat, Saint-Gilles, 35590</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >FR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >32, Hartjie My Liefie Avenue, Bloemfontein, Mangaung</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >ZA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >Street, Centurion, City of Tshwane</td>\n", " <td id=\"T_12dd9eba_33a6_11eb_8fb5_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >ZA</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20eb7a87cc8>"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["data.show_batch(10)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# TextClassifier model"]}, {"cell_type": "markdown", "metadata": {}, "source": ["`TextClassifier` model in `arcgis.learn.text` is built on top of [Hugging Face Transformers](https://huggingface.co/transformers/v3.0.2/index.html) library. The model training and inferencing workflow are similar to computer vision models in `arcgis.learn`. \n", "\n", "Run the command below to see what backbones are supported for the text classification task."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['BERT', 'RoBERTa', 'DistilBERT', 'ALBERT', 'FlauBERT', 'CamemBERT', 'XLNet', 'XLM', 'XLM-RoBERTa', 'Bart', 'ELECTRA', 'Longformer', 'MobileBERT']\n"]}], "source": ["print(TextClassifier.supported_backbones)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Call the model's `available_backbone_models()` method with the backbone name to get the available models for that backbone. The call to **available_backbone_models** method will list out only few of the available models for each backbone. Visit [this](https://huggingface.co/transformers/pretrained_models.html) link to get a complete list of models for each backbone."]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["('xlm-roberta-base', 'xlm-roberta-large')\n"]}], "source": ["print(TextClassifier.available_backbone_models(\"xlm-roberta\"))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Load model architecture"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Invoke the `TextClassifier` class by passing the data and the backbone you have chosen. The dataset consists of house addresses in multiple languages like Japanese, English, French, Spanish, etc., hence we will use a [multi-lingual transformer backbone](https://huggingface.co/transformers/v3.0.2/multilingual.html) to train our model."]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/html": [], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/html": [], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model = TextClassifier(data, backbone=\"xlm-roberta-base\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Model training"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The `learning rate`[[1]](#References) is a **tuning parameter** that determines the step size at each iteration while moving toward a minimum of a loss function, it represents the speed at which a machine learning model **\"learns\"**. `arcgis.learn` includes a learning rate finder, and is accessible through the model's `lr_find()` method, that can automatically select an **optimum learning rate**, without requiring repeated experiments."]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"image/png": 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"text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}, {"data": {"text/plain": ["0.001202264434617413"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["model.lr_find()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Training the model is an iterative process. We can train the model using its `fit()` method till the validation loss (or error rate) continues to go down with each training pass also known as an epoch. This is indicative of the model learning the task."]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"data": {"text/html": ["<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: left;\">\n", " <th>epoch</th>\n", " <th>train_loss</th>\n", " <th>valid_loss</th>\n", " <th>accuracy</th>\n", " <th>error_rate</th>\n", " <th>time</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>0.308638</td>\n", " <td>0.182150</td>\n", " <td>0.929600</td>\n", " <td>0.070400</td>\n", " <td>05:28</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>0.103615</td>\n", " <td>0.068711</td>\n", " <td>0.970600</td>\n", " <td>0.029400</td>\n", " <td>05:46</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>0.076326</td>\n", " <td>0.041269</td>\n", " <td>0.981600</td>\n", " <td>0.018400</td>\n", " <td>05:30</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>0.055707</td>\n", " <td>0.034307</td>\n", " <td>0.986300</td>\n", " <td>0.013700</td>\n", " <td>05:33</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>0.041812</td>\n", " <td>0.032772</td>\n", " <td>0.986400</td>\n", " <td>0.013600</td>\n", " <td>05:27</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>0.049993</td>\n", " <td>0.032165</td>\n", " <td>0.986600</td>\n", " <td>0.013400</td>\n", " <td>05:26</td>\n", " </tr>\n", " </tbody>\n", "</table>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model.fit(epochs=6, lr=0.001)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Validate results"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Once we have the trained model, we can see the results to see how it performs."]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070 th {\n", " text-align: left;\n", " 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" text-align: left;\n", " }</style><table id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >text</th> <th class=\"col_heading level0 col1\" >target</th> <th class=\"col_heading level0 col2\" >prediction</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >SN, AVENIDA JOSE MARIA MORELOS Y PAVON OTE., APATZING\u00c1N DE LA CONSTITUCI\u00d3N, Apatzing\u00e1n, Michoac\u00e1n de Ocampo</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row0_col2\" class=\"data row0 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >906, AVENIDA JOSEFA ORT\u00cdZ DE DOM\u00cdNGUEZ, CIUDAD MENDOZA, Camerino Z. Mendoza, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row1_col2\" class=\"data row1 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >32, CIRCUITO JOS\u00c9 MAR\u00cdA URIARTE, FRACCIONAMIENTO RANCHO ALEGRE, Tlajomulco de Z\u00fa\u00f1iga, Jalisco</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row2_col2\" class=\"data row2 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >SN, ESTRADA SP 250 SENTIDO GRAMADAO, LADO DIREITO FAZENDA SAO RAFAEL CASA 4, S\u00e3o Miguel Arcanjo, SP, 18230-000</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >BR</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row3_col2\" class=\"data row3 col2\" >BR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >SN, CALLE JOSEFA ORT\u00cdZ DE DOM\u00cdNGUEZ, RINC\u00d3N DE BUENA VISTA, Omealca, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row4_col2\" class=\"data row4 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >SN, CALLE MICHOACAN, DOLORES HIDALGO CUNA DE LA INDEPENDENCIA NACIONAL, Dolores Hidalgo Cuna de la Independencia Nacional, Guanajuato</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row5_col2\" class=\"data row5 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >SN, CALLE VERDUZCO, COALCOM\u00c1N DE V\u00c1ZQUEZ PALLARES, Coalcom\u00e1n de V\u00e1zquez Pallares, Michoac\u00e1n de Ocampo</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row6_col2\" class=\"data row6 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >1712, CALLE M\u00c1RTIRES DEL 7 DE ENERO, CIUDAD MENDOZA, Camerino Z. Mendoza, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row7_col2\" class=\"data row7 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >SN, AVENIDA JACOBO G\u00c1LVEZ, FRACCIONAMIENTO RANCHO ALEGRE, Tlajomulco de Z\u00fa\u00f1iga, Jalisco</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row8_col2\" class=\"data row8 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >SN, ANDADOR MZNA 6 AMP. LOS ROBLES, EL PUEBLITO (CRUCERO NACIONAL), C\u00f3rdoba, Veracruz de Ignacio de la Llave</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row9_col2\" class=\"data row9 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row10_col0\" class=\"data row10 col0\" >SN, CALLE S\u00c9PTIMA PONIENTE SUR (EJE VIAL), COMIT\u00c1N DE DOM\u00cdNGUEZ, Comit\u00e1n de Dom\u00ednguez, Chiapas</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row10_col1\" class=\"data row10 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row10_col2\" class=\"data row10 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row11_col0\" class=\"data row11 col0\" >18, CALLE FELIPE GORRITI / FELIPE GORRITI KALEA, Pamplona / Iru\u00f1a, Pamplona / Iru\u00f1a, Navarra, 31004</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row11_col1\" class=\"data row11 col1\" >ES</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row11_col2\" class=\"data row11 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row12_col0\" class=\"data row12 col0\" >SN, RUA X VINTE E SEIS, QUADRA 14 LOTE 35 SALA 3, Aparecida de Goi\u00e2nia, GO, 74922-680</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row12_col1\" class=\"data row12 col1\" >BR</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row12_col2\" class=\"data row12 col2\" >BR</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row13_col0\" class=\"data row13 col0\" >SN, CALLE NINGUNO, HEROICA CIUDAD DE JUCHIT\u00c1N DE ZARAGOZA, Heroica Ciudad de Juchit\u00e1n de Zaragoza, Oaxaca</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row13_col1\" class=\"data row13 col1\" >MX</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row13_col2\" class=\"data row13 col2\" >MX</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row14_col0\" class=\"data row14 col0\" >1169, RUA DOUTOR ALBUQUERQUE LINS, BLOCO B ANDAR 11 APARTAMENTO 112B, S\u00e3o Paulo, SP, 01203-001</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row14_col1\" class=\"data row14 col1\" >BR</td>\n", " <td id=\"T_eca9ff1e_33ab_11eb_b034_a4bb6dafa070row14_col2\" class=\"data row14 col2\" >BR</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20ebbd53bc8>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model.show_results(15)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Test the model prediction on an input text"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["('1016, 8A, CL RICARDO LEON - SANTA ANA (CARTAGENA), 30319', 'ES', 1.0)\n"]}], "source": ["text = \"\"\"1016, 8A, CL RICARDO LEON - SANTA ANA (CARTAGENA), 30319\"\"\"\n", "print(model.predict(text))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Model metrics\n", "\n", "To get a sense of how well the model is trained, we will calculate some important metrics for our `text-classifier` model. First, to find how accurate[[2]](#References) the model is in correctly predicting the classes in the dataset, we will call the model's `accuracy()` method."]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": {"text/plain": ["0.9866"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["model.accuracy()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Other important metrics to look at are Precision, Recall & F1-measures [[3]](#References). To find `precision`, `recall` & `f1` scores per label/class we will call the model's `metrics_per_label()` method."]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", " <div>\n", " <style>\n", " /* Turns off some styling */\n", " progress {\n", " /* gets rid of default border in Firefox and Opera. */\n", " border: none;\n", " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", " background-size: auto;\n", " }\n", " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", " background: #F44336;\n", " }\n", " </style>\n", " <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " 100.00% [10000/10000 05:05<00:00]\n", " </div>\n", " "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}, {"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>Precision_score</th>\n", " <th>Recall_score</th>\n", " <th>F1_score</th>\n", " <th>Support</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>AU</th>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>929.0</td>\n", " </tr>\n", " <tr>\n", " <th>BE</th>\n", " <td>0.9990</td>\n", " <td>0.9990</td>\n", " <td>0.9990</td>\n", " <td>1043.0</td>\n", " </tr>\n", " <tr>\n", " <th>BR</th>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>950.0</td>\n", " </tr>\n", " <tr>\n", " <th>CA</th>\n", " <td>0.9088</td>\n", " <td>0.9709</td>\n", " <td>0.9388</td>\n", " <td>996.0</td>\n", " </tr>\n", " <tr>\n", " <th>ES</th>\n", " <td>0.9969</td>\n", " <td>0.9980</td>\n", " <td>0.9975</td>\n", " <td>982.0</td>\n", " </tr>\n", " <tr>\n", " <th>FR</th>\n", " <td>1.0000</td>\n", " <td>0.9990</td>\n", " <td>0.9995</td>\n", " <td>1009.0</td>\n", " </tr>\n", " <tr>\n", " <th>JP</th>\n", " <td>1.0000</td>\n", " <td>0.9990</td>\n", " <td>0.9995</td>\n", " <td>989.0</td>\n", " </tr>\n", " <tr>\n", " <th>MX</th>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1024.0</td>\n", " </tr>\n", " <tr>\n", " <th>US</th>\n", " <td>0.9691</td>\n", " <td>0.9093</td>\n", " <td>0.9383</td>\n", " <td>1070.0</td>\n", " </tr>\n", " <tr>\n", " <th>ZA</th>\n", " <td>0.9990</td>\n", " <td>0.9980</td>\n", " <td>0.9985</td>\n", " <td>1008.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>"], "text/plain": [" Precision_score Recall_score F1_score Support\n", "AU 1.0000 1.0000 1.0000 929.0\n", "BE 0.9990 0.9990 0.9990 1043.0\n", "BR 1.0000 1.0000 1.0000 950.0\n", "CA 0.9088 0.9709 0.9388 996.0\n", "ES 0.9969 0.9980 0.9975 982.0\n", "FR 1.0000 0.9990 0.9995 1009.0\n", "JP 1.0000 0.9990 0.9995 989.0\n", "MX 1.0000 1.0000 1.0000 1024.0\n", "US 0.9691 0.9093 0.9383 1070.0\n", "ZA 0.9990 0.9980 0.9985 1008.0"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["model.metrics_per_label()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Get misclassified records\n", "\n", "Its always a good idea to see the cases where your model is not performing well. This step will help us to:\n", "- Identify if there is a problem in the dataset.\n", "- Identify if there is a problem with text/documents belonging to a specific label/class. \n", "- Identify if there is a class imbalance in your dataset, due to which the model didn't see much of the labeled data for a particular class, hence not able to learn properly about that class.\n", "\n", "To get the **misclassified records** we will call the model's `get_misclassified_records` method."]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", " <div>\n", " <style>\n", " /* Turns off some styling */\n", " progress {\n", " /* gets rid of default border in Firefox and Opera. */\n", " border: none;\n", " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", " background-size: auto;\n", " }\n", " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", " background: #F44336;\n", " }\n", " </style>\n", " <progress value='10000' class='' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " 100.00% [10000/10000 05:07<00:00]\n", " </div>\n", " "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["misclassified_records = model.get_misclassified_records()"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [{"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_7224c262_33ad_11eb_b039_a4bb6dafa070 th {\n", " text-align: left;\n", " 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" text-align: left;\n", " }</style><table id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Address</th> <th class=\"col_heading level0 col1\" >Target</th> <th class=\"col_heading level0 col2\" >Prediction</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >107, HAMILTON CT, EASLEY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row0_col2\" class=\"data row0 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >40443, CHEAKAMUS WAY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row1_col2\" class=\"data row1 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >309, SOUTH STREET, BARABOO</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row2_col2\" class=\"data row2 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >19109, DUTY ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row3_col2\" class=\"data row3 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >8171, CR 29, 43357</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row4_col2\" class=\"data row4 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >6565, WISCONSIN AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row5_col2\" class=\"data row5 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >7332, 25TH AVENUE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row6_col2\" class=\"data row6 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >14778, CAMINITO PUNTA ARENAS, Del Mar, 92014</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row7_col2\" class=\"data row7 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >916, PINE ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row8_col2\" class=\"data row8 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >168, BROAD SOUND PL, Iredell</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row9_col2\" class=\"data row9 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row10_col0\" class=\"data row10 col0\" >316, BEAUMIER LANE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row10_col1\" class=\"data row10 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row10_col2\" class=\"data row10 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row11_col0\" class=\"data row11 col0\" >1518, BARCLAY ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row11_col1\" class=\"data row11 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row11_col2\" class=\"data row11 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row12_col0\" class=\"data row12 col0\" >235, GLADEFIELD DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row12_col1\" class=\"data row12 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row12_col2\" class=\"data row12 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row13_col0\" class=\"data row13 col0\" >2701, CURRANT CV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row13_col1\" class=\"data row13 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row13_col2\" class=\"data row13 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row14_col0\" class=\"data row14 col0\" >94, ASPETUCK VILLAGE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row14_col1\" class=\"data row14 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row14_col2\" class=\"data row14 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row15_col0\" class=\"data row15 col0\" >27, South 10Th Avenue</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row15_col1\" class=\"data row15 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row15_col2\" class=\"data row15 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row16_col0\" class=\"data row16 col0\" >254, GREEN HILLS DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row16_col1\" class=\"data row16 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row16_col2\" class=\"data row16 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row17_col0\" class=\"data row17 col0\" >1025, BROOKFORD RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row17_col1\" class=\"data row17 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row17_col2\" class=\"data row17 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row18_col0\" class=\"data row18 col0\" >8981, FAIRMOUNT RD SE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row18_col1\" class=\"data row18 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row18_col2\" class=\"data row18 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row19_col0\" class=\"data row19 col0\" >5, PICKWICK LA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row19_col1\" class=\"data row19 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row19_col2\" class=\"data row19 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row20_col0\" class=\"data row20 col0\" >540, CHARLESTON HWY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row20_col1\" class=\"data row20 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row20_col2\" class=\"data row20 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row21_col0\" class=\"data row21 col0\" >1763, RD, McDowell</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row21_col1\" class=\"data row21 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row21_col2\" class=\"data row21 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row22_col0\" class=\"data row22 col0\" >40022, GOVERNMENT RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row22_col1\" class=\"data row22 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row22_col2\" class=\"data row22 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row23_col0\" class=\"data row23 col0\" >435, EMORY RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row23_col1\" class=\"data row23 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row23_col2\" class=\"data row23 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row24_col0\" class=\"data row24 col0\" >1, Bokomo Road, Malmesbury, Swartland</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row24_col1\" class=\"data row24 col1\" >ZA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row24_col2\" class=\"data row24 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row25_col0\" class=\"data row25 col0\" >3529, BRADLEY AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row25_col1\" class=\"data row25 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row25_col2\" class=\"data row25 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row26_col0\" class=\"data row26 col0\" >710, 9TH ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row26_col1\" class=\"data row26 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row26_col2\" class=\"data row26 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row27_col0\" class=\"data row27 col0\" >1421, PINOT NOIR DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row27_col1\" class=\"data row27 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row27_col2\" class=\"data row27 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row28_col0\" class=\"data row28 col0\" >1224, ST LUKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row28_col1\" class=\"data row28 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row28_col2\" class=\"data row28 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row29_col0\" class=\"data row29 col0\" >1822, RT 6</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row29_col1\" class=\"data row29 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row29_col2\" class=\"data row29 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row30_col0\" class=\"data row30 col0\" >140</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row30_col1\" class=\"data row30 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row30_col2\" class=\"data row30 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row31_col0\" class=\"data row31 col0\" >2302, RIVER MIST RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row31_col1\" class=\"data row31 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row31_col2\" class=\"data row31 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row32_col0\" class=\"data row32 col0\" >4159, Maher St</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row32_col1\" class=\"data row32 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row32_col2\" class=\"data row32 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row33_col0\" class=\"data row33 col0\" >24, DEARBORN STREET, Franklin</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row33_col1\" class=\"data row33 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row33_col2\" class=\"data row33 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row34_col0\" class=\"data row34 col0\" >2109, MALDON PL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row34_col1\" class=\"data row34 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row34_col2\" class=\"data row34 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row35_col0\" class=\"data row35 col0\" >Flora Road, Moquini Coastal Estate, Mossel Bay</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row35_col1\" class=\"data row35 col1\" >ZA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row35_col2\" class=\"data row35 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row36_col0\" class=\"data row36 col0\" >5990, THIROS CIR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row36_col1\" class=\"data row36 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row36_col2\" class=\"data row36 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row37_col0\" class=\"data row37 col0\" >167, CARLSBAD CAVERNS ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row37_col1\" class=\"data row37 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row37_col2\" class=\"data row37 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row38_col0\" class=\"data row38 col0\" >2119, E 3RD AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row38_col1\" class=\"data row38 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row38_col2\" class=\"data row38 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row39_col0\" class=\"data row39 col0\" >505, HARLEY WAY, SHARON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row39_col1\" class=\"data row39 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row39_col2\" class=\"data row39 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row40_col0\" class=\"data row40 col0\" >1354, ST LUKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row40_col1\" class=\"data row40 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row40_col2\" class=\"data row40 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row41_col0\" class=\"data row41 col0\" >3140, SOUTHWOOD RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row41_col1\" class=\"data row41 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row41_col2\" class=\"data row41 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row42_col0\" class=\"data row42 col0\" >4205, Glenn</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row42_col1\" class=\"data row42 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row42_col2\" class=\"data row42 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row43_col0\" class=\"data row43 col0\" >103, BILLETS BRIDGE RD, Courthouse, Camden</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row43_col1\" class=\"data row43 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row43_col2\" class=\"data row43 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row44_col0\" class=\"data row44 col0\" >838087, 4TH LINE EAST, TOWNSHIP OF MULMUR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row44_col1\" class=\"data row44 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row44_col2\" class=\"data row44 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row45_col0\" class=\"data row45 col0\" >3317, Doncaster DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row45_col1\" class=\"data row45 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row45_col2\" class=\"data row45 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row46_col0\" class=\"data row46 col0\" >2726, E TRUESDALE DRIVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row46_col1\" class=\"data row46 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row46_col2\" class=\"data row46 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row47_col0\" class=\"data row47 col0\" >2131, SHAMROCK DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row47_col1\" class=\"data row47 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row47_col2\" class=\"data row47 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row48_col0\" class=\"data row48 col0\" >1185, ST ANNES RD, Unit 99</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row48_col1\" class=\"data row48 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row48_col2\" class=\"data row48 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row49_col0\" class=\"data row49 col0\" >9109, CONTESSA CT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row49_col1\" class=\"data row49 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row49_col2\" class=\"data row49 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row50_col0\" class=\"data row50 col0\" >408, RUBY RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row50_col1\" class=\"data row50 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row50_col2\" class=\"data row50 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row51_col0\" class=\"data row51 col0\" >2101, FONTAINE RD, 10</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row51_col1\" class=\"data row51 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row51_col2\" class=\"data row51 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row52_col0\" class=\"data row52 col0\" >52, OLD HWY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row52_col1\" class=\"data row52 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row52_col2\" class=\"data row52 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row53_col0\" class=\"data row53 col0\" >200, EAGLE SHORE DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row53_col1\" class=\"data row53 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row53_col2\" class=\"data row53 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row54_col0\" class=\"data row54 col0\" >1450, MEADOW AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row54_col1\" class=\"data row54 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row54_col2\" class=\"data row54 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row55_col0\" class=\"data row55 col0\" >0, BEECH ST, Rockingham</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row55_col1\" class=\"data row55 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row55_col2\" class=\"data row55 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row56_col0\" class=\"data row56 col0\" >291, SPRY POINT RD, LITTLE POND, KNS</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row56_col1\" class=\"data row56 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row56_col2\" class=\"data row56 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row57_col0\" class=\"data row57 col0\" >10905, YORKTOWN CV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row57_col1\" class=\"data row57 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row57_col2\" class=\"data row57 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row58_col0\" class=\"data row58 col0\" >3903, TATTLE BRANCH RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row58_col1\" class=\"data row58 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row58_col2\" class=\"data row58 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row59_col0\" class=\"data row59 col0\" >682, ISLAND 90 SIX MILE LAKE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row59_col1\" class=\"data row59 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row59_col2\" class=\"data row59 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row60_col0\" class=\"data row60 col0\" >1887, LITITZ PIKE, UNIT 4, MANHEIM TOWNSHIP</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row60_col1\" class=\"data row60 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row60_col2\" class=\"data row60 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row61_col0\" class=\"data row61 col0\" >2821, E 18TH AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row61_col1\" class=\"data row61 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row61_col2\" class=\"data row61 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row62_col0\" class=\"data row62 col0\" >2106, MARK ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row62_col1\" class=\"data row62 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row62_col2\" class=\"data row62 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row63_col0\" class=\"data row63 col0\" >25890, 119TH STREET</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row63_col1\" class=\"data row63 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row63_col2\" class=\"data row63 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row64_col0\" class=\"data row64 col0\" >1222, VAN STEFFY AV, WYOMISSING</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row64_col1\" class=\"data row64 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row64_col2\" class=\"data row64 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row65_col0\" class=\"data row65 col0\" >16772, Heritage Ln</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row65_col1\" class=\"data row65 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row65_col2\" class=\"data row65 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row66_col0\" class=\"data row66 col0\" >450, LINCOLN AVENUE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row66_col1\" class=\"data row66 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row66_col2\" class=\"data row66 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row67_col0\" class=\"data row67 col0\" >27, GRANTHAM GLEN</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row67_col1\" class=\"data row67 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row67_col2\" class=\"data row67 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row68_col0\" class=\"data row68 col0\" >14972, GREENBRAE ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row68_col1\" class=\"data row68 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row68_col2\" class=\"data row68 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row69_col0\" class=\"data row69 col0\" >35, CR 1322</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row69_col1\" class=\"data row69 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row69_col2\" class=\"data row69 col2\" >ES</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row70_col0\" class=\"data row70 col0\" >2438, DOUBLETREE DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row70_col1\" class=\"data row70 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row70_col2\" class=\"data row70 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row71_col0\" class=\"data row71 col0\" >6999, SHIELDS DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row71_col1\" class=\"data row71 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row71_col2\" class=\"data row71 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row72_col0\" class=\"data row72 col0\" >232, COUNTY RD 5, JACKSON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row72_col1\" class=\"data row72 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row72_col2\" class=\"data row72 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row73_col0\" class=\"data row73 col0\" >2265, Coronado Parkway North, Unit B</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row73_col1\" class=\"data row73 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row73_col2\" class=\"data row73 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row74_col0\" class=\"data row74 col0\" >1026, E 18TH AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row74_col1\" class=\"data row74 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row74_col2\" class=\"data row74 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row75_col0\" class=\"data row75 col0\" >224, PINE CREST PL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row75_col1\" class=\"data row75 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row75_col2\" class=\"data row75 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row76_col0\" class=\"data row76 col0\" >6259, ROGERS RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row76_col1\" class=\"data row76 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row76_col2\" class=\"data row76 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row77_col0\" class=\"data row77 col0\" >576, WYCHE ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row77_col1\" class=\"data row77 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row77_col2\" class=\"data row77 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row78_col0\" class=\"data row78 col0\" >1109, GLENN AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row78_col1\" class=\"data row78 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row78_col2\" class=\"data row78 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row79_col0\" class=\"data row79 col0\" >4821, POSTON DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row79_col1\" class=\"data row79 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row79_col2\" class=\"data row79 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row80_col0\" class=\"data row80 col0\" >1610, WALNUT AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row80_col1\" class=\"data row80 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row80_col2\" class=\"data row80 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row81_col0\" class=\"data row81 col0\" >4134, TN SUNP.A-3 T.JARAL SEC1 UE1, 29749</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row81_col1\" class=\"data row81 col1\" >ES</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row81_col2\" class=\"data row81 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row82_col0\" class=\"data row82 col0\" >6, HUQUENIN CT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row82_col1\" class=\"data row82 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row82_col2\" class=\"data row82 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row83_col0\" class=\"data row83 col0\" >3761, OLD CLAYBURN RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row83_col1\" class=\"data row83 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row83_col2\" class=\"data row83 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row84_col0\" class=\"data row84 col0\" >4832-8</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row84_col1\" class=\"data row84 col1\" >JP</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row84_col2\" class=\"data row84 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row85_col0\" class=\"data row85 col0\" >1209, ALSON MILLS WAY</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row85_col1\" class=\"data row85 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row85_col2\" class=\"data row85 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row86_col0\" class=\"data row86 col0\" >262, BASSETT ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row86_col1\" class=\"data row86 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row86_col2\" class=\"data row86 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row87_col0\" class=\"data row87 col0\" >216, 3RD ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row87_col1\" class=\"data row87 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row87_col2\" class=\"data row87 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row88_col0\" class=\"data row88 col0\" >3749, CLARITY RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row88_col1\" class=\"data row88 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row88_col2\" class=\"data row88 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row89_col0\" class=\"data row89 col0\" >2619, SQUIRE PL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row89_col1\" class=\"data row89 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row89_col2\" class=\"data row89 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row90_col0\" class=\"data row90 col0\" >1950, PITTMAN CENTER RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row90_col1\" class=\"data row90 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row90_col2\" class=\"data row90 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row91_col0\" class=\"data row91 col0\" >WILDCAT TR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row91_col1\" class=\"data row91 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row91_col2\" class=\"data row91 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row92_col0\" class=\"data row92 col0\" >28, SUNKIST VALLEY RD, Caledon</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row92_col1\" class=\"data row92 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row92_col2\" class=\"data row92 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row93_col0\" class=\"data row93 col0\" >31, 4780</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row93_col1\" class=\"data row93 col1\" >BE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row93_col2\" class=\"data row93 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row94_col0\" class=\"data row94 col0\" >Cabarrus</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row94_col1\" class=\"data row94 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row94_col2\" class=\"data row94 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row95_col0\" class=\"data row95 col0\" >494, Oxbow Creek</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row95_col1\" class=\"data row95 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row95_col2\" class=\"data row95 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row96_col0\" class=\"data row96 col0\" >0, HIGH ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row96_col1\" class=\"data row96 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row96_col2\" class=\"data row96 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row97_col0\" class=\"data row97 col0\" >121, WHITETAIL ARCHERY AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row97_col1\" class=\"data row97 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row97_col2\" class=\"data row97 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row98_col0\" class=\"data row98 col0\" >676, STATE ROUTE 179</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row98_col1\" class=\"data row98 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row98_col2\" class=\"data row98 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row99_col0\" class=\"data row99 col0\" >8455, BACARDI AVENUE, INVER GROVE HEIGHTS</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row99_col1\" class=\"data row99 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row99_col2\" class=\"data row99 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row100_col0\" class=\"data row100 col0\" >41, WILLIAM BLAYDES ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row100_col1\" class=\"data row100 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row100_col2\" class=\"data row100 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row101_col0\" class=\"data row101 col0\" >1539, 29 AV N</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row101_col1\" class=\"data row101 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row101_col2\" class=\"data row101 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row102_col0\" class=\"data row102 col0\" >4250, OREGON AVE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row102_col1\" class=\"data row102 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row102_col2\" class=\"data row102 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row103_col0\" class=\"data row103 col0\" >845, NORTH MARY LAKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row103_col1\" class=\"data row103 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row103_col2\" class=\"data row103 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row104_col0\" class=\"data row104 col0\" >3338, FALLS DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row104_col1\" class=\"data row104 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row104_col2\" class=\"data row104 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row105_col0\" class=\"data row105 col0\" >3301, CONFLANS RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row105_col1\" class=\"data row105 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row105_col2\" class=\"data row105 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row106_col0\" class=\"data row106 col0\" >3750, WEINBRENNER RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row106_col1\" class=\"data row106 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row106_col2\" class=\"data row106 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row107_col0\" class=\"data row107 col0\" >9, BROOK SIDE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row107_col1\" class=\"data row107 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row107_col2\" class=\"data row107 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row108_col0\" class=\"data row108 col0\" >312, WHEATON STREET</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row108_col1\" class=\"data row108 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row108_col2\" class=\"data row108 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row109_col0\" class=\"data row109 col0\" >RAILROAD RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row109_col1\" class=\"data row109 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row109_col2\" class=\"data row109 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row110_col0\" class=\"data row110 col0\" >1001, Steinerwaeldel, Volksberg, 67290</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row110_col1\" class=\"data row110 col1\" >FR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row110_col2\" class=\"data row110 col2\" >BE</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row111_col0\" class=\"data row111 col0\" >1919, POCO FARM RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row111_col1\" class=\"data row111 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row111_col2\" class=\"data row111 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row112_col0\" class=\"data row112 col0\" >166, OLGA DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row112_col1\" class=\"data row112 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row112_col2\" class=\"data row112 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row113_col0\" class=\"data row113 col0\" >CALEDONIA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row113_col1\" class=\"data row113 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row113_col2\" class=\"data row113 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row114_col0\" class=\"data row114 col0\" >708, FAIRMEADOW DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row114_col1\" class=\"data row114 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row114_col2\" class=\"data row114 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row115_col0\" class=\"data row115 col0\" >126, POAS CL</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row115_col1\" class=\"data row115 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row115_col2\" class=\"data row115 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row116_col0\" class=\"data row116 col0\" >1136, CLARENDON CIR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row116_col1\" class=\"data row116 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row116_col2\" class=\"data row116 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row117_col0\" class=\"data row117 col0\" >CREEK RD, DOUGLASS</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row117_col1\" class=\"data row117 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row117_col2\" class=\"data row117 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row118_col0\" class=\"data row118 col0\" >625505, 15TH SIDEROAD, TOWNSHIP OF MELANCTHON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row118_col1\" class=\"data row118 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row118_col2\" class=\"data row118 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row119_col0\" class=\"data row119 col0\" >529, WENGLER AVE, SHARON</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row119_col1\" class=\"data row119 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row119_col2\" class=\"data row119 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row120_col0\" class=\"data row120 col0\" >40114, TN SECTOR 8, 45646</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row120_col1\" class=\"data row120 col1\" >ES</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row120_col2\" class=\"data row120 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row121_col0\" class=\"data row121 col0\" >9955, East 138Th Place</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row121_col1\" class=\"data row121 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row121_col2\" class=\"data row121 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row122_col0\" class=\"data row122 col0\" >1032, HENEY LAKE RD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row122_col1\" class=\"data row122 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row122_col2\" class=\"data row122 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row123_col0\" class=\"data row123 col0\" >1021, WOODCREEK OAKS BLVD</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row123_col1\" class=\"data row123 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row123_col2\" class=\"data row123 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row124_col0\" class=\"data row124 col0\" >514, CLEARFIELD ST</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row124_col1\" class=\"data row124 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row124_col2\" class=\"data row124 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row125_col0\" class=\"data row125 col0\" >1991, Braeburn Circle SE</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row125_col1\" class=\"data row125 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row125_col2\" class=\"data row125 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row126_col0\" class=\"data row126 col0\" >Boiling Spring Lakes, Brunswick</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row126_col1\" class=\"data row126 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row126_col2\" class=\"data row126 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row127_col0\" class=\"data row127 col0\" >3965, SAGE DR</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row127_col1\" class=\"data row127 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row127_col2\" class=\"data row127 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row128_col0\" class=\"data row128 col0\" >3175, W 34TH AV</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row128_col1\" class=\"data row128 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row128_col2\" class=\"data row128 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row129_col0\" class=\"data row129 col0\" >83, ST ANDREWS CRESCENT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row129_col1\" class=\"data row129 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row129_col2\" class=\"data row129 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row130_col0\" class=\"data row130 col0\" >10990, 1ST STREET, HEWITT</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row130_col1\" class=\"data row130 col1\" >US</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row130_col2\" class=\"data row130 col2\" >CA</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row131_col0\" class=\"data row131 col0\" >22, POLLETT LN, DIEPPE, NB</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row131_col1\" class=\"data row131 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row131_col2\" class=\"data row131 col2\" >US</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row132_col0\" class=\"data row132 col0\" >Kent Street, Richibucto, Kent</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row132_col1\" class=\"data row132 col1\" >CA</td>\n", " <td id=\"T_7224c262_33ad_11eb_b039_a4bb6dafa070row132_col2\" class=\"data row132 col2\" >ZA</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20ebbe63108>"]}, "execution_count": 24, "metadata": {}, "output_type": "execute_result"}], "source": ["misclassified_records.style.set_table_styles([dict(selector='th', props=[('text-align', 'left')])])\\\n", " .set_properties(**{'text-align': \"left\"}).hide_index()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Saving the trained model\n", "\n", "Once you are satisfied with the model, you can save it using the save() method. This creates an Esri Model Definition (EMD file) that can be used for inferencing on unseen data."]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Computing model metrics...\n"]}, {"data": {"text/plain": ["WindowsPath('models/country-classifier')"]}, "execution_count": 25, "metadata": {}, "output_type": "execute_result"}], "source": ["model.save(\"country-classifier\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Model inference\n", "\n", "The trained model can be used to classify new text documents using the predict method. This method accepts a string or a list of strings to predict the labels of these new documents/text."]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", " <div>\n", " <style>\n", " /* Turns off some styling */\n", " progress {\n", " /* gets rid of default border in Firefox and Opera. */\n", " border: none;\n", " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", " background-size: auto;\n", " }\n", " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", " background: #F44336;\n", " }\n", " </style>\n", " <progress value='15' class='' max='15' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " 100.00% [15/15 00:00<00:00]\n", " </div>\n", " "], "text/plain": ["<IPython.core.display.HTML object>"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/html": ["<style type=\"text/css\" >\n", " #T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070 th {\n", " text-align: left;\n", " }#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col2,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col0,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col1,#T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col2{\n", " text-align: left;\n", " }</style><table id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Address</th> <th class=\"col_heading level0 col1\" >CountryCode</th> <th class=\"col_heading level0 col2\" >Confidence</th> </tr></thead><tbody>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col0\" class=\"data row0 col0\" >179, RUA JOSE BARBALHO FILHO, APARTAMENTO 103 BLOCO G, Jo\u00e3o Pessoa, PB, 58027-000</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col1\" class=\"data row0 col1\" >BR</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row0_col2\" class=\"data row0 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col0\" class=\"data row1 col0\" >2531, PARTRIDGE CRES</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col1\" class=\"data row1 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row1_col2\" class=\"data row1 col2\" >0.834484</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col0\" class=\"data row2 col0\" >SN, CALLE ESCUINAPA, URUAPAN, Uruapan, Michoac\u00e1n de Ocampo</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col1\" class=\"data row2 col1\" >MX</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row2_col2\" class=\"data row2 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col0\" class=\"data row3 col0\" >44, WOODFORD DR, FREDERICKSBURG, Stafford County, VA, 22405</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col1\" class=\"data row3 col1\" >US</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row3_col2\" class=\"data row3 col2\" >0.999997</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col0\" class=\"data row4 col0\" >587, CALLE CABO SAN LUCAS, ENSENADA, Ensenada, Baja California</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col1\" class=\"data row4 col1\" >MX</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row4_col2\" class=\"data row4 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col0\" class=\"data row5 col0\" >80009, Street, Fernie, Chief Albert Luthuli</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col1\" class=\"data row5 col1\" >ZA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row5_col2\" class=\"data row5 col2\" >0.999997</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col0\" class=\"data row6 col0\" >1906, Pelton Mountain Rd, Chipman Brook, Kings County</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col1\" class=\"data row6 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row6_col2\" class=\"data row6 col2\" >0.999895</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col0\" class=\"data row7 col0\" >1, Chemin de Promelles, 1472</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col1\" class=\"data row7 col1\" >BE</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row7_col2\" class=\"data row7 col2\" >0.999912</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col0\" class=\"data row8 col0\" >1408, Cedarglen Court, Oakville, ON</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col1\" class=\"data row8 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row8_col2\" class=\"data row8 col2\" >0.998583</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col0\" class=\"data row9 col0\" >70, POPLAR ST N</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col1\" class=\"data row9 col1\" >CA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row9_col2\" class=\"data row9 col2\" >0.942083</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col0\" class=\"data row10 col0\" >48, CL RAMON TURRO, 8389</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col1\" class=\"data row10 col1\" >ES</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row10_col2\" class=\"data row10 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col0\" class=\"data row11 col0\" >454, NORTH MANNHEIM ROAD, Hillside, 60162</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col1\" class=\"data row11 col1\" >US</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row11_col2\" class=\"data row11 col2\" >0.999981</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col0\" class=\"data row12 col0\" >43, Qoqonga Street, Mfuleni, City of Cape Town</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col1\" class=\"data row12 col1\" >ZA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row12_col2\" class=\"data row12 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col0\" class=\"data row13 col0\" >1 B, TRAVESSA GENESIO SILVEIRA, Mossor\u00f3, RN, 59600-000</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col1\" class=\"data row13 col1\" >BR</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row13_col2\" class=\"data row13 col2\" >1.000000</td>\n", " </tr>\n", " <tr>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col0\" class=\"data row14 col0\" >GaMaphale, Greater Letaba</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col1\" class=\"data row14 col1\" >ZA</td>\n", " <td id=\"T_9cae2de4_33ae_11eb_a1e5_a4bb6dafa070row14_col2\" class=\"data row14 col2\" >0.999998</td>\n", " </tr>\n", " </tbody></table>"], "text/plain": ["<pandas.io.formats.style.Styler at 0x20ebbe53848>"]}, "execution_count": 26, "metadata": {}, "output_type": "execute_result"}], "source": ["text_list = data._train_df.sample(15).Address.values\n", "result = model.predict(text_list)\n", "\n", "df = pd.DataFrame(result, columns=[\"Address\", \"CountryCode\", \"Confidence\"])\n", "\n", "df.style.set_table_styles([dict(selector='th', props=[('text-align', 'left')])])\\\n", " .set_properties(**{'text-align': \"left\"}).hide_index()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Conclusion"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In this notebook, we have built a text classifier using `TextClassifier` class of `arcgis.learn.text` module. The dataset consisted of house addresses of 10 countries written in languages like English, Japanese, French, Spanish, etc. To achieve this we used a [multi-lingual transformer backbone](https://huggingface.co/transformers/v3.0.2/multilingual.html) like `XLM-RoBERTa` to build a classifier to predict the country for an input house address. "]}, {"cell_type": "markdown", "metadata": {}, "source": ["# References"]}, {"cell_type": "markdown", "metadata": {}, "source": ["[1] [Learning Rate](https://en.wikipedia.org/wiki/Learning_rate)\n", "\n", "[2] [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision)\n", "\n", "[3] [Precision, recall and F1-measures](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures)"]}], "metadata": {"esriNotebookRuntime": {"notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced with GPU support", "notebookRuntimeVersion": "5.0"}, "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": "3.8.2"}, "toc": {"base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false}}, "nbformat": 4, "nbformat_minor": 4}
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below error message is displayed
TypeError: batch_text_or_text_pairs has to be a list (got <class 'numpy.ndarray'>)


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review-notebook-app bot commented Nov 14, 2023

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sumanttyagi commented on 2023-11-14T07:25:15Z
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change "inference_raster" to "sample_inference_raster" in map1.add_layer(inference_raster)


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sumanttyagi commented on 2023-11-14T07:25:16Z
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this cell has some different output of map . Please check


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sumanttyagi commented on 2023-11-14T07:41:46Z
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warnings

C:\Users\sum11525\AppData\Local\ESRI\conda\envs\dl-envsep23\lib\site-packages\mmseg\models\decode_heads\decode_head.py:94: UserWarning:

For binary segmentation, we suggest usingout_channels = 1 to define the outputchannels of segmentor, and use thresholdto convert seg_logist into a predictionapplying a threshold

C:\Users\sum11525\AppData\Local\ESRI\conda\envs\dl-envsep23\lib\site-packages\mmseg\models\losses\cross_entropy_loss.py:235: UserWarning:

Default avg_non_ignore is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set avg_non_ignore=True.

Downloading: "https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth" to C:\Users\sum11525/.cache\torch\hub\checkpoints\fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth



shivanip32 commented on 2023-12-04T11:37:48Z
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not able to reproduce the warning.

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sumanttyagi commented on 2023-11-14T07:58:55Z
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input_raster to be changes to input_raster_layer


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sumanttyagi commented on 2023-11-14T07:58:56Z
----------------------------------------------------------------

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_29932\1772424429.py in <cell line: 1>()
----> 1 ds.search()

AttributeError: 'NoneType' object has no attribute 'search'


shivanip32 commented on 2024-01-29T09:05:39Z
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This notebook needs to be run using an enterprise portal.

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sumanttyagi commented on 2023-11-14T07:58:56Z
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AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_29932\2424663473.py in <cell line: 1>()
----> 1 rasterstore = ds.get("/rasterStores/LocalRasterStore")
      2 rasterstore

AttributeError: 'NoneType' object has no attribute 'get'


shivanip32 commented on 2024-01-29T09:07:15Z
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This notebook needs to be run using an enterprise portal.

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sumanttyagi commented on 2023-11-14T07:58:57Z
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NameError: name 'building_label' is not defined

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review-notebook-app bot commented Nov 22, 2023

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sumanttyagi commented on 2023-11-22T06:53:08Z
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Please move it from playground to geosaurus as the playground is getting deprecated.


shivanip32 commented on 2024-01-23T06:23:39Z
----------------------------------------------------------------

Will do it later in separate pr

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review-notebook-app bot commented Nov 22, 2023

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sumanttyagi commented on 2023-11-22T06:53:09Z
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Please move it from playground to geosaurus as the playground is getting deprecated.


shivanip32 commented on 2024-01-23T06:24:15Z
----------------------------------------------------------------

Will do it later in separate pr

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sumanttyagi commented on 2023-11-22T06:53:10Z
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Please move it from playground to geosaurus as the playground is getting deprecated.


shivanip32 commented on 2024-01-23T06:24:29Z
----------------------------------------------------------------

Will do it later in separate pr

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review-notebook-app bot commented Nov 22, 2023

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sumanttyagi commented on 2023-11-22T07:01:28Z
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This item is not accessibly


shivanip32 commented on 2024-01-29T08:53:44Z
----------------------------------------------------------------

This will be generated when this cell will run.

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review-notebook-app bot commented Nov 22, 2023

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sumanttyagi commented on 2023-11-22T07:01:28Z
----------------------------------------------------------------

Please move it from playground to geosaurus as the playground is getting deprecated.


shivanip32 commented on 2024-01-29T13:01:10Z
----------------------------------------------------------------

This notebook is expected to work on enterprise, geosaurus org is ArcGIS Online.

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review-notebook-app bot commented Nov 22, 2023

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sumanttyagi commented on 2023-11-22T07:01:29Z
----------------------------------------------------------------

Please move it from playground to geosaurus as the playground is getting deprecated.


shivanip32 commented on 2024-01-29T13:00:22Z
----------------------------------------------------------------

This notebook is expected to work on enterprise, geosaurus org is ArcGIS Online.

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review-notebook-app bot commented Nov 22, 2023

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sumanttyagi commented on 2023-11-22T07:01:30Z
----------------------------------------------------------------

Please move it from playground to geosaurus as the playground is getting deprecated.


shivanip32 commented on 2024-01-29T12:59:34Z
----------------------------------------------------------------

This notebook is expected to work on enterprise, geosaurus org is ArcGIS Online.

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not able to reproduce the warning.


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Will do it later in separate pr


View entire conversation on ReviewNB

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Contributor Author

Will do it later in separate pr


View entire conversation on ReviewNB

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Will do it later in separate pr


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Contributor Author

This will be generated when this cell will run.


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Contributor Author

This notebook needs to be run using an enterprise portal.


View entire conversation on ReviewNB

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Contributor Author

This notebook needs to be run using an enterprise portal.


View entire conversation on ReviewNB

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Contributor Author

Will do it later.


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Contributor Author

Will do it later.


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Contributor Author

This notebook is expected to work on enterprise, geosaurus org is ArcGIS Online.


View entire conversation on ReviewNB

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Contributor Author

This notebook is expected to work on enterprise, geosaurus org is ArcGIS Online.


View entire conversation on ReviewNB

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Contributor Author

This notebook is expected to work on enterprise, geosaurus org is ArcGIS Online.


View entire conversation on ReviewNB

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