predict-restaurant-rating.ipynb

{"cells":[{"cell_type":"markdown","source":["# Predicting the rating a reviewer will give a restaurant using Featuretools and the nlp-primitives library\n\n<a style=\"margin:30px\" href=\"https://www.featuretools.com\">\n    <img width=50% src=\"https://www.featuretools.com/wp-content/uploads/2017/12/FeatureLabs-Logo-Tangerine-800.png\" alt=\"Featuretools\" />\n</a>\n\n**When customers visit restaurants, they will oftentimes leave a review of some sort. Using data from TripAdvisor, we investigate how this text data can be used to predict the overall thoughts of the customer on that restuarant represented in a star rating.**\n\nIn this tutorial, we show how [Featuretools](https://www.featuretools.com) can be used alongside the [nlp-primitives](https://github.com/FeatureLabs/nlp_primitives) library to train an accurate machine learning model that can predict a customer's rating of a restaurant based on the text of their review and some information about the restaurant.\n\n*Note: If you are running this notebook yourself, refer to the [readme](https://github.com/FeatureLabs/predict-restaurant-rating/blob/master/README.md) on Github for instructions to download the dataset*\n\n*Also, make sure to `pip install -r requirements.txt` before running this yourself!*\n\n## Highlights\n\n* We use the nlp-primitives library to create structured data from unstructured, hard to parse, textual data\n* We acheive an accuracy rating 40% higher than the baseline\n* We use these primitives alongside Featuretools' `dfs` method to create as much information as possible from a dataset containing only two entities.\n* The `dfs` method stacks the default primitives on top of the nlp-primitives to create new, data-rich, features.\n* We build a pipeline that it can be reused for numerous NLP prediction problems (You can try this yourself!)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"7cb4c6da-2df6-430c-9d8e-ddf5e81ce518"}}},{"cell_type":"code","source":["import featuretools as ft\nimport json\nimport pandas as pd\nimport warnings\nimport sys\nimport matplotlib.pyplot as plt\nimport sklearn\nimport numpy as np\n\nfrom pandas.io.json import json_normalize\n\ndef file_to_es(basefile):\n    with open(basefile) as f:\n        d = json.load(f)\n\n    restaurants = json_normalize(d['restaurants'])\n\n    restaurants['id']=restaurants.index\n\n    # the pair of name, address forms a unique key for each restaurant.\n    unique_index = restaurants[['id','name','address']]\n\n    restaurants = restaurants.drop(columns=['trip_advisor_url', 'name', 'country', 'locality', 'region', 'address', 'address_extended', 'reviews','tel', 'email', 'chain_name', 'fax', 'longitude', 'latitude', 'website','cuisine', 'hours.monday', 'hours.tuesday', 'hours.wednesday', 'hours.thursday', 'hours.friday', 'hours.saturday', 'hours.sunday'])\n\n    reviews = json_normalize(d['restaurants'], record_path='reviews', meta=['name', 'address'])\n    reviews['index']=reviews.index\n\n    reviews = reviews.merge(unique_index, how='left', on=['name', 'address'])\n    reviews = reviews.drop(columns=['name','address', 'review_url', 'review_website'])\n    reviews = reviews.rename(columns={\"id\": \"restaurant_id\"})\n\n    revs_types = {'review_title': ft.variable_types.Text,\n                  'review_rating': ft.variable_types.Categorical}\n\n    rest_types = {'rating': ft.variable_types.Categorical,\n                  'name': ft.variable_types.Text}\n\n\n    entities = {\n        \"restaurants\" : (restaurants, \"id\"),\n        \"reviews\" : (reviews, \"index\", None, revs_types)\n    }\n\n    relationships = [(\"restaurants\", \"id\", \"reviews\", \"restaurant_id\")]\n\n\n    es = ft.EntitySet(\"es\", entities, relationships)\n\n    es['reviews'].convert_variable_type('review_rating', ft.variable_types.Categorical)\n\n    es.add_interesting_values()\n\n    return es\n\ndef plot_confusion_matrix(y_true, y_pred, classes,\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    From: https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py\n    \"\"\"\n    title = 'Normalized confusion matrix'\n\n    # Compute confusion matrix\n    cm = sklearn.metrics.confusion_matrix(y_true, y_pred)\n    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n    fig, ax = plt.subplots()\n    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n    ax.figure.colorbar(im, ax=ax)\n    # We want to show all ticks...\n    ax.set(xticks=np.arange(cm.shape[1]),\n           yticks=np.arange(cm.shape[0]),\n           # ... and label them with the respective list entries\n           xticklabels=classes, yticklabels=classes,\n           title=title,\n           ylabel='True label',\n           xlabel='Predicted label')\n\n    ax.set_ylim(len(cm)-0.5, -0.5)\n\n    # Rotate the tick labels and set their alignment.\n    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n             rotation_mode=\"anchor\")\n\n    # Loop over data dimensions and create text annotations.\n    thresh = cm.max() / 2.\n    for i in range(cm.shape[0]):\n        for j in range(cm.shape[1]):\n            ax.text(j, i, format(cm[i, j], '.2f'),\n                    ha=\"center\", va=\"center\",\n                    color=\"white\" if cm[i, j] > thresh else \"black\")\n    return ax"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"56ed6cf9-5cfe-4f52-8300-a1bce95e80a5"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">2021-03-26 12:30:48,264 featuretools - WARNING    Featuretools failed to load plugin nlp_primitives from library nlp_primitives. For a full stack trace, set logging to debug.\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">2021-03-26 12:30:48,264 featuretools - WARNING    Featuretools failed to load plugin nlp_primitives from library nlp_primitives. For a full stack trace, set logging to debug.\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["import warnings\nwarnings.filterwarnings('ignore')\nwarnings.simplefilter('ignore')\n\nimport nltk\nimport pandas as pd\nimport json\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport nlp_primitives\nimport featuretools as ft\nimport featuretools.variable_types as vtypes\nimport seaborn as sns\nimport sklearn\nimport numpy as np\n# import helpers\n\nfrom featuretools.primitives import *\nfrom nlp_primitives import (\n    DiversityScore,\n    LSA,\n    MeanCharactersPerWord,\n    PartOfSpeechCount,\n    PolarityScore, \n    PunctuationCount,\n    StopwordCount,\n    TitleWordCount,\n    UniversalSentenceEncoder,\n    UpperCaseCount)\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.experimental import enable_hist_gradient_boosting\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.naive_bayes import BernoulliNB\nfrom sklearn.ensemble import VotingClassifier\n\nprint('Featuretools version {}'.format(ft.__version__))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"72d7430f-0f51-4ba3-aa87-d54f2093bcda"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Featuretools version 0.23.2\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Featuretools version 0.23.2\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Step 1. Load Data\nWe start by loading in the data from our machine. This data is from a kaggle dataset, and instuctions on how to download it and can be found in the read me on github. Once it has been downloaded, rename the file `reviews.json`, and ensure that it is in the `predict-restaurant-rating` folder. This data includes two entity sets: The restaurants entityset, which includes the name, address, price, star-rating, and several other pieces of information about each restaurant in the data, and the reviews entityset, which includes the title of the review, date of the review, and text of the review."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c9f237c2-d433-47df-8144-1e0014bcdc86"}}},{"cell_type":"code","source":["%sh sudo apt-get install -y graphviz"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c5bea751-8016-4985-be9a-49684b7d6378"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Reading package lists...\nBuilding dependency tree...\nReading state information...\nThe following additional packages will be installed:\n  fonts-liberation libann0 libcdt5 libcgraph6 libgts-0.7-5 libgts-bin libgvc6\n  libgvpr2 liblab-gamut1 libpathplan4\nSuggested packages:\n  graphviz-doc\nThe following NEW packages will be installed:\n  fonts-liberation graphviz libann0 libcdt5 libcgraph6 libgts-0.7-5 libgts-bin\n  libgvc6 libgvpr2 liblab-gamut1 libpathplan4\n0 upgraded, 11 newly installed, 0 to remove and 17 not upgraded.\nNeed to get 2,670 kB of archives.\nAfter this operation, 11.0 MB of additional disk space will be used.\nGet:1 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 fonts-liberation all 1:1.07.4-7~18.04.1 [822 kB]\nGet:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libann0 amd64 1.1.2+doc-6 [24.8 kB]\nGet:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libcdt5 amd64 2.40.1-2 [19.6 kB]\nGet:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libcgraph6 amd64 2.40.1-2 [40.8 kB]\nGet:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgts-0.7-5 amd64 0.7.6+darcs121130-4 [150 kB]\nGet:6 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libpathplan4 amd64 2.40.1-2 [22.6 kB]\nGet:7 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgvc6 amd64 2.40.1-2 [601 kB]\nGet:8 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgvpr2 amd64 2.40.1-2 [169 kB]\nGet:9 http://archive.ubuntu.com/ubuntu bionic/universe amd64 liblab-gamut1 amd64 2.40.1-2 [178 kB]\nGet:10 http://archive.ubuntu.com/ubuntu bionic/universe amd64 graphviz amd64 2.40.1-2 [601 kB]\nGet:11 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgts-bin amd64 0.7.6+darcs121130-4 [41.3 kB]\ndebconf: delaying package configuration, since apt-utils is not installed\nFetched 2,670 kB in 1s (1,940 kB/s)\nSelecting previously unselected package fonts-liberation.\n(Reading database ... \n(Reading database ... 5%\n(Reading database ... 10%\n(Reading database ... 15%\n(Reading database ... 20%\n(Reading database ... 25%\n(Reading database ... 30%\n(Reading database ... 35%\n(Reading database ... 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unselected package libgts-0.7-5:amd64.\nPreparing to unpack .../04-libgts-0.7-5_0.7.6+darcs121130-4_amd64.deb ...\nUnpacking libgts-0.7-5:amd64 (0.7.6+darcs121130-4) ...\nSelecting previously unselected package libpathplan4.\nPreparing to unpack .../05-libpathplan4_2.40.1-2_amd64.deb ...\nUnpacking libpathplan4 (2.40.1-2) ...\nSelecting previously unselected package libgvc6.\nPreparing to unpack .../06-libgvc6_2.40.1-2_amd64.deb ...\nUnpacking libgvc6 (2.40.1-2) ...\nSelecting previously unselected package libgvpr2.\nPreparing to unpack .../07-libgvpr2_2.40.1-2_amd64.deb ...\nUnpacking libgvpr2 (2.40.1-2) ...\nSelecting previously unselected package liblab-gamut1.\nPreparing to unpack .../08-liblab-gamut1_2.40.1-2_amd64.deb ...\nUnpacking liblab-gamut1 (2.40.1-2) ...\nSelecting previously unselected package graphviz.\nPreparing to unpack .../09-graphviz_2.40.1-2_amd64.deb ...\nUnpacking graphviz (2.40.1-2) ...\nSelecting previously unselected package libgts-bin.\nPreparing to unpack .../10-libgts-bin_0.7.6+darcs121130-4_amd64.deb ...\nUnpacking libgts-bin (0.7.6+darcs121130-4) ...\nSetting up libgts-0.7-5:amd64 (0.7.6+darcs121130-4) ...\nSetting up libpathplan4 (2.40.1-2) ...\nSetting up liblab-gamut1 (2.40.1-2) ...\nSetting up fonts-liberation (1:1.07.4-7~18.04.1) ...\nSetting up libann0 (1.1.2+doc-6) ...\nSetting up libgts-bin (0.7.6+darcs121130-4) ...\nSetting up libcdt5 (2.40.1-2) ...\nSetting up libcgraph6 (2.40.1-2) ...\nSetting up libgvc6 (2.40.1-2) ...\nSetting up libgvpr2 (2.40.1-2) ...\nSetting up graphviz (2.40.1-2) ...\nProcessing triggers for man-db (2.8.3-2ubuntu0.1) ...\nProcessing triggers for fontconfig (2.12.6-0ubuntu2) ...\nProcessing triggers for libc-bin (2.27-3ubuntu1.4) ...\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Reading package lists...\nBuilding dependency tree...\nReading state information...\nThe following additional packages will be installed:\n  fonts-liberation libann0 libcdt5 libcgraph6 libgts-0.7-5 libgts-bin libgvc6\n  libgvpr2 liblab-gamut1 libpathplan4\nSuggested packages:\n  graphviz-doc\nThe following NEW packages will be installed:\n  fonts-liberation graphviz libann0 libcdt5 libcgraph6 libgts-0.7-5 libgts-bin\n  libgvc6 libgvpr2 liblab-gamut1 libpathplan4\n0 upgraded, 11 newly installed, 0 to remove and 17 not upgraded.\nNeed to get 2,670 kB of archives.\nAfter this operation, 11.0 MB of additional disk space will be used.\nGet:1 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 fonts-liberation all 1:1.07.4-7~18.04.1 [822 kB]\nGet:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libann0 amd64 1.1.2+doc-6 [24.8 kB]\nGet:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libcdt5 amd64 2.40.1-2 [19.6 kB]\nGet:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libcgraph6 amd64 2.40.1-2 [40.8 kB]\nGet:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgts-0.7-5 amd64 0.7.6+darcs121130-4 [150 kB]\nGet:6 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libpathplan4 amd64 2.40.1-2 [22.6 kB]\nGet:7 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgvc6 amd64 2.40.1-2 [601 kB]\nGet:8 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgvpr2 amd64 2.40.1-2 [169 kB]\nGet:9 http://archive.ubuntu.com/ubuntu bionic/universe amd64 liblab-gamut1 amd64 2.40.1-2 [178 kB]\nGet:10 http://archive.ubuntu.com/ubuntu bionic/universe amd64 graphviz amd64 2.40.1-2 [601 kB]\nGet:11 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgts-bin amd64 0.7.6+darcs121130-4 [41.3 kB]\ndebconf: delaying package configuration, since apt-utils is not installed\nFetched 2,670 kB in 1s (1,940 kB/s)\nSelecting previously unselected package fonts-liberation.\n(Reading database ... \n(Reading database ... 5%\n(Reading database ... 10%\n(Reading database ... 15%\n(Reading database ... 20%\n(Reading database ... 25%\n(Reading database ... 30%\n(Reading database ... 35%\n(Reading database ... 40%\n(Reading database ... 45%\n(Reading database ... 50%\n(Reading database ... 55%\n(Reading database ... 60%\n(Reading database ... 65%\n(Reading database ... 70%\n(Reading database ... 75%\n(Reading database ... 80%\n(Reading database ... 85%\n(Reading database ... 90%\n(Reading database ... 95%\n(Reading database ... 100%\n(Reading database ... 110195 files and directories currently installed.)\nPreparing to unpack .../00-fonts-liberation_1%3a1.07.4-7~18.04.1_all.deb ...\nUnpacking fonts-liberation (1:1.07.4-7~18.04.1) ...\nSelecting previously unselected package libann0.\nPreparing to unpack .../01-libann0_1.1.2+doc-6_amd64.deb ...\nUnpacking libann0 (1.1.2+doc-6) ...\nSelecting previously unselected package libcdt5.\nPreparing to unpack .../02-libcdt5_2.40.1-2_amd64.deb ...\nUnpacking libcdt5 (2.40.1-2) ...\nSelecting previously unselected package libcgraph6.\nPreparing to unpack .../03-libcgraph6_2.40.1-2_amd64.deb ...\nUnpacking libcgraph6 (2.40.1-2) ...\nSelecting previously unselected package libgts-0.7-5:amd64.\nPreparing to unpack .../04-libgts-0.7-5_0.7.6+darcs121130-4_amd64.deb ...\nUnpacking libgts-0.7-5:amd64 (0.7.6+darcs121130-4) ...\nSelecting previously unselected package libpathplan4.\nPreparing to unpack .../05-libpathplan4_2.40.1-2_amd64.deb ...\nUnpacking libpathplan4 (2.40.1-2) ...\nSelecting previously unselected package libgvc6.\nPreparing to unpack .../06-libgvc6_2.40.1-2_amd64.deb ...\nUnpacking libgvc6 (2.40.1-2) ...\nSelecting previously unselected package libgvpr2.\nPreparing to unpack .../07-libgvpr2_2.40.1-2_amd64.deb ...\nUnpacking libgvpr2 (2.40.1-2) ...\nSelecting previously unselected package liblab-gamut1.\nPreparing to unpack .../08-liblab-gamut1_2.40.1-2_amd64.deb ...\nUnpacking liblab-gamut1 (2.40.1-2) ...\nSelecting previously unselected package graphviz.\nPreparing to unpack .../09-graphviz_2.40.1-2_amd64.deb ...\nUnpacking graphviz (2.40.1-2) ...\nSelecting previously unselected package libgts-bin.\nPreparing to unpack .../10-libgts-bin_0.7.6+darcs121130-4_amd64.deb ...\nUnpacking libgts-bin (0.7.6+darcs121130-4) ...\nSetting up libgts-0.7-5:amd64 (0.7.6+darcs121130-4) ...\nSetting up libpathplan4 (2.40.1-2) ...\nSetting up liblab-gamut1 (2.40.1-2) ...\nSetting up fonts-liberation (1:1.07.4-7~18.04.1) ...\nSetting up libann0 (1.1.2+doc-6) ...\nSetting up libgts-bin (0.7.6+darcs121130-4) ...\nSetting up libcdt5 (2.40.1-2) ...\nSetting up libcgraph6 (2.40.1-2) ...\nSetting up libgvc6 (2.40.1-2) ...\nSetting up libgvpr2 (2.40.1-2) ...\nSetting up graphviz (2.40.1-2) ...\nProcessing triggers for man-db (2.8.3-2ubuntu0.1) ...\nProcessing triggers for fontconfig (2.12.6-0ubuntu2) ...\nProcessing triggers for libc-bin (2.27-3ubuntu1.4) ...\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["es = file_to_es('/dbfs/FileStore/tables/factual_tripadvisor_restaurant_data_all_100_reviews.json')\nes.plot()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1aa6c592-b411-4f8c-b15c-6de98c209e70"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[4]: &lt;graphviz.dot.Digraph at 0x7f64fa671e10&gt;</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[4]: &lt;graphviz.dot.Digraph at 0x7f64fa671e10&gt;</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### Visualize Data Distribution\nThis is a multiclass classification problem, and we can see here that the data is skewed towards higher reviews, but that there are reviews in every category, from 1-5 stars."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"b64a3f81-29e4-4f73-999d-7c322e3d7ab2"}}},{"cell_type":"code","source":["df = es['reviews'].df\n\ncol = sns.color_palette(\"Blues\")\nsns.countplot(x=df['review_rating'], palette = sns.cubehelix_palette(8, start=1, rot=-.75))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"438996dc-41c7-4ae8-be75-b4a0f8756c8e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[5]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[5]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/202f08d4-2281-45fb-9dba-0278bc9453d9.png","removedWidgets":[],"addedWidgets":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f64fb6a1310&gt;</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f64fb6a1310&gt;</div>"]}}],"execution_count":0},{"cell_type":"code","source":["es['reviews'].df = es['reviews'].df.sample(500)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"49ea6041-6057-477b-95ba-09d2371ed599"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Step 2. Compare Against Baseline\nIn order to see how much of an effect these primitives have, we will first create a baseline, and then compare the accuracy of the two models."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"21b69a5a-b4b1-4020-827a-9f7eeb58321e"}}},{"cell_type":"code","source":["rating_df = es['reviews'].df[['index', 'review_rating']]\nignore = {'restaurants': ['rating'],\n          'reviews': ['review_rating']}\n\nbaseline_feature_matrix, baseline_features = ft.dfs(entityset=es,\n                                             target_entity='reviews',\n                                             verbose=True,\n                                             ignore_variables=ignore)\n\nbaseline_fm = baseline_feature_matrix.reindex(rating_df.index)\n\ny = rating_df['review_rating']\n\nbaseline_train_fm, baseline_test_fm, baseline_y_train, baseline_y_test = train_test_split(baseline_fm, y, test_size=0.15, stratify=y)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"36701b26-3d12-4797-b445-33678669e68b"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Built 33 features\n\rElapsed: 00:00 | Progress:   0%|          \rElapsed: 00:00 | Progress:  47%|████▋     \rElapsed: 00:00 | Progress: 100%|██████████\rElapsed: 00:00 | Progress: 100%|██████████\rElapsed: 00:00 | Progress: 100%|██████████\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Built 33 features\n\rElapsed: 00:00 | Progress:   0%|          \rElapsed: 00:00 | Progress:  47%|████▋     \rElapsed: 00:00 | Progress: 100%|██████████\rElapsed: 00:00 | Progress: 100%|██████████\rElapsed: 00:00 | Progress: 100%|██████████\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["base_rfc = RandomForestClassifier(n_estimators=100, class_weight = \"balanced\", n_jobs=-1)\nbase_rfc.fit(baseline_train_fm, baseline_y_train)\nbase_rfc.score(baseline_test_fm, baseline_y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"23ce939d-5c6b-4004-bd68-c4fd7da2e756"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[10]: 0.5174603174603175</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[10]: 0.5174603174603175</div>"]}}],"execution_count":0},{"cell_type":"code","source":["plot_confusion_matrix(baseline_y_test, \n                      base_rfc.predict(baseline_test_fm), \n                      ['1', '2', '3', '4', '5'])"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a5216dc9-a65c-4f91-82b2-c4b22a08321a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[11]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  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"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f94ef65fe50&gt;</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f94ef65fe50&gt;</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["This baseline has an accuracy of about 50%, with most of the samples being classified as a 5, the most common class. This accuracy comes from classifying most reviews as a 4 or a 5, the two most common categories, so it is clear that this model's accuracy only comes from guessing the most common classes and getting it right only due to the frequency of those categories."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"006ad50d-dedb-4ec2-a901-f8a1b00ce83a"}}},{"cell_type":"markdown","source":["## Step 3. Create Feature Matrix\nHere we choose the features we wish to include in the training (in this case all of them), and calculate the training feature matrix. We add some transformation primitives, but this dfs will also include many default primitive functions to create features, and setting the `max_depth=4` will ensure that these nlp-primitives create features that other features then stack on."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"12f6ef6b-ea53-4344-a78a-02d3814adb5b"}}},{"cell_type":"code","source":["trans = [DiversityScore,\n         LSA,\n         MeanCharactersPerWord,\n         PartOfSpeechCount,\n         PolarityScore, \n         PunctuationCount,\n         StopwordCount,\n         TitleWordCount,\n         UniversalSentenceEncoder,\n         UpperCaseCount]\n\nignore = {'restaurants': ['rating'],\n          'reviews': ['review_rating']}\n\ndrop_contains = ['(reviews.UNIVERSAL']\n\nfeatures = ft.dfs(entityset=es,\n                  target_entity='reviews',\n                  trans_primitives=trans,\n                  verbose=True,\n                  features_only=True,\n                  ignore_variables=ignore,\n                  drop_contains=drop_contains,\n                  max_depth=4)"],"metadata":{"scrolled":true,"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5c51f947-7e75-4333-bf54-53e778246c00"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Built 333 features\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Built 333 features\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["This `dfs` call created 333 features, which is about 10 times more features than in the baseline. Now, we will calculate these features."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"61821b05-a60b-46a5-b7b6-f211a906decf"}}},{"cell_type":"code","source":["feature_matrix = ft.calculate_feature_matrix(features=features,\n                                             entityset=es,\n                                             verbose=True)"],"metadata":{"scrolled":true,"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"277f908c-bb52-465b-b3eb-6d98c1564d9e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">\rElapsed: 00:00 | Progress:   0%|          \rElapsed: 00:00 | Progress:   1%|          \rElapsed: 00:08 | Progress:   1%|          \rElapsed: 00:09 | Progress:   1%|          \rElapsed: 00:19 | Progress:   1%|          \rElapsed: 00:53 | Progress:   1%|▏         \rElapsed: 01:02 | Progress:   1%|▏         \rElapsed: 01:12 | Progress:   2%|▏         \rElapsed: 01:12 | Progress:   2%|▏         \rElapsed: 01:30 | Progress:   2%|▏         \rElapsed: 01:38 | Progress:   2%|▏         \rElapsed: 01:39 | Progress:   2%|▏         \rElapsed: 01:39 | Progress:   2%|▏         \rElapsed: 01:41 | Progress:   2%|▏         \rElapsed: 01:50 | Progress:   2%|▏         \rElapsed: 01:54 | Progress:   3%|▎         \rElapsed: 02:04 | Progress:   3%|▎         \rElapsed: 02:07 | Progress:   3%|▎         \rElapsed: 02:14 | Progress:   3%|▎         \rElapsed: 02:15 | Progress:  47%|████▋     \rElapsed: 02:30 | Progress:  48%|████▊     \rElapsed: 02:33 | Progress:  48%|████▊     \rElapsed: 02:59 | Progress:  48%|████▊     \rElapsed: 03:04 | Progress:  48%|████▊     \rElapsed: 03:20 | Progress:  49%|████▉     INFO:tensorflow:Saver not created because there are no variables in the graph to restore\nINFO:tensorflow:Saver not created because there are no variables in the graph to restore\n\rElapsed: 03:28 | Progress:  49%|████▉     \rElapsed: 03:28 | Progress:  49%|████▉     \rElapsed: 03:48 | Progress:  49%|████▉     INFO:tensorflow:Saver not created because there are no variables in the graph to restore\nINFO:tensorflow:Saver not created because there are no variables in the graph to restore\n\rElapsed: 04:05 | Progress:  49%|████▉     \rElapsed: 04:07 | Progress:  49%|████▉     \rElapsed: 04:17 | Progress:  50%|████▉     \rElapsed: 04:24 | Progress:  50%|████▉     \rElapsed: 04:34 | Progress:  50%|█████     \rElapsed: 05:19 | Progress:  50%|█████     \rElapsed: 05:20 | Progress:  51%|█████     \rElapsed: 05:21 | Progress:  95%|█████████▌\rElapsed: 05:21 | Progress: 100%|██████████\rElapsed: 05:21 | Progress: 100%|██████████\rElapsed: 05:21 | Progress: 100%|██████████\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">\rElapsed: 00:00 | Progress:   0%|          \rElapsed: 00:00 | Progress:   1%|          \rElapsed: 00:08 | Progress:   1%|          \rElapsed: 00:09 | Progress:   1%|          \rElapsed: 00:19 | Progress:   1%|          \rElapsed: 00:53 | Progress:   1%|▏         \rElapsed: 01:02 | Progress:   1%|▏         \rElapsed: 01:12 | Progress:   2%|▏         \rElapsed: 01:12 | Progress:   2%|▏         \rElapsed: 01:30 | Progress:   2%|▏         \rElapsed: 01:38 | Progress:   2%|▏         \rElapsed: 01:39 | Progress:   2%|▏         \rElapsed: 01:39 | Progress:   2%|▏         \rElapsed: 01:41 | Progress:   2%|▏         \rElapsed: 01:50 | Progress:   2%|▏         \rElapsed: 01:54 | Progress:   3%|▎         \rElapsed: 02:04 | Progress:   3%|▎         \rElapsed: 02:07 | Progress:   3%|▎         \rElapsed: 02:14 | Progress:   3%|▎         \rElapsed: 02:15 | Progress:  47%|████▋     \rElapsed: 02:30 | Progress:  48%|████▊     \rElapsed: 02:33 | Progress:  48%|████▊     \rElapsed: 02:59 | Progress:  48%|████▊     \rElapsed: 03:04 | Progress:  48%|████▊     \rElapsed: 03:20 | Progress:  49%|████▉     INFO:tensorflow:Saver not created because there are no variables in the graph to restore\nINFO:tensorflow:Saver not created because there are no variables in the graph to restore\n\rElapsed: 03:28 | Progress:  49%|████▉     \rElapsed: 03:28 | Progress:  49%|████▉     \rElapsed: 03:48 | Progress:  49%|████▉     INFO:tensorflow:Saver not created because there are no variables in the graph to restore\nINFO:tensorflow:Saver not created because there are no variables in the graph to restore\n\rElapsed: 04:05 | Progress:  49%|████▉     \rElapsed: 04:07 | Progress:  49%|████▉     \rElapsed: 04:17 | Progress:  50%|████▉     \rElapsed: 04:24 | Progress:  50%|████▉     \rElapsed: 04:34 | Progress:  50%|█████     \rElapsed: 05:19 | Progress:  50%|█████     \rElapsed: 05:20 | Progress:  51%|█████     \rElapsed: 05:21 | Progress:  95%|█████████▌\rElapsed: 05:21 | Progress: 100%|██████████\rElapsed: 05:21 | Progress: 100%|██████████\rElapsed: 05:21 | Progress: 100%|██████████\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["features[0:50]"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"80bdec83-2dbc-4da6-86f0-dda1b27e9498"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[14]: [&lt;Feature: restaurant_id&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_text)&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_title)&gt;,\n &lt;Feature: LSA(review_text)&gt;,\n &lt;Feature: LSA(review_title)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_text)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_title)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_text)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_title)&gt;,\n &lt;Feature: POLARITY_SCORE(review_text)&gt;,\n &lt;Feature: POLARITY_SCORE(review_title)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_text)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_title)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_text)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_title)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_text)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_title)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_text)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_title)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_text)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_title)&gt;,\n &lt;Feature: restaurants.price&gt;,\n &lt;Feature: restaurants.parking&gt;,\n &lt;Feature: restaurants.parking_valet&gt;,\n &lt;Feature: restaurants.parking_garage&gt;,\n &lt;Feature: restaurants.parking_street&gt;,\n &lt;Feature: restaurants.parking_lot&gt;,\n &lt;Feature: restaurants.parking_validated&gt;,\n &lt;Feature: restaurants.parking_free&gt;,\n &lt;Feature: restaurants.smoking&gt;,\n &lt;Feature: restaurants.accessible_wheelchair&gt;,\n &lt;Feature: restaurants.wifi&gt;,\n &lt;Feature: restaurants.meal_breakfast&gt;,\n &lt;Feature: restaurants.meal_deliver&gt;,\n &lt;Feature: restaurants.meal_dinner&gt;,\n &lt;Feature: restaurants.meal_lunch&gt;,\n &lt;Feature: restaurants.meal_takeout&gt;,\n &lt;Feature: restaurants.meal_cater&gt;,\n &lt;Feature: restaurants.options_healthy&gt;,\n &lt;Feature: restaurants.options_organic&gt;,\n &lt;Feature: restaurants.options_vegetarian&gt;,\n &lt;Feature: restaurants.options_vegan&gt;,\n &lt;Feature: restaurants.options_glutenfree&gt;,\n &lt;Feature: restaurants.options_lowfat&gt;,\n &lt;Feature: restaurants.COUNT(reviews)&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.POLARITY_SCORE(review_text))&gt;]</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[14]: [&lt;Feature: restaurant_id&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_text)&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_title)&gt;,\n &lt;Feature: LSA(review_text)&gt;,\n &lt;Feature: LSA(review_title)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_text)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_title)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_text)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_title)&gt;,\n &lt;Feature: POLARITY_SCORE(review_text)&gt;,\n &lt;Feature: POLARITY_SCORE(review_title)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_text)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_title)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_text)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_title)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_text)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_title)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_text)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_title)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_text)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_title)&gt;,\n &lt;Feature: restaurants.price&gt;,\n &lt;Feature: restaurants.parking&gt;,\n &lt;Feature: restaurants.parking_valet&gt;,\n &lt;Feature: restaurants.parking_garage&gt;,\n &lt;Feature: restaurants.parking_street&gt;,\n &lt;Feature: restaurants.parking_lot&gt;,\n &lt;Feature: restaurants.parking_validated&gt;,\n &lt;Feature: restaurants.parking_free&gt;,\n &lt;Feature: restaurants.smoking&gt;,\n &lt;Feature: restaurants.accessible_wheelchair&gt;,\n &lt;Feature: restaurants.wifi&gt;,\n &lt;Feature: restaurants.meal_breakfast&gt;,\n &lt;Feature: restaurants.meal_deliver&gt;,\n &lt;Feature: restaurants.meal_dinner&gt;,\n &lt;Feature: restaurants.meal_lunch&gt;,\n &lt;Feature: restaurants.meal_takeout&gt;,\n &lt;Feature: restaurants.meal_cater&gt;,\n &lt;Feature: restaurants.options_healthy&gt;,\n &lt;Feature: restaurants.options_organic&gt;,\n &lt;Feature: restaurants.options_vegetarian&gt;,\n &lt;Feature: restaurants.options_vegan&gt;,\n &lt;Feature: restaurants.options_glutenfree&gt;,\n &lt;Feature: restaurants.options_lowfat&gt;,\n &lt;Feature: restaurants.COUNT(reviews)&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.POLARITY_SCORE(review_text))&gt;]</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Step 4. Dividing the data\nWe must do two things in this step: split the data into a training set and a testing set, and separate the labels from the feature matrix. We will use the train_test_split functionality of sklearn to do this."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ceb86ed6-0371-4d82-98aa-734f38ae6d4e"}}},{"cell_type":"code","source":["feature_matrix = feature_matrix.reindex(rating_df.index)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"8bb3d6be-317a-4ff1-b605-ad17e2fa2ef9"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["y = rating_df['review_rating']\n\ntrain_feature_matrix, test_feature_matrix, y_train, y_test = train_test_split(feature_matrix, y, test_size=0.15, stratify=y)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d0c2a310-24f0-4585-8c99-8352ee410eb7"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Step 5. Machine Learning\nHere we create and test various machine learning models from sklearn using the feature matrix."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"288b98c2-66a0-40fc-93e6-f53a99e7d016"}}},{"cell_type":"code","source":["lgr = LogisticRegression(solver='liblinear', multi_class='auto', max_iter=100)\nlgr.fit(train_feature_matrix, y_train)\nlgr.score(test_feature_matrix, y_test)"],"metadata":{"scrolled":false,"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"3d6efc26-b7eb-4397-9109-8a0a03f3aa2d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[17]: 0.6394557823129252</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[17]: 0.6394557823129252</div>"]}}],"execution_count":0},{"cell_type":"code","source":["hgbc = HistGradientBoostingClassifier()\nhgbc.fit(train_feature_matrix, y_train)\nhgbc.score(test_feature_matrix, y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fc8dd0bd-bcc7-4d7d-be5f-35cae18425eb"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[18]: 0.6884353741496598</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[18]: 0.6884353741496598</div>"]}}],"execution_count":0},{"cell_type":"code","source":["rfc = RandomForestClassifier(n_estimators=100, class_weight = \"balanced\", n_jobs=-1)\nrfc.fit(train_feature_matrix, y_train)\nrfc.score(test_feature_matrix, y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"847a185a-141a-4553-a0d1-e0ccdb07ce2a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[19]: 0.6421768707482993</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[19]: 0.6421768707482993</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["Here we can observe the feature importances for this model, and see that the Universal Sentence Encoder features and the polarity score are the most influential in this model."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e4417ffe-fef9-4004-bddf-52434b7aa091"}}},{"cell_type":"code","source":["headers = [\"name\", \"score\"]\nvalues = pd.DataFrame(sorted(zip(train_feature_matrix.columns, rfc.feature_importances_), key=lambda x: x[1] * -1))\n\nvalues.head(10)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"28d4835f-8f19-4c63-92ac-85f02af6ee64"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[20]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[20]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[415]</td>\n      <td>0.015511</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[415]</td>\n      <td>0.011831</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[28]</td>\n      <td>0.010463</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>POLARITY_SCORE(review_text)</td>\n      <td>0.009986</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[289]</td>\n      <td>0.008896</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>POLARITY_SCORE(review_title)</td>\n      <td>0.007442</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[237]</td>\n      <td>0.006792</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[289]</td>\n      <td>0.006208</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[259]</td>\n      <td>0.005780</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[28]</td>\n      <td>0.005718</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[415]</td>\n      <td>0.015511</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[415]</td>\n      <td>0.011831</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[28]</td>\n      <td>0.010463</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>POLARITY_SCORE(review_text)</td>\n      <td>0.009986</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[289]</td>\n      <td>0.008896</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>POLARITY_SCORE(review_title)</td>\n      <td>0.007442</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[237]</td>\n      <td>0.006792</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[289]</td>\n      <td>0.006208</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[259]</td>\n      <td>0.005780</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[28]</td>\n      <td>0.005718</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["vot = VotingClassifier(voting='soft', estimators=[('lgr', lgr), ('rfc', rfc), ('hgbc', hgbc)], weights=[3, 1, 6])\nvot.fit(train_feature_matrix, y_train)\nvot.score(test_feature_matrix, y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"40417c0c-ecfc-44f9-9cdd-e73811189c05"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[21]: 0.6952380952380952</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[21]: 0.6952380952380952</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["So, we can see that the highest score we can get with a model is around 70%, which might seem like a pretty poor result. However, given that it is a 5 class classification problem, it's not a bad score. Furthermore, by examining the confusion matrix, we can see where the model goes wrong and get a better sense of how far off the model is with its predictions."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4c1aabaf-b2b1-454f-a8ca-6fa34a9ee0ae"}}},{"cell_type":"code","source":["plot_confusion_matrix(y_test, \n                      vot.predict(test_feature_matrix), \n                      ['1', '2', '3', '4', '5'])"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d956faab-5047-40ca-a2cb-92d148dc4f1f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[22]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: 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"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div 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kGTluPqVJM1unHsWFwPHP692NnB9VS0Frm/zACcAS9vfKuACGIQLcC5wFHAkcO50wEiS5s7YwqKqbgK2PK+8AljTptcAJw3VL6mBW4B9kxwEHAesq6otVfUEsI5tA0iSNGZzfc7iwKp6DKC9HtDqBwOPDI3b2Gqz1beRZFWS9UnWb968eac3LkkL2Xw5wZ0ZarWd+rbFqgurallVLZuamtqpzUnSQjfXYfF4O7xEe93U6huBQ4bGLQYe3U5dkjSH5jos1gLTVzStBK4aqr+3XRV1NPBUO0x1HbA8yaJ2Ynt5q0mS5tDYnmeR5AvAm4H9k2xkcFXTXwCXJTkDeBg4pQ2/BjgR2AD8GDgdoKq2JDkfuL2NO6+qnn/SXJI0ZmMLi6p61yyLjp1hbAFnzrKd1cDqndiaJGkHzZcT3JKkecywkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1DW2Gwlq/vv87TdNuoWxePevv2nSLUgvOu5ZSJK6DAtJUpdhIUnqMiwkSV0L7gT3rQ/fM+kWxuKoV/zqpFuQ9CLmnoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeraZcIiyfFJvpVkQ5KzJ92PJC0ku0RYJNkd+GvgBOAw4F1JDptsV5K0cOwSYQEcCWyoqger6qfApcCKCfckSQtGqmrSPXQlORk4vqr+Q5s/DTiqqv5waMwqYFWbfTXwrTlvdFv7A9+ddBPzhN/FVn4XW/ldbDUfvotfqqqpmRbsKveGygy156RcVV0IXDg37YwmyfqqWjbpPuYDv4ut/C628rvYar5/F7vKYaiNwCFD84uBRyfUiyQtOLtKWNwOLE1yaJKXAKcCayfckyQtGLvEYaiqeibJHwLXAbsDq6vq3gm3NYp5dVhswvwutvK72MrvYqt5/V3sEie4JUmTtaschpIkTZBhIUnqMizGIMnqJJuSfGPSvUxSkkOS3JDk/iT3Jjlr0j1NSpKfS3Jbkq+17+JDk+5p0pLsnuSuJFdPupdJSvJQkq8nuTvJ+kn3MxvPWYxBkjcBPwQuqapfmXQ/k5LkIOCgqrozyT7AHcBJVXXfhFubc0kC7F1VP0yyJ3AzcFZV3TLh1iYmyR8By4CXV9XbJt3PpCR5CFhWVZP+Qd52uWcxBlV1E7Bl0n1MWlU9VlV3tukfAPcDB0+2q8mogR+22T3b34L9P7Uki4HfBj4z6V40GsNCcyLJEuD1wK2T7WRy2mGXu4FNwLqqWrDfBfBx4IPAv0y6kXmggL9Pcke7bdG8ZFho7JK8DLgCeH9VfX/S/UxKVT1bVYczuAPBkUkW5CHKJG8DNlXVHZPuZZ44pqqOYHBX7TPbYex5x7DQWLXj81cAn6+qL026n/mgqp4EbgSOn3Ark3IM8PZ2rP5S4C1J/udkW5qcqnq0vW4CrmRwl+15x7DQ2LSTuhcB91fVRyfdzyQlmUqyb5t+KfBW4JuT7WoyquqcqlpcVUsY3LrnK1X1ngm3NRFJ9m4Xf5Bkb2A5MC+vojQsxiDJF4CvAq9OsjHJGZPuaUKOAU5j8H+Od7e/Eyfd1IQcBNyQ5B4G9zpbV1UL+pJRAXAgcHOSrwG3Af+7qq6dcE8z8tJZSVKXexaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFtAOSXDP947oJvf9JSQ4bmj8vyVsn1Y8WDn9noQWr/cI8VTWvbmaXZPeqenaWZRcDV1fV5XPblRY69yy0oCRZ0h7G9GngTuC0JF9NcmeS/5XkZUlOSHLZ0DpvTvJ3bfqhJPu36fe0BxrdneRv2l1l35nko235WUkebNOvTHLzdvp6KMl/bWNOSfIHSW5vD0u6IsnPJ/kN4O3AX7X3fGWSi5OcPLSND7XP8vUkr2n1qSTrWv1vknx7+jNIozIstBC9GrgE+HfAGcBb210/1wN/BKwDjm736gH4XeCLwxtI8tpWP6bdSfZZ4N3ATcBvtmG/CXwvycHAG4F/6PT1k6p6Y1VdCnypqn69ql7H4DkgZ1TVPwJrgQ9U1eFV9U8zbOO77bNcAPxJq53L4P5LRzC4Ud0rOn1I29hj0g1IE/Dtqrql3Sr7MOD/Do5I8RLgq1X1TJJrgd9JcjmDh/R88HnbOBb4NeD2tu5LGdx2+ztt72Qf4BDgb4E3MQiO3l13hwPpV5J8GNgXeBlw3Yifbfo97gDe0abfCPx7gKq6NskTI25L+hnDQgvRj9prGNzQ710zjPkicCaDJx7e3p70NyzAmqo6Z4Z1vwqcDnyLwd7E7wNvAP54xL4ALmbwCNqvJfk94M2ddac93V6fZet/3xlxXWlWHobSQnYLcEySVwG08wL/ti27ETgC+AOedwiquR44OckBbd39kvxSW3YTg0NANwF3Ab8FPF1VT+1Ab/sAj7Xngbx7qP6DtmxH3Ay8s/W5HFi0g+tLhoUWrqraDPwe8IV26/BbgNe0Zc8CVzN4etk2txKvqvuA/8LgcZj3MDjPcVBb/A8MDkHd1LbzCIN/sHfEnzF4BO06nvvci0uBDyS5K8krR9zWh4DlSe5sn+cxBqEjjcxLZ6UXuSR7Ac+2czFvAC5oJ+WlkXnOQnrxewVwWZLdgJ8yOLQm7RD3LKQ5lORK4NDnlf+0qka92kmaCMNCktTlCW5JUpdhIUnqMiwkSV2GhSSp6/8BDN/3vvh69b8AAAAASUVORK5CYII="}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f92fb29c750&gt;</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f92fb29c750&gt;</div>"]}}],"execution_count":0},{"cell_type":"code","source":["sns.countplot(x=vot.predict(test_feature_matrix), palette = sns.cubehelix_palette(8, start=1, rot=-.75))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fb1ed129-2d72-42af-842c-520959915c2f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[24]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[24]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/63270375-2497-442f-823d-5cbb4f191921.png","removedWidgets":[],"addedWidgets":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f93d86bd7d0&gt;</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f93d86bd7d0&gt;</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["It is clear that the model is pretty accurate, for when it predicts the star rating wrong, it is generally within one star of the correct answer, a vast improvement from the baseline model.\n\nThe predictions also have about the same distribution of star ratings as the training data, which suggests that this model is pretty accurate, and doesn't just predict all of the test data to be in the most frequent category. (Unlike the baseline)\n\nWhen comparing this model with the baseline model, we see an accuracy improvement of almost 40%, which is a huge improvement. Furthermore, when considering the fact that when the model using nlp-primitives is wrong, it is a lot closer to the right answer than the original model, we can see that using these primitives improves the model further beyond the 40% increase in raw accuracy."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9bc7c303-ac27-4f9a-a9b5-a715c91041a4"}}},{"cell_type":"markdown","source":["## Step 6. Next Steps\n\nThis is an end-to-end example of going from raw text data to a machine learning model with Natural Language Processing Primitives. However, in a problem such as this one, ideally there would be more data available, especially pertaining to the customer who wrote each review. \n\nFeaturetools makes it easy to deal with relational datasets, and so this new extra data would be relatively simple to add into the pipeline, just requiring the addition of a new entity to the entityset, and a recalculation of DFS and the following steps with the newly modified entityset.\n\n<p>\n    <img src=\"https://www.featurelabs.com/wp-content/uploads/2017/12/logo.png\" alt=\"Featuretools\" />\n</p>\n\nFeaturetools was created by the developers at [Feature Labs](https://www.featurelabs.com/). If building impactful data science pipelines is important to you or your business, please [get in touch](https://www.featurelabs.com/contact/)."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0756727d-386f-4a08-b23d-05494d0adb7a"}}},{"cell_type":"code","source":["df"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a21d317b-9bdb-4bdd-9023-3e9574b3eca8"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[6]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[6]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>index</th>\n      <th>review_text</th>\n      <th>review_date</th>\n      <th>restaurant_id</th>\n      <th>review_title</th>\n      <th>review_rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>They have great local craft beers and probably...</td>\n      <td>2016-03-28</td>\n      <td>0</td>\n      <td>Great drinks and food</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>We went to the downtown SF location. The resta...</td>\n      <td>2016-03-27</td>\n      <td>0</td>\n      <td>Good food &amp;amp; beer</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>I just came to this place for drinks with an o...</td>\n      <td>2016-03-16</td>\n      <td>0</td>\n      <td>Pretty good beers</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Mediocre food (not bad, just mediocre, you can...</td>\n      <td>2016-03-08</td>\n      <td>0</td>\n      <td>Ridiculously overpriced (yes I live in SF)</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>We headed out for our team dinner to this esta...</td>\n      <td>2016-03-01</td>\n      <td>0</td>\n      <td>Team dinner</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>14695</td>\n      <td>Try the chicken and waffles or the biscuit san...</td>\n      <td>2012-07-19</td>\n      <td>146</td>\n      <td>Yum, don&amp;#39;t miss it!</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>14696</td>\n      <td>We have eaten here many times and have always ...</td>\n      <td>2012-07-11</td>\n      <td>146</td>\n      <td>Bad Taste</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>14697</td>\n      <td>The restaurant is hard to find unless you know...</td>\n      <td>2012-07-02</td>\n      <td>146</td>\n      <td>Hidden gem</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>14698</td>\n      <td>I stayed at the Farmer's Daughter hotel from A...</td>\n      <td>2012-06-22</td>\n      <td>146</td>\n      <td>Good food, great atmosphere, even better staff</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>14699</td>\n      <td>So it is a pleasent place with an acomadating ...</td>\n      <td>2012-05-28</td>\n      <td>146</td>\n      <td>Cocktails in mason jars !</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 6 columns</p>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>index</th>\n      <th>review_text</th>\n      <th>review_date</th>\n      <th>restaurant_id</th>\n      <th>review_title</th>\n      <th>review_rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>They have great local craft beers and probably...</td>\n      <td>2016-03-28</td>\n      <td>0</td>\n      <td>Great drinks and food</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>We went to the downtown SF location. The resta...</td>\n      <td>2016-03-27</td>\n      <td>0</td>\n      <td>Good food &amp;amp; beer</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>I just came to this place for drinks with an o...</td>\n      <td>2016-03-16</td>\n      <td>0</td>\n      <td>Pretty good beers</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>Mediocre food (not bad, just mediocre, you can...</td>\n      <td>2016-03-08</td>\n      <td>0</td>\n      <td>Ridiculously overpriced (yes I live in SF)</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>We headed out for our team dinner to this esta...</td>\n      <td>2016-03-01</td>\n      <td>0</td>\n      <td>Team dinner</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>14695</td>\n      <td>Try the chicken and waffles or the biscuit san...</td>\n      <td>2012-07-19</td>\n      <td>146</td>\n      <td>Yum, don&amp;#39;t miss it!</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>14696</td>\n      <td>We have eaten here many times and have always ...</td>\n      <td>2012-07-11</td>\n      <td>146</td>\n      <td>Bad Taste</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>14697</td>\n      <td>The restaurant is hard to find unless you know...</td>\n      <td>2012-07-02</td>\n      <td>146</td>\n      <td>Hidden gem</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>14698</td>\n      <td>I stayed at the Farmer's Daughter hotel from A...</td>\n      <td>2012-06-22</td>\n      <td>146</td>\n      <td>Good food, great atmosphere, even better staff</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>14699</td>\n      <td>So it is a pleasent place with an acomadating ...</td>\n      <td>2012-05-28</td>\n      <td>146</td>\n      <td>Cocktails in mason jars !</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 6 columns</p>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["dft5_1 = df[['review_text','review_rating']].rename({'review_text':'input_text','review_rating':'target_text'},axis=1)\ndft5_1['prefix'] = ['t5-encode' for x in range(len(dft5_1))]\ndft5_1['target_text'] = dft5_1['target_text'].astype(str)\ndft5_1"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"062a3c1d-fea2-4f25-856f-5ddaaadd562f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[7]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[7]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>They have great local craft beers and probably...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>We went to the downtown SF location. The resta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>I just came to this place for drinks with an o...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Mediocre food (not bad, just mediocre, you can...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>We headed out for our team dinner to this esta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>Try the chicken and waffles or the biscuit san...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>We have eaten here many times and have always ...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>The restaurant is hard to find unless you know...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>I stayed at the Farmer's Daughter hotel from A...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>So it is a pleasent place with an acomadating ...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 3 columns</p>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>They have great local craft beers and probably...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>We went to the downtown SF location. The resta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>I just came to this place for drinks with an o...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Mediocre food (not bad, just mediocre, you can...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>We headed out for our team dinner to this esta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>Try the chicken and waffles or the biscuit san...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>We have eaten here many times and have always ...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>The restaurant is hard to find unless you know...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>I stayed at the Farmer's Daughter hotel from A...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>So it is a pleasent place with an acomadating ...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 3 columns</p>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["dft5_2 = df[['review_title','review_rating']].rename({'review_title':'input_text','review_rating':'target_text'},axis=1)\ndft5_2['prefix'] = ['t5-encode' for x in range(len(dft5_2))]\ndft5_2['target_text'] = dft5_2['target_text'].astype(str)\ndft5_2"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"eff0b14a-2fa4-430b-9576-db87c6580134"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[8]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[8]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Great drinks and food</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Good food &amp;amp; beer</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pretty good beers</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Ridiculously overpriced (yes I live in SF)</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Team dinner</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>Yum, don&amp;#39;t miss it!</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>Bad Taste</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>Hidden gem</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>Good food, great atmosphere, even better staff</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>Cocktails in mason jars !</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 3 columns</p>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Great drinks and food</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Good food &amp;amp; beer</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Pretty good beers</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Ridiculously overpriced (yes I live in SF)</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Team dinner</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>Yum, don&amp;#39;t miss it!</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>Bad Taste</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>Hidden gem</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>Good food, great atmosphere, even better staff</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>Cocktails in mason jars !</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 3 columns</p>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["dft5 = pd.concat([dft5_1, dft5_2])\ndft5"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5ba8345d-c8dc-4945-bc87-687b1e76f8b5"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[9]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[9]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>They have great local craft beers and probably...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>We went to the downtown SF location. The resta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>I just came to this place for drinks with an o...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Mediocre food (not bad, just mediocre, you can...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>We headed out for our team dinner to this esta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>Yum, don&amp;#39;t miss it!</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>Bad Taste</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>Hidden gem</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>Good food, great atmosphere, even better staff</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>Cocktails in mason jars !</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>29400 rows × 3 columns</p>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>They have great local craft beers and probably...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>We went to the downtown SF location. The resta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>I just came to this place for drinks with an o...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Mediocre food (not bad, just mediocre, you can...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>We headed out for our team dinner to this esta...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>Yum, don&amp;#39;t miss it!</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>Bad Taste</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>Hidden gem</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>Good food, great atmosphere, even better staff</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>Cocktails in mason jars !</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>29400 rows × 3 columns</p>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["import pandas as pd\nfrom simpletransformers.t5 import T5Model\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import shuffle\n\ndft5 = shuffle(dft5)\ndft5 = shuffle(dft5.sample(frac=.5))\ntrain_df, eval_df = train_test_split(dft5)\ntrain_df"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"92dc5f47-a581-4395-a377-e6e75fa26193"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[10]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[10]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12909</th>\n      <td>Back to your roots with a meal</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>6599</th>\n      <td>Meh...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>12131</th>\n      <td>Excellent French food in My Favorite Alley</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>13387</th>\n      <td>Beautiful place in a great spot. It is a littl...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>13377</th>\n      <td>Nice bayside lunch</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>353</th>\n      <td>great service</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1237</th>\n      <td>Incredible food - must have platter!</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>11655</th>\n      <td>Spent time in the Pig this week as we always d...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>13704</th>\n      <td>Good service, great value, nice atmosphere, we...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>9785</th>\n      <td>My parents came into town and wanted to go out...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>11025 rows × 3 columns</p>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>input_text</th>\n      <th>target_text</th>\n      <th>prefix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12909</th>\n      <td>Back to your roots with a meal</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>6599</th>\n      <td>Meh...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>12131</th>\n      <td>Excellent French food in My Favorite Alley</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>13387</th>\n      <td>Beautiful place in a great spot. It is a littl...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>13377</th>\n      <td>Nice bayside lunch</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>353</th>\n      <td>great service</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>1237</th>\n      <td>Incredible food - must have platter!</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>11655</th>\n      <td>Spent time in the Pig this week as we always d...</td>\n      <td>5</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>13704</th>\n      <td>Good service, great value, nice atmosphere, we...</td>\n      <td>4</td>\n      <td>t5-encode</td>\n    </tr>\n    <tr>\n      <th>9785</th>\n      <td>My parents came into town and wanted to go out...</td>\n      <td>3</td>\n      <td>t5-encode</td>\n    </tr>\n  </tbody>\n</table>\n<p>11025 rows × 3 columns</p>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["from simpletransformers.t5 import T5Model\n\nmodel = T5Model('t5',\n                'outputs/best_model/',\n                use_cuda=True)\n\nmodel.args"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fbc8e32d-2d54-4656-be9e-6eb2a8f52263"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">404 Client Error: Not Found for url: https://huggingface.co/outputs/best_model//resolve/main/config.json\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">404 Client Error: Not Found for url: https://huggingface.co/outputs/best_model//resolve/main/config.json\n</div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">HTTPError</span>                                 Traceback (most recent call last)\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/configuration_utils.py</span> in <span class=\"ansi-cyan-fg\">get_config_dict</span><span class=\"ansi-blue-fg\">(cls, pretrained_model_name_or_path, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    437</span>                 local_files_only<span class=\"ansi-blue-fg\">=</span>local_files_only<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-fg\">--&gt; 438</span><span class=\"ansi-red-fg\">                 </span>use_auth_token<span class=\"ansi-blue-fg\">=</span>use_auth_token<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    439</span>             )\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/file_utils.py</span> in <span class=\"ansi-cyan-fg\">cached_path</span><span class=\"ansi-blue-fg\">(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1141</span>             use_auth_token<span class=\"ansi-blue-fg\">=</span>use_auth_token<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-fg\">-&gt; 1142</span><span class=\"ansi-red-fg\">             </span>local_files_only<span class=\"ansi-blue-fg\">=</span>local_files_only<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1143</span>         )\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/file_utils.py</span> in <span class=\"ansi-cyan-fg\">get_from_cache</span><span class=\"ansi-blue-fg\">(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1299</span>             r <span class=\"ansi-blue-fg\">=</span> requests<span class=\"ansi-blue-fg\">.</span>head<span class=\"ansi-blue-fg\">(</span>url<span class=\"ansi-blue-fg\">,</span> headers<span class=\"ansi-blue-fg\">=</span>headers<span class=\"ansi-blue-fg\">,</span> allow_redirects<span class=\"ansi-blue-fg\">=</span><span class=\"ansi-green-fg\">False</span><span class=\"ansi-blue-fg\">,</span> proxies<span class=\"ansi-blue-fg\">=</span>proxies<span class=\"ansi-blue-fg\">,</span> timeout<span class=\"ansi-blue-fg\">=</span>etag_timeout<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">-&gt; 1300</span><span class=\"ansi-red-fg\">             </span>r<span class=\"ansi-blue-fg\">.</span>raise_for_status<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1301</span>             etag <span class=\"ansi-blue-fg\">=</span> r<span class=\"ansi-blue-fg\">.</span>headers<span class=\"ansi-blue-fg\">.</span>get<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;X-Linked-Etag&#34;</span><span class=\"ansi-blue-fg\">)</span> <span class=\"ansi-green-fg\">or</span> r<span class=\"ansi-blue-fg\">.</span>headers<span class=\"ansi-blue-fg\">.</span>get<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;ETag&#34;</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/requests/models.py</span> in <span class=\"ansi-cyan-fg\">raise_for_status</span><span class=\"ansi-blue-fg\">(self)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    939</span>         <span class=\"ansi-green-fg\">if</span> http_error_msg<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 940</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">raise</span> HTTPError<span class=\"ansi-blue-fg\">(</span>http_error_msg<span class=\"ansi-blue-fg\">,</span> response<span class=\"ansi-blue-fg\">=</span>self<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    941</span> \n\n<span class=\"ansi-red-fg\">HTTPError</span>: 404 Client Error: Not Found for url: https://huggingface.co/outputs/best_model//resolve/main/config.json\n\nDuring handling of the above exception, another exception occurred:\n\n<span class=\"ansi-red-fg\">OSError</span>                                   Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3094418732417175&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> model = T5Model(&#39;t5&#39;,\n<span class=\"ansi-green-intense-fg ansi-bold\">      4</span>                 <span class=\"ansi-blue-fg\">&#39;outputs/best_model/&#39;</span><span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-fg\">----&gt; 5</span><span class=\"ansi-red-fg\">                 use_cuda=True)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">      6</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">      7</span> model<span class=\"ansi-blue-fg\">.</span>args\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/simpletransformers/t5/t5_model.py</span> in <span class=\"ansi-cyan-fg\">__init__</span><span class=\"ansi-blue-fg\">(self, model_type, model_name, args, tokenizer, use_cuda, cuda_device, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    118</span>             self<span class=\"ansi-blue-fg\">.</span>model <span class=\"ansi-blue-fg\">=</span> model_class<span class=\"ansi-blue-fg\">(</span>config<span class=\"ansi-blue-fg\">=</span>self<span class=\"ansi-blue-fg\">.</span>config<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    119</span>         <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 120</span><span class=\"ansi-red-fg\">             </span>self<span class=\"ansi-blue-fg\">.</span>config <span class=\"ansi-blue-fg\">=</span> config_class<span class=\"ansi-blue-fg\">.</span>from_pretrained<span class=\"ansi-blue-fg\">(</span>model_name<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>self<span class=\"ansi-blue-fg\">.</span>args<span class=\"ansi-blue-fg\">.</span>config<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    121</span>             self<span class=\"ansi-blue-fg\">.</span>model <span class=\"ansi-blue-fg\">=</span> model_class<span class=\"ansi-blue-fg\">.</span>from_pretrained<span class=\"ansi-blue-fg\">(</span>model_name<span class=\"ansi-blue-fg\">,</span> config<span class=\"ansi-blue-fg\">=</span>self<span class=\"ansi-blue-fg\">.</span>config<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    122</span> \n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/configuration_utils.py</span> in <span class=\"ansi-cyan-fg\">from_pretrained</span><span class=\"ansi-blue-fg\">(cls, pretrained_model_name_or_path, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    384</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    385</span>         &#34;&#34;&#34;\n<span class=\"ansi-green-fg\">--&gt; 386</span><span class=\"ansi-red-fg\">         </span>config_dict<span class=\"ansi-blue-fg\">,</span> kwargs <span class=\"ansi-blue-fg\">=</span> cls<span class=\"ansi-blue-fg\">.</span>get_config_dict<span class=\"ansi-blue-fg\">(</span>pretrained_model_name_or_path<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kwargs<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    387</span>         <span class=\"ansi-green-fg\">return</span> cls<span class=\"ansi-blue-fg\">.</span>from_dict<span class=\"ansi-blue-fg\">(</span>config_dict<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kwargs<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    388</span> \n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/configuration_utils.py</span> in <span class=\"ansi-cyan-fg\">get_config_dict</span><span class=\"ansi-blue-fg\">(cls, pretrained_model_name_or_path, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    448</span>                 <span class=\"ansi-blue-fg\">f&#34;- or &#39;{pretrained_model_name_or_path}&#39; is the correct path to a directory containing a {CONFIG_NAME} file\\n\\n&#34;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    449</span>             )\n<span class=\"ansi-green-fg\">--&gt; 450</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">raise</span> EnvironmentError<span class=\"ansi-blue-fg\">(</span>msg<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    451</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    452</span>         <span class=\"ansi-green-fg\">except</span> json<span class=\"ansi-blue-fg\">.</span>JSONDecodeError<span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-red-fg\">OSError</span>: Can&#39;t load config for &#39;outputs/best_model/&#39;. Make sure that:\n\n- &#39;outputs/best_model/&#39; is a correct model identifier listed on &#39;https://huggingface.co/models&#39;\n\n- or &#39;outputs/best_model/&#39; is the correct path to a directory containing a config.json file\n\n</div>","errorSummary":"<span class=\"ansi-red-fg\">OSError</span>: Can&#39;t load config for &#39;outputs/best_model/&#39;. Make sure that:","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">HTTPError</span>                                 Traceback (most recent call last)\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/configuration_utils.py</span> in <span class=\"ansi-cyan-fg\">get_config_dict</span><span class=\"ansi-blue-fg\">(cls, pretrained_model_name_or_path, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    437</span>                 local_files_only<span class=\"ansi-blue-fg\">=</span>local_files_only<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-fg\">--&gt; 438</span><span class=\"ansi-red-fg\">                 </span>use_auth_token<span class=\"ansi-blue-fg\">=</span>use_auth_token<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    439</span>             )\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/file_utils.py</span> in <span class=\"ansi-cyan-fg\">cached_path</span><span class=\"ansi-blue-fg\">(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1141</span>             use_auth_token<span class=\"ansi-blue-fg\">=</span>use_auth_token<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-fg\">-&gt; 1142</span><span class=\"ansi-red-fg\">             </span>local_files_only<span class=\"ansi-blue-fg\">=</span>local_files_only<span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1143</span>         )\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/file_utils.py</span> in <span class=\"ansi-cyan-fg\">get_from_cache</span><span class=\"ansi-blue-fg\">(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1299</span>             r <span class=\"ansi-blue-fg\">=</span> requests<span class=\"ansi-blue-fg\">.</span>head<span class=\"ansi-blue-fg\">(</span>url<span class=\"ansi-blue-fg\">,</span> headers<span class=\"ansi-blue-fg\">=</span>headers<span class=\"ansi-blue-fg\">,</span> allow_redirects<span class=\"ansi-blue-fg\">=</span><span class=\"ansi-green-fg\">False</span><span class=\"ansi-blue-fg\">,</span> proxies<span class=\"ansi-blue-fg\">=</span>proxies<span class=\"ansi-blue-fg\">,</span> timeout<span class=\"ansi-blue-fg\">=</span>etag_timeout<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">-&gt; 1300</span><span class=\"ansi-red-fg\">             </span>r<span class=\"ansi-blue-fg\">.</span>raise_for_status<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1301</span>             etag <span class=\"ansi-blue-fg\">=</span> r<span class=\"ansi-blue-fg\">.</span>headers<span class=\"ansi-blue-fg\">.</span>get<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;X-Linked-Etag&#34;</span><span class=\"ansi-blue-fg\">)</span> <span class=\"ansi-green-fg\">or</span> r<span class=\"ansi-blue-fg\">.</span>headers<span class=\"ansi-blue-fg\">.</span>get<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;ETag&#34;</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/requests/models.py</span> in <span class=\"ansi-cyan-fg\">raise_for_status</span><span class=\"ansi-blue-fg\">(self)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    939</span>         <span class=\"ansi-green-fg\">if</span> http_error_msg<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 940</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">raise</span> HTTPError<span class=\"ansi-blue-fg\">(</span>http_error_msg<span class=\"ansi-blue-fg\">,</span> response<span class=\"ansi-blue-fg\">=</span>self<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    941</span> \n\n<span class=\"ansi-red-fg\">HTTPError</span>: 404 Client Error: Not Found for url: https://huggingface.co/outputs/best_model//resolve/main/config.json\n\nDuring handling of the above exception, another exception occurred:\n\n<span class=\"ansi-red-fg\">OSError</span>                                   Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3094418732417175&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> model = T5Model(&#39;t5&#39;,\n<span class=\"ansi-green-intense-fg ansi-bold\">      4</span>                 <span class=\"ansi-blue-fg\">&#39;outputs/best_model/&#39;</span><span class=\"ansi-blue-fg\">,</span>\n<span class=\"ansi-green-fg\">----&gt; 5</span><span class=\"ansi-red-fg\">                 use_cuda=True)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">      6</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">      7</span> model<span class=\"ansi-blue-fg\">.</span>args\n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/simpletransformers/t5/t5_model.py</span> in <span class=\"ansi-cyan-fg\">__init__</span><span class=\"ansi-blue-fg\">(self, model_type, model_name, args, tokenizer, use_cuda, cuda_device, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    118</span>             self<span class=\"ansi-blue-fg\">.</span>model <span class=\"ansi-blue-fg\">=</span> model_class<span class=\"ansi-blue-fg\">(</span>config<span class=\"ansi-blue-fg\">=</span>self<span class=\"ansi-blue-fg\">.</span>config<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    119</span>         <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 120</span><span class=\"ansi-red-fg\">             </span>self<span class=\"ansi-blue-fg\">.</span>config <span class=\"ansi-blue-fg\">=</span> config_class<span class=\"ansi-blue-fg\">.</span>from_pretrained<span class=\"ansi-blue-fg\">(</span>model_name<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>self<span class=\"ansi-blue-fg\">.</span>args<span class=\"ansi-blue-fg\">.</span>config<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    121</span>             self<span class=\"ansi-blue-fg\">.</span>model <span class=\"ansi-blue-fg\">=</span> model_class<span class=\"ansi-blue-fg\">.</span>from_pretrained<span class=\"ansi-blue-fg\">(</span>model_name<span class=\"ansi-blue-fg\">,</span> config<span class=\"ansi-blue-fg\">=</span>self<span class=\"ansi-blue-fg\">.</span>config<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    122</span> \n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/configuration_utils.py</span> in <span class=\"ansi-cyan-fg\">from_pretrained</span><span class=\"ansi-blue-fg\">(cls, pretrained_model_name_or_path, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    384</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    385</span>         &#34;&#34;&#34;\n<span class=\"ansi-green-fg\">--&gt; 386</span><span class=\"ansi-red-fg\">         </span>config_dict<span class=\"ansi-blue-fg\">,</span> kwargs <span class=\"ansi-blue-fg\">=</span> cls<span class=\"ansi-blue-fg\">.</span>get_config_dict<span class=\"ansi-blue-fg\">(</span>pretrained_model_name_or_path<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kwargs<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    387</span>         <span class=\"ansi-green-fg\">return</span> cls<span class=\"ansi-blue-fg\">.</span>from_dict<span class=\"ansi-blue-fg\">(</span>config_dict<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kwargs<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    388</span> \n\n<span class=\"ansi-green-fg\">/databricks/python/lib/python3.7/site-packages/transformers/configuration_utils.py</span> in <span class=\"ansi-cyan-fg\">get_config_dict</span><span class=\"ansi-blue-fg\">(cls, pretrained_model_name_or_path, **kwargs)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    448</span>                 <span class=\"ansi-blue-fg\">f&#34;- or &#39;{pretrained_model_name_or_path}&#39; is the correct path to a directory containing a {CONFIG_NAME} file\\n\\n&#34;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    449</span>             )\n<span class=\"ansi-green-fg\">--&gt; 450</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">raise</span> EnvironmentError<span class=\"ansi-blue-fg\">(</span>msg<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    451</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    452</span>         <span class=\"ansi-green-fg\">except</span> json<span class=\"ansi-blue-fg\">.</span>JSONDecodeError<span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-red-fg\">OSError</span>: Can&#39;t load config for &#39;outputs/best_model/&#39;. Make sure that:\n\n- &#39;outputs/best_model/&#39; is a correct model identifier listed on &#39;https://huggingface.co/models&#39;\n\n- or &#39;outputs/best_model/&#39; is the correct path to a directory containing a config.json file\n\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["model_args = {\n    \"max_seq_length\": 196,\n    \"train_batch_size\": 8,\n    \"eval_batch_size\": 8,\n    \"num_train_epochs\": 1,\n    \"evaluate_during_training\": True,\n    \"evaluate_during_training_steps\": 15000,\n    \"evaluate_during_training_verbose\": True,\n    \"use_multiprocessing\": False,\n    \"fp16\": False,\n    \"save_steps\": -1,\n    \"save_eval_checkpoints\": False,\n    \"save_model_every_epoch\": False,\n    \"reprocess_input_data\": True,\n    \"overwrite_output_dir\": True,\n    \"wandb_project\": None,\n}\n\n# model = T5Model(\"t5\", \"t5-base\", args=model_args)\n\nmodel.train_model(train_df, eval_data=eval_df)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"bc54fed0-9c18-4477-97ca-90938ad0e3dd"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"","errorSummary":"Command skipped","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>"]}}],"execution_count":0},{"cell_type":"code","source":["from simpletransformers.t5 import T5Model\n\nmodel = T5Model('t5',\n                '/dbfs/FileStore/tables/outputs/best_model/',\n                use_cuda=True)\n\nmodel.args"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5b9287b4-c49d-40fa-8613-076b70acd18e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[12]: T5Args(adafactor_beta1=None, adafactor_clip_threshold=1.0, adafactor_decay_rate=-0.8, adafactor_eps=[1e-30, 0.001], adafactor_relative_step=False, adafactor_scale_parameter=False, adafactor_warmup_init=False, adam_epsilon=1e-08, best_model_dir=&#39;outputs/best_model&#39;, cache_dir=&#39;cache_dir/&#39;, config={}, cosine_schedule_num_cycles=0.5, custom_layer_parameters=[], custom_parameter_groups=[], dataloader_num_workers=0, do_lower_case=False, dynamic_quantize=False, early_stopping_consider_epochs=False, early_stopping_delta=0, early_stopping_metric=&#39;eval_loss&#39;, early_stopping_metric_minimize=True, early_stopping_patience=3, encoding=None, eval_batch_size=8, evaluate_during_training=True, evaluate_during_training_silent=True, evaluate_during_training_steps=15000, evaluate_during_training_verbose=True, evaluate_each_epoch=True, fp16=False, gradient_accumulation_steps=1, learning_rate=0.001, local_rank=-1, logging_steps=50, manual_seed=None, max_grad_norm=1.0, max_seq_length=196, model_name=&#39;/dbfs/FileStore/tables/outputs/best_model/&#39;, model_type=&#39;t5&#39;, multiprocessing_chunksize=-1, n_gpu=1, no_cache=False, no_save=False, not_saved_args=[], num_train_epochs=1, optimizer=&#39;Adafactor&#39;, output_dir=&#39;outputs/&#39;, overwrite_output_dir=True, polynomial_decay_schedule_lr_end=1e-07, polynomial_decay_schedule_power=1.0, process_count=10, quantized_model=False, reprocess_input_data=True, save_best_model=True, save_eval_checkpoints=False, save_model_every_epoch=False, save_optimizer_and_scheduler=True, save_steps=-1, scheduler=&#39;constant_schedule_with_warmup&#39;, silent=False, skip_special_tokens=True, tensorboard_dir=None, thread_count=None, tokenizer_name=None, tokenizer_type=None, train_batch_size=8, train_custom_parameters_only=False, use_cached_eval_features=False, use_early_stopping=False, use_hf_datasets=False, use_multiprocessing=False, use_multiprocessing_for_evaluation=True, wandb_kwargs={}, wandb_project=None, warmup_ratio=0.06, warmup_steps=83, weight_decay=0.0, model_class=&#39;T5Model&#39;, dataset_class=None, do_sample=False, early_stopping=True, evaluate_generated_text=False, length_penalty=2.0, max_length=20, max_steps=-1, num_beams=1, num_return_sequences=1, preprocess_inputs=True, repetition_penalty=1.0, special_tokens_list=[], top_k=None, top_p=None, use_multiprocessed_decoding=True)</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[12]: T5Args(adafactor_beta1=None, adafactor_clip_threshold=1.0, adafactor_decay_rate=-0.8, adafactor_eps=[1e-30, 0.001], adafactor_relative_step=False, adafactor_scale_parameter=False, adafactor_warmup_init=False, adam_epsilon=1e-08, best_model_dir=&#39;outputs/best_model&#39;, cache_dir=&#39;cache_dir/&#39;, config={}, cosine_schedule_num_cycles=0.5, custom_layer_parameters=[], custom_parameter_groups=[], dataloader_num_workers=0, do_lower_case=False, dynamic_quantize=False, early_stopping_consider_epochs=False, early_stopping_delta=0, early_stopping_metric=&#39;eval_loss&#39;, early_stopping_metric_minimize=True, early_stopping_patience=3, encoding=None, eval_batch_size=8, evaluate_during_training=True, evaluate_during_training_silent=True, evaluate_during_training_steps=15000, evaluate_during_training_verbose=True, evaluate_each_epoch=True, fp16=False, gradient_accumulation_steps=1, learning_rate=0.001, local_rank=-1, logging_steps=50, manual_seed=None, max_grad_norm=1.0, max_seq_length=196, model_name=&#39;/dbfs/FileStore/tables/outputs/best_model/&#39;, model_type=&#39;t5&#39;, multiprocessing_chunksize=-1, n_gpu=1, no_cache=False, no_save=False, not_saved_args=[], num_train_epochs=1, optimizer=&#39;Adafactor&#39;, output_dir=&#39;outputs/&#39;, overwrite_output_dir=True, polynomial_decay_schedule_lr_end=1e-07, polynomial_decay_schedule_power=1.0, process_count=10, quantized_model=False, reprocess_input_data=True, save_best_model=True, save_eval_checkpoints=False, save_model_every_epoch=False, save_optimizer_and_scheduler=True, save_steps=-1, scheduler=&#39;constant_schedule_with_warmup&#39;, silent=False, skip_special_tokens=True, tensorboard_dir=None, thread_count=None, tokenizer_name=None, tokenizer_type=None, train_batch_size=8, train_custom_parameters_only=False, use_cached_eval_features=False, use_early_stopping=False, use_hf_datasets=False, use_multiprocessing=False, use_multiprocessing_for_evaluation=True, wandb_kwargs={}, wandb_project=None, warmup_ratio=0.06, warmup_steps=83, weight_decay=0.0, model_class=&#39;T5Model&#39;, dataset_class=None, do_sample=False, early_stopping=True, evaluate_generated_text=False, length_penalty=2.0, max_length=20, max_steps=-1, num_beams=1, num_return_sequences=1, preprocess_inputs=True, repetition_penalty=1.0, special_tokens_list=[], top_k=None, top_p=None, use_multiprocessed_decoding=True)</div>"]}}],"execution_count":0},{"cell_type":"code","source":["model_pretuned_sentiment = T5Model('t5',\n                                   'mrm8488/t5-base-finetuned-imdb-sentiment',\n                                   use_cuda=True)\nmodel_pretuned_sentiment.args"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d6aa4c11-abe6-4b99-a846-57e939e5c1f0"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">\rDownloading:   0%|          | 0.00/1.21k [00:00&lt;?, ?B/s]\rDownloading: 100%|██████████| 1.21k/1.21k [00:00&lt;00:00, 670kB/s]\n\rDownloading:   0%|          | 0.00/892M [00:00&lt;?, ?B/s]\rDownloading:   1%|          | 4.46M/892M [00:00&lt;00:19, 44.6MB/s]\rDownloading:   1%|          | 8.92M/892M [00:00&lt;00:30, 28.6MB/s]\rDownloading:   2%|▏         | 13.6M/892M [00:00&lt;00:25, 35.0MB/s]\rDownloading:   2%|▏         | 18.2M/892M [00:00&lt;00:22, 38.8MB/s]\rDownloading:   3%|▎         | 22.8M/892M [00:00&lt;00:21, 41.0MB/s]\rDownloading:   3%|▎         | 27.4M/892M [00:00&lt;00:33, 26.0MB/s]\rDownloading:   4%|▎         | 31.3M/892M [00:00&lt;00:29, 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custom_parameter_groups=[], dataloader_num_workers=0, do_lower_case=False, dynamic_quantize=False, early_stopping_consider_epochs=False, early_stopping_delta=0, early_stopping_metric=&#39;eval_loss&#39;, early_stopping_metric_minimize=True, early_stopping_patience=3, encoding=None, eval_batch_size=8, evaluate_during_training=False, evaluate_during_training_silent=True, evaluate_during_training_steps=2000, evaluate_during_training_verbose=False, evaluate_each_epoch=True, fp16=True, gradient_accumulation_steps=1, learning_rate=0.001, local_rank=-1, logging_steps=50, manual_seed=None, max_grad_norm=1.0, max_seq_length=128, model_name=&#39;mrm8488/t5-base-finetuned-imdb-sentiment&#39;, model_type=&#39;t5&#39;, multiprocessing_chunksize=-1, n_gpu=1, no_cache=False, no_save=False, not_saved_args=[], num_train_epochs=1, optimizer=&#39;Adafactor&#39;, output_dir=&#39;outputs/&#39;, overwrite_output_dir=False, polynomial_decay_schedule_lr_end=1e-07, polynomial_decay_schedule_power=1.0, process_count=10, quantized_model=False, reprocess_input_data=True, save_best_model=True, save_eval_checkpoints=True, save_model_every_epoch=True, save_optimizer_and_scheduler=True, save_steps=2000, scheduler=&#39;constant_schedule_with_warmup&#39;, silent=False, skip_special_tokens=True, tensorboard_dir=None, thread_count=None, tokenizer_name=None, tokenizer_type=None, train_batch_size=8, train_custom_parameters_only=False, use_cached_eval_features=False, use_early_stopping=False, use_hf_datasets=False, use_multiprocessing=True, use_multiprocessing_for_evaluation=True, wandb_kwargs={}, wandb_project=None, warmup_ratio=0.06, warmup_steps=0, weight_decay=0.0, model_class=&#39;T5Model&#39;, dataset_class=None, do_sample=False, early_stopping=True, evaluate_generated_text=False, length_penalty=2.0, max_length=20, max_steps=-1, num_beams=1, num_return_sequences=1, preprocess_inputs=True, repetition_penalty=1.0, special_tokens_list=[], top_k=None, top_p=None, use_multiprocessed_decoding=True)</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">\rDownloading:   0%|          | 0.00/1.21k [00:00&lt;?, ?B/s]\rDownloading: 100%|██████████| 1.21k/1.21k [00:00&lt;00:00, 670kB/s]\n\rDownloading:   0%|          | 0.00/892M [00:00&lt;?, ?B/s]\rDownloading:   1%|          | 4.46M/892M [00:00&lt;00:19, 44.6MB/s]\rDownloading:   1%|          | 8.92M/892M [00:00&lt;00:30, 28.6MB/s]\rDownloading:   2%|▏         | 13.6M/892M [00:00&lt;00:25, 35.0MB/s]\rDownloading:   2%|▏         | 18.2M/892M [00:00&lt;00:22, 38.8MB/s]\rDownloading:   3%|▎   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adam_epsilon=1e-08, best_model_dir=&#39;outputs/best_model&#39;, cache_dir=&#39;cache_dir/&#39;, config={}, cosine_schedule_num_cycles=0.5, custom_layer_parameters=[], custom_parameter_groups=[], dataloader_num_workers=0, do_lower_case=False, dynamic_quantize=False, early_stopping_consider_epochs=False, early_stopping_delta=0, early_stopping_metric=&#39;eval_loss&#39;, early_stopping_metric_minimize=True, early_stopping_patience=3, encoding=None, eval_batch_size=8, evaluate_during_training=False, evaluate_during_training_silent=True, evaluate_during_training_steps=2000, evaluate_during_training_verbose=False, evaluate_each_epoch=True, fp16=True, gradient_accumulation_steps=1, learning_rate=0.001, local_rank=-1, logging_steps=50, manual_seed=None, max_grad_norm=1.0, max_seq_length=128, model_name=&#39;mrm8488/t5-base-finetuned-imdb-sentiment&#39;, model_type=&#39;t5&#39;, multiprocessing_chunksize=-1, n_gpu=1, no_cache=False, no_save=False, not_saved_args=[], num_train_epochs=1, optimizer=&#39;Adafactor&#39;, output_dir=&#39;outputs/&#39;, overwrite_output_dir=False, polynomial_decay_schedule_lr_end=1e-07, polynomial_decay_schedule_power=1.0, process_count=10, quantized_model=False, reprocess_input_data=True, save_best_model=True, save_eval_checkpoints=True, save_model_every_epoch=True, save_optimizer_and_scheduler=True, save_steps=2000, scheduler=&#39;constant_schedule_with_warmup&#39;, silent=False, skip_special_tokens=True, tensorboard_dir=None, thread_count=None, tokenizer_name=None, tokenizer_type=None, train_batch_size=8, train_custom_parameters_only=False, use_cached_eval_features=False, use_early_stopping=False, use_hf_datasets=False, use_multiprocessing=True, use_multiprocessing_for_evaluation=True, wandb_kwargs={}, wandb_project=None, warmup_ratio=0.06, warmup_steps=0, weight_decay=0.0, model_class=&#39;T5Model&#39;, dataset_class=None, do_sample=False, early_stopping=True, evaluate_generated_text=False, length_penalty=2.0, max_length=20, max_steps=-1, num_beams=1, num_return_sequences=1, preprocess_inputs=True, repetition_penalty=1.0, special_tokens_list=[], top_k=None, top_p=None, use_multiprocessed_decoding=True)</div>"]}}],"execution_count":0},{"cell_type":"code","source":["t = ['Great drinks and food', \n     'Good food &amp; beer', \n     'Pretty good beers']\n\nlist(np.array(model.predict(t)).astype(float))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fe9be489-f850-4ee5-b423-404a7c78b291"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">\rGenerating outputs:   0%|          | 0/1 [00:00&lt;?, ?it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  3.17it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  3.16it/s]\n\rDecoding outputs:   0%|          | 0/3 [00:00&lt;?, ?it/s]\rDecoding outputs:  33%|███▎      | 1/3 [00:00&lt;00:01,  1.14it/s]\rDecoding outputs: 100%|██████████| 3/3 [00:00&lt;00:00,  3.43it/s]\nOut[14]: [4.0, 4.0, 4.0]</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">\rGenerating outputs:   0%|          | 0/1 [00:00&lt;?, ?it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  3.17it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  3.16it/s]\n\rDecoding outputs:   0%|          | 0/3 [00:00&lt;?, ?it/s]\rDecoding outputs:  33%|███▎      | 1/3 [00:00&lt;00:01,  1.14it/s]\rDecoding outputs: 100%|██████████| 3/3 [00:00&lt;00:00,  3.43it/s]\nOut[14]: [4.0, 4.0, 4.0]</div>"]}}],"execution_count":0},{"cell_type":"code","source":["list(np.where(np.array(model_pretuned_sentiment.predict(t))=='positive',1.0,0.0))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ae0316d3-348f-42fa-899e-d2624917bde3"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">\rGenerating outputs:   0%|          | 0/1 [00:00&lt;?, ?it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  7.57it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  7.56it/s]\n\rDecoding outputs:   0%|          | 0/3 [00:00&lt;?, ?it/s]\rDecoding outputs:  33%|███▎      | 1/3 [00:00&lt;00:01,  1.17it/s]\rDecoding outputs: 100%|██████████| 3/3 [00:00&lt;00:00,  3.50it/s]\nOut[15]: [1.0, 1.0, 1.0]</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">\rGenerating outputs:   0%|          | 0/1 [00:00&lt;?, ?it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  7.57it/s]\rGenerating outputs: 100%|██████████| 1/1 [00:00&lt;00:00,  7.56it/s]\n\rDecoding outputs:   0%|          | 0/3 [00:00&lt;?, ?it/s]\rDecoding outputs:  33%|███▎      | 1/3 [00:00&lt;00:01,  1.17it/s]\rDecoding outputs: 100%|██████████| 3/3 [00:00&lt;00:00,  3.50it/s]\nOut[15]: [1.0, 1.0, 1.0]</div>"]}}],"execution_count":0},{"cell_type":"code","source":["import numpy as np\nfrom featuretools.primitives.base import TransformPrimitive\nfrom featuretools.variable_types import Numeric, Text\n\n\nclass T5Encoder(TransformPrimitive):\n    \"\"\"Determines the number of title words in a string.\n\n    Description:\n        Given list of strings, determine the number of title words\n        in each string. A title word is defined as any word starting\n        with a capital letter. Words at the start of a sentence will\n        be counted.\n\n        If a string is missing, return `NaN`.\n\n    Examples:\n        >>> x = ['My favorite movie is Jaws.', 'this is a string', 'AAA']\n        >>> title_word_count = TitleWordCount()\n        >>> title_word_count(x).tolist()\n        [2.0, 0.0, 1.0]\n    \"\"\"\n    name = \"t5_encoder\"\n    input_types = [Text]\n    return_type = Numeric\n    default_value = 0\n    \n    def __init__(self, model=model):\n      self.model = model\n\n    def get_function(self):\n\n        def t5_encoder(x):\n            model.args.use_multiprocessing = True\n            return list(np.array(model.predict(x.tolist())).astype(float))\n        return t5_encoder\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"01d30ae1-f214-4e24-83b6-0ca3493ab11b"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["\nclass T5SentimentEncoder(TransformPrimitive):\n    \"\"\"Determines the number of title words in a string.\n\n    Description:\n        Given list of strings, determine the number of title words\n        in each string. A title word is defined as any word starting\n        with a capital letter. Words at the start of a sentence will\n        be counted.\n\n        If a string is missing, return `NaN`.\n\n    Examples:\n        >>> x = ['My favorite movie is Jaws.', 'this is a string', 'AAA']\n        >>> title_word_count = TitleWordCount()\n        >>> title_word_count(x).tolist()\n        [2.0, 0.0, 1.0]\n    \"\"\"\n    name = \"t5_sentiment_encoder\"\n    input_types = [Text]\n    return_type = Numeric\n    default_value = 0\n    \n    def __init__(self, model=model_pretuned_sentiment):\n      self.model = model\n\n    def get_function(self):\n\n        def t5_sentiment_encoder(x):\n            model.args.use_multiprocessing = True\n            return list(np.where(np.array(model_pretuned_sentiment.predict(x.tolist()))=='positive',1.0,0.0))\n        return t5_sentiment_encoder"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4bbeac5f-1efa-4520-a6fa-e2cd4eff15f6"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["trans = [\n           T5Encoder,\n           T5SentimentEncoder,\n           DiversityScore,\n           LSA,\n           MeanCharactersPerWord,\n           PartOfSpeechCount,\n           PolarityScore, \n           PunctuationCount,\n           StopwordCount,\n           TitleWordCount,\n           UniversalSentenceEncoder,\n           UpperCaseCount\n        ]\n\nignore = {'restaurants': ['rating'],\n          'reviews': ['review_rating']}\n\ndrop_contains = ['(reviews.UNIVERSAL']\n\nfeatures = ft.dfs(entityset=es,\n                  target_entity='reviews',\n                  trans_primitives=trans,\n                  verbose=True,\n                  features_only=True,\n                  ignore_variables=ignore,\n                  drop_contains=drop_contains,\n                  max_depth=4)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"97a6e061-81ac-436e-96be-e08c79d57810"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">INFO:absl:Using /tmp/tfhub_modules to cache modules.\nINFO:absl:Downloading TF-Hub Module &#39;https://tfhub.dev/google/universal-sentence-encoder/2&#39;.\nINFO:absl:Downloaded https://tfhub.dev/google/universal-sentence-encoder/2, Total size: 993.27MB\nINFO:absl:Downloaded TF-Hub Module &#39;https://tfhub.dev/google/universal-sentence-encoder/2&#39;.\nBuilt 347 features\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">INFO:absl:Using /tmp/tfhub_modules to cache modules.\nINFO:absl:Downloading TF-Hub Module &#39;https://tfhub.dev/google/universal-sentence-encoder/2&#39;.\nINFO:absl:Downloaded https://tfhub.dev/google/universal-sentence-encoder/2, Total size: 993.27MB\nINFO:absl:Downloaded TF-Hub Module &#39;https://tfhub.dev/google/universal-sentence-encoder/2&#39;.\nBuilt 347 features\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["# es['reviews'].df = es['reviews'].df.sample(20)\n# es"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"410bc054-d283-43a8-8cbc-5b0f0ad7725d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"","errorSummary":"Command skipped","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>"]}}],"execution_count":0},{"cell_type":"code","source":["feature_matrix = ft.calculate_feature_matrix(features=features,\n                                             entityset=es,\n                                             verbose=True)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1d3e8668-20b5-4e85-bde2-788ea508f451"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">\rElapsed: 00:00 | Progress:   0%|          \rElapsed: 00:00 | Progress:   1%|          \rElapsed: 00:08 | Progress:   1%|          \rElapsed: 00:09 | Progress:   1%|          \rElapsed: 00:20 | Progress:   1%|          \rElapsed: 00:56 | Progress:   1%|          \rElapsed: 01:04 | Progress:   1%|▏         \rElapsed: 01:14 | Progress:   2%|▏         \rElapsed: 01:14 | Progress:   2%|▏         \n\rGenerating outputs:   0%|          | 0/1838 [00:00&lt;?, 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[00:06&lt;00:00, 2336.46it/s]\n\rElapsed: 37:03 | Progress:  50%|████▉     \rElapsed: 37:06 | Progress:  50%|████▉     \rElapsed: 37:19 | Progress:  50%|█████     \rElapsed: 37:29 | Progress:  50%|█████     \rElapsed: 37:30 | Progress:  51%|█████     \rElapsed: 37:46 | Progress:  51%|█████     \rElapsed: 38:15 | Progress:  51%|█████     \rElapsed: 38:15 | Progress:  51%|█████     \rElapsed: 38:16 | Progress:  95%|█████████▍\rElapsed: 38:17 | Progress: 100%|██████████\rElapsed: 38:17 | Progress: 100%|██████████\rElapsed: 38:17 | Progress: 100%|██████████\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["features"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e0c2d04a-917b-4e58-a863-69cbc5cbcd0d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[20]: [&lt;Feature: restaurant_id&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_text)&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_title)&gt;,\n &lt;Feature: LSA(review_text)&gt;,\n &lt;Feature: LSA(review_title)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_text)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_title)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_text)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_title)&gt;,\n &lt;Feature: POLARITY_SCORE(review_text)&gt;,\n &lt;Feature: POLARITY_SCORE(review_title)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_text)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_title)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_text)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_title)&gt;,\n &lt;Feature: T5_ENCODER(review_title)&gt;,\n &lt;Feature: T5_SENTIMENT_ENCODER(review_title)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_text)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_title)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_text)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_title)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_text)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_title)&gt;,\n &lt;Feature: restaurants.price&gt;,\n &lt;Feature: restaurants.parking&gt;,\n &lt;Feature: restaurants.parking_valet&gt;,\n &lt;Feature: restaurants.parking_garage&gt;,\n &lt;Feature: restaurants.parking_street&gt;,\n &lt;Feature: restaurants.parking_lot&gt;,\n &lt;Feature: restaurants.parking_validated&gt;,\n &lt;Feature: restaurants.parking_free&gt;,\n &lt;Feature: restaurants.smoking&gt;,\n &lt;Feature: restaurants.accessible_wheelchair&gt;,\n &lt;Feature: restaurants.wifi&gt;,\n &lt;Feature: restaurants.meal_breakfast&gt;,\n &lt;Feature: restaurants.meal_deliver&gt;,\n &lt;Feature: restaurants.meal_dinner&gt;,\n &lt;Feature: restaurants.meal_lunch&gt;,\n &lt;Feature: restaurants.meal_takeout&gt;,\n &lt;Feature: restaurants.meal_cater&gt;,\n &lt;Feature: restaurants.options_healthy&gt;,\n &lt;Feature: restaurants.options_organic&gt;,\n &lt;Feature: restaurants.options_vegetarian&gt;,\n &lt;Feature: restaurants.options_vegan&gt;,\n &lt;Feature: restaurants.options_glutenfree&gt;,\n &lt;Feature: restaurants.options_lowfat&gt;,\n &lt;Feature: restaurants.COUNT(reviews)&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;]</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[20]: [&lt;Feature: restaurant_id&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_text)&gt;,\n &lt;Feature: DIVERSITY_SCORE(review_title)&gt;,\n &lt;Feature: LSA(review_text)&gt;,\n &lt;Feature: LSA(review_title)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_text)&gt;,\n &lt;Feature: MEAN_CHARACTERS_PER_WORD(review_title)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_text)&gt;,\n &lt;Feature: PART_OF_SPEECH_COUNT(review_title)&gt;,\n &lt;Feature: POLARITY_SCORE(review_text)&gt;,\n &lt;Feature: POLARITY_SCORE(review_title)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_text)&gt;,\n &lt;Feature: PUNCTUATION_COUNT(review_title)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_text)&gt;,\n &lt;Feature: STOPWORD_COUNT(review_title)&gt;,\n &lt;Feature: T5_ENCODER(review_title)&gt;,\n &lt;Feature: T5_SENTIMENT_ENCODER(review_title)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_text)&gt;,\n &lt;Feature: TITLE_WORD_COUNT(review_title)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_text)&gt;,\n &lt;Feature: UNIVERSAL_SENTENCE_ENCODER(review_title)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_text)&gt;,\n &lt;Feature: UPPER_CASE_COUNT(review_title)&gt;,\n &lt;Feature: restaurants.price&gt;,\n &lt;Feature: restaurants.parking&gt;,\n &lt;Feature: restaurants.parking_valet&gt;,\n &lt;Feature: restaurants.parking_garage&gt;,\n &lt;Feature: restaurants.parking_street&gt;,\n &lt;Feature: restaurants.parking_lot&gt;,\n &lt;Feature: restaurants.parking_validated&gt;,\n &lt;Feature: restaurants.parking_free&gt;,\n &lt;Feature: restaurants.smoking&gt;,\n &lt;Feature: restaurants.accessible_wheelchair&gt;,\n &lt;Feature: restaurants.wifi&gt;,\n &lt;Feature: restaurants.meal_breakfast&gt;,\n &lt;Feature: restaurants.meal_deliver&gt;,\n &lt;Feature: restaurants.meal_dinner&gt;,\n &lt;Feature: restaurants.meal_lunch&gt;,\n &lt;Feature: restaurants.meal_takeout&gt;,\n &lt;Feature: restaurants.meal_cater&gt;,\n &lt;Feature: restaurants.options_healthy&gt;,\n &lt;Feature: restaurants.options_organic&gt;,\n &lt;Feature: restaurants.options_vegetarian&gt;,\n &lt;Feature: restaurants.options_vegan&gt;,\n &lt;Feature: restaurants.options_glutenfree&gt;,\n &lt;Feature: restaurants.options_lowfat&gt;,\n &lt;Feature: restaurants.COUNT(reviews)&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MAX(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MEAN(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MIN(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.MIN(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SKEW(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.STD(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.STD(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.DIVERSITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.DIVERSITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.MEAN_CHARACTERS_PER_WORD(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.MEAN_CHARACTERS_PER_WORD(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.POLARITY_SCORE(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.POLARITY_SCORE(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.PUNCTUATION_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.PUNCTUATION_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.STOPWORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.STOPWORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.T5_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.T5_SENTIMENT_ENCODER(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.TITLE_WORD_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.TITLE_WORD_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.SUM(reviews.UPPER_CASE_COUNT(review_text))&gt;,\n &lt;Feature: restaurants.SUM(reviews.UPPER_CASE_COUNT(review_title))&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.MAX(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.MEAN(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.MIN(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.SKEW(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_text)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_text)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_title)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.LSA(review_title)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[10])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[11])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[12])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[13])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[14])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[2])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[3])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[4])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[5])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[6])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[7])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[8])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[9])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[0])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[10])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[11])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[12])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[13])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[14])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[1])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[2])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[3])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[4])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[5])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[6])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[7])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[8])&gt;,\n &lt;Feature: restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[9])&gt;]</div>"]}}],"execution_count":0},{"cell_type":"code","source":["feature_matrix = feature_matrix.reindex(rating_df.index)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"da41e0c4-5c33-4cbc-b6df-148f6e090b86"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["feature_matrix.to_csv('/dbfs/FileStore/tables/feature_matrix.csv')\nfeature_matrix"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d98faa3a-bd3d-4beb-80f3-a5791b693c73"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[25]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[25]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>restaurant_id</th>\n      <th>DIVERSITY_SCORE(review_text)</th>\n      <th>DIVERSITY_SCORE(review_title)</th>\n      <th>LSA(review_text)[0]</th>\n      <th>LSA(review_text)[1]</th>\n      <th>LSA(review_title)[0]</th>\n      <th>LSA(review_title)[1]</th>\n      <th>MEAN_CHARACTERS_PER_WORD(review_text)</th>\n      <th>MEAN_CHARACTERS_PER_WORD(review_title)</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[0]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[1]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[2]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[3]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[4]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[5]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[6]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[7]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[8]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[9]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[10]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[11]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[12]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[13]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[14]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[0]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[1]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[2]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[3]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[4]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[5]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[6]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[7]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[8]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[9]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[10]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[11]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[12]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[13]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[14]</th>\n      <th>POLARITY_SCORE(review_text)</th>\n      <th>...</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[4])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[5])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[6])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[7])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[8])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[9])</th>\n      <th>restaurants.SUM(reviews.LSA(review_text)[0])</th>\n      <th>restaurants.SUM(reviews.LSA(review_text)[1])</th>\n      <th>restaurants.SUM(reviews.LSA(review_title)[0])</th>\n      <th>restaurants.SUM(reviews.LSA(review_title)[1])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[0])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[10])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[11])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[12])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[13])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[14])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[1])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[2])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[3])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[4])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[5])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[6])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[7])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[8])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[9])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[0])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[10])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[11])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[12])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[13])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[14])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[1])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[2])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[3])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[4])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[5])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[6])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[7])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[8])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[9])</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.928571</td>\n      <td>1.0</td>\n      <td>0.041056</td>\n      <td>0.013206</td>\n      <td>0.016110</td>\n      <td>0.000714</td>\n      <td>3.814815</td>\n      <td>4.500000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>14.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.453</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0.909091</td>\n      <td>1.0</td>\n      <td>0.049185</td>\n      <td>0.010027</td>\n      <td>0.011093</td>\n      <td>0.014796</td>\n      <td>4.875000</td>\n      <td>4.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.432</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.067794</td>\n      <td>-0.012766</td>\n      <td>0.011982</td>\n      <td>0.018294</td>\n      <td>4.250000</td>\n      <td>5.000000</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.406</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0.871795</td>\n      <td>1.0</td>\n      <td>0.042773</td>\n      <td>0.054985</td>\n      <td>0.009839</td>\n      <td>0.015895</td>\n      <td>3.792683</td>\n      <td>5.142857</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.028</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0.862069</td>\n      <td>1.0</td>\n      <td>0.034249</td>\n      <td>0.036915</td>\n      <td>0.001419</td>\n      <td>0.001373</td>\n      <td>4.477273</td>\n      <td>5.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.540</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>146</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.014748</td>\n      <td>0.021926</td>\n      <td>0.010668</td>\n      <td>0.025098</td>\n      <td>4.650000</td>\n      <td>4.500000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.478</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>146</td>\n      <td>0.793814</td>\n      <td>1.0</td>\n      <td>0.096209</td>\n      <td>0.011562</td>\n      <td>0.004561</td>\n      <td>0.006928</td>\n      <td>4.100559</td>\n      <td>4.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>3.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>25.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.086</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>146</td>\n      <td>0.933333</td>\n      <td>1.0</td>\n      <td>0.067557</td>\n      <td>0.008218</td>\n      <td>0.000691</td>\n      <td>-0.000097</td>\n      <td>4.000000</td>\n      <td>4.500000</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.158</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>146</td>\n      <td>0.644068</td>\n      <td>1.0</td>\n      <td>0.069147</td>\n      <td>0.006603</td>\n      <td>0.025474</td>\n      <td>0.016240</td>\n      <td>4.198171</td>\n      <td>5.428571</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>39.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>65.0</td>\n      <td>3.0</td>\n      <td>13.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.302</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>146</td>\n      <td>0.901961</td>\n      <td>1.0</td>\n      <td>0.061839</td>\n      <td>0.003632</td>\n      <td>0.000384</td>\n      <td>0.000108</td>\n      <td>4.420455</td>\n      <td>5.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>26.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.020</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 1399 columns</p>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>restaurant_id</th>\n      <th>DIVERSITY_SCORE(review_text)</th>\n      <th>DIVERSITY_SCORE(review_title)</th>\n      <th>LSA(review_text)[0]</th>\n      <th>LSA(review_text)[1]</th>\n      <th>LSA(review_title)[0]</th>\n      <th>LSA(review_title)[1]</th>\n      <th>MEAN_CHARACTERS_PER_WORD(review_text)</th>\n      <th>MEAN_CHARACTERS_PER_WORD(review_title)</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[0]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[1]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[2]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[3]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[4]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[5]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[6]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[7]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[8]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[9]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[10]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[11]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[12]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[13]</th>\n      <th>PART_OF_SPEECH_COUNT(review_text)[14]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[0]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[1]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[2]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[3]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[4]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[5]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[6]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[7]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[8]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[9]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[10]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[11]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[12]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[13]</th>\n      <th>PART_OF_SPEECH_COUNT(review_title)[14]</th>\n      <th>POLARITY_SCORE(review_text)</th>\n      <th>...</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[4])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[5])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[6])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[7])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[8])</th>\n      <th>restaurants.STD(reviews.PART_OF_SPEECH_COUNT(review_title)[9])</th>\n      <th>restaurants.SUM(reviews.LSA(review_text)[0])</th>\n      <th>restaurants.SUM(reviews.LSA(review_text)[1])</th>\n      <th>restaurants.SUM(reviews.LSA(review_title)[0])</th>\n      <th>restaurants.SUM(reviews.LSA(review_title)[1])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[0])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[10])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[11])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[12])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[13])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[14])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[1])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[2])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[3])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[4])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[5])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[6])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[7])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[8])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_text)[9])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[0])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[10])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[11])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[12])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[13])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[14])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[1])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[2])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[3])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[4])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[5])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[6])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[7])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[8])</th>\n      <th>restaurants.SUM(reviews.PART_OF_SPEECH_COUNT(review_title)[9])</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.928571</td>\n      <td>1.0</td>\n      <td>0.041056</td>\n      <td>0.013206</td>\n      <td>0.016110</td>\n      <td>0.000714</td>\n      <td>3.814815</td>\n      <td>4.500000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>14.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.453</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0.909091</td>\n      <td>1.0</td>\n      <td>0.049185</td>\n      <td>0.010027</td>\n      <td>0.011093</td>\n      <td>0.014796</td>\n      <td>4.875000</td>\n      <td>4.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.432</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.067794</td>\n      <td>-0.012766</td>\n      <td>0.011982</td>\n      <td>0.018294</td>\n      <td>4.250000</td>\n      <td>5.000000</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.406</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0.871795</td>\n      <td>1.0</td>\n      <td>0.042773</td>\n      <td>0.054985</td>\n      <td>0.009839</td>\n      <td>0.015895</td>\n      <td>3.792683</td>\n      <td>5.142857</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.028</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0.862069</td>\n      <td>1.0</td>\n      <td>0.034249</td>\n      <td>0.036915</td>\n      <td>0.001419</td>\n      <td>0.001373</td>\n      <td>4.477273</td>\n      <td>5.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.540</td>\n      <td>...</td>\n      <td>0.242878</td>\n      <td>0.745288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.076611</td>\n      <td>0.1</td>\n      <td>4.264033</td>\n      <td>1.474902</td>\n      <td>0.977778</td>\n      <td>0.566323</td>\n      <td>101.0</td>\n      <td>359.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>725.0</td>\n      <td>8.0</td>\n      <td>122.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>105.0</td>\n      <td>879.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>1773.0</td>\n      <td>110.0</td>\n      <td>7.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>99.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14695</th>\n      <td>146</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>0.014748</td>\n      <td>0.021926</td>\n      <td>0.010668</td>\n      <td>0.025098</td>\n      <td>4.650000</td>\n      <td>4.500000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.478</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14696</th>\n      <td>146</td>\n      <td>0.793814</td>\n      <td>1.0</td>\n      <td>0.096209</td>\n      <td>0.011562</td>\n      <td>0.004561</td>\n      <td>0.006928</td>\n      <td>4.100559</td>\n      <td>4.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>3.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>25.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.086</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14697</th>\n      <td>146</td>\n      <td>0.933333</td>\n      <td>1.0</td>\n      <td>0.067557</td>\n      <td>0.008218</td>\n      <td>0.000691</td>\n      <td>-0.000097</td>\n      <td>4.000000</td>\n      <td>4.500000</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.158</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14698</th>\n      <td>146</td>\n      <td>0.644068</td>\n      <td>1.0</td>\n      <td>0.069147</td>\n      <td>0.006603</td>\n      <td>0.025474</td>\n      <td>0.016240</td>\n      <td>4.198171</td>\n      <td>5.428571</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>39.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>65.0</td>\n      <td>3.0</td>\n      <td>13.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.302</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14699</th>\n      <td>146</td>\n      <td>0.901961</td>\n      <td>1.0</td>\n      <td>0.061839</td>\n      <td>0.003632</td>\n      <td>0.000384</td>\n      <td>0.000108</td>\n      <td>4.420455</td>\n      <td>5.000000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>26.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.020</td>\n      <td>...</td>\n      <td>0.140705</td>\n      <td>0.753376</td>\n      <td>0.0</td>\n      <td>0.1</td>\n      <td>0.941254</td>\n      <td>0.1</td>\n      <td>4.636595</td>\n      <td>0.571311</td>\n      <td>1.080607</td>\n      <td>0.518212</td>\n      <td>92.0</td>\n      <td>391.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>795.0</td>\n      <td>9.0</td>\n      <td>150.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>128.0</td>\n      <td>948.0</td>\n      <td>0.0</td>\n      <td>42.0</td>\n      <td>1859.0</td>\n      <td>113.0</td>\n      <td>8.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>91.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>173.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>14700 rows × 1399 columns</p>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["y = rating_df['review_rating']\n\ntrain_feature_matrix, test_feature_matrix, y_train, y_test = train_test_split(feature_matrix, y, test_size=0.15, stratify=y)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c076e3a1-6fa3-4ec1-a0ee-a7d23ff41311"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["lgr = LogisticRegression(solver='liblinear', multi_class='auto', max_iter=100)\nlgr.fit(train_feature_matrix, y_train)\nlgr.score(test_feature_matrix, y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fc9e97f9-b0e0-482e-92bd-027fae0d011f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[27]: 0.6421768707482993</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[27]: 0.6421768707482993</div>"]}}],"execution_count":0},{"cell_type":"code","source":["hgbc = HistGradientBoostingClassifier()\nhgbc.fit(train_feature_matrix, y_train)\nhgbc.score(test_feature_matrix, y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5709108a-ff89-4ad0-911e-bd26ca0cf1ef"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[28]: 0.6807256235827664</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[28]: 0.6807256235827664</div>"]}}],"execution_count":0},{"cell_type":"code","source":["rfc = RandomForestClassifier(n_estimators=100, class_weight = \"balanced\", n_jobs=-1)\nrfc.fit(train_feature_matrix, y_train)\nrfc.score(test_feature_matrix, y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9fb9d0a9-3e52-4015-9039-7848d1761e6a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[29]: 0.6535147392290249</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[29]: 0.6535147392290249</div>"]}}],"execution_count":0},{"cell_type":"code","source":["headers = [\"name\", \"score\"]\nvalues = pd.DataFrame(sorted(zip(train_feature_matrix.columns, rfc.feature_importances_), key=lambda x: x[1] * -1))\n\nvalues.head(10)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"25b59e90-be7c-4cbd-9f01-bbcc71ca14ae"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[30]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[30]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<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>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>T5_SENTIMENT_ENCODER(review_title)</td>\n      <td>0.013718</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[415]</td>\n      <td>0.012710</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[28]</td>\n      <td>0.012192</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>POLARITY_SCORE(review_text)</td>\n      <td>0.011586</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[415]</td>\n      <td>0.011579</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[237]</td>\n      <td>0.008201</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>POLARITY_SCORE(review_title)</td>\n      <td>0.008028</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[289]</td>\n      <td>0.006696</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[28]</td>\n      <td>0.006162</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[348]</td>\n      <td>0.006059</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>T5_SENTIMENT_ENCODER(review_title)</td>\n      <td>0.013718</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[415]</td>\n      <td>0.012710</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[28]</td>\n      <td>0.012192</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>POLARITY_SCORE(review_text)</td>\n      <td>0.011586</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[415]</td>\n      <td>0.011579</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[237]</td>\n      <td>0.008201</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>POLARITY_SCORE(review_title)</td>\n      <td>0.008028</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[289]</td>\n      <td>0.006696</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[28]</td>\n      <td>0.006162</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[348]</td>\n      <td>0.006059</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["vot = VotingClassifier(voting='soft', estimators=[('lgr', lgr), ('rfc', rfc), ('hgbc', hgbc)], weights=[3, 1, 6])\nvot.fit(train_feature_matrix, y_train)\nvot.score(test_feature_matrix, y_test)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"90ceb448-7db0-4209-8b16-1c2e42ba00d9"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[31]: 0.6952380952380952</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[31]: 0.6952380952380952</div>"]}}],"execution_count":0},{"cell_type":"code","source":["plot_confusion_matrix(y_test, \n                      vot.predict(test_feature_matrix), \n                      ['1', '2', '3', '4', '5'])"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"f3928e57-1b27-41f6-834c-ce986e82bd20"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[32]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[32]: 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"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div 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"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f644813a6d0&gt;</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f644813a6d0&gt;</div>"]}}],"execution_count":0},{"cell_type":"code","source":["sns.countplot(x=vot.predict(test_feature_matrix), palette = sns.cubehelix_palette(8, start=1, rot=-.75))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"eb957384-5fa6-43bb-89ef-82ccafd66ae3"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[34]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[34]: 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index=train_feature_matrix.columns)\nfeat_importances.nlargest(10).plot(kind='bar')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d19642f3-ff83-4cf9-b8ea-d6635d20ecec"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[41]: </div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[41]: 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"}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div 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"}}],"execution_count":0},{"cell_type":"code","source":["values.head(15)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1e832bb3-760b-4384-a954-2f0040563bad"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div 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<td>0.006696</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[28]</td>\n      <td>0.006162</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[348]</td>\n      <td>0.006059</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[289]</td>\n      <td>0.005857</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>T5_ENCODER(review_title)</td>\n      <td>0.005753</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[348]</td>\n      <td>0.005459</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[259]</td>\n      <td>0.005395</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[375]</td>\n      <td>0.005111</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"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>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>T5_SENTIMENT_ENCODER(review_title)</td>\n      <td>0.013718</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[415]</td>\n      <td>0.012710</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[28]</td>\n      <td>0.012192</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>POLARITY_SCORE(review_text)</td>\n      <td>0.011586</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[415]</td>\n      <td>0.011579</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[237]</td>\n      <td>0.008201</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>POLARITY_SCORE(review_title)</td>\n      <td>0.008028</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[289]</td>\n      <td>0.006696</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[28]</td>\n      <td>0.006162</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[348]</td>\n      <td>0.006059</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_title)[289]</td>\n      <td>0.005857</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>T5_ENCODER(review_title)</td>\n      <td>0.005753</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[348]</td>\n      <td>0.005459</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[259]</td>\n      <td>0.005395</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>UNIVERSAL_SENTENCE_ENCODER(review_text)[375]</td>\n      <td>0.005111</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]}}],"execution_count":0}],"metadata":{"kernelspec":{"display_name":"ccg_blogs","language":"python","name":"ccg_blogs"},"language_info":{"mimetype":"text/x-python","name":"python","pygments_lexer":"ipython3","codemirror_mode":{"name":"ipython","version":3},"version":"3.8.3","nbconvert_exporter":"python","file_extension":".py"},"application/vnd.databricks.v1+notebook":{"notebookName":"predict-restaurant-rating","dashboards":[],"language":"python","widgets":{},"notebookOrigID":2839549113129639}},"nbformat":4,"nbformat_minor":0}