Part 4.5: AutoNLP_Sentiment_Analysis.ipynb

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            "\u001b[?25hRequirement already satisfied: jupyter in /usr/local/lib/python3.7/dist-packages (from autoviml) (1.0.0)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from autoviml) (2019.12.20)\n",
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            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
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            "Building wheels for collected packages: shap\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.39.0-cp37-cp37m-linux_x86_64.whl size=491626 sha256=28850761c361b98cd2d3ca9d64d73739326552ea0438c403d91c1bcdc9bdea4d\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "fUbFvR7LDB3U"
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDH43tw-DN4y",
        "colab": {
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        },
        "outputId": "bf7f13e5-f342-4ce1-8bad-b718c9eae37b"
      },
      "source": [
        "dataset, info = tfds.load('amazon_us_reviews/Personal_Care_Appliances_v1_00', with_info=True, batch_size=-1)\n",
        "train_dataset = dataset['train']"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0 (download: 16.82 MiB, generated: Unknown size, total: 16.82 MiB) to /root/tensorflow_datasets/amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c55581be078486daf39a4468435a047",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {
            "tags": []
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        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0.incompleteF1PUSO/amazon_us_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset amazon_us_reviews downloaded and prepared to /root/tensorflow_datasets/amazon_us_reviews/Personal_Care_Appliances_v1_00/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1diITDtD9xD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "405f6a4d-21fc-4712-e863-261dcad41c91"
      },
      "source": [
        "info"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='amazon_us_reviews',\n",
              "    version=0.1.0,\n",
              "    description='Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.\n",
              "\n",
              "Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).\n",
              "\n",
              "Each Dataset contains the following columns : \n",
              "  marketplace       - 2 letter country code of the marketplace where the review was written.\n",
              "  customer_id       - Random identifier that can be used to aggregate reviews written by a single author.\n",
              "  review_id         - The unique ID of the review.\n",
              "  product_id        - The unique Product ID the review pertains to. In the multilingual dataset the reviews\n",
              "                      for the same product in different countries can be grouped by the same product_id.\n",
              "  product_parent    - Random identifier that can be used to aggregate reviews for the same product.\n",
              "  product_title     - Title of the product.\n",
              "  product_category  - Broad product category that can be used to group reviews \n",
              "                      (also used to group the dataset into coherent parts).\n",
              "  star_rating       - The 1-5 star rating of the review.\n",
              "  helpful_votes     - Number of helpful votes.\n",
              "  total_votes       - Number of total votes the review received.\n",
              "  vine              - Review was written as part of the Vine program.\n",
              "  verified_purchase - The review is on a verified purchase.\n",
              "  review_headline   - The title of the review.\n",
              "  review_body       - The review text.\n",
              "  review_date       - The date the review was written.',\n",
              "    homepage='https://s3.amazonaws.com/amazon-reviews-pds/readme.html',\n",
              "    features=FeaturesDict({\n",
              "        'data': FeaturesDict({\n",
              "            'customer_id': tf.string,\n",
              "            'helpful_votes': tf.int32,\n",
              "            'marketplace': tf.string,\n",
              "            'product_category': tf.string,\n",
              "            'product_id': tf.string,\n",
              "            'product_parent': tf.string,\n",
              "            'product_title': tf.string,\n",
              "            'review_body': tf.string,\n",
              "            'review_date': tf.string,\n",
              "            'review_headline': tf.string,\n",
              "            'review_id': tf.string,\n",
              "            'star_rating': tf.int32,\n",
              "            'total_votes': tf.int32,\n",
              "            'verified_purchase': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
              "            'vine': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
              "        }),\n",
              "    }),\n",
              "    total_num_examples=85981,\n",
              "    splits={\n",
              "        'train': 85981,\n",
              "    },\n",
              "    supervised_keys=None,\n",
              "    citation=\"\"\"\"\"\",\n",
              "    redistribution_info=,\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeuwcjT2oLWd"
      },
      "source": [
        "dataset=tfds.as_numpy(train_dataset)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCy_iKoLwZWH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bd91991-5e1c-4bce-f348-1d7920ff76c5"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'data': {'customer_id': array([b'13986323', b'50574716', b'50593972', ..., b'40719682',\n",
              "         b'35596948', b'29430209'], dtype=object),\n",
              "  'helpful_votes': array([0, 3, 0, ..., 0, 0, 0], dtype=int32),\n",
              "  'marketplace': array([b'US', b'US', b'US', ..., b'US', b'US', b'US'], dtype=object),\n",
              "  'product_category': array([b'Personal_Care_Appliances', b'Personal_Care_Appliances',\n",
              "         b'Personal_Care_Appliances', ..., b'Personal_Care_Appliances',\n",
              "         b'Personal_Care_Appliances', b'Personal_Care_Appliances'],\n",
              "        dtype=object),\n",
              "  'product_id': array([b'B00847JQZ6', b'B00N5HD340', b'B0077L1X24', ..., b'B000UZ8X2W',\n",
              "         b'B000NURPPK', b'B001EY5GNW'], dtype=object),\n",
              "  'product_parent': array([b'997683625', b'955577225', b'120764066', ..., b'96066145',\n",
              "         b'58591097', b'986877728'], dtype=object),\n",
              "  'product_title': array([b'SE - Reading Glass - Spring Loaded Hinges, 4.0x - RTS62400',\n",
              "         b'Straight Razor',\n",
              "         b'Philips Sonicare Flexcare & Healthy White Plastic Travel Handle Case New Bulk Package',\n",
              "         ...,\n",
              "         b'Remington R-9200 Microflex Ultra TCT Shaver [Health and Beauty]',\n",
              "         b'SUNBEAM Cool Mist HUMIDIFIER with PermaFilter # 1120 \\xe2\\x80\\x93 1 Ea',\n",
              "         b'Andis Blade Set for T-Outliner Trimmer'], dtype=object),\n",
              "  'review_body': array([b\"These glasses are an excellent value.  The fit is good and they are very comfortable.  Because of my legal blindness, there aren't a lot of options to try to see better, but I believe these help with my other visual aids, and because they are reasonably priced I can have more than one pair available.\",\n",
              "         b\"Always wanted to try straight razor shaving (as a DE safety razor user), and this was a cheap way for me to determine I was not into it.<br /><br />Because the blades are disposable and always sharp, I could put a new one in and reasonably rely upon that fact that cuts were probably due to my technique and not the blade.<br /><br />It's very hard to do straight razor shaving on yourself because the ANGLE is difficult to control without switching hands. Being very right-handed, I really couldn't do that. I bet I could shave someone else's face with it though.<br /><br />An immediate upside? Using a DE safety razor (slant edged even) seems SUPER safe now! I'm increased my speed with the DE due to that confidence, and I'd been using it for years now.\",\n",
              "         b'I usually either throw my toothbrush in a plastic bag with spare head so this product is very convenient for keeping all the parts apart, dry and undamaged, and i now keep it in my travel bag all the time ready to go.',\n",
              "         ...,\n",
              "         b\"I have had a Remington before but needed a new one when the batteries died and the cutters were all but gone.  It was cheaper to buy a new one.  The new one has a nice charge level but the trimmer didn't work when I got it.\",\n",
              "         b\"I was surprised that it really didn't do much compared to the 1950s version that I'd inherited. Keeping a wet wash cloth next to my bed for when I start coughing in the middle of the night works better.\",\n",
              "         b'The blades were an excellent fit for my T-line trimmers.  Within five minutes I had my trimmers cleaned, the blades installed, and was putting them to use.  I saw the blades in several locations for almost twice the price I paid, so this worked out to be an awesome deal.'],\n",
              "        dtype=object),\n",
              "  'review_date': array([b'2015-01-04', b'2015-08-05', b'2012-11-17', ..., b'2008-02-08',\n",
              "         b'2007-09-07', b'2012-07-26'], dtype=object),\n",
              "  'review_headline': array([b'These glasses are an excellent value. The fit is good and they are ...',\n",
              "         b'A fantastic way to cheaply try straight razor shaving.',\n",
              "         b'Great for travel', ..., b'Trimmer Not Working',\n",
              "         b'Loud and ineffectual', b'Excellent product, awesomoe price'],\n",
              "        dtype=object),\n",
              "  'review_id': array([b'R3VEUFVA9QJY55', b'R2DTQV5SMJ0CK7', b'R3OJ06NK99WLNJ', ...,\n",
              "         b'R1ZQ0XZXOD9N18', b'R1FJ9OU429X00Y', b'RI28R1W94N1R6'],\n",
              "        dtype=object),\n",
              "  'star_rating': array([4, 5, 4, ..., 3, 2, 5], dtype=int32),\n",
              "  'total_votes': array([0, 3, 0, ..., 0, 0, 0], dtype=int32),\n",
              "  'verified_purchase': array([0, 0, 0, ..., 1, 0, 0]),\n",
              "  'vine': array([1, 1, 1, ..., 1, 1, 1])}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1pGPYGcoLNz"
      },
      "source": [
        "helpful_votes=dataset['data']['helpful_votes']\n",
        "review_headline=dataset['data']['review_headline']\n",
        "review_body=dataset['data']['review_body']\n",
        "rating=dataset['data']['star_rating']"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmS7qQuCoLGF"
      },
      "source": [
        "reviews_df=pd.DataFrame(np.hstack((helpful_votes[:,None],review_headline[:,None],review_body[:,None],rating[:,None])),columns=['votes','headline','reviews','rating'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0DWbO1y1Vzc"
      },
      "source": [
        "convert_dict = {'votes': int, \n",
        "                'headline': str,\n",
        "                'reviews': str,\n",
        "                'rating': int\n",
        "               } "
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWiWrKmS1lwR"
      },
      "source": [
        "reviews_df = reviews_df.astype(convert_dict) "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsmO113-vaVY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "32a69727-dbb8-45d6-fd48-4ccee29c83f4"
      },
      "source": [
        "reviews_df"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>votes</th>\n",
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              "      <th>rating</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>b'These glasses are an excellent value. The fi...</td>\n",
              "      <td>b\"These glasses are an excellent value.  The f...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>b'A fantastic way to cheaply try straight razo...</td>\n",
              "      <td>b\"Always wanted to try straight razor shaving ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Great for travel'</td>\n",
              "      <td>b'I usually either throw my toothbrush in a pl...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Five Stars'</td>\n",
              "      <td>b'Top quality.'</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>b'*Product sent not as shown'</td>\n",
              "      <td>b'Today I received  1 Fl. Oz, Natures Balance ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85976</th>\n",
              "      <td>2</td>\n",
              "      <td>b'YES!'</td>\n",
              "      <td>b\"This is the real deal. Don't bother with the...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85977</th>\n",
              "      <td>1</td>\n",
              "      <td>b'Bryton Picks'</td>\n",
              "      <td>b'I like the Bryton Picks very much. Have orde...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85978</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Trimmer Not Working'</td>\n",
              "      <td>b\"I have had a Remington before but needed a n...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85979</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Loud and ineffectual'</td>\n",
              "      <td>b\"I was surprised that it really didn't do muc...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85980</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Excellent product, awesomoe price'</td>\n",
              "      <td>b'The blades were an excellent fit for my T-li...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85981 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       votes  ... rating\n",
              "0          0  ...      4\n",
              "1          3  ...      5\n",
              "2          0  ...      4\n",
              "3          0  ...      5\n",
              "4          1  ...      3\n",
              "...      ...  ...    ...\n",
              "85976      2  ...      5\n",
              "85977      1  ...      5\n",
              "85978      0  ...      3\n",
              "85979      0  ...      2\n",
              "85980      0  ...      5\n",
              "\n",
              "[85981 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydJ-TzNZvaHR"
      },
      "source": [
        "reviews_df[\"target\"] = reviews_df[\"rating\"].apply(lambda x: 1 if x>= 4 else 0) "
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZOZarx4GGX3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "bd2e58c8-015a-49de-92b6-8578cbd5410c"
      },
      "source": [
        "reviews_df"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th></th>\n",
              "      <th>votes</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
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              "      <td>b'Great for travel'</td>\n",
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              "      <th>3</th>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>b'*Product sent not as shown'</td>\n",
              "      <td>b'Today I received  1 Fl. Oz, Natures Balance ...</td>\n",
              "      <td>3</td>\n",
              "      <td>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",
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              "    <tr>\n",
              "      <th>85976</th>\n",
              "      <td>2</td>\n",
              "      <td>b'YES!'</td>\n",
              "      <td>b\"This is the real deal. Don't bother with the...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85977</th>\n",
              "      <td>1</td>\n",
              "      <td>b'Bryton Picks'</td>\n",
              "      <td>b'I like the Bryton Picks very much. Have orde...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85978</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Trimmer Not Working'</td>\n",
              "      <td>b\"I have had a Remington before but needed a n...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85979</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Loud and ineffectual'</td>\n",
              "      <td>b\"I was surprised that it really didn't do muc...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85980</th>\n",
              "      <td>0</td>\n",
              "      <td>b'Excellent product, awesomoe price'</td>\n",
              "      <td>b'The blades were an excellent fit for my T-li...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85981 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       votes                                           headline  ... rating  target\n",
              "0          0  b'These glasses are an excellent value. The fi...  ...      4       1\n",
              "1          3  b'A fantastic way to cheaply try straight razo...  ...      5       1\n",
              "2          0                                b'Great for travel'  ...      4       1\n",
              "3          0                                      b'Five Stars'  ...      5       1\n",
              "4          1                      b'*Product sent not as shown'  ...      3       0\n",
              "...      ...                                                ...  ...    ...     ...\n",
              "85976      2                                            b'YES!'  ...      5       1\n",
              "85977      1                                    b'Bryton Picks'  ...      5       1\n",
              "85978      0                             b'Trimmer Not Working'  ...      3       0\n",
              "85979      0                            b'Loud and ineffectual'  ...      2       0\n",
              "85980      0               b'Excellent product, awesomoe price'  ...      5       1\n",
              "\n",
              "[85981 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbU4KHtEyJdM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "745140fa-57fb-474f-8c28-1deed41465eb"
      },
      "source": [
        "reviews_df.shape[0]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85981"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1p0u2X8yJaX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d593f84-02d1-4fbe-bf85-0e9471f339ca"
      },
      "source": [
        "reviews_df[\"target\"].value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    62554\n",
              "0    23427\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nkj87kcOyyhE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5100a200-3613-4fc7-aa96-e5689ec2af92"
      },
      "source": [
        "reviews_df.info()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 85981 entries, 0 to 85980\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   votes     85981 non-null  int64 \n",
            " 1   headline  85981 non-null  object\n",
            " 2   reviews   85981 non-null  object\n",
            " 3   rating    85981 non-null  int64 \n",
            " 4   target    85981 non-null  int64 \n",
            "dtypes: int64(3), object(2)\n",
            "memory usage: 3.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inj7K9BixwSl"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(reviews_df, test_size=0.25)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_9D_SnZxZ2P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "b52edf80-63bc-4657-94b6-0fe224dfc2b8"
      },
      "source": [
        "from autoviml.Auto_NLP import Auto_NLP"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Imported Auto_ViML version: 0.1.680. Call using:\n",
            "             m, feats, trainm, testm = Auto_ViML(train, target, test,\n",
            "                            sample_submission='',\n",
            "                            scoring_parameter='', KMeans_Featurizer=False,\n",
            "                            hyper_param='RS',feature_reduction=True,\n",
            "                             Boosting_Flag='CatBoost', Binning_Flag=False,\n",
            "                            Add_Poly=0, Stacking_Flag=False,Imbalanced_Flag=False,\n",
            "                            verbose=1)\n",
            "            \n",
            "Now Auto_ViML can solve multi-label, multi-output problems. Also Auto_NLP included.\n",
            "To get the latest version, perform \"pip install autoviml --no-cache-dir --ignore-installed\"\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>.container { width:95% !important; }</style>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Imported Auto_NLP version: 0.0.45.. Call using:\n",
            "     train_nlp, test_nlp, nlp_pipeline, predictions = Auto_NLP(\n",
            "                nlp_column, train, test, target, score_type='balanced_accuracy',\n",
            "                modeltype='Classification',top_num_features=200, verbose=0,\n",
            "                build_model=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJDEh3pgxZtz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5ec7a0b4-c7a9-4fd9-fe09-a023dede3571"
      },
      "source": [
        "nlp_column = 'reviews'\n",
        "target = 'target'\n",
        "train_nlp, test_nlp, nlp_transformer, preds = Auto_NLP(\n",
        "                nlp_column, train, test, target, score_type='balanced_accuracy',\n",
        "                modeltype='Classification',top_num_features=50, verbose=2,\n",
        "                build_model=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading collection 'popular'\n",
            "[nltk_data]    | \n",
            "[nltk_data]    | Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/cmudict.zip.\n",
            "[nltk_data]    | Downloading package gazetteers to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gazetteers.zip.\n",
            "[nltk_data]    | Downloading package genesis to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/genesis.zip.\n",
            "[nltk_data]    | Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/gutenberg.zip.\n",
            "[nltk_data]    | Downloading package inaugural to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/inaugural.zip.\n",
            "[nltk_data]    | Downloading package movie_reviews to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/movie_reviews.zip.\n",
            "[nltk_data]    | Downloading package names to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/names.zip.\n",
            "[nltk_data]    | Downloading package shakespeare to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/shakespeare.zip.\n",
            "[nltk_data]    | Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data]    | Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/treebank.zip.\n",
            "[nltk_data]    | Downloading package twitter_samples to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/twitter_samples.zip.\n",
            "[nltk_data]    | Downloading package omw to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/omw.zip.\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet_ic.zip.\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/words.zip.\n",
            "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping chunkers/maxent_ne_chunker.zip.\n",
            "[nltk_data]    | Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data]    | Downloading package snowball_data to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection popular\n",
            "Auto NLP processing on NLP Column: reviews\n",
            "Shape of Train Data: 64485 rows\n",
            "    Shape of Test Data: 21496 rows\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x3000 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "    Added 9 summary columns for counts of words and characters in each row\n",
            "Cleaning text in Train data for reviews column\n",
            "    Faster text processing using clean_text function, since top_num_features is below 300\n",
            "    Time Taken for Expanding emojis and abbreviations in data = 99 seconds\n",
            "        Time Taken for Processing text in data = 260 seconds\n",
            "Train data Text cleaning completed. Time taken = 381 seconds\n",
            "\n",
            "For target = target\n",
            "\n",
            "    For class = 0\n",
            "Top 200 n-grams\n",
            ": ['do', 'but', 'work', 'use', 'product', 'good', 'would', 'buy', 'they', 'if', 'will', 'like', 'me', 'time', 'when', 'make', 'no', 'well', 'what', 'after', 'money', 'your', 'try', 'these', 'back', 'really', 'even', 'think', 'them', 'off', 'because', 'much', 'go', 'look', 'return', 'great', 'take', 'give', 'could', 'come', 'purchase', 'item', 'need', 'break', 'there', 'first', 'week', 'which', 'find', 'waste', 'also', 'seem', 'order', 'day', 'as', 'small', 'price', 'hard', 'thing', 'little', 'quality', 'way', 'bad', 'want', 'fit', 'month', 'never', 'now', 'long', 'two', 'recommend', 'cheap', 'we', 'expect', 'enough', 'cannot', 'know', 'review', 'last', 'before', 'feel', 'should', 'year', 'help', 'how', 'hair', 'put', 'battery', 'however', 'problem', 'ok', 'hold', 'receive', 'keep', 'worth', 'nothing', 'see', 'another', 'plastic', 'pay', 'right', 'read', 'while', 'size', 'sure', 'big', 'different', 'send', 'wear', 'difficult', 'less', 'stay', 'something', 'lot', 'start', 'anything', 'easy', 'tell', 'old', 'amazon', 'stop', 'new', 'since', 'fine', 'without', 'nice', 'piece', 'star', 'around', 'place', 'though', 'leave', 'large', 'do not', 'not work', 'b do', 'b not', 'would not', 'b product', 'will not', 'not buy', 'but not', 'b buy', 'but do', 'work well', 'not recommend', 'waste money', 'your money', 'they do', 'b these', 'product do', 'could not', 'not good', 'not fit', 'not worth', 'not even', 'b purchase', 'b item', 'not like', 'b use', 'not do', 'waste your', 'not what', 'there no', 'b good', 'as well', 'do not work', 'b do not', 'but do not', 'do not buy', 'would not recommend', 'they do not', 'do not fit', 'product do not', 'do not know', 'do not like', 'save your money', 'do not waste', 'waste your money', 'not waste your', 'do not even', 'would not buy', 'do not do', 'not sure if', 'do not feel', 'do not recommend', 'do not want', 'do not seem', 'not work me', 'if do not', 'do not stay', 'not recommend product', 'first time use', 'b product do', 'not worth money', 'no matter how', 'do not think', 'do not give', 'do not hold']\n",
            "    Top n-grams that are most frequent in this class are: 199\n",
            "\n",
            "    For class = 1\n",
            "Top 200 n-grams\n",
            ": ['great', 'product', 'use', 'good', 'work', 'not', 'do', 'but', 'love', 'well', 'buy', 'like', 'they', 'would', 'me', 'these', 'easy', 'will', 'price', 'time', 'really', 'recommend', 'make', 'when', 'if', 'need', 'them', 'what', 'much', 'take', 'no', 'help', 'year', 'find', 'after', 'best', 'little', 'your', 'back', 'try', 'purchase', 'look', 'go', 'now', 'day', 'excellent', 'as', 'give', 'long', 'because', 'nice', 'perfect', 'feel', 'also', 'order', 'come', 'works', 'want', 'quality', 'we', 'could', 'thing', 'keep', 'even', 'which', 'fit', 'happy', 'item', 'last', 'problem', 'first', 'think', 'who', 'pain', 'week', 'every', 'put', 'how', 'there', 'highly', 'see', 'fast', 'hair', 'lot', 'without', 'she', 'small', 'far', 'two', 'while', 'hard', 'worth', 'seem', 'definitely', 'before', 'cannot', 'right', 'husband', 'comfortable', 'job', 'exactly', 'new', 'off', 'way', 'month', 'battery', 'her', 'he', 'wear', 'know', 'expect', 'many', 'ever', 'clean', 'old', 'size', 'hand', 'fine', 'enough', 'shave', 'since', 'razor', 'another', 'bit', 'hold', 'big', 'review', 'light', 'able', 'free', 'amazon', 'around', 'thank', 'b great', 'do not', 'b good', 'b love', 'great product', 'b works', 'work great', 'b excellent', 'work well', 'easy use', 'b use', 'b buy', 'highly recommend', 'b these', 'b product', 'b work', 'would recommend', 'as well', 'b nice', 'b best', 'good product', 'b perfect', 'works great', 'b really', 'great price', 'but do', 'could not', 'exactly what', 'excellent product', 'will not', 'recommend product', 'use product', 'help me', 'b great product', 'b works great', 'b good product', 'b excellent product', 'but do not', 'b works well', 'do not know', 'do not want', 'b love product', 'would recommend anyone', 'if do not', 'worth every penny', 'would definitely recommend', 'b love these', 'they do not', 'b really like', 'would highly recommend', 'b work great', 'do not like', 'b use product', 'highly recommend product', 'do not work', 'b do not', 'would recommend product', 'do not need', 'could not find', 'do not think', 'b work well', 'b buy these', 'do not use', 'b easy use', 'do not seem', 'b exactly what']\n",
            "    Top n-grams that are most frequent in this class are: 199\n",
            "Time Taken = 2 seconds\n",
            "##################    THIS IS FOR BUILD_MODEL = TRUE           #################\n",
            "Building Model and Pipeline for NLP column = reviews. This will take time...\n",
            "       Class  -> Counts -> Percent\n",
            "           0:   17676  ->   27.4%\n",
            "           1:   46809  ->   72.6%\n",
            "    Selected the maximum number of features limit = 310195\n",
            "Performing RandomizedSearchCV across 30 params. Optimizing for accuracy\n",
            "    Using train data = (51588,) and Cross Validation data = (12897,)\n",
            "Using a Calibrated Classifier in this Multi_Classification dataset to improve results...\n",
            "Since top_num_features = 50, Multinomial NB model selected. If you need different model, increase it >= 300.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZ6DDWuExZli"
      },
      "source": [
        "nlp_transformer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svsToSQr4y6B"
      },
      "source": [
        "nlp_transformer.predict(test[nlp_column])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYFdvCdi48cc"
      },
      "source": [
        "nlp_transformer."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5c5MseWV47Y0"
      },
      "source": [
        "-------------------------------------------"
      ]
    }
  ]
}