count_vectorizer.ipynb

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        "import pandas as pd\n",
        "import numpy as np"
      ]
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        "data = pd.DataFrame({'text':[\"Evaluating the sum gradient may require expensive evaluations of the gradients from all individual functions Ln\",\" When the training set is enormous and/or no simple formulas exist,evaluating the sums of gradients becomes very expensive.\"\n",
        "\"Consider the term PN We can reduce the amount of computation by taking a sum over a smaller set of Ln.\",\" In contrast to watch gradient descent, which uses all Ln for n = 1, . . . , N, we randomly choose a subset of Ln for mini-batch gradient descent.\",\n",
        "\" In the extreme case, we randomly select only a single Ln to estimate the gradient.\",\"The key insight about why taking a subset of data is sensible is to realize that for gradient descent to converge, we only require that the gradient is an unbiased estimate of the true gradient.\"]})"
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              "                                                text\n",
              "0  Evaluating the sum gradient may require expens...\n",
              "1   When the training set is enormous and/or no s...\n",
              "2   In contrast to watch gradient descent, which ...\n",
              "3   In the extreme case, we randomly select only ...\n",
              "4  The key insight about why taking a subset of d..."
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \" When the training set is enormous and/or no simple formulas exist,evaluating the sums of gradients becomes very expensive.Consider the term PN We can reduce the amount of computation by taking a sum over a smaller set of Ln.\",\n          \"The key insight about why taking a subset of data is sensible is to realize that for gradient descent to converge, we only require that the gradient is an unbiased estimate of the true gradient.\",\n          \" In contrast to watch gradient descent, which uses all Ln for n = 1, . . . , N, we randomly choose a subset of Ln for mini-batch gradient descent.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "metadata": {
        "id": "txUI5bRlzHzF"
      },
      "execution_count": 9,
      "outputs": []
    },
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        "cv = CountVectorizer()"
      ],
      "metadata": {
        "id": "6zkUVIs_1Jkv"
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      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bow = cv.fit_transform(data['text'])"
      ],
      "metadata": {
        "id": "TZOVPTOF1U49"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cv.vocabulary_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBO3Z-T22ZRI",
        "outputId": "77e15e62-d8dd-4db7-9196-bb74452ee9ea"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'evaluating': 19, 'the': 60, 'sum': 55, 'gradient': 28, 'may': 36, 'require': 47, 'expensive': 22, 'evaluations': 20, 'of': 39, 'gradients': 29, 'from': 26, 'all': 1, 'individual': 31, 'functions': 27, 'ln': 35, 'when': 69, 'training': 62, 'set': 50, 'is': 33, 'enormous': 17, 'and': 4, 'or': 41, 'no': 38, 'simple': 51, 'formulas': 25, 'exist': 21, 'sums': 56, 'becomes': 6, 'very': 66, 'consider': 12, 'term': 58, 'pn': 43, 'we': 68, 'can': 8, 'reduce': 46, 'amount': 2, 'computation': 11, 'by': 7, 'taking': 57, 'over': 42, 'smaller': 53, 'in': 30, 'contrast': 13, 'to': 61, 'watch': 67, 'descent': 16, 'which': 70, 'uses': 65, 'for': 24, 'randomly': 44, 'choose': 10, 'subset': 54, 'mini': 37, 'batch': 5, 'extreme': 23, 'case': 9, 'select': 48, 'only': 40, 'single': 52, 'estimate': 18, 'key': 34, 'insight': 32, 'about': 0, 'why': 71, 'data': 15, 'sensible': 49, 'realize': 45, 'that': 59, 'converge': 14, 'an': 3, 'unbiased': 64, 'true': 63}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(bow[0].toarray())\n",
        "print(bow[1].toarray())\n",
        "print(bow[2].toarray())\n",
        "print(bow[3].toarray())\n",
        "print(bow[4].toarray())"
      ],
      "metadata": {
        "colab": {
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          "name": "stdout",
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      "cell_type": "code",
      "source": [
        "df = cv.transform(['the training set is very enormous and evaluating it is vert difficult task']).toarray()"
      ],
      "metadata": {
        "id": "r1CLvwvD26AH"
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      "execution_count": 23,
      "outputs": []
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        "df"
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          "data": {
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              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "        1, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}