codecops.ipynb

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      "metadata": {
        "id": "udAclZIKOaBV"
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      "source": [
        "import re \n",
        "import pandas as pd \n",
        "from time import time  \n",
        "from collections import defaultdict  "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy  \n",
        "\n",
        "import logging \n",
        "logging.basicConfig(format=\"%(levelname)s - %(asctime)s: %(message)s\", datefmt= '%H:%M:%S', level=logging.INFO)"
      ],
      "metadata": {
        "id": "dfY1woLmOmRY"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/dataset (1).csv')\n",
        "df.shape"
      ],
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        },
        "id": "dAgTEPg4Op8H",
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            "text/plain": [
              "(27, 2)"
            ]
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          "metadata": {},
          "execution_count": 4
        }
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      "source": [
        "df.head()"
      ],
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          "height": 206
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        "id": "j6qDt50HOzYo",
        "outputId": "1742af1d-57b0-42be-b7cf-560f4dc903d9"
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                               code   \\\n",
              "0                                                NaN   \n",
              "1    int main() {printf(\"Hello, World!\"); return 0;}   \n",
              "2  int main() {scanf(\"%d\",&n); printf(\"%d\",n); re...   \n",
              "3  int main() {scanf(\"%d\",&m); printf(\"%d\",m); re...   \n",
              "4         int main() {printf(\"Himanshu\"); return 0;}   \n",
              "\n",
              "                                         explanation  \n",
              "0                                                NaN  \n",
              "1        The code prints the string \"Hello, World!\".  \n",
              "2  The code scans the value of n and then prints ...  \n",
              "3  The code scans the value of m and then prints ...  \n",
              "4             The code prints the string \"Himanshu\".  "
            ],
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              "      <th>1</th>\n",
              "      <td>int main() {printf(\"Hello, World!\"); return 0;}</td>\n",
              "      <td>The code prints the string \"Hello, World!\".</td>\n",
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              "      <th>2</th>\n",
              "      <td>int main() {scanf(\"%d\",&amp;n); printf(\"%d\",n); re...</td>\n",
              "      <td>The code scans the value of n and then prints ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>int main() {scanf(\"%d\",&amp;m); printf(\"%d\",m); re...</td>\n",
              "      <td>The code scans the value of m and then prints ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>int main() {printf(\"Himanshu\"); return 0;}</td>\n",
              "      <td>The code prints the string \"Himanshu\".</td>\n",
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      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
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              "code           2\n",
              "explanation    4\n",
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      "cell_type": "code",
      "source": [
        "df = df.dropna().reset_index(drop=True)\n",
        "df.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "syQc8dNoPOE_",
        "outputId": "804ed6f4-1cfa-4fbb-f119-b3a7e2974e46"
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              "explanation    0\n",
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          "metadata": {},
          "execution_count": 7
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      "cell_type": "code",
      "source": [
        "nlp = spacy.load('en_core_web_sm', disable=['ner', 'parser']) # disabling Named Entity Recognition for speed\n",
        "\n",
        "def cleaning(doc):\n",
        "    txt = [token.lemma_ for token in doc if not token.is_stop]\n",
        "    if len(txt) > 2:\n",
        "        return ' '.join(txt)\n"
      ],
      "metadata": {
        "id": "140wkLkXPVc5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "brief_cleaning = (re.sub(\"[^A-Za-z']+\", ' ', str(row)).lower() for row in df['explanation'])\n"
      ],
      "metadata": {
        "id": "LUPg00GDQEWx"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t = time()\n",
        "\n",
        "txt = [cleaning(doc) for doc in nlp.pipe(brief_cleaning, batch_size=5000)]\n",
        "\n",
        "print('Time to clean up everything: {} mins'.format(round((time() - t) / 60, 2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWQErxqMhoQz",
        "outputId": "21b6e6dc-b859-40d1-84de-8aee97179131"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to clean up everything: 0.0 mins\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean = pd.DataFrame({'clean': txt})\n",
        "df_clean = df_clean.dropna().drop_duplicates()\n",
        "df_clean.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uyJmCWBSHG7",
        "outputId": "00a8e38f-c374-4958-b306-28c18d218148"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models.phrases import Phrases, Phraser"
      ],
      "metadata": {
        "id": "CTUVnsFKSyS5"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sent = [row.split() for row in df_clean['clean']]\n"
      ],
      "metadata": {
        "id": "zYplKAJtS79P"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "phrases = Phrases(sent, min_count=30, progress_per=10000)"
      ],
      "metadata": {
        "id": "RX0bwJtRTADg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bigram = Phraser(phrases)\n",
        "sentences = bigram[sent]"
      ],
      "metadata": {
        "id": "lWmb9JoTa-fl"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_freq = defaultdict(int)\n",
        "for sent in sentences:\n",
        "    for i in sent:\n",
        "        word_freq[i] += 1\n",
        "len(word_freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWcEU24_TCfJ",
        "outputId": "9b59f59c-fba4-4d90-8bce-416607baa19c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted(word_freq, key=word_freq.get, reverse=True)[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezuwQCogb6nE",
        "outputId": "d29e2e52-0c09-4d74-ef22-29a58edc8622"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['value', 'code', 'print', 'scan', 'b', 'c', \"'\", 'sum', 'assign', 'string']"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import multiprocessing\n",
        "\n",
        "from gensim.models import Word2Vec"
      ],
      "metadata": {
        "id": "MuzZuyL0cH_1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cores = multiprocessing.cpu_count() # Count the number of cores in a computer"
      ],
      "metadata": {
        "id": "lntTVP--d_aE"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w2v_model = Word2Vec(min_count=1,\n",
        "                     window=2,\n",
        "                     vector_size=300,\n",
        "                     sample=6e-5, \n",
        "                     alpha=0.03, \n",
        "                     min_alpha=0.0007, \n",
        "                     negative=20,\n",
        "                      workers=cores-1)"
      ],
      "metadata": {
        "id": "alaLiJ9fcMgc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t = time()\n",
        "\n",
        "w2v_model.build_vocab(sentences, progress_per=10000)\n",
        "\n",
        "print('Time to build vocab: {} mins'.format(round((time() - t) / 60, 2)))"
      ],
      "metadata": {
        "id": "oZ7TD2hscNiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c448182c-6e92-42c1-d7f6-0c35bd83a445"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to build vocab: 0.0 mins\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t = time()\n",
        "\n",
        "w2v_model.train(sentences, total_examples=w2v_model.corpus_count, epochs=30, report_delay=1)\n",
        "\n",
        "print('Time to train the model: {} mins'.format(round((time() - t) / 60, 2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrvTC26fh4nW",
        "outputId": "6523b7ea-5c78-4247-f7f5-8f62981c63aa"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to train the model: 0.0 mins\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w2v_model.init_sims(replace=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFqeRX6UiBuz",
        "outputId": "7f66725a-3e5e-440d-e748-d569a0c0222c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-c7757d71a30b>:1: DeprecationWarning: Call to deprecated `init_sims` (Gensim 4.0.0 implemented internal optimizations that make calls to init_sims() unnecessary. init_sims() is now obsoleted and will be completely removed in future versions. See https:/RaRe-Technologies/gensim/wiki/Migrating-from-Gensim-3.x-to-4).\n",
            "  w2v_model.init_sims(replace=True)\n",
            "WARNING:gensim.models.keyedvectors:destructive init_sims(replace=True) deprecated & no longer required for space-efficiency\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w2v_model.wv.most_similar(positive=[\"string\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExjtYL4FmRlw",
        "outputId": "bc0516b0-e94a-4a76-bce5-99fe66db83e9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('x', 0.12529587745666504),\n",
              " ('value', 0.11045122891664505),\n",
              " (\"'\", 0.10891108959913254),\n",
              " ('check', 0.09878621995449066),\n",
              " ('m', 0.08243879675865173),\n",
              " ('code', 0.06600061804056168),\n",
              " ('hello', 0.05713500455021858),\n",
              " ('programming', 0.042763981968164444),\n",
              " ('yes', 0.04138921946287155),\n",
              " ('divisible', 0.040869440883398056)]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        " \n",
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE"
      ],
      "metadata": {
        "id": "qqFccStHo7Cv"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tsnescatterplot(model, word, list_names):\n",
        "    \"\"\" Plot in seaborn the results from the t-SNE dimensionality reduction algorithm of the vectors of a query word,\n",
        "    its list of most similar words, and a list of words.\n",
        "    \"\"\"\n",
        "    arrays = np.empty((0, 300), dtype='f')\n",
        "    word_labels = [word]\n",
        "    color_list  = ['red']\n",
        "\n",
        "    # adds the vector of the query word\n",
        "    arrays = np.append(arrays, model.wv.__getitem__([word]), axis=0)\n",
        "    \n",
        "    # gets list of most similar words\n",
        "    close_words = model.wv.most_similar([word])\n",
        "    for wrd_score in close_words:\n",
        "        wrd_vector = model.wv.__getitem__([wrd_score[0]])\n",
        "        word_labels.append(wrd_score[0])\n",
        "        color_list.append('blue')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "    \n",
        "    # adds the vector for each of the words from list_names to the array\n",
        "    for wrd in list_names:\n",
        "        wrd_vector = model.wv.__getitem__([wrd])\n",
        "        word_labels.append(wrd)\n",
        "        color_list.append('green')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "        \n",
        "    # Reduces the dimensionality from 300 to 50 dimensions with PCA\n",
        "    reduc = PCA(n_components=5).fit_transform(arrays)\n",
        "    \n",
        "    # Finds t-SNE coordinates for 2 dimensions\n",
        "    np.set_printoptions(suppress=True)\n",
        "    Y = TSNE(n_components=2, random_state=0, perplexity=15).fit_transform(reduc)\n",
        "    \n",
        "    # Sets everything up to plot\n",
        "    df = pd.DataFrame({'x': [x for x in Y[:, 0]],\n",
        "                    'y': [y for y in Y[:, 1]],\n",
        "                    'words': word_labels,\n",
        "                    'color': color_list})\n",
        "    \n",
        "    fig, _ = plt.subplots()\n",
        "    fig.set_size_inches(9, 9)\n",
        "    \n",
        "    # Basic plot\n",
        "    p1 = sns.regplot(data=df,\n",
        "                     x=\"x\",\n",
        "                     y=\"y\",\n",
        "                     fit_reg=False,\n",
        "                     marker=\"o\",\n",
        "                     scatter_kws={'s': 40,\n",
        "                                  'facecolors': df['color']\n",
        "                                 }\n",
        "                   )\n",
        "    for line in range(0, df.shape[0]):\n",
        "                p1.text(df[\"x\"][line],\n",
        "                 df['y'][line],\n",
        "                 '  ' + df[\"words\"][line].title(),\n",
        "                 horizontalalignment='left',\n",
        "                 verticalalignment='bottom', size='medium',\n",
        "                 color=df['color'][line],\n",
        "                 weight='normal'\n",
        "                ).set_size(15)\n",
        "\n",
        "    \n",
        "    plt.xlim(Y[:, 0].min()-50, Y[:, 0].max()+50)\n",
        "    plt.ylim(Y[:, 1].min()-50, Y[:, 1].max()+50)\n",
        "            \n",
        "    plt.title('t-SNE visualization for {}'.format(word.title()))"
      ],
      "metadata": {
        "id": "yD8XbvYsmg0d"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu3c_mu7ri-X",
        "outputId": "2eab4046-20a3-4adf-c283-64d98d43db5e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tsnescatterplot(w2v_model, 'string', ['c', 'h', 'print', 'm', 'value', 'b', 'd', 'scan'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795
        },
        "id": "i2WXL2gEpiOB",
        "outputId": "6fba378c-1b58-4945-e9e4-ada73b29fca8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tsnescatterplot(w2v_model, 'print', [i[0] for i in w2v_model.wv.most_similar(negative=[\"print\"])])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795
        },
        "id": "5oVxFvAm8xLw",
        "outputId": "3ce262b2-db4b-414c-d5b6-1d7fe9c7b30e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bNew5VJ9933t"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}