Jokes_.ipynb

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        "<a href=\"https://colab.research.google.com/github/frm1789/100DaysOfPython/blob/main/Jokes_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
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      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_colwidth', 120)\n",
        "\n",
        "jokes = pd.read_csv(\"http://facweb.cs.depaul.edu/mobasher/classes/csc478/data/jokes.csv\", usecols=[1], header=None)\n",
        "jokes.head(10)"
      ],
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              "                                                                                                                         1\n",
              "0  A man visits the doctor. The doctor says \"I have bad news for you.You have cancer and Alzheimer's disease\". The man ...\n",
              "1  This couple had an excellent relationship going until one day he came home from work to find his girlfriend packing....\n",
              "2                                    Q. What's 200 feet long and has 4 teeth? A. The front row at a Willie Nelson Concert.\n",
              "3             Q. What's the difference between a man and a toilet? A. A toilet doesn't follow you around after you use it.\n",
              "4                                Q. What's O. J. Simpson's Internet address? A.\\tSlash slash backslash slash slash escape.\n",
              "5  Bill & Hillary are on a trip back to Arkansas. They're almost out of gas so Bill pulls into a service station on the...\n",
              "6                                               How many feminists does it take to screw in a light bulb?That's not funny.\n",
              "7                                       Q. Did you hear about the dyslexic devil worshipper? A. He sold his soul to Santa.\n",
              "8  A country guy goes into a city bar that has a dress code and the maitred' demands he wear a tie. Discouraged the guy...\n",
              "9                            Two cannibals are eating a clown one turns to other and says: \"Does this taste funny to you? "
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              "      <td>A man visits the doctor. The doctor says \"I have bad news for you.You have cancer and Alzheimer's disease\". The man ...</td>\n",
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              "      <td>Q. What's 200 feet long and has 4 teeth? A. The front row at a Willie Nelson Concert.</td>\n",
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              "      <td>Q. What's O. J. Simpson's Internet address? A.\\tSlash slash backslash slash slash escape.</td>\n",
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              "      <td>How many feminists does it take to screw in a light bulb?That's not funny.</td>\n",
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              "      <td>Q. Did you hear about the dyslexic devil worshipper? A. He sold his soul to Santa.</td>\n",
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              "      <th>8</th>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Two cannibals are eating a clown one turns to other and says: \"Does this taste funny to you?</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "def get_joke_text(jokes, id):\n",
        "\treturn np.array(jokes)[id]"
      ],
      "metadata": {
        "id": "0bF-NKZ_grkE"
      },
      "execution_count": 3,
      "outputs": []
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      "source": [
        "print(get_joke_text(jokes, 99))"
      ],
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\"Q: What's the difference between greeting a Queen and greeting thePresident of the United  States?A: You only have to get on one knee to greet the queen.\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataMat = pd.read_csv(\"http://facweb.cs.depaul.edu/mobasher/classes/csc478/data/modified_jester_data.csv\", header=None)\n",
        "\n",
        "dataMat.shape\n"
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              "(1000, 100)"
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          "execution_count": 5
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        "pd.set_option('display.max_colwidth', 40)\n",
        "dataMat.head(10)"
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              "      0      1      2      3      4      5      6      7      8      9   ...  \\\n",
              "0   3.18  19.79   1.34   2.84   3.48   2.50   1.15  15.17   2.02   6.24  ...   \n",
              "1  15.08  10.71  17.36  15.37   8.62   1.34  10.27   5.66  19.88  20.22  ...   \n",
              "2   0.00   0.00   0.00   0.00  20.03  20.27  20.03  20.27   0.00   0.00  ...   \n",
              "3   0.00  19.35   0.00   0.00  12.80  19.16   8.18  17.21   0.00  12.84  ...   \n",
              "4  19.50  15.61   6.83   5.61  12.36  12.60  18.04  15.61  10.56  16.73  ...   \n",
              "5   4.83   7.46  11.44   2.50   3.91   6.68   2.31  10.13   4.35   9.20  ...   \n",
              "6   0.00   0.00   0.00   0.00  19.59   1.15  18.72  19.79   0.00   0.00  ...   \n",
              "7  17.84  14.16  20.17   4.79   2.84   9.30  20.27  12.41   5.81   6.58  ...   \n",
              "8   7.21   7.46   1.58   4.11   2.26  10.71   5.71   2.07   3.14   9.40  ...   \n",
              "9  14.01  16.15  16.15  14.01  17.41  16.15  19.93  13.52  14.01  19.16  ...   \n",
              "\n",
              "      90     91     92     93     94     95     96     97     98     99  \n",
              "0  13.82   0.00   0.00   0.00   0.00   0.00   5.37   0.00   0.00   0.00  \n",
              "1  13.82   6.05  10.71  18.86  10.81   8.86  14.06  11.34   6.68  12.07  \n",
              "2   0.00   0.00   0.00  20.08   0.00   0.00   0.00   0.00   0.00   0.00  \n",
              "3   0.00   0.00   0.00  11.53   0.00   0.00   0.00   0.00   0.00   0.00  \n",
              "4  16.19  16.58  15.27  16.19  16.73  12.55  14.11  17.55  12.80  12.60  \n",
              "5   7.46   4.11  10.32   8.04   8.82   7.65  11.05   1.92   5.95   7.55  \n",
              "6   0.00   0.00   0.00   0.00   0.00  13.33   0.00   0.00   0.00   0.00  \n",
              "7  18.23   9.88  10.90   5.32   7.84   7.65  13.14  10.95  12.31  11.00  \n",
              "8  15.37  10.71  15.17  10.71  10.71  10.71  10.71  10.71   7.60   6.05  \n",
              "9   0.00  15.47   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00  \n",
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              "      <td>3.18</td>\n",
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              "      <td>15.17</td>\n",
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              "      <td>15.37</td>\n",
              "      <td>8.62</td>\n",
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              "      <td>10.27</td>\n",
              "      <td>5.66</td>\n",
              "      <td>19.88</td>\n",
              "      <td>20.22</td>\n",
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              "      <td>13.82</td>\n",
              "      <td>6.05</td>\n",
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              "      <td>14.06</td>\n",
              "      <td>11.34</td>\n",
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              "      <td>20.03</td>\n",
              "      <td>20.27</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>20.08</td>\n",
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              "      <td>12.80</td>\n",
              "      <td>19.16</td>\n",
              "      <td>8.18</td>\n",
              "      <td>17.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12.84</td>\n",
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              "      <td>3.91</td>\n",
              "      <td>6.68</td>\n",
              "      <td>2.31</td>\n",
              "      <td>10.13</td>\n",
              "      <td>4.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>...</td>\n",
              "      <td>7.46</td>\n",
              "      <td>4.11</td>\n",
              "      <td>10.32</td>\n",
              "      <td>8.04</td>\n",
              "      <td>8.82</td>\n",
              "      <td>7.65</td>\n",
              "      <td>11.05</td>\n",
              "      <td>1.92</td>\n",
              "      <td>5.95</td>\n",
              "      <td>7.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>19.59</td>\n",
              "      <td>1.15</td>\n",
              "      <td>18.72</td>\n",
              "      <td>19.79</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>13.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>17.84</td>\n",
              "      <td>14.16</td>\n",
              "      <td>20.17</td>\n",
              "      <td>4.79</td>\n",
              "      <td>2.84</td>\n",
              "      <td>9.30</td>\n",
              "      <td>20.27</td>\n",
              "      <td>12.41</td>\n",
              "      <td>5.81</td>\n",
              "      <td>6.58</td>\n",
              "      <td>...</td>\n",
              "      <td>18.23</td>\n",
              "      <td>9.88</td>\n",
              "      <td>10.90</td>\n",
              "      <td>5.32</td>\n",
              "      <td>7.84</td>\n",
              "      <td>7.65</td>\n",
              "      <td>13.14</td>\n",
              "      <td>10.95</td>\n",
              "      <td>12.31</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7.21</td>\n",
              "      <td>7.46</td>\n",
              "      <td>1.58</td>\n",
              "      <td>4.11</td>\n",
              "      <td>2.26</td>\n",
              "      <td>10.71</td>\n",
              "      <td>5.71</td>\n",
              "      <td>2.07</td>\n",
              "      <td>3.14</td>\n",
              "      <td>9.40</td>\n",
              "      <td>...</td>\n",
              "      <td>15.37</td>\n",
              "      <td>10.71</td>\n",
              "      <td>15.17</td>\n",
              "      <td>10.71</td>\n",
              "      <td>10.71</td>\n",
              "      <td>10.71</td>\n",
              "      <td>10.71</td>\n",
              "      <td>10.71</td>\n",
              "      <td>7.60</td>\n",
              "      <td>6.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14.01</td>\n",
              "      <td>16.15</td>\n",
              "      <td>16.15</td>\n",
              "      <td>14.01</td>\n",
              "      <td>17.41</td>\n",
              "      <td>16.15</td>\n",
              "      <td>19.93</td>\n",
              "      <td>13.52</td>\n",
              "      <td>14.01</td>\n",
              "      <td>19.16</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15.47</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 100 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-219bc910-a940-485b-a0b6-d74cdd830205')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-219bc910-a940-485b-a0b6-d74cdd830205 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-219bc910-a940-485b-a0b6-d74cdd830205');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The following function uses gradient descent optimization to factorize a rating matrix R into matrices P (the user feature matrix) and Q (the item feature matrix).\n",
        "\n"
      ],
      "metadata": {
        "id": "p9UGUmnxg-IX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def matrix_factorization(R, P, Q, K, steps=5000, alpha=0.0002, beta=0.02):\n",
        "    \n",
        "    ### R = The user x item rating matrix (m x n)\n",
        "    ### P = Initial user-factor matrix (m x k)\n",
        "    ### Q = Initial item-factor matrix (n x k)\n",
        "    ### K = The number of latent factors (features)\n",
        "    ### steps = The number of epochs in gradient descent\n",
        "    ### alpha = The learning rate for gradient descent\n",
        "    ### beta = The regularization coefficient\n",
        "    \n",
        "    Q = Q.T\n",
        "    for step in range(steps):\n",
        "        for i in range(len(R)):\n",
        "            for j in range(len(R[i])):\n",
        "                if R[i][j] > 0:\n",
        "                    eij = R[i][j] - np.dot(P[i,:],Q[:,j])\n",
        "                    for k in range(K):\n",
        "                        ### update P and Q based on the partial derivatives\n",
        "                        P[i][k] = P[i][k] + alpha * (2 * eij * Q[k][j] - beta * P[i][k])\n",
        "                        Q[k][j] = Q[k][j] + alpha * (2 * eij * P[i][k] - beta * Q[k][j])\n",
        "        eR = np.dot(P,Q)\n",
        "        e = 0\n",
        "        for i in range(len(R)):\n",
        "            for j in range(len(R[i])):\n",
        "                if R[i][j] > 0:\n",
        "                    e = e + pow(R[i][j] - np.dot(P[i,:],Q[:,j]), 2)\n",
        "                    for k in range(K):\n",
        "                        e = e + (beta/2) * ( pow(P[i][k],2) + pow(Q[k][j],2) )\n",
        "        if e < 0.001:\n",
        "            break\n",
        "        print(\"Step %d of %d; Error: %0.5f; Time: %0.2f\" %(step+1, steps, e, time()))\n",
        "    return P, Q.T"
      ],
      "metadata": {
        "id": "0j5fYUXPg6j2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "M = dataMat.shape[0]\n",
        "N = dataMat.shape[1]\n",
        "Ratings = np.array(dataMat)\n",
        "K = 5\n",
        "steps = 3000"
      ],
      "metadata": {
        "id": "nL82b4kdhNWk"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Initialize P and Q to random values\n",
        "P = np.random.rand(M,K)\n",
        "Q = np.random.rand(N,K)\n"
      ],
      "metadata": {
        "id": "JObSZ-SChPdh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from time import time\n",
        "t0 = time()\n",
        "fP, fQ = matrix_factorization(Ratings, P, Q, K, steps=steps)\n",
        "print(\"done in %0.3fs.\" % (time() - t0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvXfZ_fOhTOf",
        "outputId": "229082c6-5bc1-4794-da23-f740908b3ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step 1 of 3000; Error: 2800656.94380; Time: 1685749848.62\n",
            "Step 2 of 3000; Error: 1503146.12047; Time: 1685749852.20\n",
            "Step 3 of 3000; Error: 1385817.46021; Time: 1685749854.82\n",
            "Step 4 of 3000; Error: 1348183.32217; Time: 1685749857.34\n",
            "Step 5 of 3000; Error: 1335096.85648; Time: 1685749860.14\n",
            "Step 6 of 3000; Error: 1330035.29506; Time: 1685749863.77\n",
            "Step 7 of 3000; Error: 1327763.21115; Time: 1685749866.34\n",
            "Step 8 of 3000; Error: 1326489.33797; Time: 1685749868.71\n",
            "Step 9 of 3000; Error: 1325566.87058; Time: 1685749871.23\n",
            "Step 10 of 3000; Error: 1324751.32584; Time: 1685749874.07\n",
            "Step 11 of 3000; Error: 1323943.85799; Time: 1685749877.74\n",
            "Step 12 of 3000; Error: 1323099.10885; Time: 1685749880.39\n",
            "Step 13 of 3000; Error: 1322191.27352; Time: 1685749882.93\n",
            "Step 14 of 3000; Error: 1321201.30691; Time: 1685749886.06\n",
            "Step 15 of 3000; Error: 1320111.96239; Time: 1685749889.73\n",
            "Step 16 of 3000; Error: 1318905.79213; Time: 1685749892.41\n",
            "Step 17 of 3000; Error: 1317564.30462; Time: 1685749895.28\n",
            "Step 18 of 3000; Error: 1316067.60987; Time: 1685749898.15\n",
            "Step 19 of 3000; Error: 1314394.31077; Time: 1685749901.39\n",
            "Step 20 of 3000; Error: 1312521.56450; Time: 1685749904.75\n",
            "Step 21 of 3000; Error: 1310425.30293; Time: 1685749907.38\n",
            "Step 22 of 3000; Error: 1308080.62647; Time: 1685749910.12\n",
            "Step 23 of 3000; Error: 1305462.39312; Time: 1685749912.57\n",
            "Step 24 of 3000; Error: 1302546.01994; Time: 1685749916.43\n",
            "Step 25 of 3000; Error: 1299308.49966; Time: 1685749919.21\n",
            "Step 26 of 3000; Error: 1295729.61115; Time: 1685749921.79\n",
            "Step 27 of 3000; Error: 1291793.26853; Time: 1685749924.56\n",
            "Step 28 of 3000; Error: 1287488.91505; Time: 1685749927.95\n",
            "Step 29 of 3000; Error: 1282812.82987; Time: 1685749931.20\n",
            "Step 30 of 3000; Error: 1277769.18995; Time: 1685749933.82\n",
            "Step 31 of 3000; Error: 1272370.72797; Time: 1685749936.50\n",
            "Step 32 of 3000; Error: 1266638.85986; Time: 1685749939.34\n",
            "Step 33 of 3000; Error: 1260603.22624; Time: 1685749943.18\n",
            "Step 34 of 3000; Error: 1254300.68942; Time: 1685749945.59\n",
            "Step 35 of 3000; Error: 1247773.92816; Time: 1685749948.12\n",
            "Step 36 of 3000; Error: 1241069.84343; Time: 1685749950.74\n",
            "Step 37 of 3000; Error: 1234238.00049; Time: 1685749954.08\n",
            "Step 38 of 3000; Error: 1227329.27555; Time: 1685749956.92\n",
            "Step 39 of 3000; Error: 1220394.76582; Time: 1685749959.68\n",
            "Step 40 of 3000; Error: 1213484.90102; Time: 1685749962.31\n",
            "Step 41 of 3000; Error: 1206648.61287; Time: 1685749965.19\n",
            "Step 42 of 3000; Error: 1199932.41087; Time: 1685749968.71\n",
            "Step 43 of 3000; Error: 1193379.28315; Time: 1685749971.43\n",
            "Step 44 of 3000; Error: 1187027.45787; Time: 1685749974.22\n",
            "Step 45 of 3000; Error: 1180909.17460; Time: 1685749976.70\n",
            "Step 46 of 3000; Error: 1175049.67721; Time: 1685749980.10\n",
            "Step 47 of 3000; Error: 1169466.62752; Time: 1685749982.92\n",
            "Step 48 of 3000; Error: 1164170.05950; Time: 1685749985.70\n",
            "Step 49 of 3000; Error: 1159162.88081; Time: 1685749988.48\n",
            "Step 50 of 3000; Error: 1154441.82257; Time: 1685749991.83\n",
            "Step 51 of 3000; Error: 1149998.66977; Time: 1685749995.02\n",
            "Step 52 of 3000; Error: 1145821.58697; Time: 1685749997.79\n",
            "Step 53 of 3000; Error: 1141896.37875; Time: 1685750000.67\n",
            "Step 54 of 3000; Error: 1138207.57441; Time: 1685750003.37\n",
            "Step 55 of 3000; Error: 1134739.28303; Time: 1685750006.96\n",
            "Step 56 of 3000; Error: 1131475.81223; Time: 1685750009.85\n",
            "Step 57 of 3000; Error: 1128402.07637; Time: 1685750012.74\n"
          ]
        }
      ]
    }
  ]
}