iLab_case_with_BONUS_QUESTION.ipynb

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        "--- \n",
        "\n",
        "**BONUS QUESTION**\n",
        "\n",
        "ANSWER:\n",
        "\n",
        "1) Donate an expression like 'UNK' for unknown IDs to training and get recommendations for out-of-vocabulary words using this designation.\n",
        "\n",
        "2) There is the deepest category breakdown available for the newly added product to the site. A temporary suggestion is made for a while over the training information of other products at this level. When there is enough data about the purchase of the new product, the temporary method is left and the new product is now added to the training and continues as known.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "hhfSHaU1GENh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings;\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "1vUfabBVQiS6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DRIVE CONNECTION AND DATA ACQUISITION**"
      ],
      "metadata": {
        "id": "Iu15l_wh6swB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "TcwD1D8g5Hgt",
        "outputId": "70fe1214-4325-447d-cd6f-7c1b23577aa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "pSpFALZX5czy",
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              "'/content'"
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/iLab_case"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSuXrE7Z5unK",
        "outputId": "c15de42e-f3fd-43d5-bb1d-bde0fc8ce7f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/iLab_case\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
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        "outputId": "bbc93b11-693b-4227-8205-e69aa13f9a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "case_study_1.csv  model_iLab_case.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"case_study_1.csv\", index_col=0)\n",
        "df"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA PREPARING**"
      ],
      "metadata": {
        "id": "f-O_EclI6grp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Her bir client için timestamp üzerinde dizilmiş sıralı product_id serisiyle başlamak güvenilir bir başlangıç olacaktır. "
      ],
      "metadata": {
        "id": "Su4igJEQYVfD"
      }
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      "source": [
        "df.sort_values(['client','timestamp'],ascending=True, inplace=True)\n",
        "df"
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              "<p>323614 rows × 3 columns</p>\n",
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              "            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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tek alışverişli geçmişlerin bilgi kazancı verebilecek sıralı bir anlamı yoktur."
      ],
      "metadata": {
        "id": "rr5U9vW5Yklw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "repetition_clients = df.client.value_counts()\n",
        "only_one_repetition_clients = repetition_clients[repetition_clients == 1].index.tolist()\n",
        "print(len(only_one_repetition_clients))\n",
        "df = df[~df.client.isin(only_one_repetition_clients)]\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "kr3Y6N_VmVYe",
        "outputId": "b7e19f63-3f70-400d-dc85-1a9aaa065856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "                                 timestamp product_id  \n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-20bb7a42-4b21-44f0-8a27-9a35c87f1e29 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-20bb7a42-4b21-44f0-8a27-9a35c87f1e29');\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Client'lerin sıralı satın alma geçmişlerinde aynı üründen üst üste iki kayıt olmasının kurulacak algoritma kapsamında bilgi kazancı verebilecek bir anlamı yoktur.   "
      ],
      "metadata": {
        "id": "dFSELLLI8hmK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(subset=['client', 'product_id'], keep='last', inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kBUQHmJD58uI",
        "outputId": "1737b884-3004-4528-bd2c-31ed521cac5f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "                                        client  \\\n",
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              "                                 timestamp product_id  \n",
              "4471996   2021-08-29 21:23:18.288000+00:00  122060534  \n",
              "4471997   2021-08-29 21:23:30.646000+00:00   59445662  \n",
              "4471998   2021-08-29 21:23:58.150000+00:00  121380020  \n",
              "4471999   2021-08-29 21:24:01.117000+00:00  139210937  \n",
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              "...                                    ...        ...  \n",
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              "      <td>2021-08-29 21:23:58.150000+00:00</td>\n",
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              "      <th>4471999</th>\n",
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              "      <th>4472000</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>16035972</th>\n",
              "      <td>ffffded9-2ab0-4294-83a0-a047ca9355d9</td>\n",
              "      <td>2021-09-02 12:34:01.443000+00:00</td>\n",
              "      <td>769137961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11142151</th>\n",
              "      <td>ffffded9-2ab0-4294-83a0-a047ca9355d9</td>\n",
              "      <td>2021-09-02 12:35:41.639000+00:00</td>\n",
              "      <td>33769960</td>\n",
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              "    <tr>\n",
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              "      <td>ffffded9-2ab0-4294-83a0-a047ca9355d9</td>\n",
              "      <td>2021-09-02 12:38:13.879000+00:00</td>\n",
              "      <td>43439084</td>\n",
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              "    <tr>\n",
              "      <th>8696055</th>\n",
              "      <td>ffffded9-2ab0-4294-83a0-a047ca9355d9</td>\n",
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              "      <td>68807238</td>\n",
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              "      <th>13589451</th>\n",
              "      <td>ffffded9-2ab0-4294-83a0-a047ca9355d9</td>\n",
              "      <td>2021-09-02 12:42:03.438000+00:00</td>\n",
              "      <td>45757152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>238947 rows × 3 columns</p>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Eğer eksik veri varsa ve çok yüksek seviyede değillerse düşürmek mantıklı olacaktır."
      ],
      "metadata": {
        "id": "apjvUIbOE_8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0EE-LB_--UH",
        "outputId": "b9b5864d-d7c7-416f-b3b2-e9a62ffadc19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "client        0\n",
              "timestamp     0\n",
              "product_id    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['product_id'] = df['product_id'].astype(str)"
      ],
      "metadata": {
        "id": "sM3AjADwJQfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9AnK9O--9oW",
        "outputId": "f5745baf-d510-4759-9318-1f70424bcee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 238947 entries, 4471996 to 13589451\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count   Dtype \n",
            "---  ------      --------------   ----- \n",
            " 0   client      238947 non-null  object\n",
            " 1   timestamp   238947 non-null  object\n",
            " 2   product_id  238947 non-null  object\n",
            "dtypes: object(3)\n",
            "memory usage: 7.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Müşterilerin %10 luk bir dilimi validasyon için ayrılmıştır."
      ],
      "metadata": {
        "id": "d8RvrxodFHPn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "clients = df[\"client\"].unique().tolist() \n",
        "\n",
        "random.shuffle(clients)\n",
        "\n",
        "clients_train = [clients[i] for i in range(round(0.9*len(clients)))]\n",
        "\n",
        "train_df = df[df['client'].isin(clients_train)]\n",
        "validation_df = df[~df['client'].isin(clients_train)]"
      ],
      "metadata": {
        "id": "zR2N-4uFAKUL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "İlgili müşterilerin alışveriş geçmişleri eğitim ve validasyon listelerine çekiliyor."
      ],
      "metadata": {
        "id": "xOJG24sMGahh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "purchases_train = []\n",
        "\n",
        "for i in tqdm(clients_train):\n",
        "    temp = train_df[train_df[\"client\"] == i][\"product_id\"].tolist()\n",
        "    purchases_train.append(temp)\n",
        "\n",
        "purchases_val = []\n",
        "\n",
        "for i in tqdm(validation_df['client'].unique()):\n",
        "    temp = validation_df[validation_df[\"client\"] == i][\"product_id\"].tolist()\n",
        "    purchases_val.append(temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ueWr08tMOI2",
        "outputId": "de6e2133-a03c-4432-c612-147efa9b70a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 25017/25017 [08:17<00:00, 50.25it/s]\n",
            "100%|██████████| 2780/2780 [00:05<00:00, 467.69it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MODELLING**"
      ],
      "metadata": {
        "id": "dMQyi9WIDpV7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Word2Vec modeli kuruluyor"
      ],
      "metadata": {
        "id": "EawrVzxhGgz-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "windows parametre değeri 10 olarak belirlenmiştir yani 10 product_id lik bir pencere geçerli olacaktır."
      ],
      "metadata": {
        "id": "uGEYyXVqG082"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "sg parametre değeri 0 olarak belirtilmiştir yani Skip-Gram yerine CBOW yaklaşımı kullanılacaktır.CBOW modelinde window size’ın merkezinde olmayan kelimeler input olarak alınıp, merkezinde olan kelimeler output olarak tahmin edilmeye çalışılır. CBOW için (Skip-Gram'da farklı) Input layer’daki node sayısı ile output layer’daki node sayısı eşittir çünkü input layer ve output layer “unique” bir kelimeyi one-hot encode etmiş kelime vektörleridir.\n",
        "Input layer ile hidden layer arasında aktivasyon fonksiyonu yoktur(linear aktivasyon fonksiyonu var) yani weight’lerin input layer’daki node’lar ile olan çarpımına relu, elu, tanh, sigmoid gibi herhangi bir non-linear aktivasyon fonksiyonu uygulanmaz. Hidden Layer ile Output Layer arasında ise softmax aktivasyon fonksiyonu uygulanır. Böylece herhangi bir kelime input olarak alındığında, o kelimenin output’u e’nin üzerine yazılarak tüm e^x lerin toplamına bölünür ve böylece bütün kelimelerin problarını içeren ve unique kelime sayısı kadar bir büyüklüğe sahip bir vektör elde edilir. İlk başta Random olarak(veya başka türlü) weight’ler atanır, forward-propagation yapılır, loss hesaplanır, sonra loss fonkisyonun weight’ler cinsinden partial derivative’leri alınarak back-propagation algoritması uygulanır ve weight’ler update edilir. Bu işlem epoch sayısı kadar yapılır. "
      ],
      "metadata": {
        "id": "uZS8FjdVHA-z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "alpha değeri neural ağın optimizasyonunda kullanılacak olan update denklemindeki learning_rate değeridir.0.03 bir çalışmada, değeri benzer büyüklükte bir word2vec modeli için kullanılmıştır bu sebeple bu değeri kullandım. alpha değerinin büyük gelmesi update denklemindeki '-' operatorü sebebiyle optimizasyonu bozacaktır fakat küçük gelirse de eğitim süresi uzayacaktır."
      ],
      "metadata": {
        "id": "cCicE5SjN6s4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "hs ise hierarchical softmaxtır ve 1 değeri True anlamına gelmektedir."
      ],
      "metadata": {
        "id": "qWi1-f10OCpl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Word2vec'in 2.makalesinde gradient hesabının iyileştirilmesi için 3 yöntem önerilmiştir. Bunların 2 tanesi text veriyi ilgilendirmektedir. Bir tanesi de negative sampling yöntemidir ve modele parametre olarak verilebilmektedir fakat ben vermeden devam edeceğim. Veriseti pek büyük değil. Gradient düşüş uzun sürmeyecektir. Training'in uzun sürmeyeceğini düşünüyorum. Referans: http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/"
      ],
      "metadata": {
        "id": "E8KhJ-xGOJdA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "compute_loss parametresi True olarak verilmiştir fakat gensim sadece final loss'u paylaşıyor. Sıralı olarak tüm loss değerlerine ulaşaral loss curve çizebilmek için yaptığım araştırmada https://github.com/cbellei/word2veclite repository'sini buldum clone'layarak devam edilip curve elde edilebilir. "
      ],
      "metadata": {
        "id": "EpESccDZ-7Sx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "10 epoch yeterli görülmüştür."
      ],
      "metadata": {
        "id": "vR2jCLwhOdcR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import Word2Vec \n",
        "\n",
        "model = Word2Vec(window = 10, sg = 0, hs = 1,\n",
        "                 alpha=0.03, min_alpha=0.0007)\n",
        "\n",
        "model.build_vocab(purchases_train, progress_per=200)\n",
        "\n",
        "model.train(purchases_train, total_examples = model.corpus_count, \n",
        "            epochs=10, compute_loss=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlSlafhgMy3g",
        "outputId": "5b813cc4-ead3-4520-d8c2-ece66986d0f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1201842, 2150830)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Y2JBA1FQlDg",
        "outputId": "39a6d2b4-3e07-42ae-c461-9cdd772064e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word2Vec(vocab=7070, size=100, alpha=0.03)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modelimiz 7070 benzersiz kelimeden oluşan bir kelime dağarcığına ve her biri için 100 lük vektörlere sahiptir."
      ],
      "metadata": {
        "id": "ClfokShrRfcV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install umap-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwXqK7eAR8td",
        "outputId": "21a8b078-158a-48d8-af6c-fb8e1018e2a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting umap-learn\n",
            "  Downloading umap-learn-0.5.3.tar.gz (88 kB)\n",
            "\u001b[K     |████████████████████████████████| 88 kB 4.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.0.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.7.3)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.51.2)\n",
            "Collecting pynndescent>=0.5\n",
            "  Downloading pynndescent-0.5.7.tar.gz (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 16.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from umap-learn) (4.64.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (0.34.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pynndescent>=0.5->umap-learn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n",
            "Building wheels for collected packages: umap-learn, pynndescent\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.3-py3-none-any.whl size=82829 sha256=4d67061ca06e8f995f2bca4321529591a0568b84ed8fe4fe423887ab11501fd2\n",
            "  Stored in directory: /root/.cache/pip/wheels/b3/52/a5/1fd9e3e76a7ab34f134c07469cd6f16e27ef3a37aeff1fe821\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.7-py3-none-any.whl size=54286 sha256=ae844cfa48fd47ca93416c055aa5d11db0c59ddd2272cbc593cca078be6bba5f\n",
            "  Stored in directory: /root/.cache/pip/wheels/7f/2a/f8/7bd5dcec71bd5c669f6f574db3113513696b98f3f9b51f496c\n",
            "Successfully built umap-learn pynndescent\n",
            "Installing collected packages: pynndescent, umap-learn\n",
            "Successfully installed pynndescent-0.5.7 umap-learn-0.5.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PRODUCT MAP IN GENERAL TREND**"
      ],
      "metadata": {
        "id": "y8yk_eiUQLAw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import umap\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "X = model[model.wv.vocab]\n",
        "\n",
        "cluster_embedding = umap.UMAP(n_neighbors=30, min_dist=0.0,\n",
        "                              n_components=2, random_state=42).fit_transform(X)\n",
        "\n",
        "plt.figure(figsize=(10,9))\n",
        "plt.scatter(cluster_embedding[:, 0], cluster_embedding[:, 1], s=3, cmap='jet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "aBO8v7MgRehB",
        "outputId": "423b9ab9-c8c1-433b-f30e-fa439b931e0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8419e93fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RECOMMENDATION**"
      ],
      "metadata": {
        "id": "O0wx1BWCMTOd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "validation_df['product_id']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8POI2eOET3yh",
        "outputId": "cdff365c-c409-4e95-c2ac-9f39f1954475"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15424972     775993409\n",
              "9309715      650127094\n",
              "5639276      650126929\n",
              "11755073     502631554\n",
              "9309716     a350778396\n",
              "               ...    \n",
              "710367       521853887\n",
              "710368      a257893754\n",
              "710369      a253822789\n",
              "710370       812395106\n",
              "710371       a19932193\n",
              "Name: product_id, Length: 23864, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Validasyon kümesinden rastgele seçilen 'a368901216' ürününe dair en benzer 15 ürün problarıyla beraber listelenmiştir"
      ],
      "metadata": {
        "id": "pydPEFA9QRXV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Case dokümanı part-3 bölümünde \"..modelinizi kullanarak verilen bir ürün için benzer ürünler öneri sistemi (recommendation) yapan bir fonksiyon yazmanızı bekliyoruz..\" teklifi bulunuyor fakat bir üstte gensim üzerinden word2vec kullanılabileceği belirtildiği ve bu modelin de most_similar fonksiyonu mevcut olduğu için tahmin fonksiyonunu ayrıca ve kendime özel bir şekilde oluşturmamın kastedilmediği şeklinde anlıyorum.*"
      ],
      "metadata": {
        "id": "5CpBt7xYBTi-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sims = model.wv.most_similar('a368901216', topn=15) "
      ],
      "metadata": {
        "id": "NNZEUB56R0Tl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sims"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPi3nUn4UBhP",
        "outputId": "420905a6-02ce-4ae3-b0ac-a10efa8bdf15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('575054541', 0.7752840518951416),\n",
              " ('361788461', 0.764622151851654),\n",
              " ('a521829586', 0.7403109073638916),\n",
              " ('605330176', 0.7291325330734253),\n",
              " ('605330269', 0.7117664813995361),\n",
              " ('a330960236', 0.7101277709007263),\n",
              " ('575054858', 0.7088828086853027),\n",
              " ('a704117515', 0.7065169811248779),\n",
              " ('575054460', 0.6960247159004211),\n",
              " ('521829325', 0.6946197152137756),\n",
              " ('a790913804', 0.6941524744033813),\n",
              " ('330907459', 0.685888409614563),\n",
              " ('704117507', 0.6850706338882446),\n",
              " ('a814104921', 0.6845427751541138),\n",
              " ('521853790', 0.6833316683769226)]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loss values listesinin son değeri"
      ],
      "metadata": {
        "id": "9T0NVd_WQXCs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_latest_training_loss()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXr2CkyyUCLj",
        "outputId": "4543da85-1461-4c6a-f8e1-607dc42aee1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7291455.0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SAVE THE MODEL .PKL**"
      ],
      "metadata": {
        "id": "yl2p8ZDyMjKn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('model_iLab_case.pkl', 'wb') as f:\n",
        "    pickle.dump(model, f)"
      ],
      "metadata": {
        "id": "Xsv1S9bxAj2q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('model_iLab_case.pkl', 'rb') as f:\n",
        "    model_loaded = pickle.load(f)"
      ],
      "metadata": {
        "id": "cihFbnywCwMK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sims = model_loaded.wv.most_similar('a368901216', topn=15) "
      ],
      "metadata": {
        "id": "AXD8Qw4kC2p7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sims"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gY0QRH5UC8DY",
        "outputId": "195c2dae-69ce-4da9-d499-59aff8ea25ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('575054541', 0.7752840518951416),\n",
              " ('361788461', 0.764622151851654),\n",
              " ('a521829586', 0.7403109073638916),\n",
              " ('605330176', 0.7291325330734253),\n",
              " ('605330269', 0.7117664813995361),\n",
              " ('a330960236', 0.7101277709007263),\n",
              " ('575054858', 0.7088828086853027),\n",
              " ('a704117515', 0.7065169811248779),\n",
              " ('575054460', 0.6960247159004211),\n",
              " ('521829325', 0.6946197152137756),\n",
              " ('a790913804', 0.6941524744033813),\n",
              " ('330907459', 0.685888409614563),\n",
              " ('704117507', 0.6850706338882446),\n",
              " ('a814104921', 0.6845427751541138),\n",
              " ('521853790', 0.6833316683769226)]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(sims)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URbzeUT743ZG",
        "outputId": "90e088a4-d277-4a1b-f836-710e357aac41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(sims[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDimEwOs9rjc",
        "outputId": "d59042cd-27cd-42d5-a496-d5806db1131b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(sims[0][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nAMj1mArABd",
        "outputId": "bffeedee-1f4a-44f0-a496-7ab5c912e4f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(sims[0][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwxVHRHKtvMV",
        "outputId": "fab8bd72-317f-4510-f39f-78cd31d8977f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    }
  ]
}