3_user_and_product_embeddings (1).ipynb

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        "# User and product embeddings\n",
        "\n",
        "We calculate user and product embeddings based on the training set, and evaluate the results on the unseen test set. We will evaluate the results by plotting the user and product similarity versus the review score."
      ],
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      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/fine_food_reviews_with_embeddings_1k.csv')\n",
        "df.head(1)"
      ],
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              "    ProductId          UserId  Score  \\\n",
              "0  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
              "\n",
              "                                             Summary  \\\n",
              "0  where does one  start...and stop... with a tre...   \n",
              "\n",
              "                                                Text  \\\n",
              "0  Wanted to save some to bring to my Chicago fam...   \n",
              "\n",
              "                                            combined  n_tokens  \\\n",
              "0  Title: where does one  start...and stop... wit...        52   \n",
              "\n",
              "                                           embedding  \n",
              "0  [0.0359942801296711, -0.02117965929210186, -0....  "
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It is the worst thing I have ever tasted. Straight soda water with absolutely no HINT of watermelon. I wrote to the company regarding any form of customer satisfaction guarantee and as of now I have not heard from them.  This has been 2 weeks now.\",\n          \"Often times I find herbal teas either too weak or too overpowering.  This tea is a perfect blend.... Not too sweet or too tarte.\",\n          \"I bought this dog food after the \\\"Tainted Dog Food\\\" incident this year.  I did some research to look for an alternative for my sensitive and allergic labracollie, and liked that Addiction used free-range animals and seemed to use natural ingredients.  My dog loved the taste and dug right into it.<br /><br />I had her on this diet for almost two weeks.  I bought two bags at a local mom and pop pet store, but it did not particularly improve her stool--which is a big factor for me and her digestive health.  Her stool was still softer than normal.  Another issue I found was that the Venison meal was very high in protein.  My dog is nine years-old and not as active as before, so I wasn't very fond of the protein content.  This dog food may be better-suited for active or younger dogs.<br /><br />At my local pet store, they also have Lamb, La Porchetta, Salmon Bleu, and a couple other puppy variations.  I bought Lamb and Porchetta as well.  Among the three my dog ate, she seemed to like the Venison the best.  I knew this because she always hesitated with the Lamb and Porchetta.  I have not tried the Kangaroo (which is available on Amazon), but I may buy a bag just so I can know how it is for future reference, and I can offer a review.<br /><br />As for the price, yes, it's a little on the higher side.  At $17.00 for a four-pound bag, I would try it for a couple weeks and make sure your dog does well (enough) on it before investing in a regular diet regimen of this dog food.  For me, though, the results I received did not justify me having to spend the extra money for this dog food, even though my dog actually liked eating it.<br /><br />Pros:<br />1. Apparently it tastes very good.<br />2. Good ingredients.<br />3. High protein content (good).<br /><br />Cons:<br />1. Pricey.<br />2. High protein content (bad).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"combined\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 762,\n        \"samples\": [\n          \"Title: The best sticky rice out there; Content: I love Tamanishiki rice, it's some of the best quality rice you can get. True it's a little more expensive, but a large bag like this lasts a while and I find it to be worth the cost for a rice-lover like myself.\",\n          \"Title: Very good; Content: It tastes delisious, and works efectively if yo drink it at the rigth time. Lean body is the suplement with the best flavor I've ever tasted\",\n          \"Title: Good product; Content: I like that this is a better product for my pets but really for the price of it I couldn't afford to buy this all the time. My cat isn't very picky usually and she ate this, we usually buy her the friskies prime filets, the vet said it is better to not change their diet and buy different types of food and flavors all the time so we normally stick with the chicken. I am glad this is a chicken formula since that is what she is used to. If it weren't so expensive I would buy it all the time but I do have to budget. I take good care of my animals but I have to watch the cost so that the humans can eat and pay bills. 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-0.02066737785935402, -0.03290289267897606, 0.008897346444427967, -0.008272270672023296, -0.024670518934726715, -0.027609702199697495, 0.00082456722157076, 0.005206742789596319, -8.426461863564327e-05, -0.0023623185697942972, 0.012927086092531681, 0.018765553832054138, 0.00058974843705073, -0.002363980980589986, 0.03808968514204025, 0.019457126036286354, 0.026598941534757614, 0.007487602066248655, 0.021731335669755936, -0.007653845474123955, 0.05803889036178589, -0.03840887174010277, -0.021225955337285995, 0.06027320399880409, -0.001833664602600038, 0.05665574595332146, -0.007201663684099913, -0.009336228482425213]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of records:\", df.shape)\n",
        "\n",
        "unique_users = df['UserId'].nunique()\n",
        "unique_products = df['ProductId'].nunique()\n",
        "\n",
        "print(\"Number of unique users:\", unique_users)\n",
        "print(\"Number of unique products:\", unique_products)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mtLgwtk9bsuP",
        "outputId": "2bb467ff-d693-4285-b3b9-da8c5f078423"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records: (1000, 8)\n",
            "Number of unique users: 708\n",
            "Number of unique products: 868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that most of the users and products appear within the 1k examples only once."
      ],
      "metadata": {
        "id": "GfU9Cbcd6ZqR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from ast import literal_eval\n",
        "\n",
        "df['babbage_similarity'] = df[\"embedding\"].apply(literal_eval).apply(np.array)\n",
        "df.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "id": "duGOh4BuY5wD",
        "outputId": "1d50d7c3-7b48-4baf-9954-da88a69c11d8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    ProductId          UserId  Score  \\\n",
              "0  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
              "\n",
              "                                             Summary  \\\n",
              "0  where does one  start...and stop... with a tre...   \n",
              "\n",
              "                                                Text  \\\n",
              "0  Wanted to save some to bring to my Chicago fam...   \n",
              "\n",
              "                                            combined  n_tokens  \\\n",
              "0  Title: where does one  start...and stop... wit...        52   \n",
              "\n",
              "                                           embedding  \\\n",
              "0  [0.0359942801296711, -0.02117965929210186, -0....   \n",
              "\n",
              "                                  babbage_similarity  \n",
              "0  [0.0359942801296711, -0.02117965929210186, -0....  "
            ],
            "text/html": [
              "\n",
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It is the worst thing I have ever tasted. Straight soda water with absolutely no HINT of watermelon. I wrote to the company regarding any form of customer satisfaction guarantee and as of now I have not heard from them.  This has been 2 weeks now.\",\n          \"Often times I find herbal teas either too weak or too overpowering.  This tea is a perfect blend.... Not too sweet or too tarte.\",\n          \"I bought this dog food after the \\\"Tainted Dog Food\\\" incident this year.  I did some research to look for an alternative for my sensitive and allergic labracollie, and liked that Addiction used free-range animals and seemed to use natural ingredients.  My dog loved the taste and dug right into it.<br /><br />I had her on this diet for almost two weeks.  I bought two bags at a local mom and pop pet store, but it did not particularly improve her stool--which is a big factor for me and her digestive health.  Her stool was still softer than normal.  Another issue I found was that the Venison meal was very high in protein.  My dog is nine years-old and not as active as before, so I wasn't very fond of the protein content.  This dog food may be better-suited for active or younger dogs.<br /><br />At my local pet store, they also have Lamb, La Porchetta, Salmon Bleu, and a couple other puppy variations.  I bought Lamb and Porchetta as well.  Among the three my dog ate, she seemed to like the Venison the best.  I knew this because she always hesitated with the Lamb and Porchetta.  I have not tried the Kangaroo (which is available on Amazon), but I may buy a bag just so I can know how it is for future reference, and I can offer a review.<br /><br />As for the price, yes, it's a little on the higher side.  At $17.00 for a four-pound bag, I would try it for a couple weeks and make sure your dog does well (enough) on it before investing in a regular diet regimen of this dog food.  For me, though, the results I received did not justify me having to spend the extra money for this dog food, even though my dog actually liked eating it.<br /><br />Pros:<br />1. Apparently it tastes very good.<br />2. Good ingredients.<br />3. High protein content (good).<br /><br />Cons:<br />1. Pricey.<br />2. High protein content (bad).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"combined\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 762,\n        \"samples\": [\n          \"Title: The best sticky rice out there; Content: I love Tamanishiki rice, it's some of the best quality rice you can get. True it's a little more expensive, but a large bag like this lasts a while and I find it to be worth the cost for a rice-lover like myself.\",\n          \"Title: Very good; Content: It tastes delisious, and works efectively if yo drink it at the rigth time. Lean body is the suplement with the best flavor I've ever tasted\",\n          \"Title: Good product; Content: I like that this is a better product for my pets but really for the price of it I couldn't afford to buy this all the time. My cat isn't very picky usually and she ate this, we usually buy her the friskies prime filets, the vet said it is better to not change their diet and buy different types of food and flavors all the time so we normally stick with the chicken. I am glad this is a chicken formula since that is what she is used to. If it weren't so expensive I would buy it all the time but I do have to budget. I take good care of my animals but I have to watch the cost so that the humans can eat and pay bills. I am trying to get a baby kitten to start on solid foods and I am trying with this since it is quality and it is easier for him to try and eat than the filets.<br />It is a good quality cat food but it loses one star for being more than I can afford to buy on a constant basis and the cans are very small so it would take more cans to feed a cat.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n_tokens\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74,\n        \"min\": 28,\n        \"max\": 645,\n        \"num_unique_values\": 201,\n        \"samples\": [\n          34,\n          31,\n          95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embedding\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 852,\n        \"samples\": [\n          \"[0.008448260836303234, 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-0.02066737785935402, -0.03290289267897606, 0.008897346444427967, -0.008272270672023296, -0.024670518934726715, -0.027609702199697495, 0.00082456722157076, 0.005206742789596319, -8.426461863564327e-05, -0.0023623185697942972, 0.012927086092531681, 0.018765553832054138, 0.00058974843705073, -0.002363980980589986, 0.03808968514204025, 0.019457126036286354, 0.026598941534757614, 0.007487602066248655, 0.021731335669755936, -0.007653845474123955, 0.05803889036178589, -0.03840887174010277, -0.021225955337285995, 0.06027320399880409, -0.001833664602600038, 0.05665574595332146, -0.007201663684099913, -0.009336228482425213]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"babbage_similarity\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prod_embeddings = df.groupby('ProductId').babbage_similarity.apply(np.mean)\n",
        "print(len(prod_embeddings))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtGtvIT3t86B",
        "outputId": "b6ec6382-c591-42dd-e382-4594db244a4f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(df, df.Score, test_size = 0.2, random_state=42)\n",
        "\n",
        "X_train.shape, X_test.shape, Y_train.shape, Y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Y0yOyRdtetl",
        "outputId": "c9a8d7bf-ae66-4ec3-e2d1-de08385afc8a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800, 9), (200, 9), (800,), (200,))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Calculate user and product embeddings\n",
        "\n"
      ],
      "metadata": {
        "id": "cUBWU64NWRao"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Approach 1: We calculate these embeddings simply by averaging all the reviews about the same product or written by the same user within the training set.\n",
        "\n"
      ],
      "metadata": {
        "id": "L7OkfjwnWaGH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_embeddings_method_1 = X_train.groupby('UserId').babbage_similarity.apply(np.mean)\n",
        "print(len(user_embeddings_method_1))\n",
        "user_embeddings_method_1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzyydQXiWcna",
        "outputId": "18fbb9d8-736b-45e2-ed6f-43a8f4ef2db3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "577\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserId\n",
              "A06364072LBY1F3ING9XN    [-0.027673892676830292, 0.0011768217664211988,...\n",
              "A07895621XU7DHRAAPYJU    [0.022543027997016907, -0.013131280429661274, ...\n",
              "A09539661HB8JHRFVRDC     [-0.008025233168154955, 0.025786804035305977, ...\n",
              "A10QOESY9VJ9K            [-0.019612858071923256, -0.08770409971475601, ...\n",
              "A111SHKHPDN8N            [0.012108179740607738, -0.039249029010534286, ...\n",
              "Name: babbage_similarity, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Approach 2: We calculate user embeddings by taking an weighted average over all their reviews about the same product.\n",
        "\n"
      ],
      "metadata": {
        "id": "SbM5XuOiawU9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First, create a new column that stores the value of babbage_similarity divided by Score\n",
        "X_train['babbage_similarity_per_score'] = X_train['babbage_similarity'] * X_train['Score'] / 5\n",
        "\n",
        "# Now, calculate the user embeddings by grouping by 'UserId' and taking the mean of the new column\n",
        "user_embeddings_method_2 = X_train.groupby('UserId').babbage_similarity_per_score.apply(np.mean)\n",
        "\n",
        "print(len(user_embeddings_method_2))\n",
        "user_embeddings_method_2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVlSg7FVZjZ1",
        "outputId": "b6d9e6dd-07b9-46eb-c5e8-f0af0c2e79f1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "577\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserId\n",
              "A06364072LBY1F3ING9XN    [-0.027673892676830292, 0.0011768217664211988,...\n",
              "A07895621XU7DHRAAPYJU    [0.022543027997016907, -0.013131280429661274, ...\n",
              "A09539661HB8JHRFVRDC     [-0.006420186534523965, 0.02062944322824478, -...\n",
              "A10QOESY9VJ9K            [-0.019612858071923256, -0.08770409971475601, ...\n",
              "A111SHKHPDN8N            [0.012108179740607738, -0.039249029010534286, ...\n",
              "Name: babbage_similarity_per_score, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Evaluate the embeddings\n",
        "\n",
        "To evaluate the recommendations, we look at the similarity of the user and product embeddings amongst the reviews in the unseen test set. We calculate the cosine distance between the user and product embeddings, which gives us a similarity score between 0 and 1. We then normalize the scores to be evenly split between 0 and 1, by calculating the percentile of the similarity score amongst all predicted scores."
      ],
      "metadata": {
        "id": "5JAscQt9csNY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cosine_similarity(a, b):\n",
        "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))"
      ],
      "metadata": {
        "id": "_l57r0TRbXv3"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate embeddings as recommendations on X_test\n",
        "def evaluate_single_match(row, user_embeddings):\n",
        "    user_id = row.UserId\n",
        "    product_id = row.ProductId\n",
        "    try:\n",
        "        user_embedding = user_embeddings[user_id]\n",
        "        product_embedding = prod_embeddings[product_id]\n",
        "        similarity = cosine_similarity(user_embedding, product_embedding)\n",
        "        return similarity\n",
        "    except Exception as e:\n",
        "        return np.nan"
      ],
      "metadata": {
        "id": "77vgEwfMc7Yd"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We group the cosine similarity scores by the review score, and plot the distribution of cosine similarity scores for each review score."
      ],
      "metadata": {
        "id": "VOPhQ6W0dg3i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm\n",
        "\n",
        "def evaluate_approach(X_test):\n",
        "    correlation = X_test[['percentile_cosine_similarity', 'Score']].corr().values[0,1]\n",
        "    print('Correlation between user & vector similarity percentile metric and review number of stars (score): %.2f%%' % (100*correlation))\n",
        "\n",
        "    # boxplot of cosine similarity for each score\n",
        "    X_test.boxplot(column='percentile_cosine_similarity', by='Score')\n",
        "    plt.title('')\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "metadata": {
        "id": "-5LmE9mFdi4S"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluating Approach 1"
      ],
      "metadata": {
        "id": "28upPXt9dPmj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_method_1 = X_test\n",
        "X_test_method_1['cosine_similarity'] = X_test_method_1.apply(lambda row: evaluate_single_match(row, user_embeddings_method_1), axis=1)\n",
        "X_test_method_1['percentile_cosine_similarity'] = X_test_method_1.cosine_similarity.rank(pct=True)\n",
        "\n",
        "evaluate_approach(X_test_method_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "9n-_5d0ZdIQe",
        "outputId": "50f46503-be0c-4403-a726-b1fe4df22017"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between user & vector similarity percentile metric and review number of stars (score): 19.70%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluating Approach 2"
      ],
      "metadata": {
        "id": "D-nXzQiZeVyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_method_2 = X_test\n",
        "X_test_method_2['cosine_similarity'] = X_test_method_2.apply(lambda row: evaluate_single_match(row, user_embeddings_method_2), axis=1)\n",
        "X_test_method_2['percentile_cosine_similarity'] = X_test_method_2.cosine_similarity.rank(pct=True)\n",
        "\n",
        "evaluate_approach(X_test_method_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "XoyMG76feWX2",
        "outputId": "0cb12dd2-1aba-400f-e90d-e3c06bc16da3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between user & vector similarity percentile metric and review number of stars (score): 18.19%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Assessment by Claude AI\n",
        "\n",
        "Given that both boxplots are based on the same test dataset with the same product embeddings, and the only difference is in the calculation of user embeddings used for cosine similarity, my final assessment is:\n",
        "\n",
        "1. The overall distributions across the different score values are very similar between the two plots. This suggests that the change in user embedding calculation method did not significantly alter the overall patterns in the data.\n",
        "\n",
        "2. The primary difference is the slight shift in the median value for score 2, where the second plot shows a higher median compared to the first. This could indicate that the new user embedding calculation improved the cosine similarity metric for a subset of data points with score 2.\n",
        "\n",
        "3. The disappearance of the outlier for score 4 in the second plot is likely due to the change in user embedding calculation, which may have reduced some extreme values or brought them closer to the bulk of the distribution.\n",
        "\n",
        "In summary, while the distributions are largely similar, the subtle changes in the median for score 2 and the absence of an outlier for score 4 suggest that the new user embedding calculation method has led to some improvement or refinement in the cosine similarity metric, especially for certain subsets of the data (scores 2 and 4).\n",
        "However, since the overall patterns are not drastically different, it appears that the impact of the user embedding change is relatively minor, at least based on these particular boxplot visualizations of the data."
      ],
      "metadata": {
        "id": "Hl_JlUCUwQLV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [Extension] Multi-Class Classification"
      ],
      "metadata": {
        "id": "JOCGiEycl2JD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Collaborative filtering"
      ],
      "metadata": {
        "id": "pKw1MVYwf4_V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -qU scikit-surprise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tNItzXMiKWo",
        "outputId": "cdd74335-8f38-4ea1-e470-89f99aa054dd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from surprise import Dataset, Reader, SVD\n",
        "from surprise.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Assuming your DataFrame is named 'df'\n",
        "# Columns: 'Score', 'user_id', 'product_id', 'embeddings'\n",
        "\n",
        "# Step 1: Define a Reader object specifying the rating scale\n",
        "reader = Reader(rating_scale=(1, 5))\n",
        "\n",
        "# Step 2: Load the DataFrame into a Surprise Dataset\n",
        "data = Dataset.load_from_df(df[['UserId', 'ProductId', 'Score']], reader)\n",
        "\n",
        "# Step 3: Split the data into train and test sets\n",
        "trainset, testset = train_test_split(data, test_size=0.2)\n",
        "\n",
        "# Step 4: Define and train a collaborative filtering model (SVD)\n",
        "model = SVD()\n",
        "model.fit(trainset)\n",
        "\n",
        "# Step 5: Predict ratings for the test set\n",
        "predictions = model.test(testset)\n",
        "\n",
        "# Step 6: Convert predictions to integer ratings\n",
        "predicted_ratings = [round(pred.est) for pred in predictions]\n",
        "print(\"predicted_ratings\", predicted_ratings)\n",
        "\n",
        "# Step 7: Extract actual ratings from test set\n",
        "actual_ratings = [pred.r_ui for pred in predictions]\n",
        "print(\"actual_ratings\", actual_ratings)\n",
        "\n",
        "# Step 8: Evaluate accuracy\n",
        "accuracy = accuracy_score(actual_ratings, predicted_ratings)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nm7ZMpXMgBZH",
        "outputId": "e484e508-ceb8-42ca-81f4-824b12deb9ff"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted_ratings [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4]\n",
            "actual_ratings [5.0, 5.0, 5.0, 2.0, 5.0, 5.0, 5.0, 4.0, 1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 3.0, 3.0, 1.0, 3.0, 5.0, 5.0, 5.0, 5.0, 2.0, 5.0, 3.0, 5.0, 2.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 1.0, 4.0, 2.0, 5.0, 5.0, 1.0, 5.0, 5.0, 5.0, 3.0, 5.0, 5.0, 5.0, 5.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 2.0, 5.0, 1.0, 3.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 5.0, 5.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 2.0, 5.0, 5.0, 4.0, 5.0, 4.0, 4.0, 5.0, 5.0, 1.0, 1.0, 5.0, 5.0, 5.0, 1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 2.0, 5.0, 5.0, 4.0, 1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 1.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 1.0, 5.0, 1.0, 5.0, 5.0, 4.0, 2.0, 5.0, 5.0, 5.0, 5.0, 4.0, 2.0, 2.0, 5.0, 5.0, 2.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 3.0, 3.0, 4.0, 5.0, 5.0, 1.0, 4.0, 5.0, 5.0, 5.0, 5.0, 4.0, 5.0, 5.0, 5.0, 4.0, 5.0, 1.0, 5.0, 5.0, 5.0, 4.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 5.0, 3.0, 5.0, 4.0, 2.0, 5.0, 5.0, 1.0, 5.0, 5.0, 3.0, 5.0, 5.0, 5.0, 5.0, 3.0]\n",
            "Accuracy: 0.145\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neural Networks\n",
        "\n",
        "An experiment to see how does a feed forward neural network behave on test data when most of the users are new and unseen from train data."
      ],
      "metadata": {
        "id": "rsVBxVJJf9aP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "zgVu8RLgn6cH"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Guess all reviews to be just 5\n",
        "\"\"\"\n",
        "# Create a new series with the same length and all values set to 5\n",
        "guessed_predictions = pd.Series(5, index=Y_test.index)\n",
        "# Calculate classification accuracy\n",
        "guessed_accuracy = accuracy_score(Y_test, guessed_predictions)\n",
        "print('Classification Accuracy: %.2f%%' % (100*guessed_accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGzWSA4VoCum",
        "outputId": "08357e38-677d-4a14-93c9-365ef4137ee8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Accuracy: 67.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install required packages\n",
        "! pip install -qU tensorflow\n",
        "! pip install -qU keras_tuner"
      ],
      "metadata": {
        "id": "y9406X7bsQVK"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import required packages\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import keras_tuner as kt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Z4cgrpfUNII8"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['embedding'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "7WaubndmSx-0",
        "outputId": "0f229dea-6bea-437c-b19f-d0e41514b474"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ast\n",
        "\n",
        "def parse_embedding(embedding_str):\n",
        "    \"\"\"\n",
        "    Parse the string representation of an embedding and convert it to a NumPy array.\n",
        "    \"\"\"\n",
        "    return np.array(ast.literal_eval(embedding_str))\n",
        "\n",
        "# Extract features and labels\n",
        "input_features = np.array(df['embedding'].tolist()) #  convert the resulting Pandas Series to a list using tolist()\n",
        "# Convert the 'embedding' column to a list of NumPy arrays\n",
        "embeddings = df['embedding'].apply(parse_embedding).tolist()\n",
        "# Convert the list of NumPy arrays to a single NumPy array\n",
        "input_features = np.array(embeddings)\n",
        "\n",
        "output_class = df['Score'].values\n",
        "# Convert labels to categorical one-hot encoding\n",
        "# output_class = keras.utils.to_categorical(output_class - 1)  # Adjust ratings to be zero-indexed\n",
        "\n",
        "# Split data into training and testing sets\n",
        "input_features_train, input_features_test, output_class_train, output_class_test = train_test_split(input_features, output_class, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "4NWy2ZiF2XOV"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_features_train.shape)\n",
        "print(input_features_train.dtype)\n",
        "\n",
        "print(output_class_train.shape)\n",
        "print(output_class_train.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iztFo5FtSKRP",
        "outputId": "6de6a513-eb92-40ce-fd22-32eb02d392f9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(800, 1536)\n",
            "float64\n",
            "(800,)\n",
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the build model function\n",
        "def build_model(hp):\n",
        "    model = keras.Sequential()\n",
        "\n",
        "    # Tune the number of units in the first dense layer\n",
        "    units = hp.Int('units', min_value=16, max_value=128, step=16)\n",
        "    model.add(keras.layers.Dense(units=units, activation='relu'))\n",
        "\n",
        "    # Tune the number of dense layers and their units\n",
        "    for i in range(hp.Int('num_layers', 1, 3)):\n",
        "        units = hp.Int(f'units_{i}', min_value=4, max_value=32, step=4)\n",
        "        model.add(keras.layers.Dense(units=units, activation='relu'))\n",
        "\n",
        "    # Output layer with softmax activation for multi-class classification\n",
        "    model.add(keras.layers.Dense(5, activation='softmax'))\n",
        "\n",
        "    # Tune the optimizer\n",
        "    optimizer = hp.Choice('optimizer', ['adam', 'rmsprop'])\n",
        "    if optimizer == 'adam':\n",
        "        learning_rate = hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log')\n",
        "        model.compile(optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "                      loss='sparse_categorical_crossentropy',\n",
        "                      metrics=['accuracy'])\n",
        "    else:\n",
        "        learning_rate = hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='log')\n",
        "        model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=learning_rate),\n",
        "                      loss='sparse_categorical_crossentropy',\n",
        "                      metrics=['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "# Initialize the Keras Tuner\n",
        "tuner = kt.RandomSearch(\n",
        "    build_model,\n",
        "    objective='val_accuracy',\n",
        "    max_trials=5,  # Set this to a higher value for better hyperparameter tuning\n",
        "    executions_per_trial=3,\n",
        "    directory='my_dir',\n",
        "    project_name='multi_class_classification'\n",
        ")\n",
        "\n",
        "# Perform the hyperparameter search\n",
        "tuner.search(input_features_train, output_class_train,\n",
        "             epochs=50,\n",
        "             validation_data=(input_features_test, output_class_test))"
      ],
      "metadata": {
        "id": "lci9HVg1tWiN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36880b8f-6867-40e6-9d3f-243ac5d535a6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 5 Complete [00h 00m 29s]\n",
            "val_accuracy: 0.13500000536441803\n",
            "\n",
            "Best val_accuracy So Far: 0.13500000536441803\n",
            "Total elapsed time: 00h 02m 41s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the optimal hyperparameters\n",
        "best_hps= tuner.get_best_hyperparameters(1)[0]\n",
        "print(best_hps)\n",
        "\n",
        "n_layers = best_hps.get('num_layers')\n",
        "print(f'Number of hidden layers: {n_layers}')\n",
        "for hyparam in [f'units_{i}' for i in range(n_layers)] + ['optimizer'] + ['learning_rate']:\n",
        "    print(f'{hyparam}: {best_hps.get(hyparam)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6F9_pzgxYzbm",
        "outputId": "47da9cb5-0757-415c-ea48-8a4261aea9b1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<keras_tuner.src.engine.hyperparameters.hyperparameters.HyperParameters object at 0x7b2aff343220>\n",
            "Number of hidden layers: 1\n",
            "units_0: 20\n",
            "optimizer: adam\n",
            "learning_rate: 0.0001372259330713521\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get the best model\n",
        "best_model = tuner.get_best_models(1)[0]\n",
        "print(best_model)\n",
        "\n",
        "# display model structure\n",
        "keras.utils.plot_model(best_model, 'best_model.png', show_shapes=True)\n",
        "\n",
        "# show model summary\n",
        "best_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "pQNFJ9QCY0PS",
        "outputId": "ca5b0e54-b9a9-431f-fe45-f6be4aaf17b3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<Sequential name=sequential, built=True>\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py:415: UserWarning: Skipping variable loading for optimizer 'adam', because it has 2 variables whereas the saved optimizer has 14 variables. \n",
            "  saveable.load_own_variables(weights_store.get(inner_path))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m16\u001b[0m)                    │          \u001b[38;5;34m24,592\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m20\u001b[0m)                    │             \u001b[38;5;34m340\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m5\u001b[0m)                     │             \u001b[38;5;34m105\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">24,592</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">340</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)                     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">105</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m25,037\u001b[0m (97.80 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,037</span> (97.80 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m25,037\u001b[0m (97.80 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,037</span> (97.80 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build and train the model with the best hyperparameters\n",
        "model = tuner.hypermodel.build(best_hps)\n",
        "model.fit(\n",
        "    input_features_train, output_class_train,\n",
        "    epochs=100,\n",
        "    callbacks=[tf.keras.callbacks.EarlyStopping(patience=5)]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGAx0Jx1ZfJ9",
        "outputId": "6392fa24-2f45-403e-f1e3-5c9114f1114f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.1199 - loss: nan\n",
            "Epoch 2/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/callbacks/early_stopping.py:155: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss\n",
            "  current = self.get_monitor_value(logs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.1358 - loss: nan\n",
            "Epoch 3/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1503 - loss: nan \n",
            "Epoch 4/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1218 - loss: nan \n",
            "Epoch 5/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1579 - loss: nan \n",
            "Epoch 6/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1475 - loss: nan \n",
            "Epoch 7/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1515 - loss: nan \n",
            "Epoch 8/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1278 - loss: nan \n",
            "Epoch 9/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1283 - loss: nan \n",
            "Epoch 10/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1497 - loss: nan \n",
            "Epoch 11/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1346 - loss: nan \n",
            "Epoch 12/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1302 - loss: nan \n",
            "Epoch 13/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1243 - loss: nan \n",
            "Epoch 14/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1443 - loss: nan \n",
            "Epoch 15/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1332 - loss: nan \n",
            "Epoch 16/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1327 - loss: nan \n",
            "Epoch 17/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1370 - loss: nan \n",
            "Epoch 18/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1193 - loss: nan \n",
            "Epoch 19/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1251 - loss: nan \n",
            "Epoch 20/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1320 - loss: nan \n",
            "Epoch 21/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1539 - loss: nan \n",
            "Epoch 22/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1400 - loss: nan \n",
            "Epoch 23/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1377 - loss: nan \n",
            "Epoch 24/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1420 - loss: nan \n",
            "Epoch 25/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1365 - loss: nan \n",
            "Epoch 26/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1219 - loss: nan \n",
            "Epoch 27/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1264 - loss: nan \n",
            "Epoch 28/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1399 - loss: nan \n",
            "Epoch 29/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1587 - loss: nan \n",
            "Epoch 30/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1271 - loss: nan \n",
            "Epoch 31/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1480 - loss: nan \n",
            "Epoch 32/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1477 - loss: nan \n",
            "Epoch 33/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1294 - loss: nan \n",
            "Epoch 34/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1455 - loss: nan \n",
            "Epoch 35/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1432 - loss: nan \n",
            "Epoch 36/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1278 - loss: nan \n",
            "Epoch 37/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1346 - loss: nan \n",
            "Epoch 38/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1499 - loss: nan \n",
            "Epoch 39/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1387 - loss: nan \n",
            "Epoch 40/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1496 - loss: nan \n",
            "Epoch 41/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1425 - loss: nan \n",
            "Epoch 42/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1424 - loss: nan \n",
            "Epoch 43/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1467 - loss: nan \n",
            "Epoch 44/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1434 - loss: nan \n",
            "Epoch 45/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1272 - loss: nan \n",
            "Epoch 46/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1467 - loss: nan \n",
            "Epoch 47/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1321 - loss: nan \n",
            "Epoch 48/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1491 - loss: nan \n",
            "Epoch 49/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1354 - loss: nan \n",
            "Epoch 50/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1350 - loss: nan \n",
            "Epoch 51/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1494 - loss: nan \n",
            "Epoch 52/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1462 - loss: nan \n",
            "Epoch 53/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1520 - loss: nan \n",
            "Epoch 54/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1342 - loss: nan \n",
            "Epoch 55/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1336 - loss: nan \n",
            "Epoch 56/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1346 - loss: nan \n",
            "Epoch 57/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1165 - loss: nan \n",
            "Epoch 58/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1317 - loss: nan \n",
            "Epoch 59/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1439 - loss: nan  \n",
            "Epoch 60/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1251 - loss: nan \n",
            "Epoch 61/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1492 - loss: nan \n",
            "Epoch 62/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1365 - loss: nan \n",
            "Epoch 63/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1250 - loss: nan \n",
            "Epoch 64/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1228 - loss: nan \n",
            "Epoch 65/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1389 - loss: nan \n",
            "Epoch 66/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1264 - loss: nan \n",
            "Epoch 67/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1287 - loss: nan \n",
            "Epoch 68/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1440 - loss: nan \n",
            "Epoch 69/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1420 - loss: nan \n",
            "Epoch 70/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1267 - loss: nan \n",
            "Epoch 71/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1226 - loss: nan \n",
            "Epoch 72/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1329 - loss: nan \n",
            "Epoch 73/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1349 - loss: nan \n",
            "Epoch 74/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1365 - loss: nan \n",
            "Epoch 75/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1383 - loss: nan \n",
            "Epoch 76/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1179 - loss: nan \n",
            "Epoch 77/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1387 - loss: nan \n",
            "Epoch 78/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1429 - loss: nan \n",
            "Epoch 79/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1484 - loss: nan \n",
            "Epoch 80/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1284 - loss: nan \n",
            "Epoch 81/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1516 - loss: nan \n",
            "Epoch 82/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1448 - loss: nan \n",
            "Epoch 83/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1345 - loss: nan \n",
            "Epoch 84/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1300 - loss: nan \n",
            "Epoch 85/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1349 - loss: nan \n",
            "Epoch 86/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1476 - loss: nan \n",
            "Epoch 87/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1429 - loss: nan \n",
            "Epoch 88/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1312 - loss: nan \n",
            "Epoch 89/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1489 - loss: nan \n",
            "Epoch 90/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1279 - loss: nan \n",
            "Epoch 91/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1338 - loss: nan \n",
            "Epoch 92/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1396 - loss: nan \n",
            "Epoch 93/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1359 - loss: nan \n",
            "Epoch 94/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1300 - loss: nan \n",
            "Epoch 95/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1288 - loss: nan \n",
            "Epoch 96/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1288 - loss: nan \n",
            "Epoch 97/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1576 - loss: nan \n",
            "Epoch 98/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1327 - loss: nan \n",
            "Epoch 99/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1543 - loss: nan \n",
            "Epoch 100/100\n",
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1618 - loss: nan \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7b2ae05bf040>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the result\n",
        "eval_result = model.evaluate(input_features_test, output_class_test)\n",
        "print(f\"test loss: {eval_result[0]}, test accuracy: {eval_result[1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMkNhki4d4Ai",
        "outputId": "b8ac3db3-af40-452f-a621-09a4700a10dc"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.1298 - loss: nan\n",
            "test loss: nan, test accuracy: 0.13500000536441803\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IpqcV49Kd5fz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}