4.OptimizationUsingCostFunc.ipynb

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              "            date  amount_of_precipitation  max_temperature  min_temperature  \\\n",
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              "        rain         year  \n",
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          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/graphing.py\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/microsoft_custom_linear_regressor.py\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/seattleWeather_1948-2017.csv\n",
        "from datetime import datetime\n",
        "\n",
        "# Load a file that contains our weather data\n",
        "dataset = pandas.read_csv('seattleWeather_1948-2017.csv', parse_dates=['date'])\n",
        "\n",
        "# Convert the dates into numbers so we can use them in our models\n",
        "# We make a year column that can contain fractions. For example,\n",
        "# 1948.5 is halfway through the year 1948\n",
        "dataset[\"year\"] = [(d.year + d.timetuple().tm_yday / 365.25) for d in dataset.date]\n",
        "\n",
        "\n",
        "# For the sake of this exercise, let's look at February 1 for the following years:\n",
        "desired_dates = [\n",
        "    datetime(1950,2,1),\n",
        "    datetime(1960,2,1),\n",
        "    datetime(1970,2,1),\n",
        "    datetime(1980,2,1),\n",
        "    datetime(1990,2,1),\n",
        "    datetime(2000,2,1),\n",
        "    datetime(2010,2,1),\n",
        "    datetime(2017,2,1),\n",
        "]\n",
        "\n",
        "dataset = dataset[dataset.date.isin(desired_dates)].copy()\n",
        "\n",
        "# Print the dataset\n",
        "dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "\n",
        "def sum_of_square_differences(estimate, actual):\n",
        "    # Note that with NumPy, to square each value we use **\n",
        "    return numpy.sum((estimate - actual)**2)\n",
        "\n",
        "def sum_of_absolute_differences(estimate, actual):\n",
        "    return numpy.sum(numpy.abs(estimate - actual))"
      ],
      "metadata": {
        "id": "s-Ce6iQuPFGm"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "actual_label = numpy.array([1, 3])\n",
        "model_estimate = numpy.array([2, 2])\n",
        "\n",
        "print(\"SSD:\", sum_of_square_differences(model_estimate, actual_label))\n",
        "print(\"SAD:\", sum_of_absolute_differences(model_estimate, actual_label))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xD15c_ywPMme",
        "outputId": "0a6c687f-fe8c-408d-f19f-2e81a7b1faf2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SSD: 2\n",
            "SAD: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual_label = numpy.array([1, 3])\n",
        "model_estimate = numpy.array([1, 1])\n",
        "\n",
        "print(\"SSD:\", sum_of_square_differences(model_estimate, actual_label))\n",
        "print(\"SAD:\", sum_of_absolute_differences(model_estimate, actual_label))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5GTCzkXVi7P",
        "outputId": "13a79bbc-e7f2-484a-ff47-34174d498e61"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SSD: 4\n",
            "SAD: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from microsoft_custom_linear_regressor import MicrosoftCustomLinearRegressor\n",
        "import graphing\n",
        "\n",
        "# Create and fit the model\n",
        "# We use a custom object that we've hidden from this notebook, because\n",
        "# you don't need to understand its details. This fits a linear model\n",
        "# by using a provided cost function\n",
        "\n",
        "# Fit a model by using sum of square differences\n",
        "model = MicrosoftCustomLinearRegressor().fit(X = dataset.year, \n",
        "                                             y = dataset.min_temperature, \n",
        "                                             cost_function = sum_of_square_differences)\n",
        "\n",
        "# Graph the model\n",
        "graphing.scatter_2D(dataset, \n",
        "                    label_x=\"year\", \n",
        "                    label_y=\"min_temperature\", \n",
        "                    trendline=model.predict)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "O37rDF9BXUPO",
        "outputId": "69b16fab-c161-4666-cf53-914e968c79f6"
      },
      "execution_count": 5,
      "outputs": [
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    {
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      "source": [
        "# Fit a model with SSD\n",
        "# Fit a model by using sum of square differences\n",
        "model = MicrosoftCustomLinearRegressor().fit(X = dataset.year, \n",
        "                                             y = dataset.min_temperature, \n",
        "                                             cost_function = sum_of_absolute_differences)\n",
        "\n",
        "# Graph the model\n",
        "graphing.scatter_2D(dataset, \n",
        "                    label_x=\"year\", \n",
        "                    label_y=\"min_temperature\", \n",
        "                    trendline=model.predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
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        "id": "3ptrQOUdXwpZ",
        "outputId": "46c4a7ac-9d1a-45ac-9f6c-eeedee708806"
      },
      "execution_count": 6,
      "outputs": [
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