firstcnn.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mariajmolina/UMDAOSC650/blob/main/FirstCNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Our first CNN!"
      ],
      "metadata": {
        "id": "pwgWAFL8GZWw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we have some helper functions for normalizing and standardizing our data using numpy."
      ],
      "metadata": {
        "id": "WmqM9d_sEJHu"
      }
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def z_score(da):\n",
        "    \"\"\"\n",
        "    Compute z-score for training data.\n",
        "    Normalizing data for ML model training.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.mean(da)) / np.std(da)\n",
        "\n",
        "def min_max_scale(da):\n",
        "    \"\"\"\n",
        "    Scale training data by minimum and maximum values.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.min(da)) / (np.max(da) - np.min(da))"
      ],
      "metadata": {
        "id": "uA1hA1WUC-R-"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "## **Supervised Learning Regression Example**"
      ],
      "metadata": {
        "id": "JBirR3cdICH8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we will proceed with training a neural network in a supervised learning regression application."
      ],
      "metadata": {
        "id": "rT22AWjwIIeX"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "We will use a dataset from Molina et al. (2021) consisting of thunderstorms generated using a 4-km horizontal resolution WRF simulation. We are using a small portion of the dataset, specifically thunderstorms simulated for April 27, 2011 over the continental United States."
      ],
      "metadata": {
        "id": "Z6MIe4xpeOQq"
      }
    },
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      "cell_type": "code",
      "source": [
        "!wget https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "YIDtIzo3IJFS",
        "outputId": "bf27811d-97d3-443e-dc67-e12b98e925ce"
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          "text": [
            "--2024-02-29 17:21:05--  https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc\n",
            "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.185.79.172, 188.184.98.238, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 109400556 (104M) [application/octet-stream]\n",
            "Saving to: ‘current_SP3hourly_20110427.nc’\n",
            "\n",
            "current_SP3hourly_2 100%[===================>] 104.33M  21.7MB/s    in 5.7s    \n",
            "\n",
            "2024-02-29 17:21:12 (18.4 MB/s) - ‘current_SP3hourly_20110427.nc’ saved [109400556/109400556]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's visualize the dataset. We can use python libraries for this."
      ],
      "metadata": {
        "id": "FTRe0rEpexSJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import xarray as xr"
      ],
      "metadata": {
        "id": "V-_wY-Lqewnh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "ds = xr.open_dataset('current_SP3hourly_20110427.nc')"
      ],
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        "id": "pY1Bn-N8d7pB"
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              "    col_indices  (starttime, y, x) int64 ...\n",
              "    lats         (starttime, y, x) float32 ...\n",
              "    lons         (starttime, y, x) float32 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2d36412a-ff59-4bce-91eb-8f73d092a5dc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d36412a-ff59-4bce-91eb-8f73d092a5dc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span>y</span>: 32</li><li><span>x</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff6aa02e-57c2-4d59-8e6f-c3c69263e265' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff6aa02e-57c2-4d59-8e6f-c3c69263e265' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-44722e6d-6aca-4916-9ef4-3c2085846ae4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44722e6d-6aca-4916-9ef4-3c2085846ae4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e4196bd-9d46-4e6b-b616-ecead924aac6' class='xr-var-data-in' type='checkbox'><label for='data-7e4196bd-9d46-4e6b-b616-ecead924aac6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b7e7aa89-8955-4ab5-a22f-5447adc63a0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7e7aa89-8955-4ab5-a22f-5447adc63a0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd52939-da62-48c4-9d5a-6520a8153bb6' class='xr-var-data-in' type='checkbox'><label for='data-bfd52939-da62-48c4-9d5a-6520a8153bb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-73b18da9-d197-4d5e-98fd-53b39a73ac00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73b18da9-d197-4d5e-98fd-53b39a73ac00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eb0b26f-d1c5-418b-af59-9679dbaacf9d' class='xr-var-data-in' type='checkbox'><label for='data-9eb0b26f-d1c5-418b-af59-9679dbaacf9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64f5017b-8af2-4ba0-a40f-adcec886d962' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64f5017b-8af2-4ba0-a40f-adcec886d962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e55c5ab-2750-4fbc-ad73-8ee24b948aa8' class='xr-var-data-in' type='checkbox'><label for='data-2e55c5ab-2750-4fbc-ad73-8ee24b948aa8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-024e008f-9c8c-485b-ab62-96d4d54f1ec9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-024e008f-9c8c-485b-ab62-96d4d54f1ec9' class='xr-section-summary' >Data variables: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uh25_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93e870b0-7fed-40fe-bb3a-e09d17364811' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93e870b0-7fed-40fe-bb3a-e09d17364811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb7ef0a2-ac60-4389-bee5-0d3f083b83ec' class='xr-var-data-in' type='checkbox'><label for='data-fb7ef0a2-ac60-4389-bee5-0d3f083b83ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uh03_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af182e4b-2e32-40c6-9f0a-b554052e8e38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af182e4b-2e32-40c6-9f0a-b554052e8e38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4f3f21f-4f6c-45c4-8949-3abda5d759ee' class='xr-var-data-in' type='checkbox'><label for='data-e4f3f21f-4f6c-45c4-8949-3abda5d759ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctt_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b4c14d9-8280-4ee9-972e-5e9e5a90ab6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b4c14d9-8280-4ee9-972e-5e9e5a90ab6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-315d3f41-a927-4c28-a694-48dca6996c78' class='xr-var-data-in' type='checkbox'><label for='data-315d3f41-a927-4c28-a694-48dca6996c78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dbz_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b00e18b-931e-456e-b6ef-d8f53bea65ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b00e18b-931e-456e-b6ef-d8f53bea65ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09583916-948a-43f3-abfb-ef18387623ab' class='xr-var-data-in' type='checkbox'><label for='data-09583916-948a-43f3-abfb-ef18387623ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84aaa5e3-50bb-4d3d-9da8-2b4cbfec3892' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84aaa5e3-50bb-4d3d-9da8-2b4cbfec3892' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e55f18b-412b-4c80-ba2e-92f020627807' class='xr-var-data-in' type='checkbox'><label for='data-9e55f18b-412b-4c80-ba2e-92f020627807' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>row_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c689abb1-7a56-4bd3-8e6d-1949dfef47af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c689abb1-7a56-4bd3-8e6d-1949dfef47af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eab4461-7494-4ed0-b956-5c73dbcf02bf' class='xr-var-data-in' type='checkbox'><label for='data-5eab4461-7494-4ed0-b956-5c73dbcf02bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>col_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98df2d41-c5bd-4f40-a3c4-acf50e094ecc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98df2d41-c5bd-4f40-a3c4-acf50e094ecc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06e34d19-10a2-4851-b5b8-f135fdc93ac1' class='xr-var-data-in' type='checkbox'><label for='data-06e34d19-10a2-4851-b5b8-f135fdc93ac1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lats</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a00d801-a25a-4762-a045-bb8e333d94d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a00d801-a25a-4762-a045-bb8e333d94d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d170abc4-7776-4629-b648-ced292a6832f' class='xr-var-data-in' type='checkbox'><label for='data-d170abc4-7776-4629-b648-ced292a6832f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lons</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1fb85da1-5705-4866-8733-a9ca55596397' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fb85da1-5705-4866-8733-a9ca55596397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e06a35c-12f5-4631-9158-317b3ac3da51' class='xr-var-data-in' type='checkbox'><label for='data-9e06a35c-12f5-4631-9158-317b3ac3da51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-310564f4-46ee-4b4a-992f-0982c7792081' class='xr-section-summary-in' type='checkbox'  ><label for='section-310564f4-46ee-4b4a-992f-0982c7792081' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7f0c52ed-7303-4f54-8a90-a0a788fa84a0' class='xr-index-data-in' type='checkbox'/><label for='index-7f0c52ed-7303-4f54-8a90-a0a788fa84a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "              92640.0, 92640.0, 92640.0,\n",
              "              ...\n",
              "              92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "              92661.0, 92661.0, 92661.0],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-099e9b0c-de96-4641-9747-4f0ad87eb6e1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-099e9b0c-de96-4641-9747-4f0ad87eb6e1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset contains 2,224 storm samples that were generated using the github reposity [deep-conus](https://github.com/mariajmolina/deep-conus). Each sample is 32x32 grid cells (i.e., pixels) with each grid cell spanning 4-km.\n",
        "\n",
        "Let's visualize some of the variables."
      ],
      "metadata": {
        "id": "yw6ewfZee-lr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# simulated radar reflectivity\n",
        "ds['dbz_grid'].isel(starttime=0).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "DsoCCgnSfnhJ",
        "outputId": "0f122f5f-92c8-45fe-85e5-b9a0f9e8d05e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x78a1ff3a2c50>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gridded updraft helicity from 2-5 km\n",
        "ds['uh25_grid'].isel(starttime=0).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "bFIWAsD0ewu-",
        "outputId": "98a18572-0b24-4427-eea7-8ba3699f7952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x78a1fd13e050>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gridded cloud top temperature\n",
        "ds['ctt_grid'].isel(starttime=0).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "dzBY7eYRfncK",
        "outputId": "5279c469-7547-4423-f989-6c9f6b58308c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x78a1fd22d390>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will preprocess our training and testing data. We need to split our data into a training and testing sets, and standardize."
      ],
      "metadata": {
        "id": "UlzHzBsPhpe1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def random_split(data, label, split=0.3, seed=0):\n",
        "    \"\"\"\n",
        "    Help spliting data randomly for training and testing.\n",
        "    \"\"\"\n",
        "    np.random.seed(0)\n",
        "    da_indx = np.random.permutation(data.shape[0])\n",
        "    data  = data[da_indx.astype(int)]\n",
        "    label = label[da_indx.astype(int)]\n",
        "    init_range = int(data.shape[0] * (1 - 0.3))\n",
        "    return data[:init_range], label[:init_range], data[init_range:], label[init_range:]"
      ],
      "metadata": {
        "id": "BYfUl7q0IJBl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = xr.concat([\n",
        "    ds['dbz_grid'].expand_dims(dim='feature', axis=3),\n",
        "    ds['ctt_grid'].expand_dims(dim='feature', axis=3)],\n",
        "          dim='feature')"
      ],
      "metadata": {
        "id": "QmZSDOrvd50f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(features.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rt4Sl3cii7SY",
        "outputId": "f84225ec-8392-44b0-eab3-776c7ce74f9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 32, 32, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# grab the maximum of the dbz to use as our labels (regression)\n",
        "labels = ds['dbz_grid'].expand_dims(dim='feature', axis=3).max('x').max('y')"
      ],
      "metadata": {
        "id": "HgsBcfJ2ivWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZ57QrQXi3HV",
        "outputId": "1ba0a0cd-1495-412e-acf2-822ec6855097"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<xarray.DataArray 'dbz_grid' (starttime: 2224, feature: 1)>\n",
            "array([[65.721054],\n",
            "       [58.54062 ],\n",
            "       [58.54062 ],\n",
            "       ...,\n",
            "       [45.41483 ],\n",
            "       [37.786957],\n",
            "       [42.48563 ]], dtype=float32)\n",
            "Coordinates:\n",
            "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
            "    endtime    (starttime) float64 ...\n",
            "    x_speed    (starttime) int64 ...\n",
            "    y_speed    (starttime) int64 ...\n",
            "Dimensions without coordinates: feature\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats, train_label, test_feats, test_label = random_split(features, labels)"
      ],
      "metadata": {
        "id": "1xt_5E0DjOPD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_feats.shape[0], train_label.shape[0], test_feats.shape[0], test_label.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5kQ6CXcjOek",
        "outputId": "5e5fae4e-46e3-4f8c-a214-585e20673b6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1556 1556 668 668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalization of our training data (note each feature individually processed!)\n",
        "train_feats_standardized = xr.concat([\n",
        "    z_score(train_feats.isel(feature=0).expand_dims(dim='feature', axis=3)),\n",
        "    z_score(train_feats.isel(feature=1).expand_dims(dim='feature', axis=3))],\n",
        "                                     dim='feature')\n",
        "\n",
        "train_label_standardized = z_score(train_label)"
      ],
      "metadata": {
        "id": "-UM89woPj8Ep"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats.isel(feature=0,starttime=15).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "1HV4mCLnlIMF",
        "outputId": "d17fbe49-eb21-4a20-8ce4-0811f0fa4971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x78a1f8f0c8b0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats_standardized.isel(feature=0,starttime=15).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "w2T48h8ykonf",
        "outputId": "62749587-1d43-4002-c4eb-5f0cf56d1bf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x78a1f8df0f70>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats.isel(feature=1,starttime=100).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "PJh6t50olRlG",
        "outputId": "33ba319b-9f17-45de-d09b-d03217d46836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x78a1f8ecc220>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_feats_standardized.isel(feature=1,starttime=100).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "EO_ugNfykwE2",
        "outputId": "00e2cfbb-6877-4d10-fa29-ba0f626c9574"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x78a1f8d8b160>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's proceed to build a simple convolutional neural network that will take images as the input and predict the respective storm's maximum updraft helicity."
      ],
      "metadata": {
        "id": "yS-guBx7gOkQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras import layers"
      ],
      "metadata": {
        "id": "KDM-f2a1fnlB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the seed using keras.utils.set_random_seed. This will set:\n",
        "# 1) `numpy` seed\n",
        "# 2) backend random seed\n",
        "# 3) `python` random seed\n",
        "keras.utils.set_random_seed(812)"
      ],
      "metadata": {
        "id": "AM2xE0yy6NPY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lets use two input variables (dbz_grid and ctt_grid)\n",
        "number_of_features = 2"
      ],
      "metadata": {
        "id": "94Cj7eVlhByj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(32, 32, number_of_features)))  # 32x32 images\n",
        "\n",
        "# data augmentation layers\n",
        "#model.add(layers.RandomFlip(\"horizontal_and_vertical\"))\n",
        "#model.add(layers.RandomRotation(factor=(-0.5, 0.5)))\n",
        "\n",
        "# hidden layers\n",
        "model.add(layers.Conv2D(32, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(64, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(128, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "# Now that we have 4x4 feature maps, now apply global max pooling.\n",
        "model.add(layers.GlobalMaxPooling2D())\n",
        "\n",
        "# Finally, we add last fully connected (dense) layers\n",
        "model.add(layers.Dense(64))\n",
        "model.add(layers.Dense(1, activation=\"linear\"))\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGZqhCVfII4J",
        "outputId": "7cacfe5f-e9eb-4265-f86d-389ba16393b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 32, 32, 32)        608       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 16, 16, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 16, 16, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 8, 8, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 8, 8, 64)          0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 4, 4, 128)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 4, 4, 128)         0         \n",
            "                                                                 \n",
            " global_max_pooling2d (Glob  (None, 128)               0         \n",
            " alMaxPooling2D)                                                 \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 101281 (395.63 KB)\n",
            "Trainable params: 101281 (395.63 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Proceeding with compiling our model and selecting some metrics to use."
      ],
      "metadata": {
        "id": "AXkjSNM3mD2u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some common evaluation metrics used with regression problems are:\n",
        "\n",
        "Mean absolute error (MAE): $\\frac{1}{N} \\sum\\limits_{i = 1}^{N} \\lvert \\hat{y}_i - y_i \\rvert$\n",
        "\n",
        "Mean squared error (MSE): $\\frac{1}{N} \\sum\\limits_{i = 1}^{N} (\\hat{y}_i - y_i)^2$\n",
        "\n",
        "\n",
        "More evalution metrics can be found [here](https://scikit-learn.org/stable/modules/model_evaluation.html)."
      ],
      "metadata": {
        "id": "knrWFwJ5LU2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "METRICS = [\n",
        "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
        "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
        "]"
      ],
      "metadata": {
        "id": "lPb4PhGRlZgN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=\"mean_squared_error\",\n",
        "    metrics=METRICS\n",
        ")"
      ],
      "metadata": {
        "id": "o82dwXizjNTa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "metadata": {
        "id": "QEdyoR97MTrv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=train_feats_standardized.values,\n",
        "    y=train_label_standardized.values,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfhYCYuVmXu7",
        "outputId": "8e851630-aa8b-4f67-adbf-11dfda510d3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "44/44 [==============================] - 11s 162ms/step - loss: 0.7521 - mean_squared_error: 0.7521 - root_mean_squared_error: 0.8672 - val_loss: 0.6207 - val_mean_squared_error: 0.6207 - val_root_mean_squared_error: 0.7878\n",
            "Epoch 2/10\n",
            "44/44 [==============================] - 3s 79ms/step - loss: 0.4133 - mean_squared_error: 0.4133 - root_mean_squared_error: 0.6429 - val_loss: 0.4454 - val_mean_squared_error: 0.4454 - val_root_mean_squared_error: 0.6674\n",
            "Epoch 3/10\n",
            "44/44 [==============================] - 4s 81ms/step - loss: 0.3027 - mean_squared_error: 0.3027 - root_mean_squared_error: 0.5502 - val_loss: 0.3515 - val_mean_squared_error: 0.3515 - val_root_mean_squared_error: 0.5929\n",
            "Epoch 4/10\n",
            "44/44 [==============================] - 5s 107ms/step - loss: 0.2466 - mean_squared_error: 0.2466 - root_mean_squared_error: 0.4966 - val_loss: 0.3009 - val_mean_squared_error: 0.3009 - val_root_mean_squared_error: 0.5486\n",
            "Epoch 5/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 0.1916 - mean_squared_error: 0.1916 - root_mean_squared_error: 0.4377 - val_loss: 0.2054 - val_mean_squared_error: 0.2054 - val_root_mean_squared_error: 0.4532\n",
            "Epoch 6/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 0.1348 - mean_squared_error: 0.1348 - root_mean_squared_error: 0.3672 - val_loss: 0.2482 - val_mean_squared_error: 0.2482 - val_root_mean_squared_error: 0.4982\n",
            "Epoch 7/10\n",
            "44/44 [==============================] - 4s 89ms/step - loss: 0.1415 - mean_squared_error: 0.1415 - root_mean_squared_error: 0.3762 - val_loss: 0.2775 - val_mean_squared_error: 0.2775 - val_root_mean_squared_error: 0.5268\n",
            "Epoch 8/10\n",
            "44/44 [==============================] - 4s 96ms/step - loss: 0.1140 - mean_squared_error: 0.1140 - root_mean_squared_error: 0.3376 - val_loss: 0.2109 - val_mean_squared_error: 0.2109 - val_root_mean_squared_error: 0.4592\n",
            "Epoch 9/10\n",
            "44/44 [==============================] - 3s 80ms/step - loss: 0.1008 - mean_squared_error: 0.1008 - root_mean_squared_error: 0.3175 - val_loss: 0.1528 - val_mean_squared_error: 0.1528 - val_root_mean_squared_error: 0.3909\n",
            "Epoch 10/10\n",
            "44/44 [==============================] - 4s 82ms/step - loss: 0.1179 - mean_squared_error: 0.1179 - root_mean_squared_error: 0.3433 - val_loss: 0.2002 - val_mean_squared_error: 0.2002 - val_root_mean_squared_error: 0.4474\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"mean_squared_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "KD2-89DcoW44",
        "outputId": "cd4a9372-0eb2-48d5-a70d-5ca043d7c276"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "Lets now evaluate the model using the test dataset. We need to standardize the data, but using the mean and standard deviation of the training set."
      ],
      "metadata": {
        "id": "rwxlG2G0xcQ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def z_score_testing(da, testdata):\n",
        "    \"\"\"\n",
        "    Compute z-score for testing data.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (testdata - np.mean(da)) / np.std(da)"
      ],
      "metadata": {
        "id": "7zaFmAIUorvC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# normalization of our training data (note each feature individually processed!)\n",
        "test_feats_standardized = xr.concat([\n",
        "    z_score_testing(\n",
        "        train_feats.isel(feature=0).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=0).expand_dims(dim='feature', axis=3)\n",
        "        ),\n",
        "    z_score_testing(\n",
        "        train_feats.isel(feature=1).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=1).expand_dims(dim='feature', axis=3)\n",
        "        )],\n",
        "                                     dim='feature')\n",
        "\n",
        "test_label_standardized = z_score_testing(train_label, test_label)"
      ],
      "metadata": {
        "id": "3jR6rF-lx55_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data using `evaluate`\n",
        "print(\"Evaluate on test data\")\n",
        "results = model.evaluate(test_feats_standardized.values, test_label_standardized.values, batch_size=128)\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXfumUFnor4-",
        "outputId": "4614a02d-d537-45ad-eec2-6af3af3bf0b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate on test data\n",
            "6/6 [==============================] - 0s 68ms/step - loss: 0.2082 - mean_squared_error: 0.2082 - root_mean_squared_error: 0.4563\n",
            "[0.20816494524478912, 0.20816494524478912, 0.45625096559524536]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate predictions using the trained model\n",
        "predictions = model.predict(test_feats_standardized.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSHfeMExzVqb",
        "outputId": "919c9865-21f9-497b-fb45-42a39b51e3a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21/21 [==============================] - 1s 21ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize predictions vs ground truth\n",
        "plt.scatter(predictions, test_label_standardized.values)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "xzOMgkXozb6V",
        "outputId": "b35291e9-80ce-4a9a-8846-ef0ab937f15b"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "# Tutorial Practice: How does batch size impact model training?\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "6soWOXwjyo8t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# recreate the model object"
      ],
      "metadata": {
        "id": "PZlR8mqqy3py"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# recompile the model"
      ],
      "metadata": {
        "id": "HOB_0mQEzJwz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# retrain the model"
      ],
      "metadata": {
        "id": "5oNHXJ4_zJ3I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the new model's skill"
      ],
      "metadata": {
        "id": "rMHPapkbzJ9m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Other sample questions for practice:\n",
        "\n",
        "*   How does the learning rate impact model training?\n",
        "*   Does data augmentation help with model skill?\n",
        "*   Does reducing regularization impact overfitting (hint: reduce dropout percentage)?"
      ],
      "metadata": {
        "id": "hsqlwFRY1aXd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# more cells for practicing"
      ],
      "metadata": {
        "id": "8rhORSiS4Dz3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Supervised Learning Classification Example\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "Hd9l4RhAC6gu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using our work from the previous section, we will now use the data and reframe our problem to be a classification task. As before, we will use two input features."
      ],
      "metadata": {
        "id": "CFMnVVa5zti6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "However, we will use updraft helicity values (which represent storm updraft rotation) and convert this continuous value into two classes, \"not potentially severe\" and \"potentially severe\". More strongly rotating storms have a greater likelihood of producing severe hazards like tornadoes and large hail. We will use 75 m2/s2 as the delineation mark between the two classes. The two classes will be creating using integer encoding first, with 0 representing non-severe and 1 representing potentially severe, and then converting using one-hot encoding for subsequent training."
      ],
      "metadata": {
        "id": "y5R40_674lUy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = xr.concat([\n",
        "    ds['dbz_grid'].expand_dims(dim='feature', axis=3),\n",
        "    ds['ctt_grid'].expand_dims(dim='feature', axis=3)],\n",
        "          dim='feature')\n",
        "\n",
        "labels = ds['uh25_grid'].expand_dims(dim='feature', axis=3).max('x').max('y')\n",
        "\n",
        "train_feats, train_label, test_feats, test_label = random_split(features, labels)\n",
        "\n",
        "# normalization of our training data (note each feature individually processed!)\n",
        "train_feats_standardized = xr.concat([\n",
        "    z_score(train_feats.isel(feature=0).expand_dims(dim='feature', axis=3)),\n",
        "    z_score(train_feats.isel(feature=1).expand_dims(dim='feature', axis=3))],\n",
        "                                     dim='feature')\n",
        "\n",
        "train_label_ints = xr.where(train_label >= 75, 1, 0)"
      ],
      "metadata": {
        "id": "hj6bAjZ7zt5v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_label.values[-2:])\n",
        "print(train_label_ints.values[-2:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnlyxpuOzuCl",
        "outputId": "6419f7da-9130-47c1-895a-fa5798673af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[162.55905  ]\n",
            " [  1.8748366]]\n",
            "[[1]\n",
            " [0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert labels into classes using one-hot encoding\n",
        "onehot_layer = keras.layers.CategoryEncoding(num_tokens=2, output_mode=\"one_hot\")\n",
        "train_y = onehot_layer(train_label_ints.astype(int))"
      ],
      "metadata": {
        "id": "LzGNl7wUzuKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_y[-2:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7axDV9KzuY3",
        "outputId": "c1344e4f-24c3-4697-b37a-8ad12fffba96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
              "array([[0., 1.],\n",
              "       [1., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# out of more than 1000 samples, only 189 are the \"severe\" class\n",
        "print(train_y[:,1].numpy().sum())\n",
        "print(train_y[:,1].numpy().shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsRwqJjG7_Ec",
        "outputId": "2f2df4a6-2cb9-49c5-fa35-d16040fd681c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "189.0\n",
            "(1556,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class imbalance is a major issue with a minority class that is a rare event, like severe hazards. To address this limitation, we create class weights that help the machine learning model learn that the minority class is important to get correct."
      ],
      "metadata": {
        "id": "12FrqE9C7_mS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generate class weights due to class imbalance issues\n",
        "# use the second class as the positive one (using 1 index)\n",
        "counts = np.bincount(train_y[:,1].numpy().astype(int))\n",
        "\n",
        "print(\"Number of positive samples in training data: {} ({:.2f}% of total)\".format(\n",
        "    counts[1], 100 * float(counts[1]) / len(train_y)))\n",
        "\n",
        "weight_for_0 = 1.0 / counts[0]\n",
        "weight_for_1 = 1.0 / counts[1]\n",
        "\n",
        "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
        "print(class_weight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asYu-Bio70M6",
        "outputId": "c2857cec-a728-4f21-e3b2-38e17de83d59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of positive samples in training data: 189 (12.15% of total)\n",
            "{0: 0.000731528895391368, 1: 0.005291005291005291}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# metrics that are useful for classification, particularly for two classes\n",
        "METRICS = [\n",
        "    keras.metrics.BinaryCrossentropy(name='cross entropy'),  # same as model's loss\n",
        "    keras.metrics.TruePositives(name='tp'),\n",
        "    keras.metrics.FalsePositives(name='fp'),\n",
        "    keras.metrics.TrueNegatives(name='tn'),\n",
        "    keras.metrics.FalseNegatives(name='fn'),\n",
        "    keras.metrics.BinaryAccuracy(name='accuracy'),\n",
        "    keras.metrics.Precision(name='precision'),\n",
        "    keras.metrics.Recall(name='recall'),\n",
        "    keras.metrics.AUC(name='auc'),\n",
        "    keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve\n",
        "]"
      ],
      "metadata": {
        "id": "xHmN3l2z70QS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(32, 32, number_of_features)))  # 32x32 images\n",
        "\n",
        "# data augmentation layers\n",
        "#model.add(layers.RandomFlip(\"horizontal_and_vertical\"))\n",
        "#model.add(layers.RandomRotation(factor=(-0.5,0.5)))\n",
        "\n",
        "# hidden layers\n",
        "model.add(layers.Conv2D(32, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(64, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Conv2D(128, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "# Now that we have 4x4 feature maps, now apply global max pooling.\n",
        "model.add(layers.GlobalMaxPooling2D())\n",
        "\n",
        "# Finally, we add last fully connected (dense) layers\n",
        "model.add(layers.Dense(64))\n",
        "model.add(layers.Dense(2, activation=\"softmax\"))\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHBpYKnh70WR",
        "outputId": "53175725-91e9-4f03-acaa-f468edbcacee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 32)        608       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 16, 16, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 16, 16, 32)        0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 8, 8, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 8, 8, 64)          0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 4, 4, 128)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 4, 4, 128)         0         \n",
            "                                                                 \n",
            " global_max_pooling2d_1 (Gl  (None, 128)               0         \n",
            " obalMaxPooling2D)                                               \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 101346 (395.88 KB)\n",
            "Trainable params: 101346 (395.88 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=keras.losses.BinaryCrossentropy(), # categorical loss function\n",
        "    metrics=METRICS\n",
        ")"
      ],
      "metadata": {
        "id": "xS8iZ6Ro70bD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "metadata": {
        "id": "m0jmE_7k823F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Fit model on training data\")\n",
        "history = model.fit(\n",
        "    x=train_feats_standardized.values,\n",
        "    y=train_y,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        "    class_weight=class_weight,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3wCne8Q826e",
        "outputId": "1fc23ce2-824e-4764-e222-fc4cd4422722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fit model on training data\n",
            "Epoch 1/10\n",
            "44/44 [==============================] - 8s 127ms/step - loss: 7.3008e-04 - cross entropy: 0.6251 - tp: 778.0000 - fp: 622.0000 - tn: 778.0000 - fn: 622.0000 - accuracy: 0.5557 - precision: 0.5557 - recall: 0.5557 - auc: 0.6789 - prc: 0.7216 - val_loss: 0.5818 - val_cross entropy: 0.5818 - val_tp: 83.0000 - val_fp: 73.0000 - val_tn: 83.0000 - val_fn: 73.0000 - val_accuracy: 0.5321 - val_precision: 0.5321 - val_recall: 0.5321 - val_auc: 0.7129 - val_prc: 0.7584\n",
            "Epoch 2/10\n",
            "44/44 [==============================] - 4s 99ms/step - loss: 5.8429e-04 - cross entropy: 0.5035 - tp: 947.0000 - fp: 453.0000 - tn: 947.0000 - fn: 453.0000 - accuracy: 0.6764 - precision: 0.6764 - recall: 0.6764 - auc: 0.8062 - prc: 0.8262 - val_loss: 0.4368 - val_cross entropy: 0.4368 - val_tp: 121.0000 - val_fp: 35.0000 - val_tn: 121.0000 - val_fn: 35.0000 - val_accuracy: 0.7756 - val_precision: 0.7756 - val_recall: 0.7756 - val_auc: 0.8727 - val_prc: 0.8823\n",
            "Epoch 3/10\n",
            "44/44 [==============================] - 4s 82ms/step - loss: 5.1437e-04 - cross entropy: 0.4644 - tp: 1042.0000 - fp: 358.0000 - tn: 1042.0000 - fn: 358.0000 - accuracy: 0.7443 - precision: 0.7443 - recall: 0.7443 - auc: 0.8480 - prc: 0.8557 - val_loss: 0.4306 - val_cross entropy: 0.4306 - val_tp: 129.0000 - val_fp: 27.0000 - val_tn: 129.0000 - val_fn: 27.0000 - val_accuracy: 0.8269 - val_precision: 0.8269 - val_recall: 0.8269 - val_auc: 0.8882 - val_prc: 0.8919\n",
            "Epoch 4/10\n",
            "44/44 [==============================] - 5s 108ms/step - loss: 4.2505e-04 - cross entropy: 0.3559 - tp: 1180.0000 - fp: 220.0000 - tn: 1180.0000 - fn: 220.0000 - accuracy: 0.8429 - precision: 0.8429 - recall: 0.8429 - auc: 0.9219 - prc: 0.9216 - val_loss: 0.2431 - val_cross entropy: 0.2431 - val_tp: 148.0000 - val_fp: 8.0000 - val_tn: 148.0000 - val_fn: 8.0000 - val_accuracy: 0.9487 - val_precision: 0.9487 - val_recall: 0.9487 - val_auc: 0.9866 - val_prc: 0.9870\n",
            "Epoch 5/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 4.0987e-04 - cross entropy: 0.3486 - tp: 1207.0000 - fp: 193.0000 - tn: 1207.0000 - fn: 193.0000 - accuracy: 0.8621 - precision: 0.8621 - recall: 0.8621 - auc: 0.9237 - prc: 0.9198 - val_loss: 0.2236 - val_cross entropy: 0.2236 - val_tp: 148.0000 - val_fp: 8.0000 - val_tn: 148.0000 - val_fn: 8.0000 - val_accuracy: 0.9487 - val_precision: 0.9487 - val_recall: 0.9487 - val_auc: 0.9931 - val_prc: 0.9933\n",
            "Epoch 6/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 3.7793e-04 - cross entropy: 0.3000 - tp: 1260.0000 - fp: 140.0000 - tn: 1260.0000 - fn: 140.0000 - accuracy: 0.9000 - precision: 0.9000 - recall: 0.9000 - auc: 0.9486 - prc: 0.9445 - val_loss: 0.2968 - val_cross entropy: 0.2968 - val_tp: 141.0000 - val_fp: 15.0000 - val_tn: 141.0000 - val_fn: 15.0000 - val_accuracy: 0.9038 - val_precision: 0.9038 - val_recall: 0.9038 - val_auc: 0.9685 - val_prc: 0.9686\n",
            "Epoch 7/10\n",
            "44/44 [==============================] - 5s 112ms/step - loss: 2.9649e-04 - cross entropy: 0.2460 - tp: 1269.0000 - fp: 131.0000 - tn: 1269.0000 - fn: 131.0000 - accuracy: 0.9064 - precision: 0.9064 - recall: 0.9064 - auc: 0.9624 - prc: 0.9579 - val_loss: 0.4005 - val_cross entropy: 0.4005 - val_tp: 131.0000 - val_fp: 25.0000 - val_tn: 131.0000 - val_fn: 25.0000 - val_accuracy: 0.8397 - val_precision: 0.8397 - val_recall: 0.8397 - val_auc: 0.8950 - val_prc: 0.8836\n",
            "Epoch 8/10\n",
            "44/44 [==============================] - 4s 81ms/step - loss: 2.7169e-04 - cross entropy: 0.2344 - tp: 1271.0000 - fp: 129.0000 - tn: 1271.0000 - fn: 129.0000 - accuracy: 0.9079 - precision: 0.9079 - recall: 0.9079 - auc: 0.9675 - prc: 0.9639 - val_loss: 0.3004 - val_cross entropy: 0.3004 - val_tp: 138.0000 - val_fp: 18.0000 - val_tn: 138.0000 - val_fn: 18.0000 - val_accuracy: 0.8846 - val_precision: 0.8846 - val_recall: 0.8846 - val_auc: 0.9419 - val_prc: 0.9370\n",
            "Epoch 9/10\n",
            "44/44 [==============================] - 4s 80ms/step - loss: 2.3649e-04 - cross entropy: 0.1940 - tp: 1298.0000 - fp: 102.0000 - tn: 1298.0000 - fn: 102.0000 - accuracy: 0.9271 - precision: 0.9271 - recall: 0.9271 - auc: 0.9770 - prc: 0.9746 - val_loss: 0.2613 - val_cross entropy: 0.2613 - val_tp: 137.0000 - val_fp: 19.0000 - val_tn: 137.0000 - val_fn: 19.0000 - val_accuracy: 0.8782 - val_precision: 0.8782 - val_recall: 0.8782 - val_auc: 0.9625 - val_prc: 0.9626\n",
            "Epoch 10/10\n",
            "44/44 [==============================] - 4s 93ms/step - loss: 2.0359e-04 - cross entropy: 0.1631 - tp: 1315.0000 - fp: 85.0000 - tn: 1315.0000 - fn: 85.0000 - accuracy: 0.9393 - precision: 0.9393 - recall: 0.9393 - auc: 0.9819 - prc: 0.9792 - val_loss: 0.2113 - val_cross entropy: 0.2113 - val_tp: 143.0000 - val_fp: 13.0000 - val_tn: 143.0000 - val_fn: 13.0000 - val_accuracy: 0.9167 - val_precision: 0.9167 - val_recall: 0.9167 - val_auc: 0.9783 - val_prc: 0.9788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"accuracy\"])\n",
        "plt.plot(history.history[\"val_accuracy\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "H1FKdrnY829W",
        "outputId": "0afcbf6e-6bca-4a81-fcea-bc432aba349d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalization of our training data (note each feature individually processed!)\n",
        "test_feats_standardized = xr.concat([\n",
        "    z_score_testing(\n",
        "        train_feats.isel(feature=0).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=0).expand_dims(dim='feature', axis=3)\n",
        "        ),\n",
        "    z_score_testing(\n",
        "        train_feats.isel(feature=1).expand_dims(dim='feature', axis=3),\n",
        "        test_feats.isel(feature=1).expand_dims(dim='feature', axis=3)\n",
        "        )],\n",
        "                                     dim='feature')\n",
        "\n",
        "test_label_ints = xr.where(test_label >= 75, 1, 0)\n",
        "test_y = onehot_layer(test_label_ints.astype(int))"
      ],
      "metadata": {
        "id": "zMbEUR_O83Aw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data using `evaluate`\n",
        "print(\"Evaluate on test data\")\n",
        "results = model.evaluate(test_feats_standardized.values, test_y, batch_size=128)\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZ4Zljo_-a7c",
        "outputId": "6e47a10c-f0c0-4ffd-9c13-9871dd3ef44f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate on test data\n",
            "6/6 [==============================] - 0s 70ms/step - loss: 0.2836 - cross entropy: 0.2836 - tp: 605.0000 - fp: 63.0000 - tn: 605.0000 - fn: 63.0000 - accuracy: 0.9057 - precision: 0.9057 - recall: 0.9057 - auc: 0.9548 - prc: 0.9461\n",
            "[0.28363198041915894, 0.28363198041915894, 605.0, 63.0, 605.0, 63.0, 0.9056886434555054, 0.9056886434555054, 0.9056886434555054, 0.954757034778595, 0.946064293384552]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate predictions using the trained model\n",
        "predictions = model.predict(test_feats_standardized.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fcwNVSZ-a_X",
        "outputId": "e5191ee7-11f5-45f4-e0d2-ed31e4145504"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21/21 [==============================] - 1s 21ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_ints = xr.where(predictions[:,1]>0.5, 1, 0)"
      ],
      "metadata": {
        "id": "tHLyxWLVAqed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.cluster import contingency_matrix\n",
        "contingency_matrix(test_label_ints.astype(int).values.squeeze(), pred_ints)\n",
        "# tend to predict more false positives"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrZYzjM9_eLj",
        "outputId": "1495ca11-b9ec-46d2-fe28-650495dd2151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[515,  61],\n",
              "       [  2,  90]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_label_ints.astype(int).sum().values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwAmxuqcBJ1K",
        "outputId": "0e0f8aea-f86a-4613-fa09-bbd83f770529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(92)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_ints.sum() - 92"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bb-w77AwBfUV",
        "outputId": "e49fa2a5-d2e9-42ad-81b0-b6326639c809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tutorial Practice: What happens if we do not use class weights during training?\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "KmokO1cq9_RS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# recreate the model object"
      ],
      "metadata": {
        "id": "0a43SpER-Mxd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# recompile the model"
      ],
      "metadata": {
        "id": "ITEZzW3y-M02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# retrain the model without class weights"
      ],
      "metadata": {
        "id": "Xak_WCCB-M3-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the new model's skill"
      ],
      "metadata": {
        "id": "qwdW6YEP-NCM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# References\n",
        "\n",
        "Campbell, T., G. M. Lackmann, M. J. Molina, and M. D. Parker (2024). Severe Convective Storms in Limited Instability Organized by Pattern and Distribution. Weather and Forecasting. https://journals.ametsoc.org/view/journals/wefo/39/1/WAF-D-23-0130.1.xml\n",
        "\n",
        "DuVivier, A. K., M. J. Molina, A. L. Deppenmeier, M. M. Holland, L. Landrum, K. Krumhardt, and S. Jenouvrier (2023). Projections of Winter Polynyas and Their Biophysical Impacts in the Ross Sea Antarctica. Climate Dynamics. https://link.springer.com/article/10.1007/s00382-023-06951-z\n",
        "\n",
        "Molina, M. J., D. J. Gagne, and A. F. Prein (2021). A benchmark to test generalization capabilities of deep learning methods to classify severe convective storms in a changing climate. Earth and Space Science. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EA001490\n",
        "\n",
        "Molina, M. J., T. A. O'Brien, G. Anderson, M. Ashfaq, K. E. Bennett, W. D. Collins, K. Dagon, J. M. Restrepo, and P. A. Ullrich (2023). A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena. Artificial Intelligence for the Earth Systems. https://journals.ametsoc.org/view/journals/aies/2/4/AIES-D-22-0086.1.xml\n",
        "\n",
        "Molina, M. J., J. H. Richter, A. A. Glanville, K. Dagon, J. Berner, A. Hu, and G. A. Meehl (2023). Subseasonal Representation and Predictability of North American Weather Regimes using Cluster Analysis. Artificial Intelligence for the Earth Systems. https://journals.ametsoc.org/view/journals/aies/2/2/AIES-D-22-0051.1.xml\n",
        "\n",
        "Passarella, L.S. and Mahajan, S., 2023. Assessing Tropical Pacific–Induced Predictability of Southern California Precipitation Using a Novel Multi-Input Multioutput Autoencoder. Artificial Intelligence for the Earth Systems, 2(4), p.e230003. https://journals.ametsoc.org/view/journals/aies/2/4/AIES-D-23-0003.1.xml"
      ],
      "metadata": {
        "id": "Y8DYzxtePqCH"
      }
    }
  ]
}