Copie_de_S5_1_GenerativeAI.ipynb

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              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1200x300 with 1 Axes>",
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            "_model_module_version": "1.5.0",
            "_model_name": "FloatSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
            "continuous_update": true,
            "description": "sigma_1",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_63741a6e863a4176ae5b4ef300c2a359",
            "max": 3,
            "min": 1.1,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 0.1,
            "style": "IPY_MODEL_ac30b4bf1b9b4e23a3c095fcb7bf8f9f",
            "value": 1.2
          }
        },
        "7307804399ee456ba052e10e76ebf2be": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
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            "disabled": false,
            "layout": "IPY_MODEL_35b2afdd972045ddb38622e0f0e0784c",
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            "style": "IPY_MODEL_f70e8011d4ad436b8893db12a9b11a57",
            "value": 0.1
          }
        },
        "35de007209f647c0af10af4e99605cff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
            "continuous_update": true,
            "description": "sigma_2",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_af18c7086d6f49678043d006cb99ad5a",
            "max": 3,
            "min": 1.1,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 0.1,
            "style": "IPY_MODEL_e85b55f82dc74357ad109877d1e5794b",
            "value": 1.7
          }
        },
        "a0c40f0ac4324755a13adc2d4ff43980": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_66534317ff8548e6b8e735d1305ce72a",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 600x500 with 2 Axes>",
                  "image/png": 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                "data": {
                  "text/plain": "<Figure size 1200x300 with 1 Axes>",
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\n"
                },
                "metadata": {}
              }
            ]
          }
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tanguy-linard/2024_MLEES/blob/main/Copie_de_S5_1_GenerativeAI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (Exercise) Introduction to Uncertainty Quantification and Generative Modeling\n",
        "\n",
        "<center>\n",
        "<img src=\"https://github.com/msgomez06/ML_pedagogical_materials/blob/main/images/09.01-missing.png?raw=True\" width=90%></img>\n",
        "\n",
        "We're often tasked with filling voids and to express how sure we are of our answer.\n",
        "<br> <i> How can we train an algorithm to do the same? </i>\n",
        "</center>"
      ],
      "metadata": {
        "id": "yxgxKxVNRDyY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title ### Prerequisite Imports\n",
        "#@markdown Run this cell for preliminary requirements. Double click it if you want to check out the source :)\n",
        "\n",
        "# Python ≥3.5 is required\n",
        "import sys\n",
        "assert sys.version_info >= (3, 5)\n",
        "\n",
        "# Is this notebook running on Colab or Kaggle?\n",
        "IS_COLAB = \"google.colab\" in sys.modules\n",
        "\n",
        "# Scikit-Learn ≥0.20 is required\n",
        "import sklearn\n",
        "assert sklearn.__version__ >= \"0.20\"\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "# To make this notebook's output stable across runs\n",
        "rnd_seed = 42\n",
        "rnd_gen = np.random.default_rng(rnd_seed)\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# Where to save the figures\n",
        "PROJECT_ROOT_DIR = \".\"\n",
        "CHAPTER_ID = \"classification\"\n",
        "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
        "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
        "    print(\"Saving figure\", fig_id)\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
        "\n",
        "# Import pooch - used to handle data downloading\n",
        "import pooch\n",
        "\n",
        "# Define colorblind safe colors for plotting\n",
        "colors = [np.array([215, 166, 122])/255, np.array([0, 148, 199])/255, np.array([214, 7, 114])/255]"
      ],
      "metadata": {
        "id": "POzUOOb62D3d",
        "cellView": "form"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A Quick Introduction to Uncertainty\n",
        "\n",
        "Uncertainty is one of those terms that you are likely very familiar with, whether in the colloquial sense (I'd have a hard time believing that any of us have *never* had a moment of doubt) or the scientific sense.\n",
        "\n",
        "Today, we'll be using <a href=\"https://doi.org/10.1175/AIES-D-22-0061.1\">Haynes et al.'s paper on uncertainty estimates with neural networks for environmental science applications</a> as a guide for our efforts. Let us then begin as they did - by discussing the different types of uncertainty we expect to encounter.\n",
        "\n",
        "Generally speaking, we expect to encounter two types of uncertainty - reducible and irreducible uncertainty. As you may imagine, irreducible uncertainty refers to the types of uncertainty that we can't do anything about - largely associated with *stochastic* (a.k.a. random) processes.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "v57XhsEJp-Ju"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Irreducible Uncertainty\n",
        "\n",
        "Let's imagine, for example, that you are the head of a seedy bookmaker organization that wants to make money on rubber duck races - similar to the one pictured below:"
      ],
      "metadata": {
        "id": "EGsFgYkW7fIN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Run this cell to load the embedded youtube video\n",
        "from IPython.display import HTML\n",
        "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OhRngvJlfmI?amp;start=35\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen>')\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "59LDm6fPyQIM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "156367d8-4f16-4f10-8d98-0547fad721c4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OhRngvJlfmI?amp;start=35\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You'd have a hard (even impossible) time trying to fix your odds for a single duck winning. Sure, maybe you could figure out what region of the box the winning duck is more likely to come from after enough trials. But the turbulent swirls (or *eddies*) in the flow in the river, the waterfall, and the collisions between ducks are all stochastic processes that will ultimately determine the average speed of the duck between the start and finish (and therefore the winner).\n",
        "\n",
        "Many phenomena in this world are like these ducks - we can't predict them precisely but we can talk about them in statistical terms (for example, the diffusion of milk into your morning coffee). As a result, many of the data that we are able to measure carry a degree of uncertainty that we have no way of reducing - e.g., was the mean flow that I measured influenced by turbulent eddies? And if so, by how much?\n",
        "\n",
        "In Machine Learning literature, people sometimes even extend the concept of irreducible uncertainty to the uncertainty in the data itself (e.g., the measurements we're working with). However, this type of unertainty is not strictly irreducible - better measurement technology, understanding of the phenomena, or includion of additional data might end up with us reducing the uncertainty in our current dataset. However, most of the time we're stuck with the quality of the data that we have and thus some refer to this uncertainty as irreducible. We don't recommend doing this, but hopefully this gives you context for their reasoning.\n",
        "\n",
        "As a last note, you will also hear about irreducible uncertainty being referred to as ***aleatoric*** uncertainty, which comes from the latin word for random. (This will be more apparent to those of you who speak romantic languages, as aleatorio / aléatoire / aleatório mean random in Spanish / French / Portuguese)"
      ],
      "metadata": {
        "id": "HDlLk4G-1qaB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Reducible Uncertainty\n",
        "\n",
        "This brings us to the second kind of uncertainty: reducible uncertainty. This is the type of uncertainty that results from decisions made in the data sampling and model development process, i.e., *it comes from our lack of knowledge*. This can take form in our method for measuring, sampling, or even the technical aspects of how we build our models.\n",
        "\n",
        "As a matter of fact, you've probably haven't thought about how your own decisions in programming can affect the output of your models. Let's look at the errors we can get depending on how we handle data operations."
      ],
      "metadata": {
        "id": "G1epCq7W7ifU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "KVMhM_V8Q8hw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61e75435-708a-4aeb-f189-2f25299b3720"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The value of a is 1.0\n"
          ]
        }
      ],
      "source": [
        "# We know that by adding a period we have a floating point representation of a number\n",
        "a = 1.0\n",
        "print(f\"The value of a is {a}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Additionally, we can make a list of 10 elements whose value is 0.1\n",
        "b = [0.1]*10\n",
        "print(f\"b as a list: {b}\")\n",
        "# If we were to sum those elements, we would expect a value of 1. Let's try python's built-in sum function:\n",
        "b = sum(b)\n",
        "print(f\"The sum of b is {b}\")"
      ],
      "metadata": {
        "id": "fCSW83WaR5Rk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6be74666-5439-433f-87f7-7e13fdd76475"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b as a list: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]\n",
            "The sum of b is 0.9999999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By summing 0.1 to itself multiple times, we introduce errors (and hence uncertainty) because there is no way of representing 0.1 perfectly as a floating point number, and the error accumulates over the repetitions. Let's try using numpy intead."
      ],
      "metadata": {
        "id": "p5vbFGB1R75S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c = np.array([0.1]*10)\n",
        "print(f\"c as an array: {c}\")\n",
        "c = np.sum(c)\n",
        "print(f\"The sum of c is {c}\")"
      ],
      "metadata": {
        "id": "iU-05ri1SMyt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc7664bb-779b-42b2-df9a-528c8f701920"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "c as an array: [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n",
            "The sum of c is 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Does this mean that numpy is perfect? Well, the short answer is no - it's just better at handling floating point operations that the default sum function in python. But it too accumulates errors."
      ],
      "metadata": {
        "id": "MQN7MtWOSZcg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = np.array([0.05]*20)\n",
        "print(f\"d as an array: {d}\")\n",
        "d = np.sum(d)\n",
        "print(f\"The sum of d is {d}\")"
      ],
      "metadata": {
        "id": "55HK-VWgSjwo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c347482-0075-47f7-a03f-0f5d8976654b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "d as an array: [0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05\n",
            " 0.05 0.05 0.05 0.05 0.05 0.05]\n",
            "The sum of d is 1.0000000000000002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may imagine, then, that performing many operations on values can end up with a small accumulation of error. Still, this is typically one of the smaller sources of uncertainty in our models.\n",
        "\n",
        "How accurate and precise was the instrument used to generate the data we're working with? How much information is present in the variables/covariates we've chosen to model our problem? What variables/processes important for our problem are we ignorant of or unable to capture? These are all sources of uncertainty that we're forced to deal with and, though these are reducible, in practice it's generally something that is easier said than done.\n",
        "\n",
        "Finally, before we move on to today's problem, let's introduce another term for reducible uncertainty: ***epistemic uncertainty***. This term comes from the greek epistēmē, meaning knowledge, and thus reflects that this uncertainty comes from a lack of knowledge. 🙂"
      ],
      "metadata": {
        "id": "_bVekRpaf60g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Q1) Can you think of sources of epistemic and aleatoric uncertainty in your field?**"
      ],
      "metadata": {
        "id": "nm5wdvvQPBUT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add your reflections here 🧠"
      ],
      "metadata": {
        "id": "NIfX3TD1PP-k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preparation and Analysis\n",
        "First, let's download a dataset we prepared for this notebook and plot it. Like before, we'll rely on pooch to load the data from OneDrive, and then save the file assocaited with the \"x\" values in x_file and that associated wit the \"y\" values in y_file."
      ],
      "metadata": {
        "id": "e4MbwINARR0K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve the files from the cloud using Pooch.\n",
        "data_url = 'https://unils-my.sharepoint.com/:u:/g/personal/tom_beucler_unil_ch/EUAqVt0ZDwNNlVTALKSu_foBBgaBoWZy1A2bxoVPCHBFfA?download=1'\n",
        "hash = '1ed5315572b919ed192e50ecaecff18a060db99d58119c151bbbe26028aa449c'\n",
        "\n",
        "files = pooch.retrieve(data_url, known_hash=hash, processor=pooch.Unzip())\n",
        "[print(f'File at index {idx}: {filename.split(\"/\")[-1]}') for idx, filename in enumerate(files)];\n",
        "\n",
        "# look for UQ_x in the filename to get the x file\n",
        "for filename in files:\n",
        "    if 'UQ_y' in filename:\n",
        "        y_file = filename\n",
        "    elif 'UQ_x' in filename:\n",
        "        x_file = filename"
      ],
      "metadata": {
        "id": "r5grRwXtOzE0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd974a21-725c-4f85-d543-c297dd90e2f7"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File at index 0: ch9_UQ_x.npy\n",
            "File at index 1: ch9_UQ_y.npy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's load the files. Note that they're npy files\n",
        "y = np.load(y_file)\n",
        "x = np.load(x_file)"
      ],
      "metadata": {
        "id": "luoI3IlrO7N3"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# And make a scatter plot. Be sure to use a small marker size and make the dots semi-transparent!\n",
        "fig, ax = plt.subplots(figsize = (8,2), dpi=150)\n",
        "ax.scatter(x, y, s=0.1, alpha=0.5)\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')"
      ],
      "metadata": {
        "id": "HBZvxVMzYQYF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "cfdbe759-f9fb-4783-e2e6-1f6b37bb88a8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'y')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imagine that you've taken this data from a physical variable you're very familiar with - and thus you know that you can model the variable with a 4th degree polynomial. Let's fit it quickly, e.g., with scikit-learn.\n",
        "\n",
        "#### **Q2) Model the data using a 4th degree polynomial**\n",
        "<details>\n",
        "<summary>Tips</summary>\n",
        "Scikit-learn doesn't include a polynomial regression model because they can be more easily made by extending the linear model! Since a polynomial regression is simply the linear combination of inputs to different powers, you can instead populate the feature space with these and fit a linear regression model.\n",
        "\n",
        "See <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">this scikit-learn module</a> for more details.\n",
        "\n",
        "</details>"
      ],
      "metadata": {
        "id": "NmNuLRnNpSMR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tn4KjXtznv7R"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit a 4th degree polynomial regression model using scikit-learn"
      ],
      "metadata": {
        "id": "NyP7XIVZ6eu_"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the polynomial regression and the datapoints"
      ],
      "metadata": {
        "id": "rM-4tgVK6z4j"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Here's one solution for fitting the model. Double click to see the answer.\n",
        "%%script false --no-raise-error\n",
        "# Remove %%script false --no-raise-error in order to get this cell to run\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "poly_features = PolynomialFeatures(degree=4, include_bias=False)\n",
        "x_poly = poly_features.fit_transform(x.reshape(-1, 1))\n",
        "\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(x_poly, y)\n",
        "\n",
        "x_pred = np.linspace(x.min(), x.max(), 500)\n",
        "x_pred_poly = poly_features.transform(x_pred.reshape(-1, 1))\n",
        "y_pred = lin_reg.predict(x_pred_poly)"
      ],
      "metadata": {
        "id": "WeybCf7Mw8lV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Here's a quick plot of the polynomial regression in the solution above. Double click to see the answer.\n",
        "%%script false --no-raise-error\n",
        "# Remove %%script false --no-raise-error in order to get this cell to run\n",
        "fig, ax = plt.subplots(figsize = (8,2), dpi=150)\n",
        "ax.scatter(x, y, s=0.1, alpha=0.5)\n",
        "ax.plot(x_pred, y_pred, alpha=1, color='red')\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')"
      ],
      "metadata": {
        "id": "TJTdciNKswxD"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This does a pretty good job, but we can clearly see that y isn't a simple function of x - after all, there are multiple values of y for each value of x, so technically a function relating x to y does not exist!\n",
        "\n",
        "Thus, we can say that there is a degree of uncertainty in the value we predict for y. One common way to express this in your model is to add simple error bars, which quantify how far away your predicted y is from the actual value of y - **on average**.\n",
        "\n",
        "#### **Q3) How would express the confidence in this model?**\n",
        "Imagine a stakeholder asks you for a measurement of the error and how confident you are in the model predictions.\n",
        "\n",
        "<details>\n",
        "<summary>Tips</summary>\n",
        "\n",
        "Think of the many functions you already know for quantifying the error. Given the large number of samples that you have, how can you express the average error?\n",
        "\n",
        "Once you have a way of quantifying the error, how can you express your confidence in the spread of the error?\n",
        "\n",
        "</details>"
      ],
      "metadata": {
        "id": "CVk6kvcBxPTf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Quantify the error in the predictions made by the model"
      ],
      "metadata": {
        "id": "PRUmboF65b1W"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the model with your confidence intervals"
      ],
      "metadata": {
        "id": "IDwR0o0S5fxU"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<details>\n",
        "  <summary>Click here to read about one way you could answer question 1. Read it once you have your own answer 🙂 </summary>\n",
        "\n",
        "One way that you can quantify the error is by figuring out how far away your prediction is from the truth, on average. This can be done, e.g., by calculating the *mean absolute error*, i.e. $\\text{MAE} = \\dfrac{1}{n}\\sum_{i=0}^{n}|\\hat{y}_i - y_i|$.\n",
        "\n",
        "If you were to calculate the MAE and its statistics, as well as the variance of y, you'd get the following values (see our code cell below for the calculation)\n",
        "\n",
        "> The mean absolute error is 0.91 <br>\n",
        "The standard deviation of the error is 1.03 <br>\n",
        "The maximum error is 17.95 <br>\n",
        "The variance in y is 8.94\n",
        "\n",
        "If one were to see the report of this model including this MAE, one could get the misplaced impression that the model is able to capture the data's behavior quite well - an MAE of 0.91 compared to a variance of ~9 could give the appearance that the model does well enough (and for some applications, this could indeed be true).\n",
        "\n",
        "We can see, however, that the model does significantly worse at explaining the behavior of y around values of $x = 1$, $3$, and $5$. Similarly, by having a single value to represent the error mean and standard deviation, we are underconfident in our predictions in areas where our model does better (e.g.,  around x=2 & x=4).\n",
        "\n",
        "Like we discussed before, there can be many sources for the uncertainty we observe- for example, the spread in the values could be due to a stochastic process (i.e., a process we can only describe statistically), and this may lead us to have models that are better suited to predict conditions around specific inputs.\n",
        "</details>"
      ],
      "metadata": {
        "id": "2PhmH5-v1eNf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Our solution for quantifying the average error.\n",
        "%%script false --no-raise-error\n",
        "# Remove %%script false --no-raise-error to get this cell to run\n",
        "# Quantify the error using the mean absolute error.\n",
        "error = np.abs(lin_reg.predict(x_poly) - y)\n",
        "print(f'The mean absolute error is {error.mean():.2f}')\n",
        "print(f'The standard deviation of the error is {error.std():.2f}')\n",
        "print(f'The maximum error is {error.max():.2f}')\n",
        "print(f'The variance in y is {y.std():.2f}')"
      ],
      "metadata": {
        "id": "WUq9k_RS0vmq"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Our solution for visualizing the error.\n",
        "%%script false --no-raise-error\n",
        "# Remove %%script false --no-raise-error to get this cell to run.\n",
        "# Plot the polynomial model with error bars\n",
        "fig, ax = plt.subplots(figsize = (8,2), dpi=150)\n",
        "ax.scatter(x, y, s=0.1, alpha=0.25)\n",
        "ax.plot(x_pred, y_pred, alpha=1, color='red', linewidth=0.5)\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')\n",
        "ax.fill_between(x_pred, y_pred - error.mean() - 2 * error.std(), y_pred + error.mean() + 2*error.std(), alpha=0.25, color='red')"
      ],
      "metadata": {
        "id": "bbesFHnWOIjf"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may notice that simply using the average error for all $x$ is clearly unsatisfactory as we alternate between under-estimating and over-estimating the error 😞\n",
        "\n",
        "Looking a bit more closely 🕵 it would seem as if we need a different error bar for each $x$.\n",
        "\n",
        "To get some intuition, let's look at the distribution of y values in our data near $x = 1.1$. Let's do this by finding all of the points that are within .02 of 1.1, and then plotting the histogram."
      ],
      "metadata": {
        "id": "piwe7zkfOI-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select x values within 0.01 of 1\n",
        "x_close = x[np.abs(x - 1.1) < 0.02]\n",
        "\n",
        "# Select the corresponding y values\n",
        "y_close = y[np.abs(x - 1.1) < 0.02]\n",
        "\n",
        "# Plot the distribution of y values\n",
        "fig, ax = plt.subplots(figsize = (8,2), dpi=150)\n",
        "ax.hist(y_close, density=True)\n",
        "ax.set_xlabel('y')\n",
        "ax.set_ylabel('Probability density')\n",
        "ax.set_title('Distribution of y values for x ≈ 1.1')"
      ],
      "metadata": {
        "id": "BV6RPtiZp-1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "a9cf0121-6ec6-4ae4-93ec-df1ef3032b5b"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of y values for x ≈ 1.1')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you did as we did, you should get the following plot: <br><center>\n",
        "<!-- <img src=\"https://github.com/tbeucler/2024_MLEES_Ebook/blob/main/Milton/paste_link_here.png?raw=1\" width=80% height=250></center> -->\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "OHVyP-y0vH6i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at this histogram, it looks like we could make the assumption that the distribution of y given a value of x can be approximated by a normal distribution (a.k.a. a bell curve, or a gaussian).\n",
        "\n",
        "The question now becomes, <i>how do we do this?</i> 🤔"
      ],
      "metadata": {
        "id": "SKiZJQYUHDES"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Distributional Regression"
      ],
      "metadata": {
        "id": "AI35gMJw0nIu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Run this code cell to define some functions we'll need for an interactive demo\n",
        "from ipywidgets import interact\n",
        "from ipywidgets import fixed\n",
        "def gaussian(mean, std):\n",
        "    \"\"\"\n",
        "    Plots a gaussian distribution with a given mean and standard deviation.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    mean : float\n",
        "        The mean of the gaussian distribution.\n",
        "    std : float\n",
        "        The standard deviation of the gaussian distribution.\n",
        "    \"\"\"\n",
        "    plt.close('all')\n",
        "    fig, ax = plt.subplots(figsize = (8,2), dpi=150)\n",
        "    x = np.linspace(mean - 6*std, mean + 6*std, 500)\n",
        "    y = np.exp(-0.5 * (( x - mean) / std) ** 2) / (std * np.sqrt(2 * np.pi))\n",
        "    ax.plot(x, y)\n",
        "    ax.set_xlabel('x')\n",
        "    ax.set_ylabel('Probability Density')\n",
        "    ax.set_title(f'Gaussian with mean {mean} and std {std}')\n",
        "    ax.set_xlim(-30, 40)\n",
        "    ax.set_ylim(0, max(y.max()*1.1, 0.5))\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "Sx2glU7gczcj"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to make this adjustment, we can write a model that predicts a distribution for each value of x. In this way, we are writing a model for the joint probability of x and y. As you may know, the shape of a normal distribution is given by two values: the mean (mu, $\\mu$) and the standard deviation (sigma, $\\sigma$).\n",
        "\n",
        "If you run the code below, you can play around with a gaussian distribution and see how the two parameters change the output."
      ],
      "metadata": {
        "id": "Pju6YnEJcycK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "interact(gaussian, mean=(-5, 5, 0.1), std=(0.5, 10, 0.5))"
      ],
      "metadata": {
        "id": "T6dnDnYReHCb",
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610,
          "referenced_widgets": [
            "0b6ff778a8f04fc4af9b7308a2042908",
            "b3f8f0a954ec497ab550df355b79689e",
            "a69fbfb3c4a048629d7a5dc7f25724a1",
            "d783feb1891c4b349eafbc9cc2acb6cd",
            "dfa4bdb8cefa4a629f720cd484addae7",
            "b0be8c72ee4a4ec384c11f181c453583",
            "4771a666d0c64dc89e6d22a2aab8bf62",
            "a13fd45f2a7442b4a2b30c419a728b60",
            "0d6e19da6e8841cc93ec3df27fd7589e",
            "be4b1001244d411187cf8577e9d982c0"
          ]
        },
        "outputId": "5f91bd58-74da-4dab-9331-b2e31c0e624e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='mean', max=5.0, min=-5.0), FloatSlider(value=5.0, de…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b6ff778a8f04fc4af9b7308a2042908"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.gaussian(mean, std)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>gaussian</b><br/>def gaussian(mean, std)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/content/&lt;ipython-input-50-c8bb6f712375&gt;</a>Plots a gaussian distribution with a given mean and standard deviation.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "mean : float\n",
              "    The mean of the gaussian distribution.\n",
              "std : float\n",
              "    The standard deviation of the gaussian distribution.</pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given that we can do this, why don't we train a neural network to predict the mean (*$\\mu$*) and standard deviation (*$\\sigma$*) of y given a value of x? Well, if you use the techniques we've been using so far, this might seem straightforward, until you reach the following question: <br> *what should I use as a training loss?*\n",
        "\n",
        "Before, you trained neural networks to reduce some kind of deterministic metric (e.g., the mean average error, root mean square error, or accuracy). However, these metrics require that we compare singular values against other singular values - if we predict $\\mu$ and $\\sigma$, we will need a function that will return a singular value when comparing distributions.\n",
        "\n",
        "Here is where the Continuous Ranked Probability Score (CRPS) comes in! Let's walk through what we're going to do in order for us to understand how our network will learn."
      ],
      "metadata": {
        "id": "21JJa_C90z27"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Explaining CRPS\n",
        "\n",
        "The CRPS is a value that quantifies the absolute area between the <a href=\"https://en.wikipedia.org/wiki/Cumulative_distribution_function\"> cumulative distribution function</a> of two distributions. Let's say that we have two gaussians we want to compare to the standard gaussian distribution ($\\mu = 0$ and $\\sigma = 1$), the first with $\\mu = 0.5$ and $\\sigma = 1.2$, and the second with $\\mu=0.1$ and $\\sigma = 1.7$. Which of these two is closer to the standard gaussian?"
      ],
      "metadata": {
        "id": "36RM_AxwP6ub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Let's plot the three gaussians. As always, feel free to check the code by double clicking :)\n",
        "from scipy.stats import norm\n",
        "fig, ax = plt.subplots(figsize = (8.5,3), dpi=150)\n",
        "x = np.linspace(-5, 5, 500)\n",
        "y1 = norm(0, 1).pdf(x)\n",
        "y2 = norm(0.5, 1.2).pdf(x)\n",
        "y3 = norm(0.1, 1.7).pdf(x)\n",
        "\n",
        "colors = [np.array([215, 166, 122])/255, np.array([0, 148, 199])/255, np.array([214, 7, 114])/255]\n",
        "labels = ['Standard Gaussian', 'Gaussian 1', 'Gaussian 2']\n",
        "\n",
        "for i, y in enumerate([y1, y2, y3]):\n",
        "    ax.plot(x, y, color=colors[i], label=labels[i])\n",
        "    # shade the area underneath the curve\n",
        "    ax.fill_between(x, 0, y, color=colors[i], alpha=0.2)\n",
        "\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('probability density')\n",
        "ax.set_title('Comparison of three gaussians')\n",
        "ax.legend()\n",
        "fig.tight_layout();"
      ],
      "metadata": {
        "id": "zEeSOSDTRIEv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "cdeaf013-2df1-46bb-8d31-7742a01ee309"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1275x450 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Like we said, the CRPS depends on the area between the CDFs. Let's plot the CDFs of the standard Gaussian with the two Gaussians. Calculating the CRPS between two Gaussians requires a bit of math that we don't want to get into, so we'll just give you the function to calculate it 😀"
      ],
      "metadata": {
        "id": "TAk2lgsmUj9S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown This cells defines the gaussian_crps function, which takes in $\\mu_1,\\; \\sigma_1,\\; \\mu_2,\\; \\sigma_2$\n",
        "from scipy.stats import norm\n",
        "def gaussians_crps(mu_F, sigma_F, mu_G, sigma_G):\n",
        "    \"\"\"\n",
        "    Calculates the CRPS between two gaussians.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    mu_F : float\n",
        "        The mean of the first gaussian.\n",
        "    sigma_F : float\n",
        "        The standard deviation of the first gaussian.\n",
        "    mu_G : float\n",
        "        The mean of the second gaussian.\n",
        "    sigma_G : float\n",
        "        The standard deviation of the second gaussian.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    crps : float\n",
        "        The CRPS between the two gaussians.\n",
        "    \"\"\"\n",
        "    # Calculate the intersection\n",
        "    x_inter = (mu_F * sigma_G - mu_G * sigma_F) / (sigma_G - sigma_F)\n",
        "\n",
        "\n",
        "    min_bound = min(mu_F - 4 * sigma_F, mu_G - 4 * sigma_G)\n",
        "    max_bound = max(mu_F + 4 * sigma_F, mu_G + 4 * sigma_G)\n",
        "\n",
        "    first_area = None\n",
        "    if x_inter > min_bound:\n",
        "        x_lower = np.linspace(min_bound, x_inter, 100)\n",
        "        # Calculate the CDF values for x_lower\n",
        "        y_lower_F = norm(mu_F, sigma_F).cdf(x_lower)\n",
        "        y_lower_G = norm(mu_G, sigma_G).cdf(x_lower)\n",
        "\n",
        "        # Calculate the area under the curve\n",
        "        first_area = np.abs(np.trapz(y_lower_F, x_lower) - np.trapz(y_lower_G, x_lower))\n",
        "\n",
        "    second_area = None\n",
        "    if x_inter < max_bound:\n",
        "        x_upper = np.linspace(x_inter, max_bound, 100)\n",
        "        # Calculate the CDF values for x_upper\n",
        "        y_upper_F = norm(mu_F, sigma_F).cdf(x_upper)\n",
        "        y_upper_G = norm(mu_G, sigma_G).cdf(x_upper)\n",
        "        # Calculate the area under the curve\n",
        "        second_area = np.abs(np.trapz(y_upper_F, x_upper) - np.trapz(y_upper_G, x_upper))\n",
        "\n",
        "    # Calculate the CRPS\n",
        "    crps = (first_area if first_area is not None else 0) + (second_area if second_area is not None else 0)\n",
        "\n",
        "    return crps"
      ],
      "metadata": {
        "id": "Ok4iHh04Y2YO",
        "cellView": "form"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Run this cell to get an interactive plot visualizing the two distributions, the area between them, and the resulting CRPS.\n",
        "def compare_gaussians(mu_1=0.5, sigma_1=1.2, mu_2=0.1, sigma_2=1.7):\n",
        "    \"\"\"\n",
        "    Compares two gaussians.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    mu_1 : float\n",
        "        The mean of the first gaussian.\n",
        "    sigma_1 : float\n",
        "        The standard deviation of the first gaussian.\n",
        "    mu_2 : float\n",
        "        The mean of the second gaussian.\n",
        "    sigma_2 : float\n",
        "        The standard deviation of the second gaussian.\n",
        "    \"\"\"\n",
        "    plt.close('all')\n",
        "\n",
        "    fig, axs = plt.subplots(2, 1, figsize = (6,5), dpi=100, sharex=True)\n",
        "\n",
        "    fig.set_facecolor(np.array([64,64,62])/255)\n",
        "    standard_gaussian = norm(0, 1)\n",
        "\n",
        "    x_lower = min(mu_1 - 4 * sigma_1, mu_2 - 4 * sigma_2)\n",
        "    x_upper = max(mu_1 + 4 * sigma_1, mu_2 + 4 * sigma_2)\n",
        "    x = np.linspace(x_lower, x_upper, 500)\n",
        "    ys = []\n",
        "    ys.append(standard_gaussian.cdf(x))\n",
        "    ys.append(norm(mu_1, sigma_1).cdf(x))\n",
        "    ys.append(norm(mu_2, sigma_2).cdf(x))\n",
        "\n",
        "    crps_scores = []\n",
        "    crps_scores.append(gaussians_crps(0, 1, mu_1, sigma_1))\n",
        "    crps_scores.append(gaussians_crps(0, 1, mu_2, sigma_2))\n",
        "\n",
        "    for idx, ax in enumerate(axs):\n",
        "        ax.plot(x, ys[0], color = colors[0], label=labels[0])\n",
        "        ax.plot(x, ys[idx+1], color=colors[idx+1], label=labels[idx+1], linewidth=0.1)\n",
        "        legend = ax.legend()\n",
        "\n",
        "\n",
        "\n",
        "        ax.fill_between(x, ys[0], ys[idx+1], color=colors[idx+1], alpha=0.2)\n",
        "        ax.set_title(f'Gaussian {idx+1} CRPS {crps_scores[idx]:.2f}')\n",
        "        ax.set_ylabel('Cumulative Prob.')\n",
        "        ax.set_ylim(-0.05, 1.05)\n",
        "\n",
        "        # Change the legend facecolor\n",
        "        legend.get_frame().set_facecolor(np.array([217, 217, 217])/255)\n",
        "        # And make the legend lines more visibile\n",
        "        for line in legend.get_lines():\n",
        "            line.set_linewidth(2)\n",
        "\n",
        "        # set the labels, ticks, tickmarks, axis labels, and title colors to white):\n",
        "        for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
        "                    ax.get_xticklabels() + ax.get_yticklabels()):\n",
        "            item.set_color('white')\n",
        "        ax.tick_params(axis='both', which='both', color='white')\n",
        "\n",
        "        # draw axis lines\n",
        "        xaxis = ax.axhline(y=0, color='k', linestyle='--', linewidth=0.3)\n",
        "        yaxis = ax.axvline(x=0, color='k', linestyle='--', linewidth=0.3)\n",
        "\n",
        "        xaxis.set_dashes([20, 10])\n",
        "        yaxis.set_dashes([20, 10])\n",
        "\n",
        "    axs[1].set_xlabel('x')\n",
        "    fig.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "compare_gaussians.__doc__ = None\n",
        "\n",
        "interact(compare_gaussians,\n",
        "         mu_1=(-1, 1, 0.1), sigma_1=(1.1, 3, 0.1), # Parameter range of distribution 1\n",
        "         mu_2=(-1, 1, 0.1), sigma_2=(1.1, 3, 0.1), # Parameter range of distribution 2\n",
        "         )"
      ],
      "metadata": {
        "id": "Ajg1V2b_UjaZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722,
          "referenced_widgets": [
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            "e85b55f82dc74357ad109877d1e5794b",
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          ]
        },
        "outputId": "b764c8ad-df0f-40a0-c7ec-c3b8965b0854"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.5, description='mu_1', max=1.0, min=-1.0), FloatSlider(value=1.2, de…"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.compare_gaussians(mu_1=0.5, sigma_1=1.2, mu_2=0.1, sigma_2=1.7)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>compare_gaussians</b><br/>def compare_gaussians(mu_1=0.5, sigma_1=1.2, mu_2=0.1, sigma_2=1.7)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/content/&lt;ipython-input-54-170383dc0f84&gt;</a>&lt;no docstring&gt;</pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "We now have a way to evaluate the distance between two distributions (and it's even a [*proper score*](https://en.wikipedia.org/wiki/Scoring_rule#:~:text=Any%20given%20proper%20scoring%20rule,the%20predicted%20probabilities%20will%20ultimately) !!!) - but if you recall what we're going to be doing is comparing the gaussian that we predict to deterministic observations (i.e., we'll predict a distribution but our target is a single value).\n",
        "\n",
        "How do we go about doing this? Well, one way that we can do this is to represent our observations with what is known as a <a href='https://en.wikipedia.org/wiki/Degenerate_distribution'>*degenerate distribution*</a> (a.k.a. a dirac distribution), whose PDF is a step function with a value between 0 and 1, where the change happens at the value of our observation. Let's make a quick plot, since a picture is worth 1000 words!\n",
        "📸\n"
      ],
      "metadata": {
        "id": "SgwAvSMQsp7e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Run this cell to get an interactive plot of a degenerate distribution.\n",
        "def degenerate_distribution(x):\n",
        "    \"\"\"\n",
        "    Plots a degenerate distribution.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    x : float\n",
        "        The value of the degenerate distribution.\n",
        "        \"\"\"\n",
        "    plt.close('all')\n",
        "    fig, ax = plt.subplots(figsize = (8,2), dpi=150)\n",
        "\n",
        "    x_range = np.linspace(x - 5, x + 5, 500)\n",
        "    Y_heaviside = np.heaviside(x_range - x, 0)\n",
        "    ax.plot(x_range, Y_heaviside, color=np.array([215, 166, 122])/255)\n",
        "\n",
        "    ax.set_xlabel('x')\n",
        "    ax.set_ylabel('Cumulative Prob.')\n",
        "    ax.set_title(f'Degenerate distribution with x = {x}')\n",
        "    ax.set_xlim(x-5, x+5)\n",
        "    ax.set_ylim(-.05, 1.05)\n",
        "    ax.axhline(y=0, color='k', linestyle='--', linewidth=0.3)\n",
        "    ax.axvline(x=0, color='k', linestyle='--', linewidth=0.3)\n",
        "    plt.show()\n",
        "\n",
        "interact(degenerate_distribution, x=(-4.9, 4.9, 0.1))"
      ],
      "metadata": {
        "id": "5F2EDFe4t3wq",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584,
          "referenced_widgets": [
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        },
        "outputId": "2931487e-105c-4677-a7b2-75be5a5db40d"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='x', max=4.9, min=-4.9), Output()), _dom_classes=('wi…"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "689fae8994a344e8b71c61feb1d5d91f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.degenerate_distribution(x)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>degenerate_distribution</b><br/>def degenerate_distribution(x)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/content/&lt;ipython-input-55-932925fd350d&gt;</a>Plots a degenerate distribution.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "x : float\n",
              "    The value of the degenerate distribution.\n",
              "    </pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With this, we have two distributions that we can compare using the CRPS! Let's take a single value of x in our dataset, and see if we can manually minimize the CRPS value for the observations for that single value of X."
      ],
      "metadata": {
        "id": "8oD-dkLW3tUe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's load the files again. Note that they're npy files\n",
        "y = np.load(y_file)\n",
        "x = np.load(x_file)\n",
        "\n",
        "# 1.02 is one of the unique values in x, so let's use it\n",
        "# to get a sample of y_values.\n",
        "sample_x = 1.02\n",
        "\n",
        "# Let's get the corresponding y values\n",
        "sample_y = y[x == sample_x]\n",
        "\n",
        "# Print out some statistics\n",
        "mean = sample_y.mean()\n",
        "std = sample_y.std()\n",
        "print(f'There are {len(sample_y)} y values for x = {sample_x}')\n",
        "print(f'The mean of the sample is {mean:.2f} and the standard deviation is {std:.2f}')\n",
        "\n",
        "# and 5 points\n",
        "sample_y = np.random.choice(sample_y, 5)"
      ],
      "metadata": {
        "id": "KR9onydZ4Ox2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afc3227a-e5d9-4a9b-eb89-21a1bdc4d7c4"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 98 y values for x = 1.02\n",
            "The mean of the sample is 44.87 and the standard deviation is 0.24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Code based on the properscoring library\n",
        "# https://github.com/properscoring/properscoring/\n",
        "def crps_gaussian(y, mu, sig):\n",
        "    \"\"\"\n",
        "    Computes the CRPS of observations y relative to normally distributed\n",
        "    forecast with mean, mu, and standard deviation, sig.\n",
        "\n",
        "    CRPS(N(mu, sig^2); x)\n",
        "\n",
        "    Formula taken from Equation (5):\n",
        "\n",
        "    Calibrated Probablistic Forecasting Using Ensemble Model Output\n",
        "    Statistics and Minimum CRPS Estimation. Gneiting, Raftery,\n",
        "    Westveld, Goldman. Monthly Weather Review 2004\n",
        "\n",
        "    http://journals.ametsoc.org/doi/pdf/10.1175/MWR2904.1\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    x : scalar or np.ndarray\n",
        "        The observation or set of observations.\n",
        "    mu : scalar or np.ndarray\n",
        "        The mean of the forecast normal distribution\n",
        "    sig : scalar or np.ndarray\n",
        "        The standard deviation of the forecast distribution\n",
        "    grad : boolean\n",
        "        If True the gradient of the CRPS w.r.t. mu and sig\n",
        "        is returned along with the CRPS.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    crps : scalar or np.ndarray or tuple of\n",
        "        The CRPS of each observation y relative to mu and sig.\n",
        "\n",
        "    \"\"\"\n",
        "    y = np.asarray(y)\n",
        "    mu = mu\n",
        "    sig = sig\n",
        "\n",
        "    # Normalize the y values using mu and sigma\n",
        "    y_norm = (y - mu) / sig\n",
        "\n",
        "    # Generate the standard gaussian\n",
        "    std_distribution = norm(0, 1)\n",
        "\n",
        "    # Calculate the pdf and cdf function values at which the target y value\n",
        "    # is located, as well as the constant value we'll need\n",
        "    pdf = std_distribution.pdf(y_norm)\n",
        "    cdf = std_distribution.cdf(y_norm)\n",
        "    c = 1. / np.sqrt(np.pi)\n",
        "\n",
        "    # Calculate the crps using the analytical formula from the reference\n",
        "    crps = sig * (y_norm * (2 * cdf - 1) + 2 * pdf - c)\n",
        "\n",
        "    return crps"
      ],
      "metadata": {
        "id": "Olixw49MPBz8"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def degenerate_gaussian(obs, mu = mean-1, sigma = std-.2):\n",
        "    \"\"\"\n",
        "    Plots a degenerate distribution cdf of a set of observations alongside a gaussian distribution cdf. Also outputs the CRPS value for each observation, as well as the mean CRPS value.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    obs : np.ndarray\n",
        "        The observations.\n",
        "    mu : float\n",
        "        The mean of the gaussian distribution.\n",
        "    sigma : float\n",
        "        The standard deviation of the gaussian distribution.\n",
        "    \"\"\"\n",
        "    lower_bound = min(mu - 3 * sigma, np.min(obs).item() - 3 * sigma)\n",
        "    upper_bound = max(mu + 3 * sigma, np.max(obs).item() + 3 * sigma)\n",
        "\n",
        "    norm_distribution = norm(mu, sigma)\n",
        "    obs_range = np.linspace(lower_bound, upper_bound, 500)\n",
        "\n",
        "    cdf = norm_distribution.cdf(obs_range)\n",
        "\n",
        "    crps_vals = np.zeros(len(obs))\n",
        "    for idx, obs_val in enumerate(obs):\n",
        "        crps_vals[idx] = crps_gaussian(y=obs_val, mu=mu, sig=sigma)\n",
        "\n",
        "    num_rows = len(obs)\n",
        "    fig, axs = plt.subplots(num_rows, 1, figsize = (6,8), dpi=100, sharey=True)\n",
        "    fig.set_facecolor(np.array([64,64,62])/255)\n",
        "    for idx, ax in enumerate(axs):\n",
        "        degenerate_distribution = np.heaviside(obs_range - obs[idx], 0)\n",
        "        ax.plot(obs_range, cdf, color=colors[0])\n",
        "        ax.plot(obs_range, degenerate_distribution, color=np.array([0, 148, 199])/255)\n",
        "\n",
        "        ax.fill_between(obs_range, cdf, degenerate_distribution, color=colors[0], alpha=0.2)\n",
        "\n",
        "        # add label on the right side\n",
        "        # Add a label on the right border\n",
        "        label = f'CRPS: {crps_vals[idx]:.2f}'\n",
        "        ax.annotate(label, xy=(1, 0.5), xycoords='axes fraction',\n",
        "            xytext=(10, 0), textcoords='offset points',\n",
        "            ha='left', va='center', rotation=90, color='white')\n",
        "\n",
        "        # set the labels, ticks, tickmarks, axis labels, and title colors to white):\n",
        "        for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
        "                    ax.get_xticklabels() + ax.get_yticklabels()):\n",
        "            item.set_color('white')\n",
        "        ax.tick_params(axis='both', which='both', color='white')\n",
        "\n",
        "    fig.suptitle(f'Distribution with mean {mu:.2f} and std {sigma:.2f}\\nMean CRPS: {crps_vals.mean():.2f}', color='white')\n",
        "    fig.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Remove the docstring for a better user experience with interact()\n",
        "degenerate_gaussian.__doc__ = None\n"
      ],
      "metadata": {
        "id": "hni7X15BOboH"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown This cell will let you do what you will train your network to do! Can you find the mu and sigma that will minimize the value of the crps for these 5 observations?\n",
        "mu_min = sample_y.min()\n",
        "mu_max = sample_y.max()\n",
        "sigma_min = sample_y.std() / 6\n",
        "sigma_max = sample_y.std() * 6\n",
        "\n",
        "interact(degenerate_gaussian, mu=(mu_min, mu_max, 0.1), sigma=(sigma_min, sigma_max, 0.1), obs=fixed(sample_y))"
      ],
      "metadata": {
        "id": "L3_ay-P1NX0n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957,
          "referenced_widgets": [
            "85a324c505094521af0e0cecf15285a8",
            "82921b063acb492d8a513c90628f63b4",
            "6e83e0d133964a5d9652de3109a6bd87",
            "06fb26a20b4e4b79b332c2bb1eddfdaa",
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        },
        "outputId": "ebae092f-308d-4653-e359-3797528f4b0f"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=44.761297460605036, description='mu', max=45.05911026158328, min=44.76…"
            ],
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            }
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.degenerate_gaussian(obs, mu=43.86809334841716, sigma=0.044339984482627104)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>degenerate_gaussian</b><br/>def degenerate_gaussian(obs, mu=mean - 1, sigma=std - 0.2)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/content/&lt;ipython-input-58-2b0d22a2eaaa&gt;</a>&lt;no docstring&gt;</pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up the Neural Network"
      ],
      "metadata": {
        "id": "Y2afKafqK9x1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Today, we'll be modeling the conditional uncertainty by setting up a simple neural network using PyTorch."
      ],
      "metadata": {
        "id": "BOOi2IpRN_0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's start by importing torch, the functional api, and the module api\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch import nn"
      ],
      "metadata": {
        "id": "8pb5RPBDN-jV"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Q4) Set up a simple neural network using PyTorch**"
      ],
      "metadata": {
        "id": "CAZuqrCsUiaG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up a simple neural network by defining a class that inherits from nn.module. Replace ___ with whatever you want to name your class\n",
        "class ANN(nn.Module):\n",
        "    # Let's define what our model will look like using the init method\n",
        "    def __init__(self,\n",
        "                # Add additional arguments depending on what you need to define your neural network. We recommend have at least:\n",
        "                input_size, # the input size (i.e., the number of features in the input)\n",
        "                hidden_size, # the number of neurons in each hidden layer\n",
        "                output_size # the output size (i.e., the number of features in the output)\n",
        "                ):\n",
        "        # Call the parent's __init__ method. Replace ___ with the name of your class\n",
        "        super(ANN, self).__init__()\n",
        "\n",
        "        # Here is how you define a linear layer (i.e., a layer that combines n number of inputs linearly into m number of outputs). The values for n and m will likely depend on your arguments, or you can hard code them into your model definition if you prefer.\n",
        "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
        "\n",
        "        #You can also add as many layers as you like. Also, don't worry about the activation functions for now - we'll define them further down.\n",
        "        self.fc2 = nn.Linear(hidden_size, hidden_size)\n",
        "        self.fc3 = nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    # When the model is trained or used in inference, it calls upon its 'forward' method, which tell's it precisely what it needs to do. Let's go ahead and define it\n",
        "    def forward(self,\n",
        "                x, # Note that x is whatever inputs the neural network sees\n",
        "                ):\n",
        "        # The convention is to overwrite x whenever we call upon the model's layers. Let's assume you have a fully connected layer called fc1. This is how you would apply the leaky relu activation function:\n",
        "        # x = F.leaky_relu(self.fc1(x))\n",
        "        x = F.leaky_relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        # Write out your actual model below.\n",
        "        x = self.fc3(x)\n",
        "        # and finally, you should return the final value for x\n",
        "        return x"
      ],
      "metadata": {
        "id": "mKpTgJz0SJX6"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Our solution for defining a simple model\n",
        "%%script false --no-raise-error\n",
        "# Remove %%script false --no-raise-error to get this cell to run.\n",
        "# Now, let's create a model by defining a class that inherits from nn.module\n",
        "class ANN(nn.Module):\n",
        "    # Let's define what our model will look like using the init method\n",
        "    def __init__(self,\n",
        "                 input_size, # the number of features in the input\n",
        "                 hidden_size, # the number of neurons in the hidden layer\n",
        "                 output_size # the number of output features.\n",
        "                 ):\n",
        "        super(ANN, self).__init__()\n",
        "\n",
        "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
        "        self.fc2 = nn.Linear(hidden_size, hidden_size)\n",
        "        self.fc3 = nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    # When the model is trained or used in inference, it calls upon its 'forward' method, which tell's it precisely what it needs to do. Let's go ahead and define it\n",
        "    def forward(self, x):\n",
        "        x = F.leaky_relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "Lwy906cqPVl6"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown CRPS isn't predefined as a loss function in torch, but it *can* be written using torch. We've gone ahead and set it up as ```CRPS_ML``` for you in this cell,\n",
        "\n",
        "def CRPS_ML(y_pred, y_true, **kwargs):\n",
        "    \"\"\"Compute the Continuous Ranked Probability Score (CRPS).\n",
        "\n",
        "    The CRPS is a probabilistic metric that evaluates the accuracy of a\n",
        "    probabilistic forecast. It is defined as the integral of the squared\n",
        "    difference between the cumulative distribution function (CDF) of the\n",
        "    forecast and the CDF of the observations.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "\n",
        "    y_pred : array-like of shape (n_samples, 2*n_features)\n",
        "        The predicted probability parameters for each sample. The odd columns\n",
        "        should contain the mean (mu), and the even columns should contain the\n",
        "        standard deviation (sigma).\n",
        "\n",
        "    y_true : array-like of shape (n_samples,n_features)\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    crps : float or array-like\n",
        "        The CRPS value mean, or array of individual CRPS values.\n",
        "\n",
        "    \"\"\"\n",
        "    # adapted from https://github.com/WillyChap/ARML_Probabilistic/blob/main/Coastal_Points/Testing_and_Utility_Notebooks/CRPS_Verify.ipynb\n",
        "    # and work by Louis Poulain--Auzeau (https://github.com/louisPoulain)\n",
        "    reduction = kwargs.get(\"reduction\", \"mean\")\n",
        "    mu = y_pred[:, ::2]\n",
        "    sigma = y_pred[:, 1::2]\n",
        "\n",
        "    # prevent negative sigmas\n",
        "    sigma = torch.sqrt(sigma.pow(2))\n",
        "    loc = (y_true - mu) / sigma\n",
        "    pdf = torch.exp(-0.5 * loc.pow(2)) / torch.sqrt(\n",
        "        2 * torch.from_numpy(np.array(np.pi))\n",
        "    )\n",
        "    cdf = 0.5 * (1.0 + torch.erf(loc / torch.sqrt(torch.tensor(2.0))))\n",
        "\n",
        "    # compute CRPS for each (input, truth) pair\n",
        "    crps = sigma * (\n",
        "        loc * (2.0 * cdf - 1.0)\n",
        "        + 2.0 * pdf\n",
        "        - 1.0 / torch.from_numpy(np.array(np.sqrt(np.pi)))\n",
        "    )\n",
        "\n",
        "    return crps.mean() if reduction == \"mean\" else crps"
      ],
      "metadata": {
        "id": "FJQX-tjhRK1J",
        "cellView": "form"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need to define the other aspcets for our model training in torch as well. This includes the batch size, optimizer, & loss function. Let's make a list of the things we need to do:\n",
        "\n",
        "1. Since we know the underlying relationship is well represented using 4th degree polynomials, let's make polynomial features of the x array\n",
        "2. Convert the numpy arrays to pytorch tensors.\n",
        "3. Convert the tensors into a tensor dataset and use the dataset to make a dataloader.\n",
        "4. Instantiate the model. Remember that the model you defined above may need a number of arguments in order to be instantiated, depending on how you defined it.\n",
        "5. Define the loss function and optimizer\n",
        "6. Write and run the training loop. It should include an early stopping criterion or two :\n"
      ],
      "metadata": {
        "id": "rfS2-LbHX4us"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Q5) Implement steps 1-5 detailed above in the code cells below.**"
      ],
      "metadata": {
        "id": "ZcveonnvrV2J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make the polynomial features using sklearn's PolynomialFeatures. Remember to make sure that the shape of the data you transform is (num_samples, num_features) - otherwise sklearn will complain.\n",
        "from sklearn.preprocessing import PolynomialFeatures # Import the PolynomialFeatures class\n",
        "poly_fitter = PolynomialFeatures(degree=4, include_bias=False)\n",
        "poly_x = poly_fitter.fit_transform(x.reshape(-1, 1))"
      ],
      "metadata": {
        "id": "Q3y7bt2CVYe3"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform the numpy arrays into pytorch tensors.\n",
        "x_tensor = torch.from_numpy(poly_x).float()\n",
        "y_tensor = torch.from_numpy(y[:,None]).float()\n",
        "print(x_tensor.shape, y_tensor.shape)"
      ],
      "metadata": {
        "id": "pLCF0A9BVe-m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ba96d08-bd56-4978-f724-9c03dec56d91"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([50000, 4]) torch.Size([50000, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the x and y tensors to make a torch TensorDataset\n",
        "dataset = torch.utils.data.TensorDataset(x_tensor, y_tensor)"
      ],
      "metadata": {
        "id": "ijeFXB1DWDEn"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use your tensor dataset to make a torch DataLoader\n",
        "dataloader = torch.utils.data.DataLoader(dataset,\n",
        "                                         batch_size=256, # The number of samples in each batch\n",
        "                                         shuffle=True, # Whether to shuffle the order of the points being fed during training\n",
        "                                         )"
      ],
      "metadata": {
        "id": "vIL8LU98WJow"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Here is our solution to the above segments.\n",
        "%%script false --no-raise-error\n",
        "# Remove %%script false --no-raise-error to get this cell to run.\n",
        "poly_fitter = PolynomialFeatures(degree=4, include_bias=False)\n",
        "poly_x = poly_fitter.fit_transform(x.reshape(-1, 1))\n",
        "\n",
        "x_tensor = torch.from_numpy(poly_x).float()\n",
        "y_tensor = torch.from_numpy(y[:,None]).float()\n",
        "print(x_tensor.shape, y_tensor.shape)\n",
        "\n",
        "dataset = torch.utils.data.TensorDataset(x_tensor, y_tensor)\n",
        "dataloader = torch.utils.data.DataLoader(dataset,\n",
        "                                         batch_size=256, # The number of samples in each batch\n",
        "                                         shuffle=True, # Whether to shuffle the order of the points being fed during training\n",
        "                                         )"
      ],
      "metadata": {
        "id": "iO6y-EdRQz1e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate your model in this cell\n",
        "model = ANN(4,110,2)"
      ],
      "metadata": {
        "id": "MWTpokdnW567"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define your loss function - in this case, we'll use CRPS\n",
        "loss_function = CRPS_ML"
      ],
      "metadata": {
        "id": "hOQUJrQdW-vQ"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# And define your optimizer. You could start with Adam, but choose whatever suits your fancy.\n",
        "optimizer = torch.optim.NAdam(model.parameters(), lr=0.0001)"
      ],
      "metadata": {
        "id": "cilK0twvXLt8"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Our solution for the above segments is here\n",
        "%%script false --no-raise-error\n",
        "# Remove %%script false --no-raise-error to get this cell to run.\n",
        "\n",
        "model = ANN(4, #Number of input features\n",
        "            110, # Number of neurons / hidden layer\n",
        "            2, # Predicted features, =2 because we predict a mean and std\n",
        "            )\n",
        "\n",
        "loss_function = CRPS_ML\n",
        "optimizer = torch.optim.NAdam(model.parameters(), lr=0.0001)"
      ],
      "metadata": {
        "id": "QKV6lm1qZsPL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Q6) Update the training loop as needed, and train a model.**"
      ],
      "metadata": {
        "id": "Fz283ni2rliA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# training loop\n",
        "num_epochs = 100\n",
        "losses = []\n",
        "print('Starting training')\n",
        "for epoch in range(num_epochs):\n",
        "    batch_losses = []\n",
        "    i = 0\n",
        "    for x_batch, y_batch in dataloader:\n",
        "        i+=1\n",
        "        print(f'\\rEpoch {epoch+1}/{num_epochs}, Batch {i}/{len(dataloader)}, last loss: {losses[-1] if len(losses)>=1 else 0}', end='', flush=True)\n",
        "\n",
        "        # Reset the gradients - needs to happen for each iteration\n",
        "        optimizer.zero_grad()\n",
        "        # Forward pass\n",
        "        y_pred = model(x_batch)\n",
        "        # Compute the loss\n",
        "        loss = loss_function(y_pred, y_batch)\n",
        "        batch_losses.append(loss.item())\n",
        "\n",
        "        # Backpropagation\n",
        "        loss.backward()\n",
        "        # Update the weights\n",
        "        optimizer.step()\n",
        "    losses.append(np.mean(batch_losses))\n",
        "\n",
        "    # save the best model\n",
        "    if len(losses) > 1:\n",
        "        if losses[-1] < min(losses[:-1]):\n",
        "            torch.save(model.state_dict(), 'best_model.pth')\n",
        "\n",
        "    # early stopping\n",
        "    if len(losses) >=10:\n",
        "        if np.mean(losses[-10:]) < losses[-1]:\n",
        "            break\n",
        "\n",
        "print('\\n Training complete')"
      ],
      "metadata": {
        "id": "AareuAqvtUah",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92a58df6-8c2d-405a-972b-c7cb8c1d9b7d"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting training\n",
            "Epoch 91/100, Batch 196/196, last loss: 0.5923863880488337\n",
            " Training complete\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the training curve\n",
        "plt.plot(losses)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Curve')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "B_z8yQyz4Srl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "outputId": "0c49ed48-30bd-484a-b06b-144634412d7b"
      },
      "execution_count": 74,
      "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": [
        "### Model Performance Overview"
      ],
      "metadata": {
        "id": "ebVzq28gImdQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's go ahead and load the best model state from what we saved before\n",
        "model.load_state_dict(torch.load('best_model.pth'))"
      ],
      "metadata": {
        "id": "ZnYylBN66eQg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ea2b702-bf12-44fa-f1d5-c301bac75ff3"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-dbcff48ba996>:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model.load_state_dict(torch.load('best_model.pth'))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's go ahead and generate the plot for our predicted distributions\n",
        "x_plot = np.linspace(x.min(), x.max(), 500)\n",
        "x_plot = poly_fitter.fit_transform(x_plot[:,None])\n",
        "\n",
        "x_tensor_plot = torch.from_numpy(x_plot).float()\n",
        "y_pred = model(x_tensor_plot)\n",
        "\n",
        "mu = y_pred[:, 0].detach().numpy()\n",
        "sigma = np.abs(y_pred[:, 1].detach().numpy())\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(x, y, label='Data', alpha=0.15, s=0.05, color = colors[1])\n",
        "\n",
        "#plot the mean\n",
        "plt.plot(x_plot[:,0], mu, label='Mean', color=colors[2], linewidth=0.7)\n",
        "\n",
        "# plot 2 standard deviations above and below\n",
        "plt.fill_between(x_plot[:,0], mu + 2*sigma, mu - 2*sigma, color=colors[2], alpha=0.2)\n",
        "\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')\n",
        "plt.title('Predicted Distribution')\n",
        "# Increase the weight of the lines in the legend\n",
        "legend = plt.legend()\n",
        "for line in legend.get_lines():\n",
        "    line.set_linewidth(2)\n",
        "# increase the marker size of the scatter elements in the legend\n",
        "legend.legend_handles[0]._sizes = [60];\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "f7STcgOv80JU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "e00c842e-a1a5-48c1-e38d-81bb28515007"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Evaluation\n",
        "\n",
        "Now that we have a probabilistic model, we need to discuss ways in which to evaluate how well our model performs. However, up to now we've focused on deterministic metrics (e.g., RMSE, accuracy, MAE) in order to evaluate the performance of our models. As we saw before, these metrics are less useful when we predict a whole distribution as our outputs (whether it be via distributional regression as we did above, or even when making predictions with ensembles).\n",
        "\n",
        "Furthermore, we used the one probabilistic metric we've discussed as our optimization target (i.e., we trained the model to minimize the CRPS) -  if we use it as an evaluation metric we'll get an overly optimistic view of how well our model performs. We're therefore going to introduce two more metrics to evaluate the performance of our model.\n",
        "\n",
        "Note that these metrics also work when using other methods for quantifying uncertainty, such as cross-validation or model ensembles. However, the scores will be much worse for models whose errors aren't conditional on the inputs!"
      ],
      "metadata": {
        "id": "PDWdGQsmN947"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Spread Skill Score\n",
        "The Spread Skill Score (SSC) is used to quantify how large the *spread* predicted by your model(s) is (e.g., the predicted sigma in our case) compared to the mean error associated with predicted mean (i.e., its *skill*)."
      ],
      "metadata": {
        "id": "gOKqGKu0URnq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let's start by calculating the unique values of X. Note that normally this would done, e.g., by carrying out a random selection of inputs in order to sample the predicted spread and error. However, here we have the luxury of knowing that we have a relatively range of unique values for our univariate input.\n",
        "unique_x = np.unique(x)\n",
        "\n",
        "spread = np.zeros_like(unique_x)\n",
        "error = np.zeros_like(unique_x)\n",
        "# For each unique X value, let's get the predicted spread and the MAE\n",
        "for idx, x_val in enumerate(unique_x):\n",
        "    # Get the corresponding y values\n",
        "    y_true = y[x==x_val]\n",
        "\n",
        "    # Generate the mean and std from the model\n",
        "    mu, sigma = model(torch.from_numpy(poly_fitter.fit_transform(np.asarray(x_val)[None,None])).float()).detach().numpy().squeeze()\n",
        "\n",
        "    # Make sure that sigma is always positive\n",
        "    sigma = np.sqrt(sigma**2)\n",
        "\n",
        "    # Calculate the spread and error\n",
        "    spread[idx] = sigma\n",
        "    error[idx] = np.abs((y_true - mu)).mean() # MAE\n",
        "\n",
        "# Sort the spread and error by increasing error\n",
        "idxs = np.argsort(error)\n",
        "error = error[idxs]\n",
        "spread = spread[idxs]"
      ],
      "metadata": {
        "id": "lc6b6w4yUWjQ"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a square figure using plt.subplots\n",
        "fig, ax = plt.subplots(figsize=(6, 6), dpi=100)\n",
        "\n",
        "# set the origin to 0,0 and the top right corner to max(error_max, spread_max)\n",
        "upper_bound = max(error.max(), spread.max()) * 1.1\n",
        "ax.set_xlim(0, upper_bound)\n",
        "ax.set_ylim(0, upper_bound)\n",
        "\n",
        "# draw the diagonal line\n",
        "ax.plot([0, upper_bound],\n",
        "        [0, upper_bound],\n",
        "        color='black',\n",
        "        linestyle='--')\n",
        "\n",
        "# plot the spread vs skill\n",
        "ax.scatter(error, spread, s=0.75, color=colors[2])"
      ],
      "metadata": {
        "id": "iLqp1MIAjunc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "9bc4d30e-507b-400d-a502-c4e56fb50211"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7dae2d091c00>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that the ideal spread skill score is a line where the error in your prediction is equal to the spread that you predict!\n",
        "\n",
        "Additionally, if you use a method of uncertainty quantification that isn't conditional on the inputs (like the error bars we produced way up in Question 2), you'll have a vertical line whose intersection with the diagonal will be whereever your error is as large as the spread in the data!"
      ],
      "metadata": {
        "id": "wqSk-L_cIjQe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Probability Integral Transform (PIT) Histogram\n",
        "While this visualization carries a name that some may find intimidating (one of your T.A.'s will readily admit to waking from a nightmare muttering something about it with a far off look on his face), it's simply a way of quantifying how well calibrated your models are. It's simply a matter of calculating the CDF of random samples from the observations and plotting a histogram."
      ],
      "metadata": {
        "id": "HWQ_Uxl4UWzf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import norm\n",
        "\n",
        "# First, we need to generate a set of y values to get a global distribution of y-values.\n",
        "x_vals = np.linspace(x.min(), x.max(), 500)\n",
        "\n",
        "y_samples = []\n",
        "for x_val in x_vals:\n",
        "    # Let's generate mu and sigma\n",
        "    mu, sigma = model(torch.from_numpy(poly_fitter.fit_transform(np.asarray(x_val)[None,None])).float()).detach().numpy().squeeze()\n",
        "    # Make sure that sigma is always positive\n",
        "    sigma = np.sqrt(sigma**2)\n",
        "\n",
        "    # make the distribution using scipy and get 100 samples\n",
        "    y_samples.append(norm(mu, sigma).rvs(100))\n",
        "\n",
        "y_samples = np.concatenate(y_samples)"
      ],
      "metadata": {
        "id": "NMk0hRRw25Bo"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now that we have the predicted values for y, let's go ahead and get an estimation of the CDF of y. To do this, we'll generate a regularly spaced grid of y values going from the minimum (where CDF~0) to the maximum (where CDF~1).\n",
        "from scipy.interpolate import interp1d\n",
        "\n",
        "y_grid = np.linspace(y.min(), y.max(), 500)\n",
        "cdf_grid = np.zeros_like(y_grid)\n",
        "for idx, y_val in enumerate(y_grid):\n",
        "    cdf_grid[idx] = np.mean(y_samples <= y_val)\n",
        "\n",
        "# and then we make an interpolator\n",
        "cdf_interpolator = interp1d(y_grid,\n",
        "                            cdf_grid,\n",
        "                            kind='linear',\n",
        "                            )"
      ],
      "metadata": {
        "id": "ZUJnQtD13zr7"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# In order to make the PIT histogram, all we have to do is get a large number of samples, and plot the PDF of the CDF values calculated from the y_true samples.\n",
        "# If we obtain a relatively uniform PDF, it means that our predictions do a good job of matching the CDF of the observations.\n",
        "\n",
        "rn_gen = np.random.default_rng(seed=42)\n",
        "\n",
        "samples = rn_gen.choice(y, size=8000)\n",
        "CDF_vals = cdf_interpolator(samples)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(6, 6), dpi=100)\n",
        "\n",
        "nbins = 10\n",
        "# Plot the histogram of the CDF values using 10 bins\n",
        "ax.hist(CDF_vals, bins=nbins, density=True, color=colors[1], label='PIT Histogram')\n",
        "# set the x-axis ticks to be every 0.05 between 0 and 1\n",
        "ax.set_xticks(np.arange(0.05, 1.1, 0.1))\n",
        "# plot the hline at 1\n",
        "ax.axhline(1, color='black', linestyle='--', label='Uniform PDF')\n",
        "ax.set_xlim(0, CDF_vals.max())\n",
        "ax.legend()"
      ],
      "metadata": {
        "id": "DkaK6DP49CRu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "4c18e5de-8d57-47cc-f8e8-20620344afa3"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7dae2ce89de0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Q7) Is the model over-dispersive or under-dispersive?**"
      ],
      "metadata": {
        "id": "E3mIlQVsLtnG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Write your thoughts here 📉\n",
        "\n",
        "over-disperive because the plot shows it"
      ],
      "metadata": {
        "id": "zKZwkye2BBZI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Bonus Challenge"
      ],
      "metadata": {
        "id": "W8vQKBivsYQd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can imagine, the data that you used today does *not*, in fact, associate a gaussian distribution of values for each value of x. Instead, the distribution is a *weibull* distribution whose shape and scale parameters depend on x!\n",
        "\n",
        "We've gone ahead and prepared an interactive demo for you to get familiar with the weibull distribution."
      ],
      "metadata": {
        "id": "M5b0oMMiGd0s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def weibull(shape, scale):\n",
        "    plt.close('all')\n",
        "    fig, ax = plt.subplots(figsize = (8,2), dpi=150)\n",
        "    x = np.linspace(1e-5, 100, 500)\n",
        "    y = (shape / scale) * (x / scale) ** (shape - 1) * np.exp(-(x / scale) ** shape)\n",
        "    ax.plot(x, y)\n",
        "    ax.set_xlabel('x')\n",
        "    ax.set_ylabel('y')\n",
        "    ax.set_title(f'Weibull with shape {shape} and scale {scale}')\n",
        "    ax.set_xlim(0, 40)\n",
        "    ax.set_ylim(0, max(y.max()*1.1, 0.5))\n",
        "    plt.show()\n",
        "\n",
        "interact(weibull, shape=(1e-5, 10, 0.1), scale=(0.5, 10, 0.5))"
      ],
      "metadata": {
        "id": "AUM3uL-KeS6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The challenge, however, is to repeat the fitting of the neural network by predicting a weibull instead of a gaussian distribution. Note that this means that **the CRPS we implemented above is not correct for use with the weibull distribution** - and as your TA ran out of time and energy to figure it out for you, it's up to you to figure it out.\n",
        "\n",
        "\"You really shouldn't spend too much time on this. I'd rather you work on your final project, or have a coffee\" - that same TA."
      ],
      "metadata": {
        "id": "xRHrGaLmHeq3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You may ignore your TA and implement a CRPS loss tailored to the Weibull distribution below"
      ],
      "metadata": {
        "id": "I2OwTZvCJwX0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train your model with the new CRPS loss"
      ],
      "metadata": {
        "id": "MYOgmZnXJ-Ws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot its predictions as a function of x"
      ],
      "metadata": {
        "id": "q1aQSw3HKAS7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# What does its spread skill score look like? Does the spread match the error?"
      ],
      "metadata": {
        "id": "udkFf2tqLnBK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# What does the PIT look like? Is the model over-dispersive, under-dispersive, or just right?"
      ],
      "metadata": {
        "id": "DucwkDP9MAbA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}