lectures/23_communication.ipynb

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   "source": [
    "# Lecture 23: Communication\n",
    "\n",
    "UBC 2022-23\n",
    "\n",
    "Instructor: Varada Kolhatkar"
   ]
  },
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   "source": [
    "## Imports"
   ]
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer, TransformedTargetRegressor\n",
    "from sklearn.dummy import DummyClassifier, DummyRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import (\n",
    "    GridSearchCV,\n",
    "    cross_val_score,\n",
    "    cross_validate,\n",
    "    train_test_split,\n",
    ")\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import (\n",
    "    MinMaxScaler,\n",
    "    OneHotEncoder,\n",
    "    OrdinalEncoder,\n",
    "    StandardScaler,\n",
    ")\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 12"
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    "## Learning objectives\n",
    "\n",
    "- When communicating about applied ML, tailor an explanation to the intended audience.\n",
    "- Apply best practices of technical communication, such as bottom-up explanations and reader-centric writing.\n",
    "- Given an ML problem, analyze the decision being made and the objectives.\n",
    "- Avoid the pitfall of thinking about ML as coding in isolation; build the habit of relating your work to the surrounding context and stakeholders.\n",
    "- Interpret a confidence score or credence, e.g. what does it mean to be 5% confident that a statement is true.\n",
    "- Maintain a healthy skepticism of `predict_proba` scores and their possible interpretation as credences.\n",
    "- Be careful and precise when communicating confidence to stakeholders in an ML project.\n",
    "- Identify misleading visualizations."
   ]
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   "source": [
    "## Announcements\n",
    "\n",
    "- hw9 (last one) due on December 7th at 11:59pm\n",
    "- My OH today is moved from 12:45 to 3pm. \n",
    "- Information on the final exam is posted on [Piazza](https://piazza.com/class/l6pflbyug1230c/post/568). \n",
    "- Almost there, hang in there!"
   ]
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   "source": [
    "## Attribution\n",
    "\n",
    "- The first part of this lecture is adapted from [DSCI 542](https://github.com/UBC-MDS/DSCI_542_comm-arg), created by [David Laing](https://davidklaing.com/).\n",
    "- The visualization component of this lecture benefitted from discussions with [Firas Moosvi](http://firas.moosvi.com/) about his course, [DSCI 531](https://github.com/UBC-MDS/DSCI_531_viz-1)."
   ]
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   "source": [
    "## Motivation"
   ]
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   "source": [
    "### Why should we care about effective communication?\n",
    "\n",
    "- Most ML practitioners work in an organization with >1 people.\n",
    "- There will very likely be stakeholders other than yourself.\n",
    "- Some of them might not have any background in ML or computer science. \n",
    "- If your ML model is going to automate some important decisions in the organization you need to be able to explain \n",
    "    - What does a particular result mean? \n",
    "    - When does the model work?\n",
    "    - What are the risks? When does it fail?\n",
    "    - Why the model made a certain prediction for a particular example?  \n",
    "    - What are the consequences of using your model?\n",
    "- If you are able to convince your manager that using is model is beneficial, then only there are chances of your work going in production. \n",
    "- That said, you want to be honest when discussing the aspects above. If you mis-communicate the performance of your model, people will find out when the deployed model does not quite give similar performance.  "
   ]
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   "source": [
    "### What are the problems with the following? "
   ]
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   "source": [
    "![](img/ml-communication.png)"
   ]
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   "source": [
    "- Does \"accuracy\" here refer to the accuracy in ML?\n",
    "- Is there class imbalance?\n",
    "- Are they reporting numbers on the training data or validation or test data?"
   ]
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    "### Main issues in ML-related communication \n",
    "\n",
    "- Overstating one's results / unable to articulate the limitations\n",
    "- Unable to explain the predictions\n",
    "- Can we trust test error?\n",
    "- Why did a particular model (e.g., CatBoost) make that prediction?\n",
    "- What does it mean if `predict_proba` outputs 0.9?\n",
    "\n",
    "These issues are there because these things are actually very hard to explain!"
   ]
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   "source": [
    "<br><br>"
   ]
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   "source": [
    "## Activity: explaining `GridSearchCV` (15 min)"
   ]
  },
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   "source": [
    "Below are two possible explanations of `GridSearchCV` pitched to different audiences. Read them both and then follow the instructions at the end."
   ]
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   "source": [
    "### Explanation 1"
   ]
  },
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   "source": [
    "Machine learning algorithms, like an airplane's cockpit, typically involve a bunch of knobs and switches that need to be set.\n",
    "\n",
    "![](https://i.pinimg.com/236x/ea/43/f3/ea43f3c7f3a8c92d884ce012c77628fd--cockpit-gauges.jpg)\n",
    "\n",
    "For example, check out the documentation of the popular random forest algorithm [here](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). Here's a list of the function arguments, along with their default values (from the documentation):\n",
    "\n",
    "> class sklearn.ensemble.RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)\n",
    "\n",
    "Holy cow, that's a lot of knobs and switches! As a machine learning practitioner, how am I supposed to choose `n_estimators`? Should I leave it at the default of 100? Or try 1000? What about `criterion` or `class_weight` for that matter? Should I trust the defaults?\n",
    "\n",
    "Enter [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) to save the day. The general strategy here is to choose the settings that perform best on the specific task of interest. So I can't say `n_estimators=100` is better than `n_estimators=1000` without knowing what problem I'm working on. For a specific problem, you usually have a numerical score that measures performance. `GridSearchCV` is part of the popular [scikit-learn](https://scikit-learn.org/) Python machine learning library. It works by searching over various settings and tells you which one worked best on your problem. \n",
    "\n",
    "The \"grid\" in \"grid search\" comes from the fact that tries all possible combinations on a grid. For example, if you want it to consider setting `n_estimators` to 100, 150 or 200, and you want it to consider setting `criterion` to `'gini'` or `'entropy'`, then it will search over all 6 possible combinations in a grid of 3 possible values by 2 possible values: \n",
    "\n",
    "|                    | `criterion='gini'` | `criterion='entropy'` |\n",
    "|----------------------|--------|---------|\n",
    "| `n_estimators=100` |    1     |    2     |\n",
    "| `n_estimators=150` |    3     |    4     |\n",
    "| `n_estimators=200` |    5     |    6     |\n",
    "\n",
    "Here is a code sample that uses `GridSearchCV` to select from the 6 options we just mentioned. The problem being solved is classifying images of handwritten digits into the 10 digit categories (0-9). I chose this because the dataset is conveniently built in to scikit-learn:"
   ]
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     "data": {
      "text/plain": [
       "{'criterion': 'gini', 'n_estimators': 100}"
      ]
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     "output_type": "execute_result"
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   "source": [
    "# imports\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# load a dataset\n",
    "data = datasets.load_digits()\n",
    "X = data[\"data\"]\n",
    "y = data[\"target\"]\n",
    "\n",
    "# set up the grid search\n",
    "grid_search = GridSearchCV(\n",
    "    RandomForestClassifier(random_state=123),\n",
    "    param_grid={\"n_estimators\": [100, 150, 200], \"criterion\": [\"gini\", \"entropy\"]},\n",
    ")\n",
    "\n",
    "# run the grid search\n",
    "grid_search.fit(X, y)\n",
    "grid_search.best_params_"
   ]
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   "source": [
    "As we can see from the output above, the grid search selected `criterion='gini', n_estimators=100`, which was one of our 6 options above (specifically Option 1).\n",
    "\n",
    "By the way, these \"knobs\" we've been setting are called [_hyperparameters_](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning) and the process of setting these hyperparameters automatically is called [_hyperparameter optimization_](https://en.wikipedia.org/wiki/Hyperparameter_optimization) or _hyperparameter tuning_."
   ]
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   "source": [
    "~400 words, not including code."
   ]
  },
  {
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   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "source": [
    "<br><br><br><br><br><br>"
   ]
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   "source": [
    "### Explanation 2"
   ]
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   },
   "source": [
    "https://medium.com/datadriveninvestor/an-introduction-to-grid-search-ff57adcc0998\n",
    "\n",
    "~400 words, not including code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "skip"
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   },
   "source": [
    "<br><br><br><br><br><br>"
   ]
  },
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   },
   "source": [
    "### Discussion questions:"
   ]
  },
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   "source": [
    "- What do you like about each explanation?\n",
    "- What do you dislike about each explanation?\n",
    "- What do you think is the intended audience for each explanation?\n",
    "- Which explanation do you think is more effective overall for someone on Day 1 of CPSC 330?\n",
    "- Each explanation has an image. Which one is more effective? What are the pros/cons?\n",
    "- Each explanation has some sample code. Which one is more effective? What are the pros/cons?"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you're done reading, take ~5 min to consider the discussion questions above. Paste your answer to **at least one** of the above questions in the [Google doc](https://docs.google.com/document/d/1BJEpMm6luVOTcbC1Qth74jP40S1jErXumR6KbhnJeZM/edit#heading=h.m3ko02t7yq5b) under the appropriate question heading."
   ]
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   },
   "source": [
    "## Principles of good explanations (~15 min)"
   ]
  },
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   "source": [
    "### Concepts *then* labels, not the other way around\n",
    "\n",
    "The first explanation start with an analogy for the concept (and the label is left until the very end):\n",
    "\n",
    "> Machine learning algorithms, like an airplane's cockpit, typically involve a bunch of knobs and switches that need to be set.\n",
    "\n",
    "In the second explanation, the first sentence is wasted on anyone who doesn't already know what \"hyperparameter tuning\" means:\n",
    "\n",
    "> Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. \n",
    "\n",
    "The effectiveness of these different statements depend on your audience. \n",
    "\n",
    "See [this video](https://www.youtube.com/watch?v=px_4TxC2mXU): \n",
    "> I learned very early the difference between knowing the name of something and knowing something.\" - Richard Feynman."
   ]
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   "source": [
    "### Bottom-up explanations"
   ]
  },
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   "source": [
    "The [Curse of Knowledge](https://en.wikipedia.org/wiki/Curse_of_knowledge) leads to *top-down* explanations:\n",
    "\n",
    "![](img/top_down.png)\n",
    "\n",
    "- When you know something well, you think about things in the context of all your knowledge. \n",
    "- Those lacking the context, or frame of mind, cannot easily understand. "
   ]
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   "source": [
    "There is another way: *bottom-up* explanations:\n",
    "\n",
    "![](img/bottom_up.png)\n",
    "\n",
    "When you're brand new to a concept, you benefit from analogies, concrete examples and familiar patterns.\n"
   ]
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   "source": [
    "### New ideas in small chunks\n",
    "\n",
    "The first explanation has a hidden conceptual skeleton:\n",
    "\n",
    "1. The concept of setting a bunch of values.\n",
    "2. Random forest example.\n",
    "3. The problem / pain point.\n",
    "4. The solution.\n",
    "5. How it works - high level.\n",
    "6. How it works - written example.\n",
    "7. How it works - code example.\n",
    "8. The name of what we were discussing all this time."
   ]
  },
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   "source": [
    "### Reuse your running examples\n",
    "\n",
    "Effective explanations often use the same example throughout the text and code. This helps readers follow the line of reasoning."
   ]
  },
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   "source": [
    "### Approach from all angles\n",
    "\n",
    "When we're trying to draw mental boundaries around a concept, it's helpful to see examples on all sides of those boundaries. If we were writing a longer explanation, it might have been better to show more, e.g.\n",
    "\n",
    "- Performance with and without hyperparameter tuning. \n",
    "- Other types of hyperparameter tuning (e.g. `RandomizedSearchCV`)."
   ]
  },
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   "source": [
    "### When experimenting, show the results asap\n",
    "\n",
    "The first explanation shows the output of the code, whereas the second does not. This is easy to do and makes a big difference."
   ]
  },
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   "source": [
    "### Interesting to you != useful to the reader (aka it's not about you)\n",
    "\n",
    "Here is something which was deleted from the explanation:\n",
    "\n",
    "> Some hyperparameters, like `n_estimators` are numeric. Numeric hyperparameters are like the knobs in the cockpit: you can tune them continuously. `n_estimators` is numeric. Categorical hyperparameters are like the switches in the cockpit: they can take on (two or more) distinct values. `criterion` is categorical. \n",
    "\n",
    "It's a very elegant analogy! But is it helpful?\n",
    "\n",
    "And furthermore, what is my hidden motivation for wanting to include it? Elegance, art, and the pursuit of higher beauty? Or _making myself look smart_? So maybe another name for this principle could be **It's not about you.**"
   ]
  },
  {
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   "source": [
    "<br><br><br><br>"
   ]
  },
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   },
   "source": [
    "## ML and decision-making (5 min)"
   ]
  },
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   "source": [
    "- There is often a wide gap between what people care about and what ML can do.\n",
    "- To understand what ML can do, let's think about what **decisions** will be made using ML. \n"
   ]
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   "source": [
    "#### Decisions involve a few key pieces\n",
    "\n",
    "- The **decision variable**: the variable that is manipulated through the decision.\n",
    "  - E.g. how much should I sell my house for? (numeric)\n",
    "  - E.g. should I sell my house? (categorical)\n",
    "- The decision-maker's **objectives**: the variables that the decision-maker ultimately cares about, and wishes to manipulate indirectly through the decision variable.\n",
    "  - E.g. my total profit, time to sale, etc.\n",
    "- The **context**: the variables that mediate the relationship between the decision variable and the objectives.\n",
    "  - E.g. the housing market, cost of marketing it, my timeline, etc."
   ]
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   "source": [
    "#### How does this inform you as an ML practitioner?\n",
    "\n",
    "Questions you have to answer:\n",
    "\n",
    "- Who is the decision maker?\n",
    "- What are their objectives?\n",
    "- What are their alternatives?\n",
    "- What is their context?\n",
    "- What data do I need?"
   ]
  },
  {
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   },
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](img/eva-coffee.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "## Confidence and `predict_proba` (20 min)"
   ]
  },
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   "source": [
    "- What does it mean to be \"confident\" in your results?\n",
    "- When you perform analysis, you are responsible for many judgment calls.\n",
    "- [Your results will be different than others'](https://fivethirtyeight.com/features/science-isnt-broken/#part1).\n",
    "- As you make these judgments and start to form conclusions, how can you recognize your own uncertainties about the data so that you can communicate confidently?"
   ]
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   "source": [
    "### What does this mean for us, when we're trying to make claims about our data?\n",
    "\n",
    "Let's imagine that the following claim is true:\n",
    "\n",
    "> Vancouver has the highest cost of living of all cities in Canada.\n",
    "\n",
    "Now let's consider a few beliefs we could hold:\n",
    "\n",
    "1. Vancouver has the highest cost of living of all cities in Canada. **I am 95% sure of this.** \n",
    "2. Vancouver has the highest cost of living of all cities in Canada. **I am 55% sure of this.** \n",
    "\n",
    "The part is bold is called a [credence](https://en.wikipedia.org/wiki/Credence_(statistics)). Which belief is better?"
   ]
  },
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   "source": [
    "But what if it's actually Toronto that has the highest cost of living in Canada?\n",
    "\n",
    "1. Vancouver has the highest cost of living of all cities in Canada. **I am 95% sure of this.** \n",
    "2. Vancouver has the highest cost of living of all cities in Canada. **I am 55% sure of this.** \n",
    "\n",
    "Which belief is better now?"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "**Conclusion: We don't just want to be right. We want to be confident when we're right and hesitant when we're wrong.**"
   ]
  },
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   "source": [
    "### What do credences mean in practical terms?\n",
    "\n",
    "One of two things:\n",
    "\n",
    "* **I would accept a bet at these odds.** 99% sure means, \"For the chance of winning \\\\$1, I would bet \\\\$99 that I'm right about this.\" 75% sure means, \"For the chance of winning \\\\$25, I would bet \\\\$75 that I'm right about this.\"\n",
    "* **Long-run frequency of correctness.** 99% sure means, \"For every 100 predictions I make at this level of confidence, I would expect only 1 of them to be incorrect.\" 75% sure means, \"For every 100 predictions I make at this level of confidence, I would expect about 25 of them to be incorrect.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### It's easy enough to evaluate how good we are at being right...\n",
    "\n",
    "But if we want to evaluate *how good we are at knowing how right we are*?\n",
    "\n",
    "We would need to keep of not just the correctness of our predictions, but also our confidence in those predictions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What does this have to do with applied ML?\n",
    "\n",
    "- What if you `predict` that a credit card transaction is fraudulent?\n",
    "  - We probably want `predict_proba` a lot of the time.\n",
    "- What if `predict_proba` is 0.95 in that case?\n",
    "  - How confident are YOU?\n",
    "- What if you forecast that avocado prices will go up next week? \n",
    "  - How confident are you there?\n",
    "- Or what if you predict a house price to be \\\\$800k? \n",
    "  - That is not even a true/false statement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Preferences in `LogisticRegression`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you call `fit` for `LogisticRegression` it has similar preferences: \n",
    "<br>\n",
    "<span style=\"color:green\">**correct and confident**</span> <br> **>** <span style=\"color:cyan\"> **correct and hesitant** </span>  <br> **>** <span style=\"color:orange\">**incorrect and hesitant**</span> <br> **>**  <span style=\"color:red\">**incorrect and confident**</span> \n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is a \"loss\" or \"error\" function like mean squared error, so lower values are better.\n",
    "- When you call `fit` it tries to minimize this metric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Consider the following made-up classification example where target (true `y`) is binary: -1 or 1. \n",
    "- The true $y$ (`y_true`) and models raw scores ($w^Tx_i$) are given to you. \n",
    "- You want to figure out how do you want to punish the mistakes made by the current model.\n",
    "- How will you punish the model in each case?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_true</th>\n",
       "      <th>raw score ($w^Tx_i$)</th>\n",
       "      <th>correct? (yes/no)</th>\n",
       "      <th>confident/hesitant?</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10.00</td>\n",
       "      <td>yes</td>\n",
       "      <td>confident</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.51</td>\n",
       "      <td>yes</td>\n",
       "      <td>hesitant</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>no</td>\n",
       "      <td>hesistant</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>no</td>\n",
       "      <td>confident</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>-12.00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1</td>\n",
       "      <td>0.40</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1</td>\n",
       "      <td>18.00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y_true  raw score ($w^Tx_i$) correct? (yes/no) confident/hesitant?   loss\n",
       "0       1                 10.00               yes           confident   None\n",
       "1       1                  0.51               yes            hesitant  small\n",
       "2       1                 -0.10                no           hesistant  small\n",
       "3       1                -10.00                no           confident   high\n",
       "4      -1                -12.00                                             \n",
       "5      -1                 -1.00                                             \n",
       "6      -1                  0.40                                             \n",
       "7      -1                 18.00                                             "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    \"y_true\": [1, 1, 1, 1, -1, -1, -1, -1],\n",
    "    \"raw score ($w^Tx_i$)\": [10.0, 0.51, -0.1, -10, -12.0, -1.0, 0.4, 18.0],\n",
    "    \"correct? (yes/no)\":[\"yes\", \"yes\", \"no\", \"no\", \"\", \"\", \"\", \"\"],\n",
    "    \"confident/hesitant?\":[\"confident\", \"hesitant\", \"hesistant\", \"confident\", \"\", \"\", \"\", \"\"],\n",
    "    \"loss\":[\"None\", \"small\", \"small\", \"high\", \"\", \"\", \"\", \"\"]\n",
    "}\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss_diagram(labels_inside=False): # From Mike's notebook: https://github.com/UBC-CS/cpsc340-2020w2/blob/main/lectures/19_linear-classifiers-fit.ipynb        \n",
    "    grid = np.linspace(-2,2,1000)\n",
    "    plt.figure(figsize=(6, 4), dpi=80)\n",
    "    plt.xlabel('$y_iw^T x_i$', fontsize=18)\n",
    "    plt.ylabel('loss', fontsize=18)\n",
    "    plt.xlim(-2,2)\n",
    "    plt.ylim(-0.025,3)\n",
    "    plt.fill_between([0, 2], -1, 3, facecolor='blue', alpha=0.2);\n",
    "    plt.fill_between([-2, 0], -1, 3, facecolor='red', alpha=0.2);\n",
    "    plt.yticks([0,1,2,3]);\n",
    "\n",
    "    if labels_inside:\n",
    "        plt.text(-1.95, 2.73, \"incorrect prediction\", fontsize=15) # 2.68\n",
    "        plt.text(0.15, 2.73, \"correct prediction\", fontsize=15)\n",
    "    else:\n",
    "        plt.text(-1.95, 3.1, \"incorrect prediction\", fontsize=15) # 2.68\n",
    "        plt.text(0.15, 3.1, \"correct prediction\", fontsize=15)\n",
    "\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = np.linspace(-2, 2, 1000)\n",
    "plot_loss_diagram()\n",
    "plt.plot(grid, np.log(1 + np.exp(-grid)), color=\"green\", linewidth=2, label=\"logistic loss\")\n",
    "plt.legend(loc=\"best\", fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- When you are confident and correct, the loss is much smaller \n",
    "- When you are hesitant and correct, the loss is a bit higher\n",
    "- If you are hesitant and incorrect the loss is even higher\n",
    "- If you are confident and incorrect the loss is quite high. \n",
    "- Your loss goes to 0 as you approach 100% confidence in the correct answer.\n",
    "- Your loss goes to infinity as you approach 100% confidence in the incorrect answer.\n",
    "- The above picture shows loss for one example. \n",
    "- The real `LogisticRegression` takes summations of losses over all examples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "Some nice examples: \n",
    "- (Optional) See also the very related [How to assign partial credit on an exam of true-false questions?](https://terrytao.wordpress.com/2016/06/01/how-to-assign-partial-credit-on-an-exam-of-true-false-questions/)\n",
    "- [Scott Alexander](https://slatestarcodex.com/2019/01/22/2018-predictions-calibration-results/)\n",
    "  - Look at how the plot starts at 50%. That is because being 40% confident of \"X\" is the same as being 60% confident of \"not X\".\n",
    "- [Good Judgment Project](https://www.gjopen.com/)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Visualizing your results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Very powerful but at the same time can be misleading if not done properly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pre-viewing review from [Calling BS visualization videos](https://www.youtube.com/watch?v=T-5aLbNeGo0&list=PLPnZfvKID1Sje5jWxt-4CSZD7bUI4gSPS&index=30&t=0s):\n",
    "\n",
    "- Dataviz in the popular media.\n",
    "  - e.g. [modern NYT](https://youtu.be/T-5aLbNeGo0?t=367)\n",
    "- Misleading axes.\n",
    "  - e.g. [vaccines](https://youtu.be/9pNWVMxaFuM?t=299)\n",
    "- Manipulating bin sizes.\n",
    "  - e.g. [tax dollars](https://youtu.be/zAg1wsYfwsM?t=196)\n",
    "- Dataviz ducks.\n",
    "  - e.g. [drinking water](https://youtu.be/rmii1hfP6d4?t=169)\n",
    "  - \"look how clever we are about design\" -> making it about me instead of about you (see last class)\n",
    "- Glass slippers.\n",
    "  - e.g. [internet marketing tree](https://youtu.be/59teS0SUHtI?t=285)\n",
    "- The principle of proportional ink.\n",
    "  - e.g. [most read books](https://youtu.be/oNhusd3xFC4?t=147)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- [Demo of cleaning up a plot](https://www.darkhorseanalytics.com/blog/data-looks-better-naked)\n",
    "- [Principle of proportional ink](https://serialmentor.com/dataviz/proportional-ink.html) from a viz textbook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Dataset\n",
    "\n",
    "We'll be using [Kaggle House Prices dataset](https://www.kaggle.com/c/home-data-for-ml-course/), which we used in lecture 10. As usual, to run this notebook you'll need to download the data. For this dataset, train and test have already been separated. We'll be working with the train portion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>303</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>118.0</td>\n",
       "      <td>13704</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>205000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>768</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>12508</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>1300</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>430</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11457</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>1140</td>\n",
       "      <td>30</td>\n",
       "      <td>RL</td>\n",
       "      <td>98.0</td>\n",
       "      <td>8731</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>144000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>559</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>57.0</td>\n",
       "      <td>21872</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR2</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "302    303          20       RL        118.0    13704   Pave   NaN      IR1   \n",
       "767    768          50       RL         75.0    12508   Pave   NaN      IR1   \n",
       "429    430          20       RL        130.0    11457   Pave   NaN      IR1   \n",
       "1139  1140          30       RL         98.0     8731   Pave   NaN      IR1   \n",
       "558    559          60       RL         57.0    21872   Pave   NaN      IR2   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "302          Lvl    AllPub  ...        0    NaN   NaN         NaN       0   \n",
       "767          Lvl    AllPub  ...        0    NaN   NaN        Shed    1300   \n",
       "429          Lvl    AllPub  ...        0    NaN   NaN         NaN       0   \n",
       "1139         Lvl    AllPub  ...        0    NaN   NaN         NaN       0   \n",
       "558          HLS    AllPub  ...        0    NaN   NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "302       1   2006        WD         Normal     205000  \n",
       "767       7   2008        WD         Normal     160000  \n",
       "429       3   2009        WD         Normal     175000  \n",
       "1139      5   2007        WD         Normal     144000  \n",
       "558       8   2008        WD         Normal     175000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/housing-kaggle/train.csv\")\n",
    "train_df, test_df = train_test_split(df, test_size=0.10, random_state=123)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1314, 81)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's separate `X` and `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "X_train = train_df.drop(columns=[\"SalePrice\"])\n",
    "y_train = train_df[\"SalePrice\"]\n",
    "\n",
    "X_test = test_df.drop(columns=[\"SalePrice\"])\n",
    "y_test = test_df[\"SalePrice\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Feature types \n",
    "\n",
    "- We have mixed feature types and a bunch of missing values. \n",
    "- Now, let's identify feature types and transformations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "drop_features = [\"Id\"]\n",
    "numeric_features = [\n",
    "    \"BedroomAbvGr\",\n",
    "    \"KitchenAbvGr\",\n",
    "    \"LotFrontage\",\n",
    "    \"LotArea\",\n",
    "    \"OverallQual\",\n",
    "    \"OverallCond\",\n",
    "    \"YearBuilt\",\n",
    "    \"YearRemodAdd\",\n",
    "    \"MasVnrArea\",\n",
    "    \"BsmtFinSF1\",\n",
    "    \"BsmtFinSF2\",\n",
    "    \"BsmtUnfSF\",\n",
    "    \"TotalBsmtSF\",\n",
    "    \"1stFlrSF\",\n",
    "    \"2ndFlrSF\",\n",
    "    \"LowQualFinSF\",\n",
    "    \"GrLivArea\",\n",
    "    \"BsmtFullBath\",\n",
    "    \"BsmtHalfBath\",\n",
    "    \"FullBath\",\n",
    "    \"HalfBath\",\n",
    "    \"TotRmsAbvGrd\",\n",
    "    \"Fireplaces\",\n",
    "    \"GarageYrBlt\",\n",
    "    \"GarageCars\",\n",
    "    \"GarageArea\",\n",
    "    \"WoodDeckSF\",\n",
    "    \"OpenPorchSF\",\n",
    "    \"EnclosedPorch\",\n",
    "    \"3SsnPorch\",\n",
    "    \"ScreenPorch\",\n",
    "    \"PoolArea\",\n",
    "    \"MiscVal\",\n",
    "    \"YrSold\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex'],\n",
       " ['Po', 'Fa', 'TA', 'Gd', 'Ex']]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_features_reg = [\n",
    "    \"ExterQual\",\n",
    "    \"ExterCond\",\n",
    "    \"BsmtQual\",\n",
    "    \"BsmtCond\",\n",
    "    \"HeatingQC\",\n",
    "    \"KitchenQual\",\n",
    "    \"FireplaceQu\",\n",
    "    \"GarageQual\",\n",
    "    \"GarageCond\",\n",
    "    \"PoolQC\",\n",
    "]\n",
    "ordering = [\n",
    "    \"Po\",\n",
    "    \"Fa\",\n",
    "    \"TA\",\n",
    "    \"Gd\",\n",
    "    \"Ex\",\n",
    "]  # if N/A it will just impute something, per below\n",
    "ordering_ordinal_reg = [ordering] * len(ordinal_features_reg)\n",
    "ordering_ordinal_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_features_oth = [\n",
    "    \"BsmtExposure\",\n",
    "    \"BsmtFinType1\",\n",
    "    \"BsmtFinType2\",\n",
    "    \"Functional\",\n",
    "    \"Fence\",\n",
    "]\n",
    "ordering_ordinal_oth = [\n",
    "    [\"NA\", \"No\", \"Mn\", \"Av\", \"Gd\"],\n",
    "    [\"NA\", \"Unf\", \"LwQ\", \"Rec\", \"BLQ\", \"ALQ\", \"GLQ\"],\n",
    "    [\"NA\", \"Unf\", \"LwQ\", \"Rec\", \"BLQ\", \"ALQ\", \"GLQ\"],\n",
    "    [\"Sal\", \"Sev\", \"Maj2\", \"Maj1\", \"Mod\", \"Min2\", \"Min1\", \"Typ\"],\n",
    "    [\"NA\", \"MnWw\", \"GdWo\", \"MnPrv\", \"GdPrv\"],\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The remaining features are categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LandSlope',\n",
       " 'Condition1',\n",
       " 'BldgType',\n",
       " 'Exterior1st',\n",
       " 'Street',\n",
       " 'MoSold',\n",
       " 'LandContour',\n",
       " 'MSZoning',\n",
       " 'Neighborhood',\n",
       " 'RoofStyle',\n",
       " 'PavedDrive',\n",
       " 'Electrical',\n",
       " 'GarageType',\n",
       " 'SaleType',\n",
       " 'SaleCondition',\n",
       " 'Foundation',\n",
       " 'Condition2',\n",
       " 'Utilities',\n",
       " 'Exterior2nd',\n",
       " 'CentralAir',\n",
       " 'LotShape',\n",
       " 'MasVnrType',\n",
       " 'MiscFeature',\n",
       " 'MSSubClass',\n",
       " 'RoofMatl',\n",
       " 'Heating',\n",
       " 'Alley',\n",
       " 'HouseStyle',\n",
       " 'GarageFinish',\n",
       " 'LotConfig']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = list(\n",
    "    set(X_train.columns)\n",
    "    - set(numeric_features)\n",
    "    - set(ordinal_features_reg)\n",
    "    - set(ordinal_features_oth)\n",
    "    - set(drop_features)\n",
    ")\n",
    "categorical_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Applying feature transformations\n",
    "\n",
    "- Since we have mixed feature types, let's use `ColumnTransformer` to apply different transformations on different features types.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "\n",
    "numeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n",
    "ordinal_transformer_reg = make_pipeline(\n",
    "    SimpleImputer(strategy=\"most_frequent\"),\n",
    "    OrdinalEncoder(categories=ordering_ordinal_reg),\n",
    ")\n",
    "\n",
    "ordinal_transformer_oth = make_pipeline(\n",
    "    SimpleImputer(strategy=\"most_frequent\"),\n",
    "    OrdinalEncoder(categories=ordering_ordinal_oth),\n",
    ")\n",
    "\n",
    "categorical_transformer = make_pipeline(\n",
    "    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n",
    "    OneHotEncoder(handle_unknown=\"ignore\", sparse=False),\n",
    ")\n",
    "\n",
    "preprocessor = make_column_transformer(\n",
    "    (\"drop\", drop_features),\n",
    "    (numeric_transformer, numeric_features),\n",
    "    (ordinal_transformer_reg, ordinal_features_reg),\n",
    "    (ordinal_transformer_oth, ordinal_features_oth),\n",
    "    (categorical_transformer, categorical_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Examining the preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;drop&#x27;, &#x27;drop&#x27;, [&#x27;Id&#x27;]),\n",
       "                                (&#x27;pipeline-1&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;BedroomAbvGr&#x27;, &#x27;KitchenAbvGr&#x27;, &#x27;LotFrontage&#x27;,\n",
       "                                  &#x27;LotArea&#x27;, &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;,\n",
       "                                  &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
       "                                  &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;,\n",
       "                                  &#x27;TotalBsmtSF&#x27;, &#x27;...\n",
       "                                 [&#x27;LandSlope&#x27;, &#x27;Condition1&#x27;, &#x27;BldgType&#x27;,\n",
       "                                  &#x27;Exterior1st&#x27;, &#x27;Street&#x27;, &#x27;MoSold&#x27;,\n",
       "                                  &#x27;LandContour&#x27;, &#x27;MSZoning&#x27;, &#x27;Neighborhood&#x27;,\n",
       "                                  &#x27;RoofStyle&#x27;, &#x27;PavedDrive&#x27;, &#x27;Electrical&#x27;,\n",
       "                                  &#x27;GarageType&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;,\n",
       "                                  &#x27;Foundation&#x27;, &#x27;Condition2&#x27;, &#x27;Utilities&#x27;,\n",
       "                                  &#x27;Exterior2nd&#x27;, &#x27;CentralAir&#x27;, &#x27;LotShape&#x27;,\n",
       "                                  &#x27;MasVnrType&#x27;, &#x27;MiscFeature&#x27;, &#x27;MSSubClass&#x27;,\n",
       "                                  &#x27;RoofMatl&#x27;, &#x27;Heating&#x27;, &#x27;Alley&#x27;, &#x27;HouseStyle&#x27;,\n",
       "                                  &#x27;GarageFinish&#x27;, &#x27;LotConfig&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;drop&#x27;, &#x27;drop&#x27;, [&#x27;Id&#x27;]),\n",
       "                                (&#x27;pipeline-1&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;BedroomAbvGr&#x27;, &#x27;KitchenAbvGr&#x27;, &#x27;LotFrontage&#x27;,\n",
       "                                  &#x27;LotArea&#x27;, &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;,\n",
       "                                  &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;,\n",
       "                                  &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;,\n",
       "                                  &#x27;TotalBsmtSF&#x27;, &#x27;...\n",
       "                                 [&#x27;LandSlope&#x27;, &#x27;Condition1&#x27;, &#x27;BldgType&#x27;,\n",
       "                                  &#x27;Exterior1st&#x27;, &#x27;Street&#x27;, &#x27;MoSold&#x27;,\n",
       "                                  &#x27;LandContour&#x27;, &#x27;MSZoning&#x27;, &#x27;Neighborhood&#x27;,\n",
       "                                  &#x27;RoofStyle&#x27;, &#x27;PavedDrive&#x27;, &#x27;Electrical&#x27;,\n",
       "                                  &#x27;GarageType&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;,\n",
       "                                  &#x27;Foundation&#x27;, &#x27;Condition2&#x27;, &#x27;Utilities&#x27;,\n",
       "                                  &#x27;Exterior2nd&#x27;, &#x27;CentralAir&#x27;, &#x27;LotShape&#x27;,\n",
       "                                  &#x27;MasVnrType&#x27;, &#x27;MiscFeature&#x27;, &#x27;MSSubClass&#x27;,\n",
       "                                  &#x27;RoofMatl&#x27;, &#x27;Heating&#x27;, &#x27;Alley&#x27;, &#x27;HouseStyle&#x27;,\n",
       "                                  &#x27;GarageFinish&#x27;, &#x27;LotConfig&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">drop</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Id&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">drop</label><div class=\"sk-toggleable__content\"><pre>drop</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-1</label><div class=\"sk-toggleable__content\"><pre>[&#x27;BedroomAbvGr&#x27;, &#x27;KitchenAbvGr&#x27;, &#x27;LotFrontage&#x27;, &#x27;LotArea&#x27;, &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;, &#x27;MasVnrArea&#x27;, &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;1stFlrSF&#x27;, &#x27;2ndFlrSF&#x27;, &#x27;LowQualFinSF&#x27;, &#x27;GrLivArea&#x27;, &#x27;BsmtFullBath&#x27;, &#x27;BsmtHalfBath&#x27;, &#x27;FullBath&#x27;, &#x27;HalfBath&#x27;, &#x27;TotRmsAbvGrd&#x27;, &#x27;Fireplaces&#x27;, &#x27;GarageYrBlt&#x27;, &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;OpenPorchSF&#x27;, &#x27;EnclosedPorch&#x27;, &#x27;3SsnPorch&#x27;, &#x27;ScreenPorch&#x27;, &#x27;PoolArea&#x27;, &#x27;MiscVal&#x27;, &#x27;YrSold&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-2</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ExterQual&#x27;, &#x27;ExterCond&#x27;, &#x27;BsmtQual&#x27;, &#x27;BsmtCond&#x27;, &#x27;HeatingQC&#x27;, &#x27;KitchenQual&#x27;, &#x27;FireplaceQu&#x27;, &#x27;GarageQual&#x27;, &#x27;GarageCond&#x27;, &#x27;PoolQC&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;],\n",
       "                           [&#x27;Po&#x27;, &#x27;Fa&#x27;, &#x27;TA&#x27;, &#x27;Gd&#x27;, &#x27;Ex&#x27;]])</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-3</label><div class=\"sk-toggleable__content\"><pre>[&#x27;BsmtExposure&#x27;, &#x27;BsmtFinType1&#x27;, &#x27;BsmtFinType2&#x27;, &#x27;Functional&#x27;, &#x27;Fence&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;NA&#x27;, &#x27;No&#x27;, &#x27;Mn&#x27;, &#x27;Av&#x27;, &#x27;Gd&#x27;],\n",
       "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
       "                           [&#x27;NA&#x27;, &#x27;Unf&#x27;, &#x27;LwQ&#x27;, &#x27;Rec&#x27;, &#x27;BLQ&#x27;, &#x27;ALQ&#x27;, &#x27;GLQ&#x27;],\n",
       "                           [&#x27;Sal&#x27;, &#x27;Sev&#x27;, &#x27;Maj2&#x27;, &#x27;Maj1&#x27;, &#x27;Mod&#x27;, &#x27;Min2&#x27;, &#x27;Min1&#x27;,\n",
       "                            &#x27;Typ&#x27;],\n",
       "                           [&#x27;NA&#x27;, &#x27;MnWw&#x27;, &#x27;GdWo&#x27;, &#x27;MnPrv&#x27;, &#x27;GdPrv&#x27;]])</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-4</label><div class=\"sk-toggleable__content\"><pre>[&#x27;LandSlope&#x27;, &#x27;Condition1&#x27;, &#x27;BldgType&#x27;, &#x27;Exterior1st&#x27;, &#x27;Street&#x27;, &#x27;MoSold&#x27;, &#x27;LandContour&#x27;, &#x27;MSZoning&#x27;, &#x27;Neighborhood&#x27;, &#x27;RoofStyle&#x27;, &#x27;PavedDrive&#x27;, &#x27;Electrical&#x27;, &#x27;GarageType&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;, &#x27;Foundation&#x27;, &#x27;Condition2&#x27;, &#x27;Utilities&#x27;, &#x27;Exterior2nd&#x27;, &#x27;CentralAir&#x27;, &#x27;LotShape&#x27;, &#x27;MasVnrType&#x27;, &#x27;MiscFeature&#x27;, &#x27;MSSubClass&#x27;, &#x27;RoofMatl&#x27;, &#x27;Heating&#x27;, &#x27;Alley&#x27;, &#x27;HouseStyle&#x27;, &#x27;GarageFinish&#x27;, &#x27;LotConfig&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('drop', 'drop', ['Id']),\n",
       "                                ('pipeline-1',\n",
       "                                 Pipeline(steps=[('simpleimputer',\n",
       "                                                  SimpleImputer(strategy='median')),\n",
       "                                                 ('standardscaler',\n",
       "                                                  StandardScaler())]),\n",
       "                                 ['BedroomAbvGr', 'KitchenAbvGr', 'LotFrontage',\n",
       "                                  'LotArea', 'OverallQual', 'OverallCond',\n",
       "                                  'YearBuilt', 'YearRemodAdd', 'MasVnrArea',\n",
       "                                  'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n",
       "                                  'TotalBsmtSF', '...\n",
       "                                 ['LandSlope', 'Condition1', 'BldgType',\n",
       "                                  'Exterior1st', 'Street', 'MoSold',\n",
       "                                  'LandContour', 'MSZoning', 'Neighborhood',\n",
       "                                  'RoofStyle', 'PavedDrive', 'Electrical',\n",
       "                                  'GarageType', 'SaleType', 'SaleCondition',\n",
       "                                  'Foundation', 'Condition2', 'Utilities',\n",
       "                                  'Exterior2nd', 'CentralAir', 'LotShape',\n",
       "                                  'MasVnrType', 'MiscFeature', 'MSSubClass',\n",
       "                                  'RoofMatl', 'Heating', 'Alley', 'HouseStyle',\n",
       "                                  'GarageFinish', 'LotConfig'])])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor.fit(X_train)\n",
    "# Calling fit to examine all the transformers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kvarada/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "ohe_columns = list(\n",
    "    preprocessor.named_transformers_[\"pipeline-4\"]\n",
    "    .named_steps[\"onehotencoder\"]\n",
    "    .get_feature_names(categorical_features)\n",
    ")\n",
    "new_columns = (\n",
    "    numeric_features + ordinal_features_reg + ordinal_features_oth + ohe_columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageFinish_missing</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>2.312501</td>\n",
       "      <td>0.381428</td>\n",
       "      <td>0.663680</td>\n",
       "      <td>-0.512408</td>\n",
       "      <td>0.993969</td>\n",
       "      <td>0.840492</td>\n",
       "      <td>0.269972</td>\n",
       "      <td>-0.961498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1.372763</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>0.260890</td>\n",
       "      <td>0.248457</td>\n",
       "      <td>-0.054669</td>\n",
       "      <td>1.285467</td>\n",
       "      <td>-1.026793</td>\n",
       "      <td>0.016525</td>\n",
       "      <td>-0.573129</td>\n",
       "      <td>0.476092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>2.885044</td>\n",
       "      <td>0.131607</td>\n",
       "      <td>-0.054669</td>\n",
       "      <td>-0.512408</td>\n",
       "      <td>0.563314</td>\n",
       "      <td>0.161931</td>\n",
       "      <td>-0.573129</td>\n",
       "      <td>1.227559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>1.358264</td>\n",
       "      <td>-0.171468</td>\n",
       "      <td>-0.773017</td>\n",
       "      <td>-0.512408</td>\n",
       "      <td>-1.689338</td>\n",
       "      <td>-1.679877</td>\n",
       "      <td>-0.573129</td>\n",
       "      <td>0.443419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>-0.597924</td>\n",
       "      <td>1.289541</td>\n",
       "      <td>0.663680</td>\n",
       "      <td>-0.512408</td>\n",
       "      <td>0.828332</td>\n",
       "      <td>0.598149</td>\n",
       "      <td>-0.573129</td>\n",
       "      <td>0.354114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 263 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BedroomAbvGr  KitchenAbvGr  LotFrontage   LotArea  OverallQual  \\\n",
       "302       0.154795     -0.222647     2.312501  0.381428     0.663680   \n",
       "767       1.372763     -0.222647     0.260890  0.248457    -0.054669   \n",
       "429       0.154795     -0.222647     2.885044  0.131607    -0.054669   \n",
       "1139      0.154795     -0.222647     1.358264 -0.171468    -0.773017   \n",
       "558       0.154795     -0.222647    -0.597924  1.289541     0.663680   \n",
       "\n",
       "      OverallCond  YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  \\\n",
       "302     -0.512408   0.993969      0.840492    0.269972   -0.961498  ...   \n",
       "767      1.285467  -1.026793      0.016525   -0.573129    0.476092  ...   \n",
       "429     -0.512408   0.563314      0.161931   -0.573129    1.227559  ...   \n",
       "1139    -0.512408  -1.689338     -1.679877   -0.573129    0.443419  ...   \n",
       "558     -0.512408   0.828332      0.598149   -0.573129    0.354114  ...   \n",
       "\n",
       "      HouseStyle_SLvl  GarageFinish_Fin  GarageFinish_RFn  GarageFinish_Unf  \\\n",
       "302               0.0               0.0               1.0               0.0   \n",
       "767               0.0               0.0               0.0               1.0   \n",
       "429               0.0               0.0               0.0               1.0   \n",
       "1139              0.0               0.0               0.0               1.0   \n",
       "558               0.0               0.0               0.0               1.0   \n",
       "\n",
       "      GarageFinish_missing  LotConfig_Corner  LotConfig_CulDSac  \\\n",
       "302                    0.0               1.0                0.0   \n",
       "767                    0.0               0.0                0.0   \n",
       "429                    0.0               1.0                0.0   \n",
       "1139                   0.0               0.0                0.0   \n",
       "558                    0.0               0.0                0.0   \n",
       "\n",
       "      LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  \n",
       "302             0.0            0.0               0.0  \n",
       "767             0.0            0.0               1.0  \n",
       "429             0.0            0.0               0.0  \n",
       "1139            0.0            0.0               1.0  \n",
       "558             1.0            0.0               0.0  \n",
       "\n",
       "[5 rows x 263 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_enc = pd.DataFrame(\n",
    "    preprocessor.transform(X_train), index=X_train.index, columns=new_columns\n",
    ")\n",
    "X_train_enc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageFinish_missing</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>-0.025381</td>\n",
       "      <td>-0.085415</td>\n",
       "      <td>0.663680</td>\n",
       "      <td>-0.512408</td>\n",
       "      <td>0.993969</td>\n",
       "      <td>0.792023</td>\n",
       "      <td>0.438592</td>\n",
       "      <td>-0.961498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>1.372763</td>\n",
       "      <td>4.348569</td>\n",
       "      <td>-0.454788</td>\n",
       "      <td>-0.074853</td>\n",
       "      <td>-1.491366</td>\n",
       "      <td>-3.209221</td>\n",
       "      <td>-2.351883</td>\n",
       "      <td>-1.679877</td>\n",
       "      <td>-0.573129</td>\n",
       "      <td>-0.961498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>-1.790721</td>\n",
       "      <td>-0.768279</td>\n",
       "      <td>0.663680</td>\n",
       "      <td>-0.512408</td>\n",
       "      <td>1.093350</td>\n",
       "      <td>0.937429</td>\n",
       "      <td>0.084490</td>\n",
       "      <td>-0.961498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>0.260890</td>\n",
       "      <td>-0.058176</td>\n",
       "      <td>0.663680</td>\n",
       "      <td>0.386530</td>\n",
       "      <td>0.894587</td>\n",
       "      <td>0.646617</td>\n",
       "      <td>-0.573129</td>\n",
       "      <td>1.162214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>0.154795</td>\n",
       "      <td>-0.222647</td>\n",
       "      <td>-0.454788</td>\n",
       "      <td>0.073016</td>\n",
       "      <td>-0.773017</td>\n",
       "      <td>0.386530</td>\n",
       "      <td>-0.861157</td>\n",
       "      <td>-1.679877</td>\n",
       "      <td>-0.573129</td>\n",
       "      <td>0.301838</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 263 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BedroomAbvGr  KitchenAbvGr  LotFrontage   LotArea  OverallQual  \\\n",
       "147       0.154795     -0.222647    -0.025381 -0.085415     0.663680   \n",
       "676       1.372763      4.348569    -0.454788 -0.074853    -1.491366   \n",
       "1304      0.154795     -0.222647    -1.790721 -0.768279     0.663680   \n",
       "1372      0.154795     -0.222647     0.260890 -0.058176     0.663680   \n",
       "1427      0.154795     -0.222647    -0.454788  0.073016    -0.773017   \n",
       "\n",
       "      OverallCond  YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  \\\n",
       "147     -0.512408   0.993969      0.792023    0.438592   -0.961498  ...   \n",
       "676     -3.209221  -2.351883     -1.679877   -0.573129   -0.961498  ...   \n",
       "1304    -0.512408   1.093350      0.937429    0.084490   -0.961498  ...   \n",
       "1372     0.386530   0.894587      0.646617   -0.573129    1.162214  ...   \n",
       "1427     0.386530  -0.861157     -1.679877   -0.573129    0.301838  ...   \n",
       "\n",
       "      HouseStyle_SLvl  GarageFinish_Fin  GarageFinish_RFn  GarageFinish_Unf  \\\n",
       "147               0.0               1.0               0.0               0.0   \n",
       "676               0.0               0.0               0.0               1.0   \n",
       "1304              0.0               0.0               0.0               1.0   \n",
       "1372              0.0               0.0               1.0               0.0   \n",
       "1427              0.0               0.0               0.0               1.0   \n",
       "\n",
       "      GarageFinish_missing  LotConfig_Corner  LotConfig_CulDSac  \\\n",
       "147                    0.0               0.0                1.0   \n",
       "676                    0.0               0.0                0.0   \n",
       "1304                   0.0               0.0                0.0   \n",
       "1372                   0.0               1.0                0.0   \n",
       "1427                   0.0               0.0                0.0   \n",
       "\n",
       "      LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  \n",
       "147             0.0            0.0               0.0  \n",
       "676             0.0            0.0               1.0  \n",
       "1304            0.0            0.0               1.0  \n",
       "1372            0.0            0.0               0.0  \n",
       "1427            0.0            0.0               1.0  \n",
       "\n",
       "[5 rows x 263 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_enc = pd.DataFrame(\n",
    "    preprocessor.transform(X_test), index=X_test.index, columns=new_columns\n",
    ")\n",
    "X_test_enc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 80), (146, 80))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Training random forests and gradient boosted trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare sklearn's `GradientBoostingRegressor` to `RandomForestRegressor` for different values of `n_estimators`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators_values = [3, 10, 30, 100, 300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "10\n",
      "30\n",
      "100\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "score_rf_train = list()\n",
    "score_rf_test = list()\n",
    "score_gb_train = list()\n",
    "score_gb_test = list()\n",
    "\n",
    "for n_estimators in n_estimators_values:\n",
    "    print(n_estimators)\n",
    "    rf = TransformedTargetRegressor(\n",
    "        RandomForestRegressor(n_estimators=n_estimators, random_state=123),\n",
    "        func=np.log1p,\n",
    "        inverse_func=np.expm1,\n",
    "    )\n",
    "    rf.fit(X_train_enc, y_train)\n",
    "    score_rf_train.append(rf.score(X_train_enc, y_train))\n",
    "    score_rf_test.append(rf.score(X_test_enc, y_test))\n",
    "\n",
    "    gb = TransformedTargetRegressor(\n",
    "        GradientBoostingRegressor(n_estimators=n_estimators, random_state=123),\n",
    "        func=np.log1p,\n",
    "        inverse_func=np.expm1,\n",
    "    )\n",
    "    gb.fit(X_train_enc, y_train)\n",
    "    score_gb_train.append(gb.score(X_train_enc, y_train))\n",
    "    score_gb_test.append(gb.score(X_test_enc, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Here is a low-quality plot that is confusing and perhaps downright misleading:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(n_estimators_values, score_rf_train, label=\"rf train\")\n",
    "plt.plot(n_estimators_values, score_rf_test, label=\"rf test\")\n",
    "plt.ylabel(\"$R^2$ score\")\n",
    "plt.legend()\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(n_estimators_values, score_gb_train, label=\"gb train\")\n",
    "plt.plot(n_estimators_values, score_gb_test, label=\"gb test\")\n",
    "plt.xlabel(\"n estimators\")\n",
    "plt.ylabel(\"$R^2$ score\")\n",
    "plt.legend()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why is this confusing/misleading? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's create some visualizations. \n",
    "\n",
    "- Create a visualization that makes RF look better than GB.\n",
    "- Create a visualization that makes GB look better than RF.\n",
    "- Create a visualization that makes RF and GB look equally good.\n",
    "\n",
    "You can create your own misleading example and copy or screenshot it and paste it into the [Google Doc](https://docs.google.com/document/d/1y4YMSAWQGRFFn8ig0pAs5TJR5h2jKnvtY97HN-rEkyE/edit#).\n",
    "\n",
    "Here are some misleading plots. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### RF better than GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nmax = 3\n",
    "\n",
    "# plt.plot(n_estimators_values[:nmax], score_rf_train[:nmax], label=\"rf train\")\n",
    "plt.plot(n_estimators_values[:nmax], score_rf_test[:nmax], label=\"rf test\")\n",
    "# plt.plot(n_estimators_values[:nmax], score_gb_train[:nmax], label=\"gb train\")\n",
    "plt.plot(n_estimators_values[:nmax], score_gb_test[:nmax], label=\"gb test\")\n",
    "plt.xlabel(\"n estimators\")\n",
    "plt.ylabel(\"$R^2$ score\")\n",
    "plt.legend()\n",
    "plt.title(\"for most values of n_estimators, RF is betteR!!\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### GB better than RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(n_estimators_values, score_rf_train, label=\"rf train\")\n",
    "plt.plot(n_estimators_values, score_rf_test, label=\"rf test\")\n",
    "# plt.ylabel(\"$R^2$ score\");\n",
    "# plt.legend();\n",
    "# plt.subplot(2,1,2)\n",
    "# plt.plot(n_estimators_values, score_gb_train, label=\"gb train\")\n",
    "plt.plot(n_estimators_values, score_gb_test, label=\"gb test\")\n",
    "plt.xlabel(\"n estimators\")\n",
    "plt.ylabel(\"$R^2$ score\")\n",
    "plt.legend()\n",
    "plt.title(\"GB better!!!\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Equally good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nmax = 4\n",
    "\n",
    "# plt.plot(n_estimators_values, score_rf_train, label=\"rf train\")\n",
    "plt.plot(n_estimators_values[:nmax], score_rf_test[:nmax], \"b\", label=\"rf test\")\n",
    "plt.ylabel(\"RF $R^2$ score\")\n",
    "plt.ylim((0.8, 0.9))\n",
    "plt.legend(loc=2)\n",
    "plt.twinx()\n",
    "# plt.plot(n_estimators_values, score_gb_train, label=\"gb train\")\n",
    "plt.plot(n_estimators_values[:nmax], score_gb_test[:nmax], \"--r\", label=\"gb test\")\n",
    "plt.legend(loc=4)\n",
    "plt.xlabel(\"n estimators\")\n",
    "plt.ylabel(\"GB $R^2$ score\")\n",
    "plt.ylim((0.7, 0.9))\n",
    "#plt.ylim((-0.01, 0.70))\n",
    "plt.title(\"Both equally good!!!\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Things to watch out for\n",
    "\n",
    "- Chopping off the x-axis \n",
    "- Saturate the axes\n",
    "- Bar chart for a cherry-picked values\n",
    "- Different y-axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Be critical of your visualizations and try to make them as honest as possible.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What did we learn today?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Principles of effective communication\n",
    "  - Concepts then labels, not the other way around.\n",
    "  - Bottom-up explanations.\n",
    "  - New ideas in small chunks.\n",
    "  - Reuse your running examples.\n",
    "  - Approaches from all angles.  \n",
    "  - When experimenting, show the results asap.\n",
    "  - **It's not about you.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Decision variables, objectives, and context.\n",
    "- How does ML fit in?\n",
    "- Expressing your confidence about the results\n",
    "- Misleading visualizations.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Have a great weekend! \n",
    "\n",
    "![](img/eva-seeyou.png)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [conda env:cpsc330]",
   "language": "python",
   "name": "conda-env-cpsc330-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}