07. Feature Engineering/tutorial/02. mutual-information.ipynb

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    "# Introduction #\n",
    "\n",
    "First encountering a new dataset can sometimes feel overwhelming. You might be presented with hundreds or thousands of features without even a description to go by. Where do you even begin?\n",
    "\n",
    "A great first step is to construct a ranking with a **feature utility metric**, a function measuring associations between a feature and the target. Then you can choose a smaller set of the most useful features to develop initially and have more confidence that your time will be well spent.\n",
    "\n",
    "The metric we'll use is called \"mutual information\". Mutual information is a lot like correlation in that it measures a relationship between two quantities. The advantage of mutual information is that it can detect *any* kind of relationship, while correlation only detects *linear* relationships.\n",
    "\n",
    "Mutual information is a great general-purpose metric and especially useful at the start of feature development when you might not know what model you'd like to use yet. It is:\n",
    "- easy to use and interpret,\n",
    "- computationally efficient,\n",
    "- theoretically well-founded,\n",
    "- resistant to overfitting, and,\n",
    "- able to detect any kind of relationship\n",
    "\n",
    "# Mutual Information and What it Measures #\n",
    "\n",
    "Mutual information describes relationships in terms of *uncertainty*. The **mutual information** (MI) between two quantities is a measure of the extent to which knowledge of one quantity reduces uncertainty about the other. If you knew the value of a feature, how much more confident would you be about the target?\n",
    "\n",
    "Here's an example from the *Ames Housing* data. The figure shows the relationship between the exterior quality of a house and the price it sold for. Each point represents a house.\n",
    "\n",
    "<figure style=\"padding: 1em;\">\n",
    "<img src=\"https://i.imgur.com/X12ARUK.png\" width=400, alt=\"Four categories of ExterQual: Fair, Typical, Good, Excellent. A scatter plot of SalePrice within each category.\">\n",
    "<figcaption style=\"textalign: center; font-style: italic\"><center>Knowing the exterior quality of a house reduces uncertainty about its sale price.\n",
    "</center></figcaption>\n",
    "</figure>\n",
    "\n",
    "From the figure, we can see that knowing the value of `ExterQual` should make you more certain about the corresponding `SalePrice` -- each category of `ExterQual` tends to concentrate `SalePrice` to within a certain range. The mutual information that `ExterQual` has with `SalePrice` is the average reduction of uncertainty in `SalePrice` taken over the four values of `ExterQual`. Since `Fair` occurs less often than `Typical`, for instance, `Fair` gets less weight in the MI score.\n",
    "\n",
    "(Technical note: What we're calling uncertainty is measured using a quantity from information theory known as \"entropy\". The entropy of a variable means roughly: \"how many yes-or-no questions you would need to describe an occurance of that variable, on average.\" The more questions you have to ask, the more uncertain you must be about the variable. Mutual information is how many questions you expect the feature to answer about the target.)\n",
    "\n",
    "# Interpreting Mutual Information Scores #\n",
    "\n",
    "The least possible mutual information between quantities is 0.0. When MI is zero, the quantities are independent: neither can tell you anything about the other. Conversely, in theory there's no upper bound to what MI can be. In practice though values above 2.0 or so are uncommon. (Mutual information is a logarithmic quantity, so it increases very slowly.)\n",
    "\n",
    "The next figure will give you an idea of how MI values correspond to the kind and degree of association a feature has with the target.\n",
    "\n",
    "<figure style=\"padding: 1em;\">\n",
    "<img src=\"https://i.imgur.com/Dt75E1f.png\" width=800, alt=\"\">\n",
    "<figcaption style=\"textalign: center; font-style: italic\"><center><strong>Left:</strong> Mutual information increases as the dependence between feature and target becomes tighter. <strong>Right:</strong> Mutual information can capture any kind of association (not just linear, like correlation.)\n",
    "</center></figcaption>\n",
    "</figure>\n",
    "\n",
    "Here are some things to remember when applying mutual information:\n",
    "- MI can help you to understand the *relative potential* of a feature as a predictor of the target, considered by itself.\n",
    "- It's possible for a feature to be very informative when interacting with other features, but not so informative all alone. MI *can't detect interactions* between features. It is a **univariate** metric.\n",
    "- The *actual* usefulness of a feature *depends on the model you use it with*. A feature is only useful to the extent that its relationship with the target is one your model can learn. Just because a feature has a high MI score doesn't mean your model will be able to do anything with that information. You may need to transform the feature first to expose the association.\n",
    "\n",
    "# Example - 1985 Automobiles #\n",
    "\n",
    "The [*Automobile*](https://www.kaggle.com/toramky/automobile-dataset) dataset consists of 193 cars from the 1985 model year. The goal for this dataset is to predict a car's `price` (the target) from 23 of the car's features, such as `make`, `body_style`, and `horsepower`. In this example, we'll rank the features with mutual information and investigate the results by data visualization.\n",
    "\n",
    "This hidden cell imports some libraries and loads the dataset."
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num_of_doors</th>\n",
       "      <th>body_style</th>\n",
       "      <th>drive_wheels</th>\n",
       "      <th>engine_location</th>\n",
       "      <th>wheel_base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>fuel_system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression_ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak_rpm</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>highway_mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
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       "      <td>alfa-romero</td>\n",
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       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
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       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
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       "<p>5 rows × 25 columns</p>\n",
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       "   symboling         make fuel_type aspiration  num_of_doors   body_style  \\\n",
       "0          3  alfa-romero       gas        std             2  convertible   \n",
       "1          3  alfa-romero       gas        std             2  convertible   \n",
       "2          1  alfa-romero       gas        std             2    hatchback   \n",
       "3          2         audi       gas        std             4        sedan   \n",
       "4          2         audi       gas        std             4        sedan   \n",
       "\n",
       "  drive_wheels engine_location  wheel_base  length  ...  engine_size  \\\n",
       "0          rwd           front        88.6   168.8  ...          130   \n",
       "1          rwd           front        88.6   168.8  ...          130   \n",
       "2          rwd           front        94.5   171.2  ...          152   \n",
       "3          fwd           front        99.8   176.6  ...          109   \n",
       "4          4wd           front        99.4   176.6  ...          136   \n",
       "\n",
       "   fuel_system  bore stroke  compression_ratio  horsepower peak_rpm  city_mpg  \\\n",
       "0         mpfi  3.47   2.68                  9         111     5000        21   \n",
       "1         mpfi  3.47   2.68                  9         111     5000        21   \n",
       "2         mpfi  2.68   3.47                  9         154     5000        19   \n",
       "3         mpfi  3.19   3.40                 10         102     5500        24   \n",
       "4         mpfi  3.19   3.40                  8         115     5500        18   \n",
       "\n",
       "   highway_mpg  price  \n",
       "0           27  13495  \n",
       "1           27  16500  \n",
       "2           26  16500  \n",
       "3           30  13950  \n",
       "4           22  17450  \n",
       "\n",
       "[5 rows x 25 columns]"
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   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use(\"seaborn-whitegrid\")\n",
    "\n",
    "df = pd.read_csv(\"../input/fe-course-data/autos.csv\")\n",
    "df.head()"
   ]
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   "source": [
    "The scikit-learn algorithm for MI treats discrete features differently from continuous features. Consequently, you need to tell it which are which. As a rule of thumb, anything that *must* have a `float` dtype is *not* discrete. Categoricals (`object` or `categorial` dtype) can be treated as discrete by giving them a label encoding. (You can review label encodings in our [Categorical Variables](http://www.kaggle.com/alexisbcook/categorical-variables) lesson.)"
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   "source": [
    "X = df.copy()\n",
    "y = X.pop(\"price\")\n",
    "\n",
    "# Label encoding for categoricals\n",
    "for colname in X.select_dtypes(\"object\"):\n",
    "    X[colname], _ = X[colname].factorize()\n",
    "\n",
    "# All discrete features should now have integer dtypes (double-check this before using MI!)\n",
    "discrete_features = X.dtypes == int"
   ]
  },
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   "source": [
    "Scikit-learn has two mutual information metrics in its `feature_selection` module: one for real-valued targets (`mutual_info_regression`) and one for categorical targets (`mutual_info_classif`). Our target, `price`, is real-valued. The next cell computes the MI scores for our features and wraps them up in a nice dataframe."
   ]
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   "outputs": [
    {
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      "text/plain": [
       "curb_weight          1.486440\n",
       "highway_mpg          0.950989\n",
       "length               0.607955\n",
       "bore                 0.489772\n",
       "stroke               0.380041\n",
       "drive_wheels         0.332973\n",
       "compression_ratio    0.134799\n",
       "fuel_type            0.048139\n",
       "Name: MI Scores, dtype: float64"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_regression\n",
    "\n",
    "def make_mi_scores(X, y, discrete_features):\n",
    "    mi_scores = mutual_info_regression(X, y, discrete_features=discrete_features)\n",
    "    mi_scores = pd.Series(mi_scores, name=\"MI Scores\", index=X.columns)\n",
    "    mi_scores = mi_scores.sort_values(ascending=False)\n",
    "    return mi_scores\n",
    "\n",
    "mi_scores = make_mi_scores(X, y, discrete_features)\n",
    "mi_scores[::3]  # show a few features with their MI scores"
   ]
  },
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   "source": [
    "And now a bar plot to make comparisions easier:"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_mi_scores(scores):\n",
    "    scores = scores.sort_values(ascending=True)\n",
    "    width = np.arange(len(scores))\n",
    "    ticks = list(scores.index)\n",
    "    plt.barh(width, scores)\n",
    "    plt.yticks(width, ticks)\n",
    "    plt.title(\"Mutual Information Scores\")\n",
    "\n",
    "\n",
    "plt.figure(dpi=100, figsize=(8, 5))\n",
    "plot_mi_scores(mi_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438a5ccb",
   "metadata": {
    "papermill": {
     "duration": 0.018179,
     "end_time": "2022-05-05T12:49:42.942341",
     "exception": false,
     "start_time": "2022-05-05T12:49:42.924162",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Data visualization is a great follow-up to a utility ranking. Let's take a closer look at a couple of these.\n",
    "\n",
    "As we might expect, the high-scoring `curb_weight` feature exhibits a strong relationship with `price`, the target.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94058dcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T12:49:42.980996Z",
     "iopub.status.busy": "2022-05-05T12:49:42.980653Z",
     "iopub.status.idle": "2022-05-05T12:49:43.226173Z",
     "shell.execute_reply": "2022-05-05T12:49:43.225342Z"
    },
    "papermill": {
     "duration": 0.267414,
     "end_time": "2022-05-05T12:49:43.228275",
     "exception": false,
     "start_time": "2022-05-05T12:49:42.960861",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"curb_weight\", y=\"price\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308d9b1a",
   "metadata": {
    "papermill": {
     "duration": 0.019496,
     "end_time": "2022-05-05T12:49:43.267708",
     "exception": false,
     "start_time": "2022-05-05T12:49:43.248212",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The `fuel_type` feature has a fairly low MI score, but as we can see from the figure, it clearly separates two `price` populations with different trends within the `horsepower` feature. This indicates that `fuel_type` contributes an interaction effect and might not be unimportant after all. Before deciding a feature is unimportant from its MI score, it's good to investigate any possible interaction effects -- domain knowledge can offer a lot of guidance here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f08fd63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-05T12:49:43.309293Z",
     "iopub.status.busy": "2022-05-05T12:49:43.308554Z",
     "iopub.status.idle": "2022-05-05T12:49:43.781816Z",
     "shell.execute_reply": "2022-05-05T12:49:43.780242Z"
    },
    "papermill": {
     "duration": 0.496783,
     "end_time": "2022-05-05T12:49:43.784210",
     "exception": false,
     "start_time": "2022-05-05T12:49:43.287427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"horsepower\", y=\"price\", hue=\"fuel_type\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f11bbe8",
   "metadata": {
    "papermill": {
     "duration": 0.021235,
     "end_time": "2022-05-05T12:49:43.827018",
     "exception": false,
     "start_time": "2022-05-05T12:49:43.805783",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Data visualization is a great addition to your feature-engineering toolbox. Along with utility metrics like mutual information, visualizations like these can help you discover important relationships in your data. Check out our [Data Visualization](https://www.kaggle.com/learn/data-visualization) course to learn more!\n",
    "\n",
    "# Your Turn #\n",
    "\n",
    "[**Rank the features**](https://www.kaggle.com/kernels/fork/14393925) of the *Ames Housing* dataset and choose your first set of features to start developing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62846c07",
   "metadata": {
    "papermill": {
     "duration": 0.021814,
     "end_time": "2022-05-05T12:49:43.870552",
     "exception": false,
     "start_time": "2022-05-05T12:49:43.848738",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/feature-engineering/discussion) to chat with other learners.*"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "formats": "ipynb"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 12.516678,
   "end_time": "2022-05-05T12:49:44.615628",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-05-05T12:49:32.098950",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}