chp18_xgbfir.ipynb

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    "## 18.1 Feature Interactions\n",
    "In machine learning, understanding the interactions between features can be crucial for building accurate models. Imagine a chef creating a recipe: each ingredient has its own unique flavor, but when combined with other ingredients, new and complex flavors can emerge. Similarly, in a machine learning model, the relationship between two features can change the prediction in ways that cannot be captured by considering each feature individually.\n",
    "\n",
    "For example, consider a linear regression model that predicts the price of a house based on its size and location. The model might include an interaction term between size and location, represented by the formula: $price = a * size + b * location + c * size * location$. This interaction term captures the idea that the effect of size on price might depend on location: in some locations, larger houses might command a higher premium than in others.\n",
    "\n",
    "In decision trees, feature interactions are represented differently. A feature $X_1$ interacts with another feature $X2$ if $X2$ is used differently in different paths of the tree. In other words, the effect of $X1$ on the prediction depends on the value of $X2$. For example, imagine a decision tree that predicts whether someone will enjoy a movie based on its genre and runtime. The tree might split first on genre, then on runtime for certain genres. This would indicate an interaction between genre and runtime: the effect of runtime on enjoyment depends on the genre of the movie.\n",
    "\n",
    "Feature interactions occur when the effect of one feature on the prediction depends on the value of another feature. In other words, the change in the prediction that occurs by varying one feature also depends on the value of another feature. This can be represented in a formula when using linear or logistic regression by creating a new column that multiplies two features together. In decision trees, interaction occurs when one feature is used differently in different paths of the tree, and both features combined impact the result.\n",
    "\n",
    "Feature interactions can be important for improving the accuracy of machine learning models, particularly in recommendation systems and click-through rate predictions. By learning sophisticated feature interactions, deep learning methods can achieve state-of-the-art results in an end-to-end manner.\n",
    "\n",
    "If we created a formula thet represents a linear or logistic regression model that includes an interaction term between two features, $X_1 \\text{ and }X_2$. The formula is: $y = aX_1 + bX_2 + cX_1X_2$.\n",
    "\n",
    "In this formula, $y$ represents the predicted value of the dependent variable, which could be a continuous value in the case of linear regression or a probability in the case of logistic regression. $X_1 \\text{ and }X_2$ represent two independent variables or features that are used to make the prediction.\n",
    "\n",
    "The coefficients $a, b,\\text{ and }c$ represent the _weights_ assigned to each term in the formula. The coefficient $a$ represents the effect of $X_1 \\text{ on }y \\text{ when }X_2$ is held constant, while the coefficient $b$ represents the effect of $X_2\\text{ on }y\\text{ when }X_1$ is held constant. The coefficient c represents the interaction effect between $X_1\\text{ and }X2\\text{ on }y$.\n",
    "\n",
    "The interaction term, $cX_1X_2$, is created by multiplying the two features together. This term captures the idea that the effect of one feature on $y$ depends on the value of another feature. In other words, the change in $y$ that occurs by varying one feature also depends on the value of another feature.\n",
    "\n",
    "Overall, this formula represents a model that includes both main effects (the effects of each feature individually) and an interaction effect (the combined effect of two features) on the dependent variable $y$.\n",
    "\n",
    "## 18.2 xgbfir\n",
    "The xgbfir library can be used to find the top feature interactions in an XGBoost model. To use this library, you need to install both xgbfir and the openpyxl library, which is used to read Excel files. "
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    "import dtreeviz\n",
    "from feature_engine import encoding, imputation\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import base, compose, datasets, ensemble, metrics, model_selection, pipeline, preprocessing, tree\n",
    "import scikitplot\n",
    "import xgboost as xgb\n",
    "import yellowbrick.model_selection as ms\n",
    "from yellowbrick import classifier\n",
    "import urllib\n",
    "import zipfile\n",
    "from hyperopt import fmin, tpe, hp, Trials\n",
    "from EffectiveXGBoost import *\n",
    "\n",
    "import xgbfir"
   ]
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      "G:\\My Drive\\SisengAI\\AlgorithmicTrading\\code_rebuilding\\MattHarrison\\EffectiveXGBoost\\EffectiveXGBoost_MyTake\\EffectiveXGBoost.py:62: DtypeWarning: Columns (0,2,8,10,21,23,24,25,26,27,28,44,56,64,83,85,87,107,109,123,125,150,157,172,174,194,210,218,219,223,246,249,262,264,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,304,306,325,326,329,341,368,371,384,385,389,390,391,393,394) have mixed types. Specify dtype option on import or set low_memory=False.\n"
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       "              reg_alpha=0, reg_lambda=1, ...)</pre></div></div></div></div></div>"
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       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
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       "              reg_alpha=0, reg_lambda=1, ...)"
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   "source": [
    "path_url = 'https://github.com/mattharrison/datasets/raw/master/data/kaggle-survey-2018.zip'\n",
    "file_name = 'kaggle-survey-2018.zip'\n",
    "dataset = 'multipleChoiceResponses.csv'\n",
    "\n",
    "raw = extract_dataset(path_url, file_name, dataset)\n",
    "\n",
    "# Create raw X and raw y\n",
    "kag_X, kag_y = prepX_y(raw, 'Q6')\n",
    "\n",
    "# Split data\n",
    "kag_X_train, kag_X_test, kag_y_train, kag_y_test = (model_selection\n",
    "                                                    .train_test_split(kag_X, \n",
    "                                                                      kag_y, \n",
    "                                                                      test_size=.3, \n",
    "                                                                      random_state=42, \n",
    "                                                                      stratify=kag_y)\n",
    "                                                   )\n",
    "\n",
    "\n",
    "# Transform X with pipeline\n",
    "pline = pipeline.Pipeline(\n",
    "    [('tweak', PrepDataTransformer()),\n",
    "    ('cat', encoding.OneHotEncoder(top_categories=5, drop_last=True,\n",
    "    variables=['Q1', 'Q3', 'major'])),\n",
    "    ('num_impute', imputation.MeanMedianImputer(imputation_method='median',\n",
    "                variables=['education', 'years_exp']))]\n",
    "    )\n",
    "\n",
    "X_train = pline.fit_transform(kag_X_train)\n",
    "X_test = pline.transform(kag_X_test)\n",
    "\n",
    "# Transform y with label encoder\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "label_encoder.fit(kag_y_train)\n",
    "y_train = label_encoder.transform(kag_y_train)\n",
    "y_test = label_encoder.transform(kag_y_test)\n",
    "\n",
    "# Combined Data for cross validation/etc\n",
    "X = pd.concat([X_train, X_test], axis='index')\n",
    "y = pd.Series([*y_train, *y_test], index=X.index)\n",
    "\n",
    "# Default training\n",
    "xg = xgb.XGBClassifier()\n",
    "xg.fit(X_train, y_train)"
   ]
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_def = xgb.XGBClassifier()\n",
    "xgb_def.fit(X_train, y_train)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "_=(pd.Series(xgb_def.feature_importances_, index=X_train.columns)\n",
    ".sort_values()\n",
    ".plot.barh(ax=ax)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55cf48b4-67aa-49df-8158-0c47e1ca0414",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Interaction   Gain  FScore  wFScore  Average wFScore  Average Gain  \\\n",
      "2             r  517.8      84     34.2              0.4           6.2   \n",
      "0     years_exp  597.0     627     88.2              0.1           1.0   \n",
      "5     education  296.0     254     76.7              0.3           1.2   \n",
      "1  compensation  518.5     702    123.0              0.2           0.7   \n",
      "4      major_cs  327.1      96     16.1              0.2           3.4   \n",
      "\n",
      "   Expected Gain  Gain Rank  FScore Rank  wFScore Rank  Avg wFScore Rank  \\\n",
      "2          479.4          3            8             5                 2   \n",
      "0           87.9          1            2             3                12   \n",
      "5           83.7          6            4             4                 4   \n",
      "1           59.7          2            1             1                 8   \n",
      "4          174.7          5            7             8                 9   \n",
      "\n",
      "   Avg Gain Rank  Expected Gain Rank  Average Rank  Average Tree Index  \\\n",
      "2              1                   1           3.3                44.6   \n",
      "0              6                   3           4.5                45.1   \n",
      "5              5                   4           4.5                45.2   \n",
      "1             12                   5           4.8                47.5   \n",
      "4              2                   2           5.5                48.9   \n",
      "\n",
      "   Average Tree Depth  \n",
      "2                 2.6  \n",
      "0                 3.7  \n",
      "5                 3.3  \n",
      "1                 3.7  \n",
      "4                 3.6  \n"
     ]
    }
   ],
   "source": [
    "xgbfir.saveXgbFI(xgb_def, feature_names=X_train.columns, OutputXlsxFile='fir.xlsx')\n",
    "fir = pd.read_excel('fir.xlsx')\n",
    "print(fir\n",
    " .sort_values(by='Average Rank')\n",
    " .head()\n",
    " .round(1)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ea58742-94a1-4b11-b5a2-86121d330113",
   "metadata": {},
   "source": [
    "Here is an explanation of the terms we see in the output above:\n",
    "\n",
    "- **Gain**: This represents the improvement in accuracy brought by a feature to the branches on which it is used. It is calculated by taking the difference between the weighted impurity of the parent node and the sum of the weighted impurities of its child nodes.\n",
    "\n",
    "- **FScore**: This represents the number of times a feature is used to split the data in the tree structure of an XGBoost model.\n",
    "\n",
    "- **wFScore**: This represents the FScore weighted by the number of instances (samples) affected by the split.\n",
    "\n",
    "- **Average wFScore**: This represents the average wFScore for a feature, calculated by dividing the total wFScore for that feature by its FScore.\n",
    "\n",
    "- **Average Gain**: This represents the average gain for a feature, calculated by dividing the total gain for that feature by its FScore.\n",
    "\n",
    "- **Expected Gain**: This represents an estimate of the expected gain for a feature, calculated by multiplying its Average Gain by its Average wFScore.\n",
    "\n",
    "These values provide insight into the importance and impact of individual features in an XGBoost model, and can be used to guide feature selection and model interpretation.\n",
    "\n",
    "The xgbfir library is used to find the top feature interactions in an XGBoost model. After running the saveXgbFI function, the library generates an Excel file with multiple sheets containing the results. Here is a breakdown of the output:\n",
    "\n",
    "- **Interaction Depth 0**: This sheet contains information about individual features, including their importance, gain, and coverage.\n",
    "- **Interaction Depth 1**: This sheet contains information about pairwise feature interactions, including their importance, gain, and coverage.\n",
    "- **Interaction Depth 2+**: These sheets contain information about higher-order feature interactions (interactions between three or more features), including their importance, gain, and coverage.\n",
    "- **Feature Stats**: This sheet contains summary statistics for each feature, including mean, standard deviation, minimum, and maximum values.\n",
    "\n",
    "Overall, the output of the xgbfir library provides detailed information about the importance and impact of individual features and their interactions in an XGBoost model.\n",
    "\n",
    "Based on the data in the table, it appears that the `years_exp` feature has the highest Gain value, indicating that it contributes the most to the model's overall performance. The `compensation` feature has the highest FScore value, indicating that it is used the most often as a split in the trees of the XGBoost model. The `compensation` feature also has the highest wFScore value, indicating that it has a high probability of being used as a split in the trees of the model.\n",
    "\n",
    "The `r` feature has the highest Average wFScore value, indicating that on average, it has a high probability of being used as a split in the trees of the model when it is used. The `r` feature also has the highest Average Gain value, indicating that on average, it contributes the most to the model's performance when it is used as a split.\n",
    "\n",
    "The `r` feature also has the highest Expected Gain value, indicating that it is expected to contribute the most to the model's performance based on its probability of being used as a split and its average contribution when used.\n",
    "\n",
    "Overall, it appears that `years_exp`, `compensation`, and `r` are among the most important features in this XGBoost model based on their high values for various metrics.\n",
    "\n",
    "One potential next step could be to use this information to perform feature selection. This involves selecting a subset of the most important features to use in the model, based on their importance metrics. By only using the most important features, you may be able to simplify the model and potentially improve its performance.\n",
    "\n",
    "However, it's important to note that feature selection should be done carefully and with consideration of the specific problem and data at hand. Removing features from the model can sometimes lead to a loss of information and reduced performance. It may be helpful to experiment with different subsets of features and evaluate their impact on the model's performance before making a final decision.\n",
    "\n",
    "For each of the above columns, another column shows the rank. Finally, there is the _Average\n",
    "Rank, the Average Tree Index_, and _the Average Tree Depth columns._\n",
    "Let’s sort the features by the Average Rank column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83ce6db9-d519-4918-8cdf-f9d6386f25f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Interaction   Gain  FScore  wFScore  Average wFScore  Average Gain  \\\n",
      "2             r  517.8      84     34.2              0.4           6.2   \n",
      "0     years_exp  597.0     627     88.2              0.1           1.0   \n",
      "5     education  296.0     254     76.7              0.3           1.2   \n",
      "1  compensation  518.5     702    123.0              0.2           0.7   \n",
      "4      major_cs  327.1      96     16.1              0.2           3.4   \n",
      "\n",
      "   Expected Gain  Gain Rank  FScore Rank  wFScore Rank  Avg wFScore Rank  \\\n",
      "2          479.4          3            8             5                 2   \n",
      "0           87.9          1            2             3                12   \n",
      "5           83.7          6            4             4                 4   \n",
      "1           59.7          2            1             1                 8   \n",
      "4          174.7          5            7             8                 9   \n",
      "\n",
      "   Avg Gain Rank  Expected Gain Rank  Average Rank  Average Tree Index  \\\n",
      "2              1                   1           3.3                44.6   \n",
      "0              6                   3           4.5                45.1   \n",
      "5              5                   4           4.5                45.2   \n",
      "1             12                   5           4.8                47.5   \n",
      "4              2                   2           5.5                48.9   \n",
      "\n",
      "   Average Tree Depth  \n",
      "2                 2.6  \n",
      "0                 3.7  \n",
      "5                 3.3  \n",
      "1                 3.7  \n",
      "4                 3.6  \n"
     ]
    }
   ],
   "source": [
    "fir = pd.read_excel('fir.xlsx')\n",
    "print(fir\n",
    " .sort_values(by='Average Rank')\n",
    " .head()\n",
    " .round(1)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f73b607-9cfd-4181-8964-54d27b1339d8",
   "metadata": {},
   "source": [
    "This looks like the `r` and `years_exp` features are important for our model.\n",
    "\n",
    "The _Interaction Depth 1_ sheet shows how pairs of columns interact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c86caab3-e356-4f77-b3d3-0bcb9e3ba8c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Interaction    Gain  FScore  wFScore  Average wFScore  \\\n",
      "1      education|years_exp   523.8     106     14.8              0.1   \n",
      "0               major_cs|r  1210.8      15      5.4              0.4   \n",
      "6   compensation|education   207.2     103     18.8              0.2   \n",
      "11           age|education   133.2      80     27.2              0.3   \n",
      "3       major_cs|years_exp   441.3      36      4.8              0.1   \n",
      "5            age|years_exp   316.3     216     43.9              0.2   \n",
      "4         age|compensation   344.7     219     38.8              0.2   \n",
      "15    major_stat|years_exp    97.7      32      6.7              0.2   \n",
      "14             education|r   116.5      14      4.6              0.3   \n",
      "18                 age|age    90.5      66     24.7              0.4   \n",
      "\n",
      "    Average Gain  Expected Gain  Gain Rank  FScore Rank  wFScore Rank  \\\n",
      "1            4.9           77.9          2            5             8   \n",
      "0           80.7          607.6          1           45            20   \n",
      "6            2.0           34.0          7            6             7   \n",
      "11           1.7           25.6         12            8             4   \n",
      "3           12.3          108.2          4           20            25   \n",
      "5            1.5           44.0          6            3             1   \n",
      "4            1.6           30.6          5            2             2   \n",
      "15           3.1           20.4         16           25            15   \n",
      "14           8.3           72.3         15           52            27   \n",
      "18           1.4           16.6         19           11             6   \n",
      "\n",
      "    Avg wFScore Rank  Avg Gain Rank  Expected Gain Rank  Average Rank  \\\n",
      "1                 43              8                   3          11.5   \n",
      "0                  8              1                   1          12.7   \n",
      "6                 32             25                   9          14.3   \n",
      "11                12             40                  13          14.8   \n",
      "3                 46              3                   2          16.7   \n",
      "5                 26             57                   7          16.7   \n",
      "4                 34             48                  11          17.0   \n",
      "15                24             14                  14          18.0   \n",
      "14                13              5                   4          19.3   \n",
      "18                 7             62                  16          20.2   \n",
      "\n",
      "    Average Tree Index  Average Tree Depth  \n",
      "1                 38.0                 3.5  \n",
      "0                 12.3                 1.6  \n",
      "6                 50.6                 3.7  \n",
      "11                38.8                 3.6  \n",
      "3                 29.2                 3.2  \n",
      "5                 45.6                 3.9  \n",
      "4                 48.9                 3.9  \n",
      "15                25.5                 3.1  \n",
      "14                40.4                 2.4  \n",
      "18                48.0                 3.6  \n"
     ]
    }
   ],
   "source": [
    "print(pd.read_excel('fir.xlsx', sheet_name='Interaction Depth 1').iloc[:20]\n",
    " .sort_values(by='Average Rank')\n",
    " .head(10)\n",
    " .round(1)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb75c3a-3423-47f7-a3aa-6b6ce90da3e6",
   "metadata": {},
   "source": [
    "This output tells us that `education` is often followed by `years_exp` and `major_cs` is often followed by\n",
    "`r`. This hints that these features might have an impact on each other. \n",
    "\n",
    "We can create a correlation heatmap with both Pandas and Seaborn to explore this relationship further. \n",
    "\n",
    "The selected text is a code snippet that calculates the Spearman correlation between several columns of a Pandas DataFrame and displays the result as a styled table with a color gradient.\n",
    "\n",
    "Here is a step-by-step explanation of what this code does:\n",
    "\n",
    "1. `(X_train.assign(software_eng=y_train)` adds a new column to the `X_train` DataFrame, named `software_eng`, with the values from `y_train`.\n",
    "\n",
    "2. `.corr(method='spearman')` calculates the pairwise Spearman correlation between all columns of the resulting DataFrame.\n",
    "\n",
    "3. `.loc[:, ['education', 'years_exp', 'major_cs', 'r', 'compensation', 'age']]` selects a subset of columns from the resulting correlation matrix.\n",
    "\n",
    "4. `.style.background_gradient(cmap='RdBu', vmin=-1, vmax=1)` applies a color gradient to the table cells, using the 'RdBu' colormap and a value range of -1 to 1.\n",
    "\n",
    "5. `.format('{:.2f}')` formats the table cell values as floating-point numbers with two decimal places.\n",
    "\n",
    "6. `)` displays the resulting styled table.\n",
    "\n",
    "Overall, this code snippet calculates the Spearman correlation between several columns of a Pandas DataFrame, selects a subset of columns from the resulting correlation matrix, and displays the result as a styled table with a color gradient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "305eafd5-efcc-408a-bb70-2021b330bb0c",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_32929_level0_col0\" class=\"col_heading level0 col0\" >education</th>\n",
       "      <th id=\"T_32929_level0_col1\" class=\"col_heading level0 col1\" >years_exp</th>\n",
       "      <th id=\"T_32929_level0_col2\" class=\"col_heading level0 col2\" >major_cs</th>\n",
       "      <th id=\"T_32929_level0_col3\" class=\"col_heading level0 col3\" >r</th>\n",
       "      <th id=\"T_32929_level0_col4\" class=\"col_heading level0 col4\" >compensation</th>\n",
       "      <th id=\"T_32929_level0_col5\" class=\"col_heading level0 col5\" >age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row0\" class=\"row_heading level0 row0\" >age</th>\n",
       "      <td id=\"T_32929_row0_col0\" class=\"data row0 col0\" >0.35</td>\n",
       "      <td id=\"T_32929_row0_col1\" class=\"data row0 col1\" >0.64</td>\n",
       "      <td id=\"T_32929_row0_col2\" class=\"data row0 col2\" >-0.22</td>\n",
       "      <td id=\"T_32929_row0_col3\" class=\"data row0 col3\" >0.13</td>\n",
       "      <td id=\"T_32929_row0_col4\" class=\"data row0 col4\" >0.45</td>\n",
       "      <td id=\"T_32929_row0_col5\" class=\"data row0 col5\" >1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row1\" class=\"row_heading level0 row1\" >education</th>\n",
       "      <td id=\"T_32929_row1_col0\" class=\"data row1 col0\" >1.00</td>\n",
       "      <td id=\"T_32929_row1_col1\" class=\"data row1 col1\" >0.10</td>\n",
       "      <td id=\"T_32929_row1_col2\" class=\"data row1 col2\" >-0.22</td>\n",
       "      <td id=\"T_32929_row1_col3\" class=\"data row1 col3\" >0.15</td>\n",
       "      <td id=\"T_32929_row1_col4\" class=\"data row1 col4\" >0.24</td>\n",
       "      <td id=\"T_32929_row1_col5\" class=\"data row1 col5\" >0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row2\" class=\"row_heading level0 row2\" >years_exp</th>\n",
       "      <td id=\"T_32929_row2_col0\" class=\"data row2 col0\" >0.10</td>\n",
       "      <td id=\"T_32929_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
       "      <td id=\"T_32929_row2_col2\" class=\"data row2 col2\" >-0.05</td>\n",
       "      <td id=\"T_32929_row2_col3\" class=\"data row2 col3\" >0.01</td>\n",
       "      <td id=\"T_32929_row2_col4\" class=\"data row2 col4\" >0.26</td>\n",
       "      <td id=\"T_32929_row2_col5\" class=\"data row2 col5\" >0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row3\" class=\"row_heading level0 row3\" >compensation</th>\n",
       "      <td id=\"T_32929_row3_col0\" class=\"data row3 col0\" >0.24</td>\n",
       "      <td id=\"T_32929_row3_col1\" class=\"data row3 col1\" >0.26</td>\n",
       "      <td id=\"T_32929_row3_col2\" class=\"data row3 col2\" >-0.23</td>\n",
       "      <td id=\"T_32929_row3_col3\" class=\"data row3 col3\" >0.17</td>\n",
       "      <td id=\"T_32929_row3_col4\" class=\"data row3 col4\" >1.00</td>\n",
       "      <td id=\"T_32929_row3_col5\" class=\"data row3 col5\" >0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row4\" class=\"row_heading level0 row4\" >python</th>\n",
       "      <td id=\"T_32929_row4_col0\" class=\"data row4 col0\" >0.06</td>\n",
       "      <td id=\"T_32929_row4_col1\" class=\"data row4 col1\" >-0.06</td>\n",
       "      <td id=\"T_32929_row4_col2\" class=\"data row4 col2\" >-0.10</td>\n",
       "      <td id=\"T_32929_row4_col3\" class=\"data row4 col3\" >0.18</td>\n",
       "      <td id=\"T_32929_row4_col4\" class=\"data row4 col4\" >0.17</td>\n",
       "      <td id=\"T_32929_row4_col5\" class=\"data row4 col5\" >0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row5\" class=\"row_heading level0 row5\" >r</th>\n",
       "      <td id=\"T_32929_row5_col0\" class=\"data row5 col0\" >0.15</td>\n",
       "      <td id=\"T_32929_row5_col1\" class=\"data row5 col1\" >0.01</td>\n",
       "      <td id=\"T_32929_row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
       "      <td id=\"T_32929_row5_col3\" class=\"data row5 col3\" >1.00</td>\n",
       "      <td id=\"T_32929_row5_col4\" class=\"data row5 col4\" >0.17</td>\n",
       "      <td id=\"T_32929_row5_col5\" class=\"data row5 col5\" >0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row6\" class=\"row_heading level0 row6\" >sql</th>\n",
       "      <td id=\"T_32929_row6_col0\" class=\"data row6 col0\" >0.07</td>\n",
       "      <td id=\"T_32929_row6_col1\" class=\"data row6 col1\" >0.05</td>\n",
       "      <td id=\"T_32929_row6_col2\" class=\"data row6 col2\" >-0.13</td>\n",
       "      <td id=\"T_32929_row6_col3\" class=\"data row6 col3\" >0.28</td>\n",
       "      <td id=\"T_32929_row6_col4\" class=\"data row6 col4\" >0.20</td>\n",
       "      <td id=\"T_32929_row6_col5\" class=\"data row6 col5\" >0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row7\" class=\"row_heading level0 row7\" >Q1_Male</th>\n",
       "      <td id=\"T_32929_row7_col0\" class=\"data row7 col0\" >-0.08</td>\n",
       "      <td id=\"T_32929_row7_col1\" class=\"data row7 col1\" >0.08</td>\n",
       "      <td id=\"T_32929_row7_col2\" class=\"data row7 col2\" >0.06</td>\n",
       "      <td id=\"T_32929_row7_col3\" class=\"data row7 col3\" >-0.07</td>\n",
       "      <td id=\"T_32929_row7_col4\" class=\"data row7 col4\" >0.00</td>\n",
       "      <td id=\"T_32929_row7_col5\" class=\"data row7 col5\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row8\" class=\"row_heading level0 row8\" >Q1_Female</th>\n",
       "      <td id=\"T_32929_row8_col0\" class=\"data row8 col0\" >0.06</td>\n",
       "      <td id=\"T_32929_row8_col1\" class=\"data row8 col1\" >-0.10</td>\n",
       "      <td id=\"T_32929_row8_col2\" class=\"data row8 col2\" >-0.05</td>\n",
       "      <td id=\"T_32929_row8_col3\" class=\"data row8 col3\" >0.07</td>\n",
       "      <td id=\"T_32929_row8_col4\" class=\"data row8 col4\" >0.00</td>\n",
       "      <td id=\"T_32929_row8_col5\" class=\"data row8 col5\" >-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row9\" class=\"row_heading level0 row9\" >Q1_Prefer not to say</th>\n",
       "      <td id=\"T_32929_row9_col0\" class=\"data row9 col0\" >0.06</td>\n",
       "      <td id=\"T_32929_row9_col1\" class=\"data row9 col1\" >0.03</td>\n",
       "      <td id=\"T_32929_row9_col2\" class=\"data row9 col2\" >-0.05</td>\n",
       "      <td id=\"T_32929_row9_col3\" class=\"data row9 col3\" >0.00</td>\n",
       "      <td id=\"T_32929_row9_col4\" class=\"data row9 col4\" >-0.04</td>\n",
       "      <td id=\"T_32929_row9_col5\" class=\"data row9 col5\" >0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row10\" class=\"row_heading level0 row10\" >Q1_Prefer to self-describe</th>\n",
       "      <td id=\"T_32929_row10_col0\" class=\"data row10 col0\" >0.00</td>\n",
       "      <td id=\"T_32929_row10_col1\" class=\"data row10 col1\" >0.02</td>\n",
       "      <td id=\"T_32929_row10_col2\" class=\"data row10 col2\" >-0.02</td>\n",
       "      <td id=\"T_32929_row10_col3\" class=\"data row10 col3\" >-0.00</td>\n",
       "      <td id=\"T_32929_row10_col4\" class=\"data row10 col4\" >0.05</td>\n",
       "      <td id=\"T_32929_row10_col5\" class=\"data row10 col5\" >0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row11\" class=\"row_heading level0 row11\" >Q3_United States of America</th>\n",
       "      <td id=\"T_32929_row11_col0\" class=\"data row11 col0\" >0.27</td>\n",
       "      <td id=\"T_32929_row11_col1\" class=\"data row11 col1\" >0.12</td>\n",
       "      <td id=\"T_32929_row11_col2\" class=\"data row11 col2\" >-0.27</td>\n",
       "      <td id=\"T_32929_row11_col3\" class=\"data row11 col3\" >0.18</td>\n",
       "      <td id=\"T_32929_row11_col4\" class=\"data row11 col4\" >0.62</td>\n",
       "      <td id=\"T_32929_row11_col5\" class=\"data row11 col5\" >0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row12\" class=\"row_heading level0 row12\" >Q3_India</th>\n",
       "      <td id=\"T_32929_row12_col0\" class=\"data row12 col0\" >-0.27</td>\n",
       "      <td id=\"T_32929_row12_col1\" class=\"data row12 col1\" >-0.14</td>\n",
       "      <td id=\"T_32929_row12_col2\" class=\"data row12 col2\" >0.24</td>\n",
       "      <td id=\"T_32929_row12_col3\" class=\"data row12 col3\" >-0.11</td>\n",
       "      <td id=\"T_32929_row12_col4\" class=\"data row12 col4\" >-0.56</td>\n",
       "      <td id=\"T_32929_row12_col5\" class=\"data row12 col5\" >-0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row13\" class=\"row_heading level0 row13\" >Q3_China</th>\n",
       "      <td id=\"T_32929_row13_col0\" class=\"data row13 col0\" >-0.01</td>\n",
       "      <td id=\"T_32929_row13_col1\" class=\"data row13 col1\" >0.03</td>\n",
       "      <td id=\"T_32929_row13_col2\" class=\"data row13 col2\" >0.07</td>\n",
       "      <td id=\"T_32929_row13_col3\" class=\"data row13 col3\" >-0.13</td>\n",
       "      <td id=\"T_32929_row13_col4\" class=\"data row13 col4\" >-0.12</td>\n",
       "      <td id=\"T_32929_row13_col5\" class=\"data row13 col5\" >-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row14\" class=\"row_heading level0 row14\" >major_cs</th>\n",
       "      <td id=\"T_32929_row14_col0\" class=\"data row14 col0\" >-0.22</td>\n",
       "      <td id=\"T_32929_row14_col1\" class=\"data row14 col1\" >-0.05</td>\n",
       "      <td id=\"T_32929_row14_col2\" class=\"data row14 col2\" >1.00</td>\n",
       "      <td id=\"T_32929_row14_col3\" class=\"data row14 col3\" >-0.21</td>\n",
       "      <td id=\"T_32929_row14_col4\" class=\"data row14 col4\" >-0.23</td>\n",
       "      <td id=\"T_32929_row14_col5\" class=\"data row14 col5\" >-0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row15\" class=\"row_heading level0 row15\" >major_other</th>\n",
       "      <td id=\"T_32929_row15_col0\" class=\"data row15 col0\" >0.16</td>\n",
       "      <td id=\"T_32929_row15_col1\" class=\"data row15 col1\" >0.02</td>\n",
       "      <td id=\"T_32929_row15_col2\" class=\"data row15 col2\" >-0.49</td>\n",
       "      <td id=\"T_32929_row15_col3\" class=\"data row15 col3\" >0.14</td>\n",
       "      <td id=\"T_32929_row15_col4\" class=\"data row15 col4\" >0.24</td>\n",
       "      <td id=\"T_32929_row15_col5\" class=\"data row15 col5\" >0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row16\" class=\"row_heading level0 row16\" >major_eng</th>\n",
       "      <td id=\"T_32929_row16_col0\" class=\"data row16 col0\" >-0.00</td>\n",
       "      <td id=\"T_32929_row16_col1\" class=\"data row16 col1\" >0.00</td>\n",
       "      <td id=\"T_32929_row16_col2\" class=\"data row16 col2\" >-0.46</td>\n",
       "      <td id=\"T_32929_row16_col3\" class=\"data row16 col3\" >-0.02</td>\n",
       "      <td id=\"T_32929_row16_col4\" class=\"data row16 col4\" >-0.06</td>\n",
       "      <td id=\"T_32929_row16_col5\" class=\"data row16 col5\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row17\" class=\"row_heading level0 row17\" >major_stat</th>\n",
       "      <td id=\"T_32929_row17_col0\" class=\"data row17 col0\" >0.12</td>\n",
       "      <td id=\"T_32929_row17_col1\" class=\"data row17 col1\" >0.04</td>\n",
       "      <td id=\"T_32929_row17_col2\" class=\"data row17 col2\" >-0.33</td>\n",
       "      <td id=\"T_32929_row17_col3\" class=\"data row17 col3\" >0.16</td>\n",
       "      <td id=\"T_32929_row17_col4\" class=\"data row17 col4\" >0.11</td>\n",
       "      <td id=\"T_32929_row17_col5\" class=\"data row17 col5\" >0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32929_level0_row18\" class=\"row_heading level0 row18\" >software_eng</th>\n",
       "      <td id=\"T_32929_row18_col0\" class=\"data row18 col0\" >-0.27</td>\n",
       "      <td id=\"T_32929_row18_col1\" class=\"data row18 col1\" >0.15</td>\n",
       "      <td id=\"T_32929_row18_col2\" class=\"data row18 col2\" >0.32</td>\n",
       "      <td id=\"T_32929_row18_col3\" class=\"data row18 col3\" >-0.33</td>\n",
       "      <td id=\"T_32929_row18_col4\" class=\"data row18 col4\" >-0.17</td>\n",
       "      <td id=\"T_32929_row18_col5\" class=\"data row18 col5\" >-0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1eb57af8ee0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train\n",
    ".assign(software_eng=y_train)\n",
    ".corr(method='spearman')\n",
    ".loc[:, ['education', 'years_exp', 'major_cs', 'r', 'compensation', 'age']]\n",
    ".style\n",
    ".background_gradient(cmap='RdBu', vmin=-1, vmax=1)\n",
    ".format('{:.2f}')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7bfb4b-437e-434d-ba1f-65fc17c27845",
   "metadata": {},
   "source": [
    "We will again calculate the Spearman correlation between the columns and display the result as a heatmap using the Seaborn library.\n",
    "\n",
    "\n",
    "1. `import seaborn as sns` imports the Seaborn library, which provides functions for creating statistical graphics.\n",
    "\n",
    "2. `fig, ax = plt.subplots(figsize=(8, 4))` creates a new figure and axes object with the specified size.\n",
    "\n",
    "3. `sns.heatmap(` creates a heatmap using the Seaborn library.\n",
    "\n",
    "4. `X_train.assign(software_eng=y_train)` adds a new column to the `X_train` DataFrame, named `software_eng`, with the values from `y_train`.\n",
    "\n",
    "5. `.corr(method='spearman')` calculates the pairwise Spearman correlation between all columns of the resulting DataFrame.\n",
    "\n",
    "6. `.loc[:, ['age','education', 'years_exp', 'compensation', 'r', 'major_cs', 'software_eng']]` selects a subset of columns from the resulting correlation matrix.\n",
    "\n",
    "7. `cmap='RdBu', annot=True, fmt='.2f', vmin=-1, vmax=1, ax=ax)` specifies additional parameters for the heatmap, including the colormap, annotation format, value range, and axes object.\n",
    "\n",
    "8. `)` displays the resulting heatmap.\n",
    "\n",
    "Overall, this code snippet calculates the Spearman correlation between several columns of a Pandas DataFrame, selects a subset of columns from the resulting correlation matrix, and displays the result as a heatmap using the Seaborn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "df6071c9-e405-495b-ae17-fdf1e131c963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "sns.heatmap(X_train\n",
    ".assign(software_eng=y_train)\n",
    ".corr(method='spearman')\n",
    ".loc[:, ['age','education', 'years_exp', 'compensation', 'r',\n",
    "'major_cs', 'software_eng']],\n",
    "cmap='RdBu', annot=True, fmt='.2f', vmin=-1, vmax=1, ax=ax\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35532d6e-c573-4f3e-898b-be0e76612624",
   "metadata": {},
   "source": [
    "The correlation between `years_exp` and `education` is close to 0.10. We will use a scatter plot to tease out the possibility of a non-linear relationship between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "27616bb1-3294-40b0-aee2-115b8811cdd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<seaborn._core.plot.Plotter at 0x1eb58cf01f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "image/png": {
       "height": 378.25,
       "width": 611.15
      }
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn.objects as so\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4))\n",
    "\n",
    "(so.Plot(X_train.assign(software_eng=y_train), \n",
    "         x='years_exp', y='education', \n",
    "         color='software_eng')\n",
    " .add(so.Dots(alpha=.9, pointsize=2), so.Jitter(x=.7, y=1))\n",
    " .add(so.Line(), so.PolyFit()).scale(color='viridis')\n",
    " #.on(fig) # not required unless saving to image\n",
    " .plot() # ditto\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e276c41-e8f8-4039-99e8-3ba3a8f3a5ad",
   "metadata": {},
   "source": [
    "From the output above, it would seem like the education levels for software engineers are lower than for data scientists.\n",
    "\n",
    "Now, let’s explore the relationships between major_cs and R and the target label. This has\n",
    "three dimensions of categorical data. We can use Pandas grouping or the `pd.crosstab` shortcut\n",
    "to quantify this:\n",
    "\n",
    "\n",
    "Here is a step-by-step explanation of what this code does:\n",
    "\n",
    "1. `X_train.assign(software_eng=y_train)` adds a new column to the `X_train` DataFrame, named `software_eng`, with the values from `y_train`.\n",
    "\n",
    "2. `.groupby(['software_eng', 'r', 'major_cs'])` groups the resulting DataFrame by the values in the `software_eng`, `r`, and `major_cs` columns.\n",
    "\n",
    "3. `.age.count()` calculates the count of rows in each group.\n",
    "\n",
    "4. `.unstack().unstack()` reshapes the resulting Series into a multi-level table, with the levels of the index corresponding to the grouping columns.\n",
    "\n",
    "5. `)` displays the resulting table.\n",
    "\n",
    "Overall, this code snippet calculates the count of rows in a Pandas DataFrame grouped by several columns and displays the result as a multi-level table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "85d9fab0-de73-4197-8937-b715a06964ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "major_cs        0         1     \n",
      "r               0    1    0    1\n",
      "software_eng                    \n",
      "0             410  390  243  110\n",
      "1             308   53  523   73\n"
     ]
    }
   ],
   "source": [
    "print(X_train\n",
    ".assign(software_eng=y_train)\n",
    ".groupby(['software_eng', 'r', 'major_cs'])\n",
    " .age\n",
    " .count()\n",
    " .unstack()\n",
    " .unstack()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f453e856-4d90-482b-ac26-f1480589986f",
   "metadata": {},
   "source": [
    "This shows that if the user didn’t use R, they have a higher probability of being a software\n",
    "engineer.\n",
    "\n",
    "Here is the pd.crosstab version:\n",
    "\n",
    "\n",
    "Here is a step-by-step explanation of what this code does:\n",
    "\n",
    "1. `both = (X_train.assign(software_eng=y_train))` adds a new column to the `X_train` DataFrame, named `software_eng`, with the values from `y_train`, and assigns the resulting DataFrame to the variable `both`.\n",
    "\n",
    "2. `pd.crosstab(index=both.software_eng, columns=[both.major_cs, both.r])` calculates a cross-tabulation of the `major_cs` and `r` columns in the `both` DataFrame, with the rows corresponding to the values in the `software_eng` column.\n",
    "\n",
    "3. `)` displays the resulting table.\n",
    "\n",
    "Overall, this code snippet calculates a cross-tabulation of two columns in a Pandas DataFrame and displays the result as a table.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "abe3c901-947a-4507-ba11-6aebccdae8b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "major_cs        0         1     \n",
      "r               0    1    0    1\n",
      "software_eng                    \n",
      "0             410  390  243  110\n",
      "1             308   53  523   73\n"
     ]
    }
   ],
   "source": [
    "both = (X_train\n",
    " .assign(software_eng=y_train)\n",
    ")\n",
    "print(pd.crosstab(index=both.software_eng, columns=[both.major_cs, both.r]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426e9a21-6f0f-4399-817d-92626b1b6e72",
   "metadata": {},
   "source": [
    "The plot displays the count of rows in a Pandas DataFrame grouped by several columns as a stacked bar chart.\n",
    "\n",
    "Here is a step-by-step explanation of what this code does:\n",
    "\n",
    "1. `fig, ax = plt.subplots(figsize=(8, 4))` creates a new figure and axes with the specified size.\n",
    "\n",
    "2. `grey = '#999999'`, `blue = '#16a2c6'`, and `font = 'Roboto'` define some variables that will be used later to customize the appearance of the plot.\n",
    "\n",
    "3. `data = (X_train.assign(software_eng=y_train).groupby(['software_eng', 'r', 'major_cs']).age.count().unstack().unstack())` calculates the count of rows in the `X_train` DataFrame grouped by the values in the `software_eng`, `r`, and `major_cs` columns, and assigns the resulting table to the variable `data`.\n",
    "\n",
    "4. `(data.pipe(lambda adf: adf.iloc[:,-2:].plot(color=[grey,blue], linewidth=4, ax=ax, legend=None) and adf).plot(color=[grey, blue, grey, blue], ax=ax, legend=None))` creates a stacked bar chart using the data in the `data` DataFrame. The bars are colored using the specified colors and have no legend.\n",
    "\n",
    "5. `ax.set_xticks([0, 1], ['Data Scientist', 'Software Engineer'], font=font, size=12, weight=600)` sets the x-tick labels to `'Data Scientist'` and `'Software Engineer'`.\n",
    "\n",
    "6. `ax.set_yticks([])` removes the y-tick labels.\n",
    "\n",
    "7. `ax.set_xlabel('')` removes the x-axis label.\n",
    "\n",
    "8. `ax.text(x=0, y=.93, s=\"Count Data Scientist or Software Engineer by R/CS\", transform=fig.transFigure, ha='left', font=font, fontsize=10, weight=1000)` adds a title to the plot.\n",
    "\n",
    "9. `ax.text(x=0, y=.83, s=\"(Studied CS) Thick lines\\n(R) Blue\", transform=fig.transFigure, ha='left', font=font, fontsize=10, weight=300)` adds a legend to the plot.\n",
    "\n",
    "10. `for side in 'left,top,right,bottom'.split(','): ax.spines[side].set_visible(False)` removes the borders of the plot.\n",
    "\n",
    "11. The two for loops at the end add labels to the bars of the chart.\n",
    "\n",
    "Overall, this code snippet creates a plot using the Matplotlib library that displays the count of rows in a Pandas DataFrame grouped by several columns as a stacked bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9865a4d6-0762-4d10-9124-a1b77d7cb410",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Roboto'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Roboto'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Roboto'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Roboto'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "grey = '#999999'\n",
    "blue = '#16a2c6'\n",
    "font = 'Roboto'\n",
    "\n",
    "data = (X_train\n",
    " .assign(software_eng=y_train)\n",
    " .groupby(['software_eng', 'r', 'major_cs'])\n",
    " .age\n",
    " .count()\n",
    " .unstack()\n",
    " .unstack())\n",
    "\n",
    "(data\n",
    " .pipe(lambda adf: adf.iloc[:,-2:].plot(color=[grey,blue], linewidth=4, ax=ax,\n",
    " legend=None) and adf)\n",
    " .plot(color=[grey, blue, grey, blue], ax=ax, legend=None)\n",
    ")\n",
    "\n",
    "ax.set_xticks([0, 1], ['Data Scientist', 'Software Engineer'], font=font, size=12, weight=600)\n",
    "ax.set_yticks([])\n",
    "ax.set_xlabel('')\n",
    "ax.text(x=0, y=.93, s=\"Count Data Scientist or Software Engineer by R/CS\", transform=fig.transFigure, ha='left', font=font, fontsize=10, weight=1000)\n",
    "ax.text(x=0, y=.83, s=\"(Studied CS) Thick lines\\n(R) Blue\", transform=fig.transFigure, ha='left', font=font, fontsize=10, weight=300)\n",
    "\n",
    "for side in 'left,top,right,bottom'.split(','):\n",
    "    ax.spines[side].set_visible(False)\n",
    "    \n",
    "# labels\n",
    "for left,txt in zip(data.iloc[0], ['Other/No R', 'Other/R', 'CS/No R', 'CS/R']):\n",
    "    ax.text(x=-.02, y=left, s=f'{txt} ({left})', ha='right', va='center', font=font, weight=300)\n",
    "for right,txt in zip(data.iloc[1], ['Other/No R', 'Other/R', 'CS/No R', 'CS/R']):\n",
    "    ax.text(x=1.02, y=right, s=f'{txt} ({right})', ha='left', va='center', font=font, weight=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea62be6-99d6-40cb-ad27-a3aeb8fd4ab6",
   "metadata": {},
   "source": [
    "## 18.3 Deeper Interactions\n",
    "\n",
    "We can get interactions with three columns from the _Interaction Depth 2_ sheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ded2997f-0d0d-45cc-a6f9-6462016c49c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Interaction         Gain  FScore    wFScore  \\\n",
      "0               major_cs|r|years_exp  1842.711400      17   3.218940   \n",
      "7            age|education|years_exp   267.537988      53   8.216888   \n",
      "13        age|compensation|education   154.313243      55  15.013424   \n",
      "2   compensation|education|years_exp   431.541351      91   8.997655   \n",
      "14             education|r|years_exp   145.534593      17   2.879716   \n",
      "\n",
      "    Average wFScore  Average Gain  Expected Gain  Gain Rank  FScore Rank  \\\n",
      "0          0.189349    108.394788     409.261203          1           34   \n",
      "7          0.155036      5.047887      30.267047          8            8   \n",
      "13         0.272971      2.805695      17.358738         14            7   \n",
      "2          0.098875      4.742213      40.322039          3            2   \n",
      "14         0.169395      8.560858      34.975379         15           35   \n",
      "\n",
      "    wFScore Rank  Avg wFScore Rank  Avg Gain Rank  Expected Gain Rank  \\\n",
      "0             15                19              2                   1   \n",
      "7              9                27             32                  10   \n",
      "13             3                 7             50                  14   \n",
      "2              7                51             33                   7   \n",
      "14            21                23             12                   8   \n",
      "\n",
      "    Average Rank  Average Tree Index  Average Tree Depth  \n",
      "0      12.000000            2.588235            2.117647  \n",
      "7      15.666667           31.452830            3.981132  \n",
      "13     15.833333           47.381818            3.800000  \n",
      "2      17.166667           47.175824            4.010989  \n",
      "14     19.000000           34.352941            2.588235  \n"
     ]
    }
   ],
   "source": [
    "print(pd.read_excel('fir.xlsx', sheet_name='Interaction Depth 2').iloc[:20]\n",
    " .sort_values(by='Average Rank')\n",
    " .head(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e3893e-b8b9-41db-a864-a835f22fcc97",
   "metadata": {},
   "source": [
    "## 18.4 Specifying Feature Interactions\n",
    "\n",
    "The XGBoost library can limit feature interactions. This means that users can specify which features are allowed to interact with each other, potentially leading to models that are more robust, simpler, or comply with regulations. The text provides an example of how to take the top entries from the interactions and put them in a nested list, where each entry shows which groups of columns are able to follow other columns in a given tree.\n",
    "\n",
    "According to the XGBoost documentation, feature interaction constraints allow users to decide which variables are allowed to interact and which are not. Potential benefits include better predictive performance from focusing on interactions that work, less noise in predictions, better generalization, and more control to the user on what the model can fit ¹. Additionally, it is simple to enforce feature interaction constraints in XGBoost by adding a single parameter.\n",
    "\n",
    "We will take the top entries from the interactions and put them in a nested list. Each entry\n",
    "in this list shows which groups of columns are able to follow other columns in a given tree.\n",
    "For example, _education_ is in the first, third, and fourth lists. This means that _education_ can be\n",
    "followed by _years_exp_, _compensation_, or _age columns_, but not the remaining columns.\n",
    "\n",
    "This code snippet defines a list of lists named `constraints`, where each inner list represents a group of columns that are allowed to interact with each other in an XGBoost model. The `flatten` function is then defined, which takes a nested sequence as input and returns a flattened version of that sequence, where all nested elements are extracted and added to a single, one-dimensional list.\n",
    "\n",
    "The `flatten` function is then called on the `constraints` list, and the resulting flattened list is passed to the `set` function to remove any duplicate elements. The resulting set is then sorted and converted back to a list, which is assigned to the variable `small_cols`. Finally, the `small_cols` list is printed.\n",
    "\n",
    "In summary, this code snippet defines a list of feature interaction constraints for an XGBoost model, flattens the list of constraints, removes any duplicate elements, sorts the resulting list of unique elements, and then prints the resulting list of column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f2167bdf-6b97-471e-a9fd-870503760121",
   "metadata": {},
   "outputs": [],
   "source": [
    "constraints = [['education', 'years_exp'], ['major_cs', 'r'],\n",
    "['compensation', 'education'], ['age', 'education'],\n",
    "['major_cs', 'years_exp'], ['age', 'years_exp'],\n",
    "['age', 'compensation'], ['major_stat', 'years_exp'],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "181db246-c978-4c50-afaf-a87a7cd821d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'compensation', 'education', 'major_cs', 'major_stat', 'r', 'years_exp']\n"
     ]
    }
   ],
   "source": [
    "def flatten(seq):\n",
    "    res = []\n",
    "    for sub in seq:\n",
    "        res.extend(sub)\n",
    "    return res\n",
    "\n",
    "small_cols = sorted(set(flatten(constraints)))\n",
    "\n",
    "print(small_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "872bd52f-7b0c-4371-a0e7-c27360bec4fd",
   "metadata": {},
   "source": [
    "The selected text is a code snippet that shows how to create an XGBoost classifier with interaction constraints, fit the model to training data, and evaluate its performance on test data.\n",
    "\n",
    "First, an instance of the `XGBClassifier` class is created with the `interaction_constraints` parameter set to `constraints`, which is a list of lists representing the groups of columns that are allowed to interact with each other in the model. This classifier is assigned to the variable `xg_constraints`.\n",
    "\n",
    "Next, the `fit` method is called on the `xg_constraints` classifier with the training data `X_train` and labels `y_train` as arguments. The `loc` method is used to select only the columns specified in the `small_cols` list from the training data. This fits the XGBoost classifier to the training data while enforcing the specified interaction constraints.\n",
    "\n",
    "Finally, the `score` method is called on the fitted `xg_constraints` classifier with the test data `X_test` and labels `y_test` as arguments. Again, the `loc` method is used to select only the columns specified in the `small_cols` list from the test data. This returns the mean accuracy of the classifier on the given test data and labels.\n",
    "\n",
    "In summary, this code snippet shows how to create an XGBoost classifier with interaction constraints, fit it to training data, and evaluate its performance on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9884f367-2929-4325-83c4-ec408feb2d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7259668508287292"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_constraints = xgb.XGBClassifier(interaction_constraints=constraints)\n",
    "xg_constraints.fit(X_train.loc[:, small_cols], y_train)\n",
    "xg_constraints.score(X_test.loc[:, small_cols], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c97b28-9024-4114-acc1-e13a691e8e85",
   "metadata": {},
   "source": [
    "It looks like this model does ok when it uses 7 of the 18 features. But it does not exhibit\n",
    "better performance than the default model (.745) trained on all of the data.\n",
    "Here is a visualization of the first tree. You can see that the `r` column is only following by\n",
    "`major_cs` in this tree.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "985fef70-1a7f-4e0f-abd8-9088581a9e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_image_export(xg_constraints, n_trees=0, filename='img/constrains0_xg.dot', title='First Constrained Tree')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61cb7d07-17a6-41a1-bbae-ce51993faa37",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- An interaction in a machine learning model refers to the combined effect of two or more features on the target variable. In other words, the effect of one feature on the target variable depends on the value of another feature.\n",
    "- The xgbfir library is a XGBoost model dump parser, which ranks features as well as feature interactions by different metrics. It is used to analyze and understand the importance of features and their interactions in an XGBoost model.\n",
    "- The Average Rank column in the xgbfir library is calculated by taking the average rank of a feature across all trees in the XGBoost model.\n",
    "- The purpose of the Interaction Depth 1 sheet in the xgbfir library is to show the top feature interactions of depth 1 (i.e., pairwise interactions between two features) ranked by different metrics such as Gain, FScore, wFScore, etc.\n",
    "- The xgbfir library can be used to find the top feature interactions in an XGBoost model by parsing the model dump and ranking feature interactions by different metrics. The top interactions can then be analyzed to gain insights into how features interact with each other and affect the target variable.\n",
    "- The purpose of using interaction constraints in a machine learning model is to restrict the possible interactions between features. This can be useful when domain knowledge suggests that certain interactions are not meaningful or when interpretability is important.\n",
    "- The potential drawbacks of using interaction constraints in a machine learning model include reduced flexibility and potentially reduced predictive performance if important interactions are excluded.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "669b2bde-860f-4833-a2df-408e8df095fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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 },
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