{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## License \n", "\n", "Copyright 2019-2020 Patrick Hall and the H2O.ai team\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " http://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**DISCLAIMER:** This notebook is not legal compliance advice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# All models are wrong, but ... why is my model wrong? \n", "**Part 2: Residual analysis for model debugging**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"All models are wrong but some are useful\" -- George Box, 1978" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook uses variants of residual analysis to find error mechanisms and security vulnerabilities and to assess stability and fairness in a trained XGBoost model. It begins by loading the UCI credit card default data and then training an interpretable, monotonically constrained XGBoost gradient boosting machine (GBM) model. (Pearson correlation with the prediction target is used to determine the direction of the monotonicity constraints for each input variable.) After the model is trained, its logloss residuals are analyzed and explained thoroughly and the constrained GBM is compared to a benchmark linear model. These model debugging exercises uncover several accuracy, drift, and security problems such as over-emphasis of important variables and strong signal in model residuals. Several remediation mechanisms are proposed including missing value injection during training, additional data collection, and use of assertions to correct known problems during scoring. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Python imports " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321 ." ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/patrickh/workspace/interpretable_machine_learning_with_python/env_iml/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n", " from numpy.core.umath_tests import inner1d\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".... not found.\n", "Attempting to start a local H2O server...\n", " Java Version: openjdk version \"1.8.0_232\"; OpenJDK Runtime Environment (build 1.8.0_232-8u232-b09-0ubuntu1~16.04.1-b09); OpenJDK 64-Bit Server VM (build 25.232-b09, mixed mode)\n", " Starting server from /home/patrickh/workspace/interpretable_machine_learning_with_python/env_iml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n", " Ice root: /tmp/tmpb5qehacu\n", " JVM stdout: /tmp/tmpb5qehacu/h2o_patrickh_started_from_python.out\n", " JVM stderr: /tmp/tmpb5qehacu/h2o_patrickh_started_from_python.err\n", " Server is running at http://127.0.0.1:54321\n", "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n", "Warning: Your H2O cluster version is too old (4 months and 13 days)! Please download and install the latest version from http://h2o.ai/download/\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ "-------------------------- ---------------------------------------------------\n", "H2O cluster uptime: 01 secs\n", "H2O cluster timezone: America/New_York\n", "H2O data parsing timezone: UTC\n", "H2O cluster version: 3.26.0.3\n", "H2O cluster version age: 4 months and 13 days !!!\n", "H2O cluster name: H2O_from_python_patrickh_safz5l\n", "H2O cluster total nodes: 1\n", "H2O cluster free memory: 3.462 Gb\n", "H2O cluster total cores: 8\n", "H2O cluster allowed cores: 8\n", "H2O cluster status: accepting new members, healthy\n", "H2O connection url: http://127.0.0.1:54321\n", "H2O connection proxy:\n", "H2O internal security: False\n", "H2O API Extensions: Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n", "Python version: 3.6.3 final\n", "-------------------------- ---------------------------------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np # array, vector, matrix calculations\n", "import os # file-handling\n", "import pandas as pd # DataFrame handling\n", "import shap # for consistent, signed variable importance measurements\n", "import xgboost as xgb # gradient boosting machines (GBMs)\n", "import subprocess # manage external processes\n", "\n", "import h2o # import h2o python bindings to java server\n", "from h2o.backend import H2OLocalServer # for plotting local tree in-notebook\n", "from h2o.estimators.random_forest import H2ORandomForestEstimator # for single tree\n", "from h2o.estimators.glm import H2OGeneralizedLinearEstimator # for benchmark model\n", "from h2o.grid.grid_search import H2OGridSearch # for benchmark model\n", "\n", "# plotting ###########\n", "import matplotlib.pyplot as plt # general plotting\n", "from matplotlib.lines import Line2D # necessary for custom legends\n", "import seaborn as sns # facet grid for residuals\n", "from mpl_toolkits import mplot3d # 3-D scatterplots\n", "import matplotlib; matplotlib.rcParams.update({'font.size': 40}) # set legible font size\n", "\n", "\n", "# enables display of plots in notebook\n", "# in-notebook display\n", "from IPython.display import Image\n", "from IPython.display import display\n", "%matplotlib inline\n", "\n", "pd.options.display.max_columns = 999 # enable display of all dataframe columns in notebook\n", "\n", "SEED = 12345 # global random seed\n", "np.random.seed(SEED) # set random seed for reproducibility\n", "h2o.init() # start h2o java server" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Download, explore, and prepare UCI credit card default data\n", "\n", "UCI credit card default data: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n", "\n", "The UCI credit card default data contains demographic and payment information about credit card customers in Taiwan in the year 2005. The data set contains 23 input variables: \n", "\n", "* **`LIMIT_BAL`**: Amount of given credit (NT dollar)\n", "* **`SEX`**: 1 = male; 2 = female\n", "* **`EDUCATION`**: 1 = graduate school; 2 = university; 3 = high school; 4 = others \n", "* **`MARRIAGE`**: 1 = married; 2 = single; 3 = others\n", "* **`AGE`**: Age in years \n", "* **`PAY_0`, `PAY_2` - `PAY_6`**: History of past payment; `PAY_0` = the repayment status in September, 2005; `PAY_2` = the repayment status in August, 2005; ...; `PAY_6` = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above. \n", "* **`BILL_AMT1` - `BILL_AMT6`**: Amount of bill statement (NT dollar). `BILL_AMNT1` = amount of bill statement in September, 2005; `BILL_AMT2` = amount of bill statement in August, 2005; ...; `BILL_AMT6` = amount of bill statement in April, 2005. \n", "* **`PAY_AMT1` - `PAY_AMT6`**: Amount of previous payment (NT dollar). `PAY_AMT1` = amount paid in September, 2005; `PAY_AMT2` = amount paid in August, 2005; ...; `PAY_AMT6` = amount paid in April, 2005. \n", "\n", "These 23 input variables are used to predict the target variable, whether or not a customer defaulted on their credit card bill in late 2005. Because XGBoost accepts only numeric inputs, all variables will be treated as numeric." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import data and clean\n", "The credit card default data is available as an `.xls` file. Pandas reads `.xls` files automatically, so it's used to load the credit card default data and give the prediction target a shorter name: `DEFAULT_NEXT_MONTH`. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# import XLS file\n", "path = 'default_of_credit_card_clients.xls'\n", "data = pd.read_excel(path,\n", " skiprows=1) # skip the first row of the spreadsheet\n", "\n", "# remove spaces from target column name \n", "data = data.rename(columns={'default payment next month': 'DEFAULT_NEXT_MONTH'}) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Assign modeling roles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The shorthand name `y` is assigned to the prediction target. `X` is assigned to all other input variables in the credit card default data except the row identifier, `ID`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = DEFAULT_NEXT_MONTH\n", "X = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n" ] } ], "source": [ "# assign target and inputs for GBM\n", "y = 'DEFAULT_NEXT_MONTH'\n", "X = [name for name in data.columns if name not in [y, 'ID']]\n", "print('y =', y)\n", "print('X =', X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Display descriptive statistics\n", "The Pandas `describe()` function displays a brief description of the credit card default data. The input variables `SEX`, `EDUCATION`, `MARRIAGE`, `PAY_0`-`PAY_6`, and the prediction target `DEFAULT_NEXT_MONTH`, are really categorical variables, but they have already been encoded into meaningful numeric, integer values, which is great for XGBoost. Also, there are no missing values in this dataset." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTH
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mean167484.3226671.6037331.8531331.55186735.485500-0.016700-0.133767-0.166200-0.220667-0.266200-0.29110051223.33090049179.0751674.701315e+0443262.94896740311.40096738871.7604005663.5805005.921163e+035225.681504826.0768674799.3876335215.5025670.221200
std129747.6615670.4891290.7903490.5219709.2179041.1238021.1971861.1968681.1691391.1331871.14998873635.86057671173.7687836.934939e+0464332.85613460797.15577059554.10753716563.2803542.304087e+0417606.9614715666.15974415278.30567917777.4657750.415062
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25%50000.0000001.0000001.0000001.00000028.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0000003558.7500002984.7500002.666250e+032326.7500001763.0000001256.0000001000.0000008.330000e+02390.00000296.000000252.500000117.7500000.000000
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" ], "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", "mean 167484.322667 1.603733 1.853133 1.551867 35.485500 \n", "std 129747.661567 0.489129 0.790349 0.521970 9.217904 \n", "min 10000.000000 1.000000 0.000000 0.000000 21.000000 \n", "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", "50% 140000.000000 2.000000 2.000000 2.000000 34.000000 \n", "75% 240000.000000 2.000000 2.000000 2.000000 41.000000 \n", "max 1000000.000000 2.000000 6.000000 3.000000 79.000000 \n", "\n", " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", "mean -0.016700 -0.133767 -0.166200 -0.220667 -0.266200 \n", "std 1.123802 1.197186 1.196868 1.169139 1.133187 \n", "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", "\n", " PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 \\\n", "count 30000.000000 30000.000000 30000.000000 3.000000e+04 \n", "mean -0.291100 51223.330900 49179.075167 4.701315e+04 \n", "std 1.149988 73635.860576 71173.768783 6.934939e+04 \n", "min -2.000000 -165580.000000 -69777.000000 -1.572640e+05 \n", "25% -1.000000 3558.750000 2984.750000 2.666250e+03 \n", "50% 0.000000 22381.500000 21200.000000 2.008850e+04 \n", "75% 0.000000 67091.000000 64006.250000 6.016475e+04 \n", "max 8.000000 964511.000000 983931.000000 1.664089e+06 \n", "\n", " BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 \\\n", "count 30000.000000 30000.000000 30000.000000 30000.000000 \n", "mean 43262.948967 40311.400967 38871.760400 5663.580500 \n", "std 64332.856134 60797.155770 59554.107537 16563.280354 \n", "min -170000.000000 -81334.000000 -339603.000000 0.000000 \n", "25% 2326.750000 1763.000000 1256.000000 1000.000000 \n", "50% 19052.000000 18104.500000 17071.000000 2100.000000 \n", "75% 54506.000000 50190.500000 49198.250000 5006.000000 \n", "max 891586.000000 927171.000000 961664.000000 873552.000000 \n", "\n", " PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", "count 3.000000e+04 30000.00000 30000.000000 30000.000000 \n", "mean 5.921163e+03 5225.68150 4826.076867 4799.387633 \n", "std 2.304087e+04 17606.96147 15666.159744 15278.305679 \n", "min 0.000000e+00 0.00000 0.000000 0.000000 \n", "25% 8.330000e+02 390.00000 296.000000 252.500000 \n", "50% 2.009000e+03 1800.00000 1500.000000 1500.000000 \n", "75% 5.000000e+03 4505.00000 4013.250000 4031.500000 \n", "max 1.684259e+06 896040.00000 621000.000000 426529.000000 \n", "\n", " PAY_AMT6 DEFAULT_NEXT_MONTH \n", "count 30000.000000 30000.000000 \n", "mean 5215.502567 0.221200 \n", "std 17777.465775 0.415062 \n", "min 0.000000 0.000000 \n", "25% 117.750000 0.000000 \n", "50% 1500.000000 0.000000 \n", "75% 4000.000000 0.000000 \n", "max 528666.000000 1.000000 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[X + [y]].describe() # display descriptive statistics for all columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Investigate pair-wise Pearson correlations for DEFAULT_NEXT_MONTH\n", "\n", "Monotonic relationships are much easier to explain to colleagues, bosses, customers, and regulators than more complex, non-monotonic relationships and monotonic relationships may also prevent overfitting and excess error due to variance for new data.\n", "\n", "To train a transparent monotonic classifier, constraints must be supplied to XGBoost that determine whether the learned relationship between an input variable and the prediction target `DEFAULT_NEXT_MONTH` will be increasing for increases in an input variable or decreasing for increases in an input variable. Pearson correlation provides a linear measure of the direction of the relationship between each input variable and the target. If the pair-wise Pearson correlation between an input and `DEFAULT_NEXT_MONTH` is positive, it will be constrained to have an increasing relationship with the predictions for `DEFAULT_NEXT_MONTH`. If the pair-wise Pearson correlation is negative, the input will be constrained to have a decreasing relationship with the predictions for `DEFAULT_NEXT_MONTH`. \n", "\n", "Constraints are supplied to XGBoost in the form of a Python tuple with length equal to the number of inputs. Each item in the tuple is associated with an input variable based on its index in the tuple. The first constraint in the tuple is associated with the first variable in the training data, the second constraint in the tuple is associated with the second variable in the training data, and so on. The constraints themselves take the form of a 1 for a positive relationship and a -1 for a negative relationship." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Calculate Pearson correlation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Pandas `.corr()` function returns the pair-wise Pearson correlation between variables in a Pandas DataFrame. Because `DEFAULT_NEXT_MONTH` is the last column in the `data` DataFrame, the last column of the Pearson correlation matrix indicates the direction of the linear relationship between each input variable and the prediction target, `DEFAULT_NEXT_MONTH`. According to the calculated values, as a customer's balance limit (`LIMIT_BAL`), bill amounts (`BILL_AMT1`-`BILL_AMT6`), and payment amounts (`PAY_AMT1`-`PAY_AMT6`) increase, their probability of default tends to decrease. However as a customer's number of late payments increase (`PAY_0`, `PAY_2`-`PAY6`), their probability of default usually increases. In general, the Pearson correlation values make sense, and they will be used to ensure that the modeled relationships will make sense as well. (Pearson correlation values between the target variable, DEFAULT_NEXT_MONTH, and each input variable are displayed directly below.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DEFAULT_NEXT_MONTH
LIMIT_BAL-0.153520
SEX-0.039961
EDUCATION0.028006
MARRIAGE-0.024339
AGE0.013890
PAY_00.324794
PAY_20.263551
PAY_30.235253
PAY_40.216614
PAY_50.204149
PAY_60.186866
BILL_AMT1-0.019644
BILL_AMT2-0.014193
BILL_AMT3-0.014076
BILL_AMT4-0.010156
BILL_AMT5-0.006760
BILL_AMT6-0.005372
PAY_AMT1-0.072929
PAY_AMT2-0.058579
PAY_AMT3-0.056250
PAY_AMT4-0.056827
PAY_AMT5-0.055124
PAY_AMT6-0.053183
\n", "
" ], "text/plain": [ " DEFAULT_NEXT_MONTH\n", "LIMIT_BAL -0.153520\n", "SEX -0.039961\n", "EDUCATION 0.028006\n", "MARRIAGE -0.024339\n", "AGE 0.013890\n", "PAY_0 0.324794\n", "PAY_2 0.263551\n", "PAY_3 0.235253\n", "PAY_4 0.216614\n", "PAY_5 0.204149\n", "PAY_6 0.186866\n", "BILL_AMT1 -0.019644\n", "BILL_AMT2 -0.014193\n", "BILL_AMT3 -0.014076\n", "BILL_AMT4 -0.010156\n", "BILL_AMT5 -0.006760\n", "BILL_AMT6 -0.005372\n", "PAY_AMT1 -0.072929\n", "PAY_AMT2 -0.058579\n", "PAY_AMT3 -0.056250\n", "PAY_AMT4 -0.056827\n", "PAY_AMT5 -0.055124\n", "PAY_AMT6 -0.053183" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# displays last column of Pearson correlation matrix as Pandas DataFrame\n", "pd.DataFrame(data[X + [y]].corr()[y]).iloc[:-1] " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Create tuple of monotonicity constraints from Pearson correlation values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last column of the Pearson correlation matrix is transformed from a numeric column in a Pandas DataFrame into a Python tuple of `1`s and `-1`s that will be used to specify monotonicity constraints for each input variable in XGBoost. If the Pearson correlation between an input variable and `DEFAULT_NEXT_MONTH` is positive, a positive monotonic relationship constraint is specified for that variable using `1`. If the correlation is negative, a negative monotonic constraint is specified using `-1`. (Specifying `0` indicates that no constraints should be used.) The resulting tuple will be passed to XGBoost when the GBM model is trained." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# creates a tuple in which positive correlation values are assigned a 1\n", "# and negative correlation values are assigned a -1\n", "mono_constraints = tuple([int(i) for i in np.sign(data[X + [y]].corr()[y].values[:-1])])\n", "\n", "# (-1, -1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Train XGBoost with monotonicity constraints\n", "\n", "XGBoost is a very accurate, open source GBM library for regression and classification tasks. XGBoost can learn complex relationships between input variables and a target variable, but here the `monotone_constraints` tuning parameter is used to enforce monotonicity between inputs and the prediction for `DEFAULT_NEXT_MONTH`.\n", "\n", "XGBoost is available from: https://github.com/dmlc/xgboost and the implementation of XGBoost is described in detail here: http://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Split data into training and test sets for early stopping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The credit card default data is split into training and test sets to monitor and prevent overtraining. Reproducibility is another important factor in creating trustworthy models, and randomly splitting datasets can introduce randomness in model predictions and other results. A random seed is used here to ensure the data split is reproducible." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data rows = 20946, columns = 25\n", "Test data rows = 9054, columns = 25\n" ] } ], "source": [ "split_ratio = 0.70 # 70%/30% train/test split\n", "\n", "# execute split\n", "split = np.random.rand(len(data)) < split_ratio\n", "train = data[split]\n", "test = data[~split]\n", "\n", "# summarize split\n", "print('Train data rows = %d, columns = %d' % (train.shape[0], train.shape[1]))\n", "print('Test data rows = %d, columns = %d' % (test.shape[0], test.shape[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Train XGBoost GBM classifier\n", "To train an XGBoost classifier, the training and test data must be converted from Pandas DataFrames into SVMLight format. The `DMatrix()` function in the XGBoost package is used to convert the data. Many XGBoost tuning parameters must be specified as well. Typically a grid search would be performed to identify the best parameters for a given modeling task. For brevity's sake, a previously-discovered set of good tuning parameters are specified here. Notice that the monotonicity constraints are passed to XGBoost using the `monotone_constraints` parameter. Because gradient boosting methods typically resample training data, an additional random seed is also specified for XGBoost using the `seed` parameter to create reproducible predictions, error rates, and variable importance values. To calculate Shapley contributions to logloss in later sections of the notebook, early stopping is not used because it may be unsupported for calculating accurate contributions to logloss. Hence, a previously discovered best number of trees is used in this notebook." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\ttrain-logloss:0.517487\teval-logloss:0.51404\n", "[1]\ttrain-logloss:0.504097\teval-logloss:0.500144\n", "[2]\ttrain-logloss:0.49331\teval-logloss:0.489053\n", "[3]\ttrain-logloss:0.48449\teval-logloss:0.480157\n", "[4]\ttrain-logloss:0.476882\teval-logloss:0.472429\n", "[5]\ttrain-logloss:0.470593\teval-logloss:0.46606\n", "[6]\ttrain-logloss:0.465221\teval-logloss:0.460639\n", "[7]\ttrain-logloss:0.460893\teval-logloss:0.456227\n", "[8]\ttrain-logloss:0.456987\teval-logloss:0.452346\n", "[9]\ttrain-logloss:0.453662\teval-logloss:0.449056\n", "[10]\ttrain-logloss:0.450854\teval-logloss:0.446329\n", "[11]\ttrain-logloss:0.448211\teval-logloss:0.443774\n", "[12]\ttrain-logloss:0.445944\teval-logloss:0.441601\n", "[13]\ttrain-logloss:0.444034\teval-logloss:0.439837\n", "[14]\ttrain-logloss:0.442334\teval-logloss:0.438174\n", "[15]\ttrain-logloss:0.440674\teval-logloss:0.436801\n", "[16]\ttrain-logloss:0.439326\teval-logloss:0.435491\n", "[17]\ttrain-logloss:0.438064\teval-logloss:0.434513\n", "[18]\ttrain-logloss:0.436879\teval-logloss:0.433658\n", "[19]\ttrain-logloss:0.4357\teval-logloss:0.432662\n", "[20]\ttrain-logloss:0.434803\teval-logloss:0.431955\n", "[21]\ttrain-logloss:0.433965\teval-logloss:0.431188\n", "[22]\ttrain-logloss:0.433221\teval-logloss:0.430624\n", "[23]\ttrain-logloss:0.432498\teval-logloss:0.430157\n", "[24]\ttrain-logloss:0.431834\teval-logloss:0.429738\n", "[25]\ttrain-logloss:0.431169\teval-logloss:0.429235\n", "[26]\ttrain-logloss:0.430642\teval-logloss:0.428901\n", "[27]\ttrain-logloss:0.430144\teval-logloss:0.428576\n", "[28]\ttrain-logloss:0.429715\teval-logloss:0.428276\n", "[29]\ttrain-logloss:0.429267\teval-logloss:0.427921\n", "[30]\ttrain-logloss:0.428899\teval-logloss:0.427618\n", "[31]\ttrain-logloss:0.42856\teval-logloss:0.427503\n", "[32]\ttrain-logloss:0.428243\teval-logloss:0.427278\n", "[33]\ttrain-logloss:0.427974\teval-logloss:0.427111\n", "[34]\ttrain-logloss:0.427719\teval-logloss:0.427036\n", "[35]\ttrain-logloss:0.427457\teval-logloss:0.426976\n", "[36]\ttrain-logloss:0.427155\teval-logloss:0.426925\n", "[37]\ttrain-logloss:0.426861\teval-logloss:0.426893\n", "[38]\ttrain-logloss:0.426616\teval-logloss:0.426824\n", "[39]\ttrain-logloss:0.42634\teval-logloss:0.426717\n", "[40]\ttrain-logloss:0.426117\teval-logloss:0.426629\n", "[41]\ttrain-logloss:0.425885\teval-logloss:0.426643\n", "[42]\ttrain-logloss:0.425639\teval-logloss:0.426595\n", "[43]\ttrain-logloss:0.425484\teval-logloss:0.426524\n", "[44]\ttrain-logloss:0.425296\teval-logloss:0.426502\n", "[45]\ttrain-logloss:0.425151\teval-logloss:0.426528\n", "[46]\ttrain-logloss:0.42499\teval-logloss:0.426503\n", "[47]\ttrain-logloss:0.424875\teval-logloss:0.426499\n", "[48]\ttrain-logloss:0.424652\teval-logloss:0.426443\n", "[49]\ttrain-logloss:0.424516\teval-logloss:0.426425\n", "[50]\ttrain-logloss:0.424296\teval-logloss:0.426354\n", "[51]\ttrain-logloss:0.42416\teval-logloss:0.426311\n", "[52]\ttrain-logloss:0.424011\teval-logloss:0.426276\n", "[53]\ttrain-logloss:0.423911\teval-logloss:0.426338\n", "[54]\ttrain-logloss:0.423786\teval-logloss:0.426309\n", "[55]\ttrain-logloss:0.423656\teval-logloss:0.426288\n", "[56]\ttrain-logloss:0.423537\teval-logloss:0.426259\n", "[57]\ttrain-logloss:0.423442\teval-logloss:0.426248\n", "[58]\ttrain-logloss:0.423331\teval-logloss:0.426241\n", "[59]\ttrain-logloss:0.423219\teval-logloss:0.426204\n", "[60]\ttrain-logloss:0.4231\teval-logloss:0.426169\n", "[61]\ttrain-logloss:0.422985\teval-logloss:0.426156\n" ] } ], "source": [ "# XGBoost uses SVMLight data structure, not Numpy arrays or Pandas DataFrames \n", "dtrain = xgb.DMatrix(train[X], train[y])\n", "dtest = xgb.DMatrix(test[X], test[y])\n", "\n", "# used to calibrate predictions to mean of y \n", "base_y = train[y].mean()\n", "\n", "# tuning parameters\n", "params = {\n", " 'objective': 'binary:logistic', # produces 0-1 probabilities for binary classification\n", " 'booster': 'gbtree', # base learner will be decision tree\n", " 'eval_metric': 'logloss', # stop training based on\n", " 'eta': 0.08, # learning rate\n", " 'subsample': 0.9, # use 90% of rows in each decision tree\n", " 'colsample_bytree': 0.9, # use 90% of columns in each decision tree\n", " 'max_depth': 15, # allow decision trees to grow to depth of 15\n", " 'monotone_constraints':mono_constraints, # 1 = increasing relationship, -1 = decreasing relationship\n", " 'base_score': base_y, # calibrate predictions to mean of y \n", " 'seed': SEED # set random seed for reproducibility\n", "}\n", "\n", "# watchlist is used for early stopping\n", "watchlist = [(dtrain, 'train'), (dtest, 'eval')]\n", "\n", "# train model\n", "xgb_model = xgb.train(params, # set tuning parameters from above \n", " dtrain, # training data\n", " 62, # **logloss shap may not account for early stopping**\n", " evals=watchlist, # use watchlist for early stopping \n", " verbose_eval=True) # display iteration progress\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Cutoff Selection\n", "Cutoffs affect model characteristics, and sometimes drastically. Cutoffs should always be chosen with care. In this notebook, the cutoff is selected by maximizing the model's F1 statistic. This is a standard approach that could likely be improved upon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preliminaries to calculate PR-AUC\n", "A new column will be created called `p_DEFAULT_NEXT_MONTH` -- this is the model's predicted probability. P-R AUC stands for precision-recall area under the curve. The curve to be created is an established model assessment technique that helps choose a probability cutoff." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# shortcut name\n", "yhat = 'p_DEFAULT_NEXT_MONTH' \n", "\n", "# copy test data and reset index\n", "test_yhat = test.copy(deep=True)\n", "test_yhat.reset_index(drop=True, inplace=True) # Pandas joins are weird otherwise\n", "test_yhat[yhat] = pd.DataFrame(xgb_model.predict(xgb.DMatrix(test[X]))) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Function to calculate precision and recall \n", "Precision refers to the proportion of people the model predicts will default correctly *out of the total number of predictions of default*. Recall refers to the proportion of people the model predicts will default correctly *out of the total number of actual defaults*. F1 is a combination (harmonic mean) of these quantities. So by maximizing F1, a cutoff that considers true positives carefully is selected. The function below calculates precision, recall, and F1 at a number of probability cutoffs between 0 and 1. Results are displayed below." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Recall and Precision
cutoffrecallprecisionf1
0010.2193510.359783
10.010.9994960.2193610.359764
20.020.9989930.2200040.360596
30.030.9964750.2212410.362089
40.040.9934540.223140.364426
50.050.9884190.2258920.367741
60.060.9823770.2315450.37476
70.070.9687810.2391250.383573
80.080.9501510.2483550.393781
90.090.9290030.2591660.405272
100.10.9093660.2715790.418249
110.110.8952670.2847990.43213
120.120.8731120.2984510.444844
130.130.8564950.3098930.455117
140.140.8368580.3220930.465155
150.150.8136960.3370180.476626
160.160.790030.3564290.491234
170.170.764350.3736160.501901
180.180.735650.3875330.507644
190.190.718530.4027660.516187
200.20.701410.4205920.525859
210.210.684290.434880.531794
220.220.6641490.4498640.536397
230.230.6445120.4668130.541455
240.240.6273920.4865290.548054
250.250.6077540.5031260.550513
260.260.5946630.5116980.55007
270.270.5770390.5192570.546625
280.280.5664650.5309110.548112
290.290.5568980.5429550.549838
300.30.5422960.5560140.54907
310.310.5216520.5692310.544404
320.320.5065460.5838650.542464
330.330.4939580.5916770.538419
340.340.4843910.6038920.53758
350.350.4758310.6120470.535411
360.360.4687810.6202530.533983
370.370.4587110.6278430.530113
380.380.4501510.6304650.525264
390.390.4431020.6349210.521945
400.40.4355490.6397930.518274
410.410.4274920.6431820.513612
420.420.4204430.6472870.509768
430.430.4149040.6529320.507389
440.440.412890.6570510.507112
450.450.4068480.6639280.504527
460.460.4003020.6652720.499843
470.470.3972810.6680780.498263
480.480.3937560.6712450.49635
490.490.3882180.6733620.492494
500.50.38570.6760810.491183
510.510.3806650.6816950.48853
520.520.3761330.6828150.485065
530.530.3726080.6896550.483818
540.540.3655590.6920880.478418
550.550.3610270.6954410.475307
560.560.3519640.6983020.468028
570.570.3408860.6972190.457897
580.580.3282980.7025860.447495
590.590.3162130.7048260.436566
600.60.3061430.710280.427868
610.610.2945620.7116790.416667
620.620.2834840.7199490.406792
630.630.2668680.7230560.389849
640.640.2497480.7272730.371814
650.650.2406850.7286590.361847
660.660.2265860.7258060.345357
670.670.2104730.7333330.327074
680.680.1998990.7297790.313834
690.690.1883180.7405940.300281
700.70.1797580.743750.289538
710.710.1676740.7466370.273849
720.720.1596170.7547620.263508
730.730.1470290.7506430.245895
740.740.1294060.7626110.221266
750.750.1137970.7739730.19842
760.760.1017120.7651520.179556
770.770.09063440.7725320.162235
780.780.07955690.786070.14449
790.790.07049350.790960.12945
800.80.05891240.7748340.109499
810.810.04884190.763780.0918126
820.820.04531720.7692310.085592
830.830.03575030.7473680.0682364
840.840.03121850.7654320.0599903
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" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_prauc(frame, y, yhat, pos=1, neg=0, res=0.01):\n", " \n", " \"\"\" Calculates precision, recall, and f1 for a pandas dataframe of y \n", " and yhat values.\n", " \n", " Args:\n", " frame: Pandas dataframe of actual (y) and predicted (yhat) values.\n", " y: Name of actual value column.\n", " yhat: Name of predicted value column.\n", " pos: Primary target value, default 1.\n", " neg: Secondary target value, default 0.\n", " res: Resolution by which to loop through cutoffs, default 0.01.\n", " \n", " Returns:\n", " Pandas dataframe of precision, recall, and f1 values.\n", " \n", " \"\"\"\n", " \n", " frame_ = frame.copy(deep=True) # don't destroy original data\n", " dname = 'd_' + str(y) # column for predicted decisions\n", " eps = 1e-20 # for safe numerical operations\n", " \n", " # init p-r roc frame\n", " prroc_frame = pd.DataFrame(columns=['cutoff', 'recall', 'precision', 'f1'])\n", " \n", " # loop through cutoffs to create p-r roc frame\n", " for cutoff in np.arange(0, 1 + res, res):\n", "\n", " # binarize decision to create confusion matrix values\n", " frame_[dname] = np.where(frame_[yhat] > cutoff , 1, 0)\n", " \n", " # calculate confusion matrix values\n", " tp = frame_[(frame_[dname] == pos) & (frame_[y] == pos)].shape[0]\n", " fp = frame_[(frame_[dname] == pos) & (frame_[y] == neg)].shape[0]\n", " tn = frame_[(frame_[dname] == neg) & (frame_[y] == neg)].shape[0]\n", " fn = frame_[(frame_[dname] == neg) & (frame_[y] == pos)].shape[0]\n", "\n", " # calculate precision, recall, and f1\n", " recall = (tp + eps)/((tp + fn) + eps)\n", " precision = (tp + eps)/((tp + fp) + eps)\n", " f1 = 2/((1/(recall + eps)) + (1/(precision + eps)))\n", " \n", " # add new values to frame\n", " prroc_frame = prroc_frame.append({'cutoff': cutoff,\n", " 'recall': recall,\n", " 'precision': precision,\n", " 'f1': f1}, \n", " ignore_index=True)\n", " \n", " # housekeeping\n", " del frame_\n", " \n", " return prroc_frame\n", "\n", "# calculate and display recall and precision\n", "prauc_frame = get_prauc(test_yhat, y, yhat)\n", "prauc_frame.style.set_caption('Recall and Precision')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Find and display cutoff with maximum F1\n", "For this model, the best F1 value is found at a probability cutoff of 0.25." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best F1 threshold: 0.25\n" ] } ], "source": [ "gbm_cut = prauc_frame.loc[prauc_frame['f1'].idxmax(), 'cutoff'] # value associated w/ index of max. F1\n", "print('Best F1 threshold: %.2f' % gbm_cut)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot P-R AUC with best F1 and cutoff\n", "A P-R AUC curve is a curve formed by plotting precision and recall across different cutoffs. (P-R AUC curves are often a bit more robust to imbalanced target classes than ROC curves.) This curve shows some odd behavior for high precision and low recall, potentially indicating instability in model predictions, but is smooth and mostly well-behaved where the cutoff is selected. Generally a smooth tradeoff between precision and recall is preferred as different cutoffs are considered." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "title_ = 'P-R Curve: Best F1 Threshold = ' + str(gbm_cut)\n", "ax = prauc_frame.plot(x='recall', y='precision', kind='scatter', title=title_, xlim=[0,1])\n", "_ = ax.axvline(gbm_cut, color='magenta')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Function to calculate confusion matrices\n", "Once a cutoff has been established, a confusion matrix can be created. A confusion matrix is basic model assessment technique that shows how often a model is correct and incorrect, and how it is incorrect, e.g. type I (false positive) and type II (false negative) errors. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Confusion Matrix
actual: 1actual: 0
predicted: 112071192
predicted: 07795876
" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_confusion_matrix(frame, y, yhat, by=None, level=None, cutoff=0.5):\n", "\n", " \"\"\" Creates confusion matrix from pandas dataframe of y and yhat values, \n", " can be sliced by a variable and level.\n", " \n", " Args:\n", " frame: Pandas dataframe of actual (y) and predicted (yhat) values.\n", " y: Name of actual value column.\n", " yhat: Name of predicted value column.\n", " by: By variable to slice frame before creating confusion matrix, \n", " default None.\n", " level: Value of by variable to slice frame before creating confusion \n", " matrix, default None.\n", " cutoff: Cutoff threshold for confusion matrix, default 0.5. \n", "\n", " Returns:\n", " Confusion matrix as pandas dataframe. \n", " \"\"\"\n", " \n", " # determine levels of target (y) variable\n", " # sort for consistency\n", " level_list = list(frame[y].unique())\n", " level_list.sort(reverse=True)\n", "\n", " # init confusion matrix\n", " cm_frame = pd.DataFrame(columns=['actual: ' + str(i) for i in level_list], \n", " index=['predicted: ' + str(i) for i in level_list])\n", " \n", " # don't destroy original data\n", " frame_ = frame.copy(deep=True)\n", " \n", " # convert numeric predictions to binary decisions using cutoff\n", " dname = 'd_' + str(y)\n", " frame_[dname] = np.where(frame_[yhat] > cutoff , 1, 0)\n", " \n", " # slice frame\n", " if (by is not None) & (level is not None):\n", " frame_ = frame_[frame[by] == level]\n", " \n", " # calculate size of each confusion matrix value\n", " for i, lev_i in enumerate(level_list):\n", " for j, lev_j in enumerate(level_list):\n", " cm_frame.iat[j, i] = frame_[(frame_[y] == lev_i) & \n", " (frame_[dname] == lev_j)].shape[0]\n", " # i,j vs. j,i - nasty little bug updated 8/30/19\n", " \n", " return cm_frame\n", " \n", "cm = get_confusion_matrix(test_yhat, y, yhat, cutoff=gbm_cut)\n", "cm.style.set_caption('Confusion Matrix')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The confusion matrix shows that the GBM model is more accurate than not because the true positive and true negative cells contain the largest values by far. But the GBM model seems to make a large number of type I errors, or false positive predictions. False positives are a known disparity issue, because for complex reasons, many credit scoring and other models tend to overestimate the likelihood of non-reference groups - typically not white males - to default. The large amount of false positives could indicate that a lot of deserving people will not receive loans that they could actually pay back according to this model. This is a potential pathology that should definitely be investigated further." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Basic Residual Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Score test data and calculate logloss residuals\n", "Because special functionality in XGBoost to find Shapley contributions to logloss will be used later, logloss residuals are used in several places in this notebook." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean logloss residual: 0.426156\n" ] } ], "source": [ "# shortcut name\n", "resid = 'r_DEFAULT_NEXT_MONTH' \n", "\n", "# calculate logloss residuals\n", "test_yhat[resid] = -test_yhat[y]*np.log(test_yhat[yhat]) -\\\n", " (1 - test_yhat[y])*np.log(1 - test_yhat[yhat]) \n", " \n", "# check that logloss is calculated correctly\n", "# should match eval-logloss above\n", "print('Mean logloss residual: %.6f' % test_yhat[resid].mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot global logloss residuals\n", "Plotting residuals is almost always a good idea. You can see all of your model's predictions in two-dimensions!" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ " # initialize figure\n", "fig, ax_ = plt.subplots(figsize=(8, 8)) \n", "\n", "# plot groups with appropriate color\n", "color_list = ['royalblue', 'magenta'] \n", "c_idx = 0\n", "groups = test_yhat.groupby(y) # define groups for levels of PAY_0\n", "for name, group in groups:\n", " ax_.plot(group.p_DEFAULT_NEXT_MONTH, group.r_DEFAULT_NEXT_MONTH, \n", " label=' '.join([y, str(name)]),\n", " marker='o', linestyle='', color=color_list[c_idx], alpha=0.3)\n", " c_idx += 1\n", " \n", "# annotate plot\n", "_ = plt.xlabel(yhat)\n", "_ = plt.ylabel(resid)\n", "_ = ax_.legend(loc=1)\n", "_ = plt.title('Global Logloss Residuals')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some high-magnitude outlying residuals are visible. Who are these customers? Why is the model so wrong about them? And are they somehow exerting undue influence on other predictions? The model could be retrained without these individuals and retested as a potentially remediation strategy. From a security standpoint it can be interesting to examine insider attacks and false negatives. These are people who where issued a loan, but did not pay it back. Are any of these employees, contractors, consultants, or their associates? Could they have poisoned the model's training data to receive the loan? Could they have changed the model's production scoring code to receive the loan?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Examine high logloss individuals" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTHp_DEFAULT_NEXT_MONTHr_DEFAULT_NEXT_MONTH
098350000011236-2-2-2-2-2-245106812641812227229214622791181690182252736521570280501739710.0080154.826454
125772350000211330-1-1-1-1-182964685321792617966307413108868940180181805830897312448846110.0143964.240807
21157120000012230-2-2-2-2-2-248492499342475312343913226912922420294248911251711781626269434910.0227043.785196
3256131000021232-2-2-2-2-2-2201388267659938543169575082676600885431695750735010.0232833.760035
41620936000021135-2-10-1-1-194657345291062767333177593184045000100000734277759318401257710.0265043.630469
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" ], "text/plain": [ " ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 \\\n", "0 983 500000 1 1 2 36 -2 -2 -2 \n", "1 25772 350000 2 1 1 33 0 -1 -1 \n", "2 11571 200000 1 2 2 30 -2 -2 -2 \n", "3 2561 310000 2 1 2 32 -2 -2 -2 \n", "4 16209 360000 2 1 1 35 -2 -1 0 \n", "\n", " PAY_4 PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 \\\n", "0 -2 -2 -2 45106 81264 18122 27229 21462 \n", "1 -1 -1 -1 82964 68532 17926 17966 30741 \n", "2 -2 -2 -2 48492 49934 24753 123439 132269 \n", "3 -2 -2 -2 20138 8267 65993 8543 1695 \n", "4 -1 -1 -1 94657 34529 106276 73331 7759 \n", "\n", " BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 \\\n", "0 27911 81690 18225 27365 21570 28050 17397 \n", "1 31088 68940 18018 18058 30897 31244 88461 \n", "2 129224 20294 24891 125171 17816 26269 4349 \n", "3 750 8267 66008 8543 1695 750 7350 \n", "4 31840 45000 100000 73427 7759 31840 12577 \n", "\n", " DEFAULT_NEXT_MONTH p_DEFAULT_NEXT_MONTH r_DEFAULT_NEXT_MONTH \n", "0 1 0.008015 4.826454 \n", "1 1 0.014396 4.240807 \n", "2 1 0.022704 3.785196 \n", "3 1 0.023283 3.760035 \n", "4 1 0.026504 3.630469 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sort by residual and display\n", "test_yhat_sorted = test_yhat.\\\n", " sort_values(by=resid, ascending=False).\\\n", " reset_index(drop=True)\n", " \n", "test_yhat_sorted.head() # large positive residuals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "High positive residuals seem to be caused by people with excellent payment behavior who suddenly default. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Examine low logloss residuals" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTHp_DEFAULT_NEXT_MONTHr_DEFAULT_NEXT_MONTH
90492871734000021342-1-1-1-1-101398081767433440220593126543127023787355212154718890436210002000014500000.0105880.010644
90501413650000011243-1-1-1-1-1-13757510565251072770947766942059107243520907837278949429856279300.0097730.009821
90512874444000022229-1-1-1-10023147888484204540032122968026540470554452134009725456100267753000.0071820.007207
9052377550000021232-1-1-1-1-1-110388039356301441379451044913523439560302961381391046733538717725800.0071510.007176
90532347748000021232-2-2-2-2-2-211872389332347952177540055385340000234795220954005545004232100.0060110.006030
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" ], "text/plain": [ " ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 \\\n", "9049 28717 340000 2 1 3 42 -1 -1 -1 \n", "9050 14136 500000 1 1 2 43 -1 -1 -1 \n", "9051 28744 440000 2 2 2 29 -1 -1 -1 \n", "9052 3775 500000 2 1 2 32 -1 -1 -1 \n", "9053 23477 480000 2 1 2 32 -2 -2 -2 \n", "\n", " PAY_4 PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 \\\n", "9049 -1 -1 0 139808 176743 34402 205931 \n", "9050 -1 -1 -1 37575 105652 51072 77094 \n", "9051 -1 0 0 23147 88848 42045 400321 \n", "9052 -1 -1 -1 103880 39356 301441 37945 \n", "9053 -2 -2 -2 11872 38933 23479 52177 \n", "\n", " BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", "9049 265431 270237 873552 1215471 889043 621000 20000 \n", "9050 77669 42059 107243 52090 78372 78949 42985 \n", "9051 229680 265404 70554 45213 400972 5456 100267 \n", "9052 104491 35234 39560 302961 38139 104673 35387 \n", "9053 54005 53853 40000 23479 52209 54005 54500 \n", "\n", " PAY_AMT6 DEFAULT_NEXT_MONTH p_DEFAULT_NEXT_MONTH r_DEFAULT_NEXT_MONTH \n", "9049 145000 0 0.010588 0.010644 \n", "9050 62793 0 0.009773 0.009821 \n", "9051 7530 0 0.007182 0.007207 \n", "9052 177258 0 0.007151 0.007176 \n", "9053 42321 0 0.006011 0.006030 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_yhat_sorted.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model seems to have the easiest time making correct decisions about customers with good payment behavior who sustain that behavior." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Residuals plotted by important variables: `PAY_0`\n", "To take residual analysis farther, try plotting residuals by values of important variables or across bins of important variables. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# some seaborn configs\n", "sns.set(font_scale=0.9) # legible font size\n", "sns.set_style('whitegrid', {'axes.grid': False}) # white background, no grid in plots\n", "sns.set_palette(sns.color_palette([\"#4169e1\", \"#ff00ff\"])) # consistent colors\n", "\n", "# facet grid of residuals by PAY_0 \n", "sorted_ = test_yhat.sort_values(by='PAY_0') # sort for better layout of by-groups\n", "g = sns.FacetGrid(sorted_, col='PAY_0', hue=y, col_wrap=4) # init grid\n", "_ = g.map(plt.scatter, yhat, resid, alpha=0.4) # plot points\n", "_ = g.add_legend(bbox_to_anchor=(0.82, 0.2)) # legend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, a problem with the model is visible. For people whose most recent repayment status, i.e. `PAY_0` value, is good, `PAY_0 <= 1`, the model appears to struggle to predict default. When the model predicts this type of customer will not default, and they do, this results in large residuals. For people with late most recent payments, `PAY_0 > 1`, the model appears to struggle to predict on-time payment. When the model predicts people with late most recent payments will default, and then they don't, this also causes large residuals. *A mechanism for error* has been identified: over-emphasis of a feature: `PAY_0 > 2`. These problems could be become worse if market conditions change, such as in a recession, where more people with good payment behavior could default suddenly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Residuals plotted by important variables: `SEX`\n", "While not a frontline fairness tool, it can be interesting to examine residuals across demographic variables to get a sense of whether the model's errors are different across these groups. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# facet grid of residuals by SEX\n", "sorted_ = test_yhat.sort_values(by='SEX') # sort for better layout of by-groups\n", "g = sns.FacetGrid(sorted_, col='SEX', hue=y, col_wrap=4) # init grid \n", "_ = g.map(plt.scatter, yhat, resid, alpha=0.4) # plot points\n", "_ = g.add_legend(bbox_to_anchor=(0.6, 0.5)) # legend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The residual profile is not dramatically different for men (`SEX = 1`) and women (`SEX = 2`). The largest magnitude residuals appear for men and for false positive predictions. This does not ensure the model is not discriminatory, but is just a potentially interesting method to augment results from a more rigorous fairness analysis, such as: https://github.com/jphall663/interpretable_machine_learning_with_python/blob/master/dia.ipynb. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Disparate Error Analysis\n", "The analysis in this section applies simple but numerous metrics to the accuracy and error of the model across the values of important or demographic variables to discover any potential blindspots in the model's predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Represent error metrics as dictionary for use later\n", "These are the metrics that will be examined across important and demographic variables. These metrics are based on the confusion matrix, but any other metrics would likely be useful as well." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "metric_dict = {\n", "\n", "#### overall performance\n", "'Prevalence': '(tp + fn) / (tp + tn +fp + fn)', # how much default actually happens for this group\n", "#'Adverse Impact': '(tp + fp) / (tp + tn + fp + fn)', # how often the model predicted default for each group \n", "'Accuracy': '(tp + tn) / (tp + tn + fp + fn)', # how often the model predicts default and non-default correctly for this group\n", "\n", "#### predicting default will happen\n", "# (correctly)\n", "'True Positive Rate': 'tp / (tp + fn)', # out of the people in the group *that did* default, how many the model predicted *correctly* would default \n", "'Precision': 'tp / (tp + fp)', # out of the people in the group the model *predicted* would default, how many the model predicted *correctly* would default\n", "\n", "#### predicting default won't happen\n", "# (correctly)\n", "'Specificity': 'tn / (tn + fp)', # out of the people in the group *that did not* default, how many the model predicted *correctly* would not default\n", "'Negative Predicted Value': 'tn / (tn + fn)', # out of the people in the group the model *predicted* would not default, how many the model predicted *correctly* would not default \n", "\n", "#### analyzing errors - type I\n", "# false accusations \n", "'False Positive Rate': 'fp / (tn + fp)', # out of the people in the group *that did not* default, how many the model predicted *incorrectly* would default\n", "'False Discovery Rate': 'fp / (tp + fp)', # out of the people in the group the model *predicted* would default, how many the model predicted *incorrectly* would default\n", "\n", "#### analyzing errors - type II\n", "# costly ommisions\n", "'False Negative Rate': 'fn / (tp + fn)', # out of the people in the group *that did* default, how many the model predicted *incorrectly* would not default\n", "'False Omissions Rate':'fn / (tn + fn)' # out of the people in the group the model *predicted* would not default, how many the model predicted *incorrectly* would not default\n", "} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Small utility functions used to calculate and display different types of errors \n", "A number of small, tightly coupled utility functions are used to calculate and display the error metrics across the values of important or demographic variables." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# small utility functions\n", "# all tightly coupled to global names and data structures !!\n", "\n", "def cm_exp_parser(expression):\n", " \n", " \"\"\" Translates abbreviated metric expressions from metric_dict \n", " into executable Python statements.\n", " \n", " Arg: \n", " expression: Error metric expression from metric_dict.\n", " \n", " Returns:\n", " Python statements based on predefined metrics in metric_dict.\n", " \n", " \"\"\"\n", " \n", " # tp | fp cm_dict[level].iat[0, 0] | cm_dict[level].iat[0, 1]\n", " # ------- ==> --------------------------------------------\n", " # fn | tn cm_dict[level].iat[1, 0] | cm_dict[level].iat[1, 1]\n", "\n", " expression = expression.replace('tp', '(cm_dict[level].iat[0, 0] + eps)')\\\n", " .replace('fp', '(cm_dict[level].iat[0, 1] + eps)')\\\n", " .replace('fn', '(cm_dict[level].iat[1, 0] + eps)')\\\n", " .replace('tn', '(cm_dict[level].iat[1, 1] + eps)')\n", "\n", " return expression\n", "\n", "################################################################################\n", "\n", "def get_cm_dict(name, cutoff):\n", "\n", " \"\"\" Loops through levels of named variable and calculates confusion \n", " matrices per level; uses dynamically generated entities to reduce \n", " code duplication. \n", " \n", " Args:\n", " name: Name of variable for which to calculate confusion matrices.\n", " cutoff: Cutoff threshold for confusion matrices. \n", " \n", " Returns:\n", " Dictionary of confusion matrices. \n", " \n", " \"\"\"\n", "\n", " levels = sorted(list(test_yhat[name].unique())) # get levels\n", " cm_dict = {} # init dict to store confusion matrices per level\n", " for level in levels: \n", " \n", " # dynamically name confusion matrices by level\n", " # coerce to proper python names\n", " cm_name = '_' + str(level).replace('-', 'm') + '_cm' \n", " \n", " # dynamically calculate confusion matrices by level\n", " code = cm_name + ''' = get_confusion_matrix(test_yhat, \n", " y, \n", " yhat, \n", " by=name, \n", " level=level, \n", " cutoff=cutoff)'''\n", " exec(code)\n", " exec('cm_dict[level] = ' + cm_name) # store in dict\n", " \n", " return cm_dict\n", "\n", "################################################################################\n", "\n", "def get_metrics_frame(name): \n", "\n", " \"\"\" Loops through levels of named variable and metrics to calculate each \n", " error metric per each level of the variable; uses dynamically generated \n", " entities to reduce code duplication.\n", " \n", " Arg:\n", " name: Name of variable for which to calculate error metrics.\n", " \n", " Return:\n", " Pandas DataFrame of error metrics.\n", " \n", " \"\"\"\n", " \n", " levels = sorted(list(test_yhat[name].unique())) # get levels\n", " metrics_frame = pd.DataFrame(index=levels) # init Pandas frame for metrics\n", " eps = 1e-20 # for safe numerical operations\n", "\n", " # nested loop through:\n", " # - levels\n", " # - metrics \n", " for level in levels:\n", " for metric in metric_dict.keys():\n", " \n", " # parse metric expressions into executable pandas statements\n", " expression = cm_exp_parser(metric_dict[metric])\n", " \n", " # dynamically evaluate metrics to avoid code duplication\n", " metrics_frame.loc[level, metric] = eval(expression) \n", "\n", " # display results\n", " return metrics_frame\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Display all error metrics across levels of `PAY_0`" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Error Metrics for PAY_0
PrevalenceAccuracyTrue Positive RatePrecisionSpecificityNegative Predicted ValueFalse Positive RateFalse Discovery RateFalse Negative RateFalse Omissions Rate
PAY_0
-20.1240.8570.1190.30.9610.8850.0390.70.8810.115
-10.1680.8050.2670.3830.9130.8610.0870.6170.7330.139
00.1210.8640.1430.3450.9630.8910.0370.6550.8570.109
10.3250.4570.930.3680.2290.8710.7710.6320.070.129
20.7090.70910.70900.510.29100.5
30.7480.74810.74800.510.25200.5
40.5710.57110.57100.510.42900.5
50.4440.44410.44400.510.55600.5
60.250.2510.2500.510.7500.5
70.50.510.500.510.500.5
80.750.7510.7500.510.2500.5
" ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name = 'PAY_0'\n", "cm_dict = get_cm_dict(name, gbm_cut) # get dict of confusion matrices\n", "\n", "# print formatted error metrics frame: precision, title, colors\n", "PAY_0_metrics = get_metrics_frame(name)\n", "PAY_0_metrics['PAY_0'] = [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]\n", "PAY_0_metrics.set_index('PAY_0', inplace=True)\n", "PAY_0_metrics.round(3).\\\n", " style.set_caption('Error Metrics for ' + name).\\\n", " background_gradient(cmap=sns.diverging_palette(-20, 260, n=7, as_cmap=True), \n", " axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As identified by basic residual analysis, it can be seen here that the model behaves extremely differently for customers with `PAY_0 <= 1` and `PAY_0 > 1`, likely due to sparsity of data in the `PAY_0 > 1` range. Again, this may be acceptable behavior today, but what if market conditions drift and these types of behaviors become more common and model mistakes become more costly in the future?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Display all error metrics across levels of `SEX`" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Error Metrics for SEX
PrevalenceAccuracyTrue Positive RatePrecisionSpecificityNegative Predicted ValueFalse Positive RateFalse Discovery RateFalse Negative RateFalse Omissions Rate
SEX
Male0.2350.7730.6550.5130.8090.8840.1910.4870.3450.116
Female0.2090.7880.5730.4950.8450.8820.1550.5050.4270.118
" ], "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name = 'SEX'\n", "cm_dict = get_cm_dict(name, gbm_cut) # get dict of confusion matrices\n", "\n", "# print formatted error metrics frame: precision, title, colors\n", "sex_metrics = get_metrics_frame(name)\n", "sex_metrics['SEX'] = ['Male', 'Female']\n", "sex_metrics.set_index('SEX', inplace=True)\n", "sex_metrics.round(3).\\\n", " style.set_caption('Error Metrics for ' + name).\\\n", " background_gradient(cmap=sns.diverging_palette(-20, 260, n=7, as_cmap=True), \n", " axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike `PAY_0`, different types of error and accuracy measures appear fairly aligned for men and women." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Benchmark Models\n", "\n", "Benchmark models are an excellent model debugging tool. They can be used at training time to understand how a new model differs from an established, trusted model. They can also be used at scoring time to understand if a newer or more complex model is giving different predictions from a previously deployed trusted model or simpler model. If a prediction from a new model is too different from a prediction from a trusted model, this could be indicative of potential accuracy, fairness, or security problems." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Function for simple grid search across alpha for penalized GLM\n", "Instead of using a pre-existing model, a stable penalized GLM will be used as a benchmark in this notebook." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", "glm Grid Build progress: |████████████████████████████████████████████████| 100%\n", "Best penalized GLM mean logloss residual: 0.461254\n", "At threshold: 0.300562\n" ] } ], "source": [ "def glm_grid(X, y, train, valid):\n", " \n", " \"\"\" Wrapper function for penalized GLM with alpha and lambda search.\n", " \n", " :param X: List of inputs.\n", " :param y: Name of target variable.\n", " :param train: Name of training H2OFrame.\n", " :param valid: Name of validation H2OFrame.\n", " :return: Best H2Omodel from H2OGeneralizedLinearEstimator\n", "\n", " \"\"\"\n", " \n", " alpha_opts = [0.01, 0.25, 0.5, 0.99] # always keep some L2\n", " \n", " # define search criteria\n", " # i.e. over alpha\n", " # lamda search handled by lambda_search param below\n", " hyper_parameters = {'alpha': alpha_opts} \n", "\n", " # initialize grid search\n", " grid = H2OGridSearch(\n", " H2OGeneralizedLinearEstimator(\n", " family=\"binomial\",\n", " lambda_search=True,\n", " seed=SEED),\n", " hyper_params=hyper_parameters)\n", " \n", " # run grid search\n", " grid.train(y=y,\n", " x=X, \n", " training_frame=train,\n", " validation_frame=valid)\n", "\n", " # select best model from grid search\n", " return grid.get_grid()[0]\n", "\n", "best_glm = glm_grid(X, y, h2o.H2OFrame(train), h2o.H2OFrame(test))\n", "print('Best penalized GLM mean logloss residual: %.6f' % \n", " best_glm.logloss(valid=True))\n", "glm_cut = best_glm.F1(valid=True)[0][0] # get GLM cutoff to create decisions\n", "print('At threshold: %.6f' % glm_cut)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Find rows where GLM benchmark model is right, but GBM is wrong\n", "One interesting place to start comparing a more complex model to a simpler model is when the simple model is right and the complex model is wrong." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", "glm prediction progress: |████████████████████████████████████████████████| 100%\n" ] }, { "data": { "text/html": [ "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTHp_glm_DEFAULT_NEXT_MONTHp_gbm_DEFAULT_NEXT_MONTH
7360000211491-2-2-2-2-200000000000000.2780970.338660
8180000212291-2-2-2-2-200000000000000.2384630.297464
2220000021234-13222215871098782116670014140070001200000.2277270.524791
25130000232291-2-2-12-1-190-9850-9850103111016173190020161073191389900.2407430.303131
27290000212371-2-1-1-1-100315502359003155023590000.2617410.253799
\n", "
" ], "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", "7 360000 2 1 1 49 1 -2 -2 -2 \n", "8 180000 2 1 2 29 1 -2 -2 -2 \n", "22 200000 2 1 2 34 -1 3 2 2 \n", "25 130000 2 3 2 29 1 -2 -2 -1 \n", "27 290000 2 1 2 37 1 -2 -1 -1 \n", "\n", " PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 \\\n", "7 -2 -2 0 0 0 0 0 \n", "8 -2 -2 0 0 0 0 0 \n", "22 2 2 1587 1098 782 1166 700 \n", "25 2 -1 -190 -9850 -9850 10311 10161 \n", "27 -1 -1 0 0 3155 0 2359 \n", "\n", " BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 \\\n", "7 0 0 0 0 0 0 0 \n", "8 0 0 0 0 0 0 0 \n", "22 1414 0 0 700 0 1200 0 \n", "25 7319 0 0 20161 0 7319 13899 \n", "27 0 0 3155 0 2359 0 0 \n", "\n", " DEFAULT_NEXT_MONTH p_glm_DEFAULT_NEXT_MONTH p_gbm_DEFAULT_NEXT_MONTH \n", "7 0 0.278097 0.338660 \n", "8 0 0.238463 0.297464 \n", "22 0 0.227727 0.524791 \n", "25 0 0.240743 0.303131 \n", "27 0 0.261741 0.253799 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# copy test data\n", "test_yhat2 = test_yhat.copy(deep=True)\n", "\n", "# create columns for gbm and glm preds\n", "test_yhat2.rename(columns={yhat: 'p_gbm_DEFAULT_NEXT_MONTH'}, inplace=True)\n", "test_yhat2['p_glm_DEFAULT_NEXT_MONTH'] = best_glm.\\\n", " predict(h2o.H2OFrame(test))['p1'].\\\n", " as_data_frame()\n", "\n", "# create columns for gbm and glm decisions (i.e. apply cutoff)\n", "test_yhat2['gbm_DECISION'] = 0\n", "test_yhat2.loc[test_yhat2['p_gbm_DEFAULT_NEXT_MONTH'] > gbm_cut,\n", " 'gbm_DECISION'] = 1\n", "test_yhat2['glm_DECISION'] = 0\n", "test_yhat2.loc[test_yhat2['p_glm_DEFAULT_NEXT_MONTH'] > glm_cut,\n", " 'glm_DECISION'] = 1\n", "\n", "# create columns for gbm and glm wrong decisions\n", "test_yhat2['gbm_WRONG'] = 0\n", "test_yhat2.loc[test_yhat2[y] != test_yhat2['gbm_DECISION'], 'gbm_WRONG'] = 1\n", "test_yhat2['glm_WRONG'] = 0\n", "test_yhat2.loc[test_yhat2[y] != test_yhat2['glm_DECISION'], 'glm_WRONG'] = 1\n", "\n", "# create a subset of preds where gbm is wrong, but glm is right\n", "gbm_wrong = test_yhat2.loc[(test_yhat2['gbm_WRONG'] == 1) & \n", " (test_yhat2['glm_WRONG'] == 0)]\n", "\n", "gbm_wrong[X + [y, 'p_glm_DEFAULT_NEXT_MONTH', 'p_gbm_DEFAULT_NEXT_MONTH']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the subset of customers where the more complex GBM model is wrong and the GLM model is right, the predictions of two models appear to be inversely correlated. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot rows where GLM benchmark model is right, but GBM is wrong" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# custom legend\n", "custom = [Line2D([0], [0], color='royalblue', lw=2),\n", " Line2D([0], [0], color='deeppink', lw=2),\n", " Line2D([0], [0], marker='o', color='w',\n", " markerfacecolor='orange', markersize=3)]\n", "\n", "# init plot\n", "fig, ax = plt.subplots(figsize=(7, 5)) \n", "\n", "# plot sorted actuals\n", "# double index reset orders index by sort variable and\n", "# brings index into frame for plotting\n", "_ = gbm_wrong[[y,'p_gbm_DEFAULT_NEXT_MONTH']].\\\n", " sort_values(by='p_gbm_DEFAULT_NEXT_MONTH').\\\n", " reset_index(drop=True).\\\n", " reset_index().\\\n", " plot(kind='scatter', x='index', y=y, color='orange', s=3, ax=ax, \n", " legend=False)\n", "\n", "# plot sorted gbm and glm preds \n", "_ = gbm_wrong[['p_glm_DEFAULT_NEXT_MONTH', 'p_gbm_DEFAULT_NEXT_MONTH']].\\\n", " sort_values(by='p_gbm_DEFAULT_NEXT_MONTH').\\\n", " reset_index(drop=True).\\\n", " plot(ax=ax, legend=False, \n", " title='Ranked Predictions for Correct GLM and Incorrect GBM')\n", "\n", "# annotate plot\n", "_ = ax.legend(custom, ['p_glm_DEFAULT_NEXT_MONTH', 'p_gbm_DEFAULT_NEXT_MONTH', \n", " y])\n", "_ = ax.set_xlabel('Ranked Row Index')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a range of probabilities between ~0.2 and ~0.6 there exists a group of customers where a GLM model gives more correct predictions than the more complex GBM model. In the plot above, the yellow points represent the known target labels, the pink line is the sorted GBM model predictions, and the blue line is the GLM predictions for the same customers and target labels. For this group of people the GLM is obviously able to better represent some attribute of the customer's demographics or behaviors. Can the differences between this group of people and the rest of the customers be identified and leveraged to make better predictions?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Descriptive statistics for rows where GLM benchmark model is right, but GBM is wrong" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTHp_gbm_DEFAULT_NEXT_MONTHr_DEFAULT_NEXT_MONTHp_glm_DEFAULT_NEXT_MONTHgbm_DECISIONglm_DECISIONgbm_WRONGglm_WRONG
count502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.000000502.0502.0
mean13870.175299160896.4143431.5517931.8725101.48406436.7051790.123506-0.633466-0.444223-0.474104-0.575697-0.64143419479.75896419197.11553818623.70119518039.63545818077.87450217293.5338651800.5139441860.0976101525.5577691956.5776892736.4800802585.0358570.0199200.3068850.3947270.2302740.9800800.0199201.00.0
std8502.509341119357.0317370.4978060.8591880.5386698.9318070.9771261.4522041.5933611.6226711.5272791.50677756825.03216954988.15611953770.73333352307.53435151894.92391448606.1476244801.4409358087.7627575574.95357111037.03409112441.99284913390.4597370.1398660.0539740.1788670.0627700.1398660.1398660.00.0
min19.00000010000.0000001.0000001.0000000.00000022.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000-9802.000000-9850.000000-9850.000000-4894.000000-37594.000000-21295.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.1792990.2877950.0503470.0000000.0000001.00.0
25%6986.50000050000.0000001.0000001.0000001.00000030.000000-1.000000-2.000000-2.000000-2.000000-2.000000-2.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.2693040.3181160.1909011.0000000.0000001.00.0
50%13143.000000150000.0000002.0000002.0000001.00000035.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000414.500000393.000000390.000000355.000000323.000000390.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.2979460.3552700.2515311.0000000.0000001.00.0
75%20930.250000230000.0000002.0000002.0000002.00000043.0000001.0000000.0000001.5000000.0000000.0000000.0000008009.7500009862.7500009885.2500009400.2500009752.2500009392.0000001601.5000001249.000000804.750000981.0000001153.5000001070.0000000.0000000.3210030.3954030.2769521.0000000.0000001.00.0
max29956.000000710000.0000002.0000006.0000003.00000063.0000002.0000003.0000003.0000007.0000006.0000005.000000455293.000000441048.000000433541.000000425538.000000419080.000000381132.00000059068.00000097225.00000080677.000000180000.000000202317.000000164047.0000001.0000000.5647831.7187010.3465471.0000001.0000001.00.0
\n", "
" ], "text/plain": [ " ID LIMIT_BAL SEX EDUCATION MARRIAGE \\\n", "count 502.000000 502.000000 502.000000 502.000000 502.000000 \n", "mean 13870.175299 160896.414343 1.551793 1.872510 1.484064 \n", "std 8502.509341 119357.031737 0.497806 0.859188 0.538669 \n", "min 19.000000 10000.000000 1.000000 1.000000 0.000000 \n", "25% 6986.500000 50000.000000 1.000000 1.000000 1.000000 \n", "50% 13143.000000 150000.000000 2.000000 2.000000 1.000000 \n", "75% 20930.250000 230000.000000 2.000000 2.000000 2.000000 \n", "max 29956.000000 710000.000000 2.000000 6.000000 3.000000 \n", "\n", " AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", "count 502.000000 502.000000 502.000000 502.000000 502.000000 502.000000 \n", "mean 36.705179 0.123506 -0.633466 -0.444223 -0.474104 -0.575697 \n", "std 8.931807 0.977126 1.452204 1.593361 1.622671 1.527279 \n", "min 22.000000 -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", "25% 30.000000 -1.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", "50% 35.000000 0.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", "75% 43.000000 1.000000 0.000000 1.500000 0.000000 0.000000 \n", "max 63.000000 2.000000 3.000000 3.000000 7.000000 6.000000 \n", "\n", " PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 \\\n", "count 502.000000 502.000000 502.000000 502.000000 502.000000 \n", "mean -0.641434 19479.758964 19197.115538 18623.701195 18039.635458 \n", "std 1.506777 56825.032169 54988.156119 53770.733333 52307.534351 \n", "min -2.000000 -9802.000000 -9850.000000 -9850.000000 -4894.000000 \n", "25% -2.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% -1.000000 414.500000 393.000000 390.000000 355.000000 \n", "75% 0.000000 8009.750000 9862.750000 9885.250000 9400.250000 \n", "max 5.000000 455293.000000 441048.000000 433541.000000 425538.000000 \n", "\n", " BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", "count 502.000000 502.000000 502.000000 502.000000 502.000000 \n", "mean 18077.874502 17293.533865 1800.513944 1860.097610 1525.557769 \n", "std 51894.923914 48606.147624 4801.440935 8087.762757 5574.953571 \n", "min -37594.000000 -21295.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 323.000000 390.000000 0.000000 0.000000 0.000000 \n", "75% 9752.250000 9392.000000 1601.500000 1249.000000 804.750000 \n", "max 419080.000000 381132.000000 59068.000000 97225.000000 80677.000000 \n", "\n", " PAY_AMT4 PAY_AMT5 PAY_AMT6 DEFAULT_NEXT_MONTH \\\n", "count 502.000000 502.000000 502.000000 502.000000 \n", "mean 1956.577689 2736.480080 2585.035857 0.019920 \n", "std 11037.034091 12441.992849 13390.459737 0.139866 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 \n", "75% 981.000000 1153.500000 1070.000000 0.000000 \n", "max 180000.000000 202317.000000 164047.000000 1.000000 \n", "\n", " p_gbm_DEFAULT_NEXT_MONTH r_DEFAULT_NEXT_MONTH \\\n", "count 502.000000 502.000000 \n", "mean 0.306885 0.394727 \n", "std 0.053974 0.178867 \n", "min 0.179299 0.287795 \n", "25% 0.269304 0.318116 \n", "50% 0.297946 0.355270 \n", "75% 0.321003 0.395403 \n", "max 0.564783 1.718701 \n", "\n", " p_glm_DEFAULT_NEXT_MONTH gbm_DECISION glm_DECISION gbm_WRONG \\\n", "count 502.000000 502.000000 502.000000 502.0 \n", "mean 0.230274 0.980080 0.019920 1.0 \n", "std 0.062770 0.139866 0.139866 0.0 \n", "min 0.050347 0.000000 0.000000 1.0 \n", "25% 0.190901 1.000000 0.000000 1.0 \n", "50% 0.251531 1.000000 0.000000 1.0 \n", "75% 0.276952 1.000000 0.000000 1.0 \n", "max 0.346547 1.000000 1.000000 1.0 \n", "\n", " glm_WRONG \n", "count 502.0 \n", "mean 0.0 \n", "std 0.0 \n", "min 0.0 \n", "25% 0.0 \n", "50% 0.0 \n", "75% 0.0 \n", "max 0.0 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbm_wrong.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If this group of people can be isolated, either by descriptive statistics, or by more sophisticated means, the training process could be adapted to fix these errors or another model could be used at scoring time to create more accurate predictions. Even if a group cannot be isolated, the two different model predictions could potentially be blended in this range of predicted probabilities. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Explanation of Residuals\n", "A novel and very interesting application of post-hoc explanation techniques is in-depth investigation of model residuals. Below post-hoc explanation of predictions and residuals are used to further debug model predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.1 Shapley Values for Residuals and Predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Calculate Shapley values for logloss for each row\n", "A fairly recent addition to the `shap` package is the ability to calculate Shapley contributions to logloss. From this potentially very time-consuming calculation, an accurate estimate of each variable's contribution to the model's error for each row can be known. This notebook is shipped with pre-calculated values, but you can calculate them too. It might take a few hours though." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pre-calculated Shapley values loaded from disk.\n" ] } ], "source": [ "# init explainer object for access to explainer.expected_value() function below\n", "# expected value is a function for loss, not a constant\n", "explainer = shap.TreeExplainer(xgb_model, test_yhat[X], \n", " feature_dependence='independent', # necessary for logloss \n", " model_output='logloss') # necessary for logloss\n", "\n", "# load if pre-calculated\n", "if os.path.isfile('shap_error_values.csv'):\n", "\n", " shap_error_values = np.loadtxt('shap_error_values.csv', delimiter=',') # load\n", " print('Pre-calculated Shapley values loaded from disk.') # print confirmation\n", " \n", "# else, calculate\n", "else: \n", "\n", " xgb_model.set_attr(objective='binary:logistic')\n", " shap_error_values = explainer.shap_values(test_yhat[X], y=test_yhat[y]) # long step (hours!)\n", " np.savetxt('shap_error_values.csv', shap_error_values, delimiter=',') # save for immediate reuse later" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Calculate Shapley values for prediction for each row" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "shap_values = xgb_model.predict(dtest, pred_contribs=True) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot Shapley value summary for global importance measure in prediction and residuals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the local Shapley contributions to the predictions and logloss are known, they can be aggregated by their mean absolute value to create global importance metrics. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# init two pane plot\n", "fig, (ax0, ax1) = plt.subplots(ncols=2, sharey=False, figsize=(7, 5))\n", "plt.subplots_adjust(left=0.0, right=1.2, wspace=0.4)\n", "\n", "# plot global abs mean Shapley contribs to predictions\n", "global_shap_values_pd = pd.DataFrame(np.abs(shap_values[:, :-1]).mean(axis=0),\n", " index=X, columns=['Pred. Contribs.'])\n", "_ = global_shap_values_pd.sort_values(by='Pred. Contribs.').\\\n", " plot(kind='barh', ax=ax0, legend=False, color='royalblue',\n", " title='Global Shapley Contributions to Prediction')\n", "_ = ax0.set_xlabel('mean(|SHAP Contrib.|)')\n", "\n", "# plot global abs mean Shapley contribs to logloss\n", "global_shap_error_values_pd = pd.DataFrame(np.abs(shap_error_values).mean(axis=0), \n", " index=X, columns=['Loss Contribs.'])\n", "_ = global_shap_error_values_pd.sort_values(by='Loss Contribs.').\\\n", " plot(kind='barh', ax=ax1, legend=False, color='royalblue',\n", " title='Global Shapley Contributions to Logloss')\n", "_ = ax1.set_xlabel('mean(|SHAP Contrib.|)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some features are more important to the logloss than to the predictions! These include `PAY_3`, `PAY_2`, and `BILL_AMT1`. The model could potentially be retrained without these features." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Mean Shapley contributions of high and low residual customers\n", "Shapley contributions to predictions are reliably available at scoring time. It may be possible to understand what Shapley contributions look like for correct model decisions and for incorrect model decisions and take remediation steps in real-time before issuing model predictions." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# init two pane plot\n", "fig, (ax0, ax1) = plt.subplots(ncols=2, sharey=True)\n", "plt.subplots_adjust(left=0.0, right=1.5, wspace=0.1)\n", "\n", "# convert shap_values into Pandas DataFrame \n", "# merge residuals onto this frame\n", "local_shap_values_pd = pd.DataFrame(shap_values[:, :-1], columns=X)\n", "local_shap_values_pd[resid] = test_yhat[resid]\n", "\n", "# plot mean shap values of 100 lowest residual customers \n", "_ = local_shap_values_pd.sort_values(by=resid).\\\n", " reset_index(drop=True).\\\n", " iloc[:1000, :-1].mean(axis=0).\\\n", " plot(kind='barh', color='royalblue', ax=ax0, \n", " title='Mean Low Residual Pred. Contribs.')\n", "_ = ax0.set_xlabel('Pred. Contribs.')\n", "\n", "# plot mean shap values of 100 highest residual customers\n", "_ = local_shap_values_pd.sort_values(by=resid).\\\n", " reset_index(drop=True).\\\n", " iloc[-1000:, :-1].mean(axis=0).\\\n", " plot(kind='barh', color='royalblue', ax=ax1,\n", " title='Mean High Residual Pred. Contribs.')\n", "_ = ax1.set_xlabel('Pred. Contribs.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here large differences in average Shapley values are visible between low and high residual model predictions. On average, correct model decisions have negative Shapley values, while incorrect decisions have small magnitude Shapley values. If, when issuing a prediction, the Shapley values are low magnitude, the row could be diverted to another model, say the linear benchmark model, or sent for human processing. Input data, particularly features with large global or local Shapley contributions to loss, could also be corrupted with missing values in hopes of attaining a better model prediction. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Residuals vs. local Shapley prediction values in 2-D\n", "Shapley contributions to predictions can also be plotted against residual values, in hopes of understanding if certain model behaviors lead to higher errors." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# init three pane plot\n", "fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, sharey=False, figsize=(7, 5))\n", "plt.subplots_adjust(left=0.0, right=2, wspace=0.4)\n", "\n", "# plot shap vs. important variable PAY_0\n", "_ = local_shap_values_pd.plot(kind='scatter', y=resid, x='PAY_0', color='royalblue',\n", " title='PAY_0 Pred. Contribs. vs. ' + resid, ax=ax0)\n", "_ = ax0.set_xlabel('PAY_0 Pred. Contribs.')\n", "\n", "# plot shap vs. important variable PAY_3\n", "_ = local_shap_values_pd.plot(kind='scatter', y=resid, x='PAY_3', color='royalblue',\n", " title='PAY_3 Pred. Contribs. vs. ' + resid, ax=ax1)\n", "_ = ax0.set_xlabel('PAY_3 Pred. Contribs.')\n", "\n", "# plot shap vs. demographic variable SEX\n", "_ = local_shap_values_pd.plot(kind='scatter', y=resid, x='SEX', color='royalblue',\n", " title='SEX Pred. Contribs. vs. ' + resid, ax=ax2)\n", "_ = ax2.set_xlabel('SEX Pred. Contribs.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here it appears there are no single Shapley contributions that can be associated with only high residuals. Every extremely high residual value also has low residual values for the same Shapley value. However, it can be seen that some Shapley values are only associated with low residuals, for instance `0 < PAY_0` Contribs. `< 0.5` or `PAY_3` Contribs. `< -0.2`. If trying to detect scoring problems in real-time, Shapley values in these ranges for these features could indicate that these predictions are likely to be correct. Also, in terms of local feature importance, the model is not treating `SEX` very differently -- a good sign from a fairness perspective but not a definitive test. From a security perspective, high residuals above low residuals are potentially interesting. These are individuals the model usually gets right, but for some reason they did not in this case. That could indicate manipulation of the training data or model scoring logic in rare cases." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Residuals vs. local Shapley prediction values in 3-D\n", "If relationships between Shapley contributions and dependably high residuals cannot be found in two-dimensions, they may be visible in three dimensions when two features interact." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# init 3-D plot\n", "fig, ax = plt.subplots(figsize=(10, 8))\n", "ax = plt.axes(projection='3d')\n", "\n", "# z-axis: residuals values\n", "_z = local_shap_values_pd[resid].values\n", "ax.set_zlabel(resid)\n", "\n", "# x-axis: PAY_2 values\n", "_x = local_shap_values_pd['PAY_2'].values\n", "ax.set_xlabel('\\nPAY_2 Pred. Contribs.')\n", "\n", "# y-axis: PAY_3 values\n", "_y = local_shap_values_pd['PAY_3'].values\n", "ax.set_ylabel('\\nPAY_3 Pred. Contribs.')\n", "\n", "# plot and annotate\n", "_ = ax.scatter3D(_x, _y, _z, color='royalblue')\n", "fig.suptitle('PAY_2 and PAY_3 Pred. Contribs vs. ' + resid) \n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here two features that are globally more important to logloss than to the model predictions are plotted against model residuals. Like directly above, these plots can also be used to find potentially interesting regions of purely high or low residuals, differences in model behavior across demographic segments, or strange patterns indicating possible manipulation of data or model logic. Cluster profiles of this space seem particularly interesting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Shapley contributions to logloss for a specific individual\n", "Much like individual Shapley contributions to predictions, individual Shapley contributions to logloss residuals show exactly how much a variable contributes to the logloss for any individual in the training data." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Loss Contribs.
PAY_AMT10.611533
LIMIT_BAL0.403705
PAY_AMT20.352150
PAY_00.311651
PAY_30.272713
PAY_AMT30.184478
PAY_60.147152
PAY_20.128540
BILL_AMT10.123718
MARRIAGE0.100163
PAY_50.092106
PAY_AMT50.086318
PAY_AMT60.075505
PAY_40.067393
BILL_AMT20.064727
EDUCATION0.051236
PAY_AMT40.046066
BILL_AMT40.022856
BILL_AMT60.011413
BILL_AMT50.006382
BILL_AMT30.000471
AGE-0.050907
SEX-0.073163
\n", "
" ], "text/plain": [ " Loss Contribs.\n", "PAY_AMT1 0.611533\n", "LIMIT_BAL 0.403705\n", "PAY_AMT2 0.352150\n", "PAY_0 0.311651\n", "PAY_3 0.272713\n", "PAY_AMT3 0.184478\n", "PAY_6 0.147152\n", "PAY_2 0.128540\n", "BILL_AMT1 0.123718\n", "MARRIAGE 0.100163\n", "PAY_5 0.092106\n", "PAY_AMT5 0.086318\n", "PAY_AMT6 0.075505\n", "PAY_4 0.067393\n", "BILL_AMT2 0.064727\n", "EDUCATION 0.051236\n", "PAY_AMT4 0.046066\n", "BILL_AMT4 0.022856\n", "BILL_AMT6 0.011413\n", "BILL_AMT5 0.006382\n", "BILL_AMT3 0.000471\n", "AGE -0.050907\n", "SEX -0.073163" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select highest residual individual\n", "row_id = test_yhat_sorted.loc[0, 'ID']\n", "row = test_yhat[test_yhat['ID'] == row_id]\n", "\n", "# match to their shap loss contribs\n", "# create Pandas DataFrame\n", "# display\n", "s_df = pd.DataFrame(shap_error_values[row.index[0], :].reshape(23, 1), \n", " columns=['Loss Contribs.'], index=X)\n", "s_df.sort_values(by='Loss Contribs.', inplace=True, ascending=False)\n", "s_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this high-residual individual, `PAY_AMT1`, `LIMIT_BAL`, and `PAY_AMT2` have the largest positive impact on the model error." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check Shapley values for local accuracy\n", "Just to make sure Shapley is locally accurate, it can be shown that the Shapley contributions sum directly to the logloss for this individual." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Shapley contributions to logloss: 4.826473\n" ] } ], "source": [ "# should match residual value for row ... very close\n", "print('Total Shapley contributions to logloss: %.6f' % \n", " (s_df['Loss Contribs.'].sum() + explainer.expected_value(row[y].values[0])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot Shapley contributions to logloss for a specific individual" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot barchart of top five loss contribs\n", "ax = s_df.plot(kind='bar', \n", " title='Local Feature Contributions to Logloss\\n',\n", " color='royalblue',\n", " legend=False)\n", "\n", "# annotate\n", "_ = ax.set_ylabel('Loss Contribs.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Combine Shapley values with row values to explain logloss\n", "Just like reason codes, or simple written explanations, can be generated for each model prediction. Reason codes can also be generated for logloss for each model prediction by combining the ranking provided by Shapley values and the individual row values. " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6DEFAULT_NEXT_MONTHp_DEFAULT_NEXT_MONTHr_DEFAULT_NEXT_MONTH
30098350000011236-2-2-2-2-2-245106812641812227229214622791181690182252736521570280501739710.0080154.826454
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" ], "text/plain": [ " ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 \\\n", "300 983 500000 1 1 2 36 -2 -2 -2 \n", "\n", " PAY_4 PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 \\\n", "300 -2 -2 -2 45106 81264 18122 27229 \n", "\n", " BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", "300 21462 27911 81690 18225 27365 21570 28050 \n", "\n", " PAY_AMT6 DEFAULT_NEXT_MONTH p_DEFAULT_NEXT_MONTH r_DEFAULT_NEXT_MONTH \n", "300 17397 1 0.008015 4.826454 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "row # helps understand reason codes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this individual the top five characteristics contributing to the logloss residual are:\n", "\n", "\n", "* Most recent payment is NT dollar 81,690 (high)\n", "* Credit limit is NT dollar 500,000 (high)\n", "* Second most recent payment is NT dollar 18,225 (high)\n", "* Most recent repayment status is very good\n", "* Third most recent repayment status is very good\n", "\n", "All of the variables contributing to high logloss for this individual have values that would be considered positive attributes: large payments, high credit limit, and good repayment statuses. Essentially the model is surprised this person defaulted. This could mean that the model is too reliant on repayment statuses, as was seen by plotting residuals against `PAY_0` values, or it may also indicate the model requires more information to make good decisions, likely in the form of additional input variables.\n", "\n", "In general, when examining local contributions to logloss, one might consider whether local contributions to predictions and errors are parsimonious or might indicate tampering with the data or model scoring logic or whether protected variables are weighing heavily in a model decision. Also, and although tedious and time-consuming today, one could use sensitivity analysis to understand how changing model inputs could affect model outcomes for different individuals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.2 Explain LogLoss Residuals with Decision Trees\n", "A time-tested method of determining if a model is fundamentally biased, either missing data or too simple in form, is to determine whether strong patterns exist in the model residuals. One way to determine if these patterns exist is to model the residuals themselves. When the model used is a decision tree or rule-based model, this opens up the possibility of automatically generating rules that indicate when or why a model might be wrong. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Small utility functions used to generate decision trees\n", "This set of small functions is used to train decision trees on logloss residuals and display those trees." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# small utility functions\n", "# all tightly coupled to global names and data structures !!\n", "\n", "def train_cv_dt(model_id, frame):\n", " \n", " \"\"\" Utility function to train decision trees.\n", " \n", " Args:\n", " model_id: h2o model identifier. \n", " frame: Pandas DataFrame containing X and yhat on which to train \n", " the decision trees.\n", " \n", " Returns:\n", " Path to saved model MOJO (Java scoring artifact), \n", " model as h2o model object.\n", " \n", " \"\"\"\n", " \n", " # initialize single tree model\n", " tree = H2ORandomForestEstimator(ntrees=1, # use only one tree\n", " sample_rate=1, # use all rows in that tree\n", " mtries=-2, # use all columns in that tree's split search\n", " max_depth=4, # shallow trees are easier to understand\n", " seed=SEED, # set random seed for reproducibility\n", " nfolds=3, # cross-validation for stability and ...\n", " # only way to get metrics for 1 tree in h2o\n", " model_id=model_id) # gives MOJO artifact a recognizable name\n", "\n", " # train single tree model\n", " tree.train(x=X, y=resid, training_frame=h2o.H2OFrame(frame))\n", "\n", " # persist MOJO (compiled Java representation of trained model)\n", " # from which to generate plot of tree\n", " mojo_path = tree.download_mojo(path='.')\n", " print('Generated MOJO path:\\n', mojo_path)\n", " \n", " return mojo_path, tree\n", "\n", "################################################################################\n", "\n", "# \n", "def get_gv(title, model_id, mojo_path):\n", "\n", " \"\"\" Utility function to generate graphviz dot file from h2o MOJO using \n", " a subprocess.\n", " \n", " Args:\n", " title: Title for displayed decision tree.\n", " model_id: h2o model identifier. \n", " mojo_path: Path to saved model MOJO (Java scoring artifact); \n", " generated by train_cv_dt function above. \n", " \n", " \"\"\"\n", " \n", " # locate h2o jar\n", " hs = H2OLocalServer()\n", " h2o_jar_path = hs._find_jar()\n", " print('Discovered H2O jar path:\\n', h2o_jar_path)\n", "\n", " # construct command line call to generate graphviz version of \n", " # tree, see for more information: \n", " # http://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/index.html\n", " gv_file_name = model_id + '.gv'\n", " gv_args = str('-cp ' + h2o_jar_path +\n", " ' hex.genmodel.tools.PrintMojo --tree 0 -i '\n", " + mojo_path + ' -o').split()\n", " gv_args.insert(0, 'java')\n", " gv_args.append(gv_file_name)\n", " if title is not None:\n", " gv_args = gv_args + ['--title', title]\n", " \n", " # call constructed command\n", " print()\n", " print('Calling external process ...')\n", " print(' '.join(gv_args))\n", " # if the line below is failing for you, try instead:\n", " # _ = subprocess.call(gv_args, shell=True)\n", " _ = subprocess.call(gv_args)\n", "\n", "################################################################################\n", "\n", "def get_png(model_id):\n", "\n", " \"\"\" Utility function to generate PNGs from .dots using a subprocess.\n", " \n", " Arg:\n", " model_id: h2o model identifier. \n", " \n", " \"\"\"\n", " \n", " gv_file_name = model_id + '.gv'\n", " \n", " # construct call to generate PNG from \n", " # graphviz representation of the tree\n", " png_file_name = model_id + '.png'\n", " png_args = str('dot -Tpng ' + gv_file_name + ' -o ' + png_file_name)\n", " png_args = png_args.split()\n", "\n", " # call \n", " print('Calling external process ...')\n", " print(' '.join(png_args))\n", " # if the line below is failing for you, try instead:\n", " # _ = subprocess.call(png_args, shell=True) \n", " _ = subprocess.call(png_args)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Train decision trees" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", "drf Model Build progress: |███████████████████████████████████████████████| 100%\n", "Generated MOJO path:\n", " /home/patrickh/workspace/interpretable_machine_learning_with_python/tree0.zip\n", "Parse progress: |█████████████████████████████████████████████████████████| 100%\n", "drf Model Build progress: |███████████████████████████████████████████████| 100%\n", "Generated MOJO path:\n", " /home/patrickh/workspace/interpretable_machine_learning_with_python/tree1.zip\n", "H2O session _sid_8235 closed.\n" ] } ], "source": [ "# train trees\n", "mojo_path0, tree0 = train_cv_dt('tree0', test_yhat[test_yhat[y] == 0]) \n", "mojo_path1, tree1 = train_cv_dt('tree1', test_yhat[test_yhat[y] == 1]) \n", "\n", "# shutdown h2o\n", "h2o.cluster().shutdown()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Display error measures to ensure trustworthiness of models: `DEFAULT_NEXT_MONTH = 0` logloss residuals" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meansdcv_1_validcv_2_validcv_3_valid
0mae0.0554557550.00236446320.0527557320.0571566040.056454934
1mean_residual_deviance0.0069930644.908051E-40.0065171250.00749749360.0069645736
2mse0.0069930644.908051E-40.0065171250.00749749360.0069645736
3r20.887533550.0125671640.89438170.87302970.8951892
4residual_deviance0.0069930644.908051E-40.0065171250.00749749360.0069645736
5rmse0.083590270.00293205450.080728710.086588070.08345402
6rmsle0.05911880.00259428660.056364240.0615156960.059476465
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" ], "text/plain": [ " mean sd cv_1_valid \\\n", "0 mae 0.055455755 0.0023644632 0.052755732 \n", "1 mean_residual_deviance 0.006993064 4.908051E-4 0.006517125 \n", "2 mse 0.006993064 4.908051E-4 0.006517125 \n", "3 r2 0.88753355 0.012567164 0.8943817 \n", "4 residual_deviance 0.006993064 4.908051E-4 0.006517125 \n", "5 rmse 0.08359027 0.0029320545 0.08072871 \n", "6 rmsle 0.0591188 0.0025942866 0.05636424 \n", "\n", " cv_2_valid cv_3_valid \n", "0 0.057156604 0.056454934 \n", "1 0.0074974936 0.0069645736 \n", "2 0.0074974936 0.0069645736 \n", "3 0.8730297 0.8951892 \n", "4 0.0074974936 0.0069645736 \n", "5 0.08658807 0.08345402 \n", "6 0.061515696 0.059476465 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cv metrics for y = 0 tree\n", "tree0.cross_validation_metrics_summary().as_data_frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The residuals for non-default decisions can be fit relatively well with a single shallow tree and with stable errors across three folds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Display error measures to ensure trustworthiness of tree models: `DEFAULT_NEXT_MONTH = 1` logloss residuals" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meansdcv_1_validcv_2_validcv_3_valid
0mae0.167459560.0036696060.169167880.163247240.16996355
1mean_residual_deviance0.06745320.00365590890.069818510.0692986850.063242406
2mse0.06745320.00365590890.069818510.0692986850.063242406
3r20.887078050.0085361310.87988210.88484260.89650947
4residual_deviance0.06745320.00365590890.069818510.0692986850.063242406
5rmse0.259652940.0070947310.264231920.263246420.25148043
6rmsle0.0996339540.0036249490.1037406850.098281530.09687965
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" ], "text/plain": [ " mean sd cv_1_valid \\\n", "0 mae 0.16745956 0.003669606 0.16916788 \n", "1 mean_residual_deviance 0.0674532 0.0036559089 0.06981851 \n", "2 mse 0.0674532 0.0036559089 0.06981851 \n", "3 r2 0.88707805 0.008536131 0.8798821 \n", "4 residual_deviance 0.0674532 0.0036559089 0.06981851 \n", "5 rmse 0.25965294 0.007094731 0.26423192 \n", "6 rmsle 0.099633954 0.003624949 0.103740685 \n", "\n", " cv_2_valid cv_3_valid \n", "0 0.16324724 0.16996355 \n", "1 0.069298685 0.063242406 \n", "2 0.069298685 0.063242406 \n", "3 0.8848426 0.89650947 \n", "4 0.069298685 0.063242406 \n", "5 0.26324642 0.25148043 \n", "6 0.09828153 0.09687965 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cv metrics for y = 1 tree\n", "tree1.cross_validation_metrics_summary().as_data_frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The residuals for default decisions can be fit relatively well with a single shallow tree and with stable errors across three folds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create graphviz `.dot` files for visualizing decision trees" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Discovered H2O jar path:\n", " /home/patrickh/workspace/interpretable_machine_learning_with_python/env_iml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n", "\n", "Calling external process ...\n", "java -cp /home/patrickh/workspace/interpretable_machine_learning_with_python/env_iml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /home/patrickh/workspace/interpretable_machine_learning_with_python/tree0.zip -o tree0.gv --title LogLoss Residual Tree (DEFAULT_NEXT_MONTH=0)\n", "Discovered H2O jar path:\n", " /home/patrickh/workspace/interpretable_machine_learning_with_python/env_iml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n", "\n", "Calling external process ...\n", "java -cp /home/patrickh/workspace/interpretable_machine_learning_with_python/env_iml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /home/patrickh/workspace/interpretable_machine_learning_with_python/tree1.zip -o tree1.gv --title LogLoss Residual Tree (DEFAULT_NEXT_MONTH=1)\n" ] } ], "source": [ "get_gv('LogLoss Residual Tree (' + y + '=0)', 'tree0', mojo_path0) # .dot for y=0 tree\n", "get_gv('LogLoss Residual Tree (' + y + '=1)', 'tree1', mojo_path1) # .dot for y=1 tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Convert `.dot` files into `.png`s" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calling external process ...\n", "dot -Tpng tree0.gv -o tree0.png\n", "Calling external process ...\n", "dot -Tpng tree1.gv -o tree1.png\n" ] } ], "source": [ "get_png('tree0') # .png for y=0 tree\n", "get_png('tree1') # .png for y=1 tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Decision tree for `DEFAULT_NEXT_MONTH` = 0 logloss residuals" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(('tree0.png')))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This relatively accurate tree shows that strong patterns likely exist in the model residuals for non-default decisions. The highest residuals appear to occur when: `PAY_0 >= 1.5` and `PAY_5 >= 1` and `BILL_AMT2 < 18776.5` and `PAY_4 >= 1`. This scenario could be easily converted into a *model assertion* for use when scoring new data. When these conditions are present, the model is likely to be wrong and action can be taken such as scoring this individual with another model, diverting them to a human case worker, simply predicting the mean value of `DEFAULT_NEXT_MONTH`, or injecting missing values into their data in hopes of issuing a better prediction. \n", "\n", "Since these residuals relate to individuals being awarded a loan, another potentially important pattern to be aware of in the residuals would be if employees, contractors, or consultants were consistently being awarded loans, particularly by high-residual model decisions. Such a pattern could be indicative of data poisoning or tampering with model scoring logic. It seems possible that these tree rules could also be used to create adversarial examples for white-hat hacking purposes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Decision tree for `DEFAULT_NEXT_MONTH` = 1 logloss residuals" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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AAAA+lDlz5mTt2rVpaWlJnz59io4DFKi6ujrTpk3LHXfckY0bN+bll1/O4sWLM2bMmCxZsiRf+tKX8ulPfzpjx45NU1NTHnjggfzmN78pOjZHmPr6+nzve9/L//k//ydz5879wOu3bt2ajo6OnHrqqamsrMzFF1+cRx55JGvXrj0MaQEAAAA4VBTYAAAA+EBLly7NggULcuutt2b48OFFxwGOMEOGDEljY2PuuOOOPPPMM/nlL3+Ze++9N7W1tVmxYkWmTp2agQMHpqamJhdddFEeeOCBbN26tejYHAEuu+yynHfeeVmwYEGWLl36vtfeeeedueSSS0qff+Mb30ivXr1MYQMAAADo5so6Ozs7D/bwlClTkiQtLS1dFggAAIAjy6ZNmzJ69Oice+65WbRoUdFxgG5ox44dWbVqVVpbW9PW1pZVq1Zl3759qa6uTkNDQ2pra3P22Wdn6NChRUelC5WVleWzn/1s1q9f/47X/+0fSe7cuTOf+9znsm3btqxbty4nnXRS/t8/sty7d29OO+20/PKXv3zHcyoqKvL888/n5JNPfsd7w4YNyy9+8Yt33A8AAACArtEV/TET2AAAAHhP7e3tOf/881NVVZWFCxcWHQfopvr165eGhoZcf/31aWtry29+85ssW7YsX/3qV7Nx48bMnDkzp512WoYMGZIpU6bkzjvvzDPPPFN0bA6Tvn375qc//Wl2796dr3zlK+96zQMPPJArrrginZ2dB3z8+Mc/Tnt7e2655ZbDnBoAAACArmICGwAAAO/pyiuvzO23355Vq1ZZHQocMm+++WaefPLJrFixojSlbc+ePRk8eHDq6upSW1uburq6jB49OmVlZUXH5UN6twlsHR0dqaioSHt7e3r06HHA9S0tLfnyl7+cJAdMTOvo6MiYMWPS2tqaT3/60weceeutt3LKKadk79692bRpU/r27XvA+yawAQAAABxaJrABAABwyCxdujQLFizIrbfeqrwGHFJ9+vRJXV1dZs2alWXLlmXnzp1ZvXp1mpqasnv37lx99dUZO3ZsBg8enHPPPTfz5s3LmjVrsn///qKj8xG9+uqrSZJXXnnlHe9NmTIll1122TteX7x4cT71qU+9o7yWJL169crkyZOzffv23HjjjV0fGAAAAIBDToENAACAd9i8eXOmT5+eGTNmZPr06UXHAY4xFRUVGTNmTGbNmpUlS5Zk27ZtWb16debMmZPevXtn3rx5GTt2bPr3759JkyZl3rx5aWtry759+4qOzvv42c9+lq997WtJkpkzZ2b58uXvuOaGG25IbW3tAWeuuOKKrF27Nrfffvs7rn/wwQezbt26JMn111+fefPmHaL0AAAAABwqVogCAABwgPb29kyYMCFbt27N6tWr37GO7WA88cQTefjhh/NXf/VXXZAQONZ1dHRk/fr1pZWjjz76aLZt25bKysqMHz++tHK0vr4+vXr1KjruMevdVogeblaIAgAAABxaVogCAADQ5ebOnZs1a9akpaXlY5XX9u/fn4cffjif//znU1dXlx07dnRhSuBY1qNHj9TU1GTmzJlpaWnJ1q1bs2HDhixYsCCDBw9Oc3NzJk2alAEDBqSuri6zZ89Oa2trdu/eXXT0Y85bb71V6PPb29sLfT4AAAAAH6yi6AAAAAAcOZYuXZr58+dn0aJFGTFixEHdY+/evbn33ntzww03ZMOGDZkxY0YWLVqUM844o4vTAvxf1dXVmTlzZmbOnJkk2bhxY9ra2rJixYq0tLRk3rx5qaioyIgRI9LQ0JDa2trU19enf//+BSc/ur3wwgu59NJLM2TIkPzH//gfc+aZZx7yZz733HP52c9+lt/85jfZsGHDIX8eAAAAAB+PFaIAAAAkSTZv3pxRo0Zl0qRJWbx48Uc+v3Pnztx555256aabsmvXrlx88cVpamrKoEGDDkFagI9my5YtpZWjbW1tefbZZ1NeXp6RI0eWVo5OnDgxAwYMKDoqAAAAAHQbXdEfM4ENAACAtLe3Z+rUqRkwYEDuuOOOj3z+oYceyowZM/KJT3wil112WS666KL069fvECQFODhDhgxJY2NjGhsbkySvvPJKHn/88dKUtoULFyZJhg0blrq6ujQ0NGTChAkZOHBgkbEBAAAA4KinwAYAAEDmzp2bNWvWZOXKlenbt+9HPv/qq69m+/btGTlyZEaOHHlQ9wA4nKqqqg4otO3YsSOrVq0qTWhrbm7Ovn37Ul1dXVo5evbZZ2fo0KEFJwcAAACAo0t50QEAAAAo1tKlSzN//vzcfvvtGTFixEHdY+bMmXn66adz2mmn5Y//+I8zZsyY3H///Wlvb+/itACHRr9+/dLQ0JDrr78+bW1t+c1vfpNly5blq1/9ajZu3JgLL7wwp512WoYMGZIpU6bkzjvvzDPPPFN0bAAAAADo9so6Ozs7D/ZwV+wwBQAAoDibN2/OqFGjMmnSpCxevLjL7vk3f/M3ueOOOzJw4MBcccUVmTFjRvr06dMl9wcowhtvvJGnnnoqK1asKE1p27NnTwYPHpy6urrU1tamrq4uo0ePTllZWdFxAQAAAOCw6Ir+mAIbAADAMaq9vT0TJkzI1q1bs3r16i5f+/n666/n9ttvz80335yOjo585zvfSVNTU5c+A6Ao7e3tWbduXanMtnz58mzfvj1VVVU566yzUldXl4aGhowaNSrl5ZYgAAAAAHB0UmADAADgoF111VW55ZZbsnLlyoNeHfph7NmzJ/fcc09aW1tz//33H7LnABTp7UJbW1tbaUrba6+9lr59+2bcuHFpaGhIbW1txo0bl549exYdFwAAAAC6hAIbAAAAB2Xp0qWZPHlyFi1alBkzZhQdB+Co09HRkfXr15fKbI8++mi2bduWysrKjB8/vrRytL6+Pr169So67mF17733Zu/evTn//PNz3HHHFR0HAAAAgI+hK/pj9hcAAAAcYzZv3pzp06dn6tSpymsAh0iPHj1SU1OTmTNnpqWlJVu3bs2GDRuyYMGCDB48OM3NzZk0aVIGDBiQurq6zJ49O62trdm9e3fR0Q+5T3ziE7n88stTXV2dG2+8MTt37iw6EgAAAAAFMoENAADgGNLe3p4JEyZk69atWb16dfr27Vt0JIBj1saNG0srR5ctW5bnn38+FRUVGTFiRGnlaH19ffr371901C63c+fOfP/7389NN92UnTt35s///M9z6aWXZtCgQQdc9+tf/zpbt25933v17t07Q4cOPZRxAQAAAHgPVogCAADwkVx11VW55ZZbsnLlyowYMaLoOAD8G1u2bCmtHG1ra8uzzz6b8vLyjBw5srRydOLEiRkwYEDRUbvM3r17s3jx4nzve9/Lhg0bMn369HzrW9/KmWeemSSZP39+rrzyyve9R21tbdra2g5HXAAAAAD+HwpsAAAAfGhLly7N5MmTs2jRIqtDAbqBV155JY8//nhpSttTTz2VJBk2bFjq6urS0NCQCRMmZODAgQUn/fj279+fhx9+OPPmzcsTTzyR//yf/3Nuu+22omMBAAAA8AG6oj9W0VVhAAAAOHJt3rw506dPz9SpU5XXALqJqqqqNDY2prGxMUmyY8eOrFq1qjShrbm5Ofv27Ut1dXVp5ejZZ5/dLddplpeX5/d+7/cyatSo/PznP8/GjRuLjgQAAADAYaLABgAAcJRrb2/P1KlTM2DAgNxxxx1FxwHgIPXr1y8NDQ1paGhIkuzatSsrV64sTWi76667snfv3gwePLg0oa22tjY1NTUFJ39/Tz75ZL73ve+lpaUl48aNy09+8pN88YtfTJL8+te/ztatW9/3fO/evbtlaQ8AAACA31JgAwAAOMrNnTs3a9asycqVK9O3b9+i4wDQRSorKw8otL3xxht56qmnsmLFirS2tqapqSl79uwpFdpqa2tTV1eX0aNHp6ysrNDsnZ2d+V//63/lhhtuyLJlyzJ58uQ89thjqa2tPSBbc3Nzrrzyyve9V21tbdra2g51ZAAAAAAOkbLOzs7Ogz3cFTtMAQAAOHSWLl2ayZMnZ9GiRVaHAhxj2tvbs27dutLK0eXLl2f79u2pqqrKWWedVZrSNmrUqJSXlx+2XE888UQuueSS/NM//VO+8pWv5Morr8zw4cMP2/MBAAAA6Dpd0R9TYAMAADhKbd68OaNGjcqkSZOyePHiouMAULC3C21vrxxtbW3Na6+9lr59+2bcuHGllaPjxo1Lz549D1mOm2++OZs2bcpll12Wk08++ZA9BwAAAIBDT4ENAACAd9Xe3p4JEyZk69atWb16tdWhALxDR0dH1q9fXyqzPfroo9m2bVsqKyszfvz40srR+vr69OrVq+i4AAAAAByBuqI/VtFVYQAAADhyzJ07N2vWrMnKlSuV1wB4Vz169EhNTU1qamoyc+bMJMnGjRtLK0ebm5tzzTXXpE+fPhk1alRp5WhtbW169+5dcHoAAAAAjhYKbAAAAEeZpUuXZv78+Vm0aFFGjBhRdBwAupHq6urMnDnzgELb2ytHW1paMm/evFRUVGTEiBGlMlt9fX369+9fcHIAAAAAuisFNgAAgKPI5s2bM3369EydOjUzZswoOg4A3Vx1dXWqq6szbdq0JMmWLVtKK0eXLFmSG264IeXl5Rk5cmRp5ejEiRMzYMCAgpMDAAAA0F0osAEAABwl2tvbM3Xq1AwYMCB33HFH0XEAOAoNGTIkjY2NaWxsTJL86le/yvLly0tT2hYuXJgkGTZsWGnl6IQJEzJw4MAiYwMAAABwBFNgAwAAOErMnTs3a9asycqVK9O3b9+i4wBwDBg0aNABhbYdO3Zk1apVaW1tTVtbW5qbm7Nv375UV1eXVo6effbZGTp0aMHJAQAAADhSKLABAAAcBZYuXZr58+dn0aJFGTFiRNFxADhG9evXLw0NDWloaEiS7Nq1KytXrixNaLvrrruyd+/eDB48uDShrba2NjU1NQUnBwAAAKAoCmwAAADd3ObNmzN9+vRMnTo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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(('tree1.png')))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This relatively accurate tree shows that strong patterns likely also exist in the model residuals for default decisions. Rules of this tree could be used to create model assertions as well, and because the decisions of this tree result in denial of credit, any rules relating demographic variables to large residuals may be indicative of sociological bias in model predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Conclusion\n", "\n", "This notebook uses techniques related to residual analysis to: \n", "\n", "* Visualize residuals and learn about outlying, wrong predictions and to see that the trained model is too dependent on one input variable: `PAY_0`.\n", "\n", "* Examine multiple types of errors across the levels of categorical variables and found that the model behaves poorly for `PAY_0 >= 2` but seems to have roughly similar error characteristics across men and women.\n", "\n", "* Compare monotonic GBM predictions to simpler linear model predictions and discovered a group of customers for which the simpler linear model performs better than the GBM.\n", "\n", "* Analyze residuals with post-hoc explainable AI techniques to understand:\n", "\n", " * Shapley contributions to predictions and to the GBM model's logloss, and to see that some variables are actually more important globally to the logloss than to the predictions.\n", " * Shapley prediction contributions and their relationships to the model residuals, and discovered that some Shapley contribution values are only associated with correct model decisions.\n", " * Local Shapley contributions to logloss and to view the exact local contributions of variables to logloss for a high-residual individual.\n", " * Summaries of residual behavior through decision trees and potentially to create model assertions or adversarial examples automatically.\n", "\n", "Once these kinds of model bugs have been identified, new options exist to remediate them during either training or scoring. This model was found to be too dependent on `PAY_0` and there was remaining signal in the model residuals to train accurate decision trees. It seems there is additional information that would lessen this model's over-emphasis of `PAY_0` and perhaps allow the constrained GBM to make more accurate decisions in general. Local contributions to logloss were also calculated. Is it possible to retrain the model with this information in some kind of novel row and column boosting scheme? Can the Shapley prediction contributions and the decision tree rules for residuals be used to create model assertions automatically? More importantly than this one toy case, can these strategies be applied to find accuracy, fairness, or security bugs in different types of models?" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }