Predicting diabetes with Pima Indians

Author

Daniel Kapitan

Published

November 1, 2023

Objectives

  • Example end-to-end supervised learning workflow with Pima Indians
  • Focus on conceptual understanding of machine learning
  • Demonstrate use of Predictive Power Score (PPS)
  • Demonstrate capabilities of low-code tools
  • Demonstrate use of average_precision_score (link)
  • Demonstrate SHAP values

Attribution

Dataset

Python libraries

  • Altair (docs)
  • ydata-profiling (docs)
  • Predictive Power Score (PPS, GitHub, blog)
  • PyCaret: open-source, low-code machine learning library in Python that automates machine learning workflows (link)
import altair as alt
import pandas as pd
import ppscore as pps
from pycaret.classification import *
import shap
from sklearn.metrics import average_precision_score
from sklearn.model_selection import train_test_split
from ydata_profiling import ProfileReport


# customize Altair
def y_axis():
    return {
        "config": {
            "axisX": {"grid": False},
            "axisY": {
                "domain": False,
                "gridDash": [2, 4],
                "tickSize": 0,
                "titleAlign": "right",
                "titleAngle": 0,
                "titleX": -5,
                "titleY": -10,
            },
            "view": {
                "stroke": "transparent",
                # To keep the same height and width as the default theme:
                "continuousHeight": 300,
                "continuousWidth": 400,
            },
        }
    }


alt.themes.register("y_axis", y_axis)
alt.themes.enable("y_axis");

Read and explore the data

%%time
df = pd.read_csv("diabetes.csv").astype({"Outcome": bool})
train, test = train_test_split(df, test_size=0.3)
profile = ProfileReport(train, minimal=True, title="Pima Indians Profiling Report")
profile.to_file("pima-indians-profiling-report-minimal.html")
CPU times: user 5.54 s, sys: 184 ms, total: 5.72 s
Wall time: 1.7 s
profile.to_notebook_iframe()

Investigate features with largest predictive power

We use the Predictive Power Score to evaluate which features have the highest predictive power with respect to Outcome.

predictors = (
    pps.predictors(train, "Outcome")
    .round(3)
    .iloc[:, :-1]
)
base = (
    alt.Chart(predictors)
    .encode(
        y=alt.Y("x:N").sort("-x"),
        x="ppscore",
        tooltip=["x", "ppscore"],
    )
)
base.mark_bar() + base.mark_text(align="center", dy=-5)

Investigate colinearity

pps.matrix(train)
x y ppscore case is_valid_score metric baseline_score model_score model
0 Pregnancies Pregnancies 1.000000 predict_itself True None 0.000000 1.000000 None
1 Pregnancies Glucose 0.000000 regression True mean absolute error 23.651769 24.024994 DecisionTreeRegressor()
2 Pregnancies BloodPressure 0.000000 regression True mean absolute error 12.748603 12.961633 DecisionTreeRegressor()
3 Pregnancies SkinThickness 0.000000 regression True mean absolute error 13.467412 13.630828 DecisionTreeRegressor()
4 Pregnancies Insulin 0.000000 regression True mean absolute error 79.240223 85.015086 DecisionTreeRegressor()
... ... ... ... ... ... ... ... ... ...
76 Outcome Insulin 0.000000 regression True mean absolute error 79.240223 84.802415 DecisionTreeRegressor()
77 Outcome BMI 0.035133 regression True mean absolute error 5.672812 5.473511 DecisionTreeRegressor()
78 Outcome DiabetesPedigreeFunction 0.000000 regression True mean absolute error 0.226384 0.234531 DecisionTreeRegressor()
79 Outcome Age 0.000000 regression True mean absolute error 8.888268 8.997094 DecisionTreeRegressor()
80 Outcome Outcome 1.000000 predict_itself True None 0.000000 1.000000 None

81 rows × 9 columns

pps_matrix = (
    pps.matrix(
        train.loc[:, predictors.query("ppscore > 0")["x"].tolist()],
    )
    .loc[:, ["x", "y", "ppscore"]]
    .round(3)
)
(
    alt.Chart(pps_matrix)
    .mark_rect()
    .encode(
        x="x:O",
        y="y:O",
        color="ppscore:Q",
        tooltip=["x", "y", "ppscore"])
).properties(width=500, height=500)

Build models

cls = setup(data = train, 
             target = 'Outcome',
             numeric_imputation = 'mean',
             feature_selection = False,
             pca=False,
             remove_multicollinearity=True,
             remove_outliers = False,
             normalize = True,
             )
add_metric('apc', 'APC', average_precision_score, target = 'pred_proba');
  Description Value
0 Session id 5752
1 Target Outcome
2 Target type Binary
3 Original data shape (537, 9)
4 Transformed data shape (537, 9)
5 Transformed train set shape (375, 9)
6 Transformed test set shape (162, 9)
7 Numeric features 8
8 Preprocess True
9 Imputation type simple
10 Numeric imputation mean
11 Categorical imputation mode
12 Remove multicollinearity True
13 Multicollinearity threshold 0.900000
14 Normalize True
15 Normalize method zscore
16 Fold Generator StratifiedKFold
17 Fold Number 10
18 CPU Jobs -1
19 Use GPU False
20 Log Experiment False
21 Experiment Name clf-default-name
22 USI 10e4 
%%time
best_model = compare_models(include=["et", "lightgbm", "rf", "dt"], sort="APC")
  Model Accuracy AUC Recall Prec. F1 Kappa MCC APC TT (Sec)
et Extra Trees Classifier 0.7569 0.8039 0.5090 0.6832 0.5719 0.4106 0.4261 0.6795 0.1740
rf Random Forest Classifier 0.7412 0.7893 0.5013 0.6420 0.5538 0.3784 0.3896 0.6661 0.0330
lightgbm Light Gradient Boosting Machine 0.7252 0.7776 0.5090 0.5987 0.5452 0.3519 0.3574 0.6378 0.1310
dt Decision Tree Classifier 0.6691 0.6275 0.5019 0.5002 0.4904 0.2503 0.2559 0.4269 0.0050 
CPU times: user 537 ms, sys: 135 ms, total: 672 ms
Wall time: 3.96 s

Evaluation

predictions = (
    predict_model(best_model, data=test.iloc[:, :-1])
)
predictions.head()
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age prediction_label prediction_score
752 3 108 62 24 0 26.000000 0.223 25 0 0.76
295 6 151 62 31 120 35.500000 0.692 28 0 0.57
532 1 86 66 52 65 41.299999 0.917 29 0 0.88
426 0 94 0 0 0 0.000000 0.256 25 0 0.89
68 1 95 66 13 38 19.600000 0.334 25 0 0.97 
evaluate_model(best_model)

SHAP

interpret_model(best_model)

interpret_model(best_model, plot="reason", observation=1)
Visualization omitted, Javascript library not loaded!
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interpret_model(best_model, plot="reason")
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