EstimatorReport: Get insights from any scikit-learn estimator

This example shows how the skore.EstimatorReport class can be used to quickly get insights from any scikit-learn estimator.

Loading our dataset and defining our estimator

First, we load a dataset from skrub. Our goal is to predict if a healthcare manufacturing companies paid a medical doctors or hospitals, in order to detect potential conflict of interest.

from skrub.datasets import fetch_open_payments

dataset = fetch_open_payments()
df = dataset.X
y = dataset.y

Downloading 'open_payments' from https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/open_payments.zip (attempt 1/3)

from skrub import TableReport

TableReport(df)

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TableReport(y.to_frame())

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Looking at the distributions of the target, we observe that this classification task is quite imbalanced. It means that we have to be careful when selecting a set of statistical metrics to evaluate the classification performance of our predictive model. In addition, we see that the class labels are not specified by an integer 0 or 1 but instead by a string “allowed” or “disallowed”.

For our application, the label of interest is “allowed”.

pos_label, neg_label = "allowed", "disallowed"

Before training a predictive model, we need to split our dataset into a training and a validation set.

from skore import train_test_split

# If you have many dataframes to split on, you can always ask train_test_split to return
# a dictionary. Remember, it needs to be passed as a keyword argument!
split_data = train_test_split(X=df, y=y, random_state=42, as_dict=True)

╭───────────────────────────── HighClassImbalanceWarning ──────────────────────────────╮
│ It seems that you have a classification problem with a high class imbalance. In this │
│ case, using train_test_split may not be a good idea because of high variability in   │
│ the scores obtained on the test set. To tackle this challenge we suggest to use      │
│ skore's CrossValidationReport with the `splitter` parameter of your choice.          │
╰──────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────── ShuffleTrueWarning ─────────────────────────────────╮
│ We detected that the `shuffle` parameter is set to `True` either explicitly or from  │
│ its default value. In case of time-ordered events (even if they are independent),    │
│ this will result in inflated model performance evaluation because natural drift will │
│ not be taken into account. We recommend setting the shuffle parameter to `False` in  │
│ order to ensure the evaluation process is really representative of your production   │
│ release process.                                                                     │
╰──────────────────────────────────────────────────────────────────────────────────────╯

By the way, notice how skore’s train_test_split() automatically warns us for a class imbalance.

Now, we need to define a predictive model. Hopefully, skrub provides a convenient function (skrub.tabular_pipeline()) when it comes to getting strong baseline predictive models with a single line of code. As its feature engineering is generic, it does not provide some handcrafted and tailored feature engineering but still provides a good starting point.

So let’s create a classifier for our task.

from skrub import tabular_pipeline

estimator = tabular_pipeline("classifier")
estimator

Pipeline(steps=[('tablevectorizer',
                 TableVectorizer(low_cardinality=ToCategorical())),
                ('histgradientboostingclassifier',
                 HistGradientBoostingClassifier())])

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Getting insights from our estimator

Introducing the skore.EstimatorReport class

Now, we would be interested in getting some insights from our predictive model. One way is to use the skore.EstimatorReport class. This constructor will detect that our estimator is unfitted and will fit it for us on the training data.

from skore import EstimatorReport

report = EstimatorReport(estimator, **split_data, pos_label=pos_label)
report

Pipeline(steps=[('tablevectorizer',
                 TableVectorizer(low_cardinality=ToCategorical())),
                ('histgradientboostingclassifier',
                 HistGradientBoostingClassifier())])

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1 issue(s), 0 tip(s), 2 passed, 0 ignored.

Once the report is created, we get some information regarding the available tools allowing us to get some insights from our specific model on our specific task by calling the help() method.

report.help()

Be aware that we can access the help for each individual sub-accessor. For instance:

report.metrics.help()

Metrics computation with aggressive caching

At this point, we might be interested to have a first look at the statistical performance of our model on the validation set that we provided. We can access it by calling any of the metrics displayed above. Since we are greedy, we want to get several metrics at once and we will use the summarize() method.

import time

start = time.time()
metric_report = report.metrics.summarize().frame()
end = time.time()
metric_report

	HistGradientBoostingClassifier
Metric
Accuracy	0.951985
Precision	0.682231
Recall	0.451890
ROC AUC	0.941697
Log loss	0.124163
Brier score	0.035026
Fit time (s)	9.464977
Predict time (s)	1.542027

print(f"Time taken to compute the metrics: {end - start:.2f} seconds")

Time taken to compute the metrics: 0.00 seconds

An interesting feature provided by the skore.EstimatorReport is the the caching mechanism. Indeed, when we have a large enough dataset, computing the predictions for a model is not cheap anymore. For instance, on our smallish dataset, it took a couple of seconds to compute the metrics. The report will cache the predictions and if we are interested in computing a metric again or an alternative metric that requires the same predictions, it will be faster. Let’s check by requesting the same metrics report again.

start = time.time()
metric_report = report.metrics.summarize().frame()
end = time.time()
metric_report

	HistGradientBoostingClassifier
Metric
Accuracy	0.951985
Precision	0.682231
Recall	0.451890
ROC AUC	0.941697
Log loss	0.124163
Brier score	0.035026
Fit time (s)	9.464977
Predict time (s)	1.542027

print(f"Time taken to compute the metrics: {end - start:.2f} seconds")

Time taken to compute the metrics: 0.00 seconds

Note that when the model is fitted or the predictions are computed, we additionally store the time the operation took:

report.metrics.timings()

{'fit_time': 9.464977432000069, 'predict_time_test': 1.542026749999991, 'predict_time_train': 4.546076176000042}

Since we obtain a pandas dataframe, we can also use the plotting interface of pandas.

ax = metric_report.plot.barh()
_ = ax.set_title("Metrics report")

Whenever computing a metric, we check if the predictions are available in the cache and reload them if available. So for instance, let’s compute the log loss.

start = time.time()
log_loss = report.metrics.log_loss()
end = time.time()
log_loss

0.12416306414423657

print(f"Time taken to compute the log loss: {end - start:.2f} seconds")

Time taken to compute the log loss: 0.00 seconds

We can show that without initial cache, it would have taken more time to compute the log loss.

report.clear_cache()

start = time.time()
log_loss = report.metrics.log_loss()
end = time.time()
log_loss

0.12416306414423657

print(f"Time taken to compute the log loss: {end - start:.2f} seconds")

Time taken to compute the log loss: 4.73 seconds

By default, the metrics are computed on the test set only. However, if a training set is provided, we can also compute the metrics by specifying the data_source parameter.

report.metrics.log_loss(data_source="train")

0.09999389347526483

Be aware that we can also benefit from the caching mechanism with our own custom metrics. Skore only expects that we define our own metric function to take y_true and y_pred as the first two positional arguments. It can take any other arguments. Let’s see an example.

def operational_decision_cost(y_true, y_pred, amount):
    mask_true_positive = (y_true == pos_label) & (y_pred == pos_label)
    mask_true_negative = (y_true == neg_label) & (y_pred == neg_label)
    mask_false_positive = (y_true == neg_label) & (y_pred == pos_label)
    mask_false_negative = (y_true == pos_label) & (y_pred == neg_label)
    fraudulent_refuse = mask_true_positive.sum() * 50
    fraudulent_accept = -amount[mask_false_negative].sum()
    legitimate_refuse = mask_false_positive.sum() * -5
    legitimate_accept = (amount[mask_true_negative] * 0.02).sum()
    return fraudulent_refuse + fraudulent_accept + legitimate_refuse + legitimate_accept

In our use case, we have a operational decision to make that translate the classification outcome into a cost. It translate the confusion matrix into a cost matrix based on some amount linked to each sample in the dataset that are provided to us. Here, we randomly generate some amount as an illustration.

import numpy as np
from sklearn.metrics import make_scorer

rng = np.random.default_rng(42)
amount = rng.integers(low=100, high=1000, size=len(split_data["y_test"]))

report.metrics.add(metric=make_scorer(operational_decision_cost, amount=amount))

cost = report.metrics.summarize(metric="operational_decision_cost")
cost.frame()

	HistGradientBoostingClassifier
Metric
Operational Decision Cost	-136140.34

By the way, skore caches the model predictions. It is really handy because it means that we can compute some additional metrics without having to recompute the the predictions.

report.metrics.summarize(
    metric=["precision", "recall", "operational_decision_cost"]
).frame()

	HistGradientBoostingClassifier
Metric
Precision	0.682231
Recall	0.451890
Operational Decision Cost	-136140.340000

Effortless one-liner plotting

The skore.EstimatorReport class also implements a number of the most common data science plots. As for the metrics, we only provide the meaningful set of plots for the provided estimator.

report.metrics.help()

Let’s start by plotting the ROC curve for our binary classification task.

display = report.metrics.roc()
display.plot()

<Figure size 600x750 with 1 Axes>

The plot functionality is built upon the scikit-learn display objects. We return those display (slightly modified to improve the UI) in case we want to tweak some of the plot properties. We can have quick look at the available attributes and methods by calling the help method or simply by printing the display.

display.help()

fig = display.plot()
fig.axes[0].set_title("Example of a ROC curve")
fig

<Figure size 600x750 with 1 Axes>

Similarly to the metrics, we aggressively use the caching to avoid recomputing the predictions of the model. We also cache the plot display object by detection if the input parameters are the same as the previous call. Let’s demonstrate the kind of performance gain we can get.

start = time.time()
# we already trigger the computation of the predictions in a previous call
display = report.metrics.roc()
fig = display.plot()
end = time.time()
fig

<Figure size 600x750 with 1 Axes>

print(f"Time taken to compute the ROC curve: {end - start:.2f} seconds")

Time taken to compute the ROC curve: 0.10 seconds

Now, let’s clean the cache and check if we get a slowdown.

report.clear_cache()

start = time.time()
display = report.metrics.roc()
fig = display.plot()
end = time.time()
fig

<Figure size 600x750 with 1 Axes>

print(f"Time taken to compute the ROC curve: {end - start:.2f} seconds")

Time taken to compute the ROC curve: 4.87 seconds

As expected, since we need to recompute the predictions, it takes more time.

Visualizing the confusion matrix

Another useful visualization for classification tasks is the confusion matrix, which shows the counts of correct and incorrect predictions for each class.

Let’s first start with a basic confusion matrix:

cm_display = report.metrics.confusion_matrix()
cm_display.plot()

<Figure size 600x600 with 1 Axes>

In binary classification, a confusion matrix depends on the decision threshold used to convert predicted probabilities into class labels. By default, skore uses a threshold of 0.5, but confusion matrices are actually computed at every threshold internally.

# To visualize the confusion matrix at a different threshold, use the
# ``threshold_value`` parameter. For example, a threshold of 0.3 will classify
# more samples as positive:
cm_display.plot(threshold_value=0.3)

<Figure size 600x600 with 1 Axes>

We can normalize the confusion matrix to get percentages instead of raw counts. Here we normalize by true labels (rows):

cm_display.plot(normalize="true")

<Figure size 600x600 with 1 Axes>

More plotting options are available via heatmap_kwargs, which are passed to seaborn’s heatmap. For example, we can customize the colormap and number format:

cm_display.set_style(heatmap_kwargs={"cmap": "Greens", "fmt": ".2e"})
cm_display.plot()

<Figure size 600x600 with 1 Axes>

Finally, the confusion matrix can also be exported as a pandas DataFrame for further analysis:

cm_display.frame()

	true_label	predicted_label	value	split	estimator	data_source
0	allowed	allowed	526	NaN	HistGradientBoostingClassifier	test
1	allowed	disallowed	638	NaN	HistGradientBoostingClassifier	test
2	disallowed	allowed	245	NaN	HistGradientBoostingClassifier	test
3	disallowed	disallowed	16981	NaN	HistGradientBoostingClassifier	test

See also

For using the EstimatorReport to inspect your models, see EstimatorReport: Inspecting your models with the feature importance.