Scikit-Learn

Use W&B to visualize and compare scikit-learn model performance with experiment tracking and automated plot logging.

This page shows scikit-learn users how to use W&B to track experiments and automatically log charts that visualize and compare model performance. You can use wandb to visualize and compare your scikit-learn models’ performance with a few lines of code. Try an example.

Get started

Sign up and create an API key

An API key authenticates your machine to W&B. You can generate an API key from your user profile.

For a more streamlined approach, create an API key by going directly to User Settings. Copy the newly created API key immediately and save it in a secure location such as a password manager.

1. Click your user profile icon in the upper right corner.
2. Select User Settings, then scroll to the API Keys section.

Install the wandb library and log in

To install the wandb library locally and log in:

Command Line

1. Set the WANDB_API_KEY environment variable to your API key.

   export WANDB_API_KEY=[YOUR-API-KEY]
   

2. Install the wandb library and log in.

   pip install wandb
   
   wandb login
   

Python

pip install wandb

import wandb
wandb.login()

Python notebook

!pip install wandb

import wandb
wandb.login()

Log metrics

After installing and logging in, log metrics from your scikit-learn training code so you can compare runs in W&B.

import wandb

wandb.init(project="visualize-sklearn") as run:

  y_pred = clf.predict(X_test)
  accuracy = sklearn.metrics.accuracy_score(y_true, y_pred)

  # If logging metrics over time, then use run.log
  run.log({"accuracy": accuracy})

  # OR to log a final metric at the end of training you can also use run.summary
  run.summary["accuracy"] = accuracy

Make plots

In addition to logging metrics, you can generate diagnostic plots for your scikit-learn models and log them as part of a run. The following steps initialize a run and then visualize either individual plots or a full set of plots for a given model type.

Import wandb and initialize a new run

import wandb

run = wandb.init(project="visualize-sklearn")

Visualize plots

The following sections describe how to visualize individual plots or all plots for a given model type.

Individual plots

After training a model and making predictions, you can generate plots in wandb to analyze your predictions. For more information about supported charts, see the Supported plots section.

# Visualize single plot
wandb.sklearn.plot_confusion_matrix(y_true, y_pred, labels)

All plots

W&B has functions such as plot_classifier that plot several relevant plots:

# Visualize all classifier plots
wandb.sklearn.plot_classifier(
    clf,
    X_train,
    X_test,
    y_train,
    y_test,
    y_pred,
    y_probas,
    labels,
    model_name="SVC",
    feature_names=None,
)

# All regression plots
wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, model_name="Ridge")

# All clustering plots
wandb.sklearn.plot_clusterer(
    kmeans, X_train, cluster_labels, labels=None, model_name="KMeans"
)

run.finish()

Existing Matplotlib plots

If you already create plots with Matplotlib, you can log them on the W&B dashboard alongside your scikit-learn plots. To do that, you must first install plotly.

pip install plotly

Finally, log the plots on the W&B dashboard as follows:

import matplotlib.pyplot as plt
import wandb

with wandb.init(project="visualize-sklearn") as run:

  # do all the plt.plot(), plt.scatter(), etc. here.
  # ...

  # instead of doing plt.show() do:
  run.log({"plot": plt})

Supported plots

The following sections describe each plot type that wandb.sklearn can produce, along with the function signature and arguments. Use these as a reference when calling individual plot functions or interpreting the output of plot_classifier, plot_regressor, and plot_clusterer.

Learning curve

Trains model on datasets of varying lengths and generates a plot of cross-validated scores versus dataset size, for both training and test sets.

wandb.sklearn.plot_learning_curve(model, X, y)

• model (clf or reg): Takes in a fitted regressor or classifier.
• X (arr): Dataset features.
• y (arr): Dataset labels.

ROC

ROC curves plot true positive rate (y-axis) versus false positive rate (x-axis). The ideal score is a TPR = 1 and FPR = 0, which is the point on the top left. You calculate the area under the ROC curve (AUC-ROC), and the greater the AUC-ROC the better.

wandb.sklearn.plot_roc(y_true, y_probas, labels)

• y_true (arr): Test set labels.
• y_probas (arr): Test set predicted probabilities.
• labels (list): Named labels for target variable (y).

Class proportions

Plots the distribution of target classes in training and test sets. Useful for detecting imbalanced classes and ensuring that one class doesn’t have a disproportionate influence on the model.

wandb.sklearn.plot_class_proportions(y_train, y_test, ['dog', 'cat', 'owl'])

• y_train (arr): Training set labels.
• y_test (arr): Test set labels.
• labels (list): Named labels for target variable (y).

Precision recall curve

Computes the tradeoff between precision and recall for different thresholds. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.

High scores for both show that the classifier is returning accurate results (high precision), as well as returning a majority of all positive results (high recall). The precision-recall curve is useful when the classes are imbalanced.

wandb.sklearn.plot_precision_recall(y_true, y_probas, labels)

• y_true (arr): Test set labels.
• y_probas (arr): Test set predicted probabilities.
• labels (list): Named labels for target variable (y).

Feature importances

Evaluates and plots the importance of each feature for the classification task. Only works with classifiers that have a feature_importances_ attribute, such as trees.

wandb.sklearn.plot_feature_importances(model, ['width', 'height, 'length'])

• model (clf): Takes in a fitted classifier.
• feature_names (list): Names for features. Makes plots easier to read by replacing feature indexes with corresponding names.

Calibration curve

Plots how well calibrated the predicted probabilities of a classifier are and how to calibrate an uncalibrated classifier. Compares estimated predicted probabilities by a baseline logistic regression model, the model passed as an argument, and by both its isotonic calibration and sigmoid calibrations.

The closer the calibration curves are to a diagonal the better. A transposed sigmoid-like curve represents an overfitted classifier, while a sigmoid-like curve represents an underfitted classifier. By training isotonic and sigmoid calibrations of the model and comparing their curves, you can figure out whether the model is over or underfitting and, if so, which calibration (sigmoid or isotonic) might help fix this.

For more details, check out sklearn’s docs.

wandb.sklearn.plot_calibration_curve(clf, X, y, 'RandomForestClassifier')

• model (clf): Takes in a fitted classifier.
• X (arr): Training set features.
• y (arr): Training set labels.
• model_name (str): Model name. Defaults to "Classifier".

Confusion matrix

Computes the confusion matrix to evaluate the accuracy of a classification. It’s useful for assessing the quality of model predictions and finding patterns in incorrect predictions. The diagonal represents the predictions where the actual label is equal to the predicted label.

wandb.sklearn.plot_confusion_matrix(y_true, y_pred, labels)

• y_true (arr): Test set labels.
• y_pred (arr): Test set predicted labels.
• labels (list): Named labels for target variable (y).

Summary metrics

• Calculates summary metrics for classification, such as mse, mae, and r2 score.
• Calculates summary metrics for regression, such as f1, accuracy, precision, and recall.

wandb.sklearn.plot_summary_metrics(model, X_train, y_train, X_test, y_test)

• model (clf or reg): Takes in a fitted regressor or classifier.
• X (arr): Training set features.
• y (arr): Training set labels.
• X_test (arr): Test set features.
• y_test (arr): Test set labels.

Elbow plot

Measures and plots the percentage of variance explained as a function of the number of clusters, along with training times. Useful in picking the optimal number of clusters.

wandb.sklearn.plot_elbow_curve(model, X_train)

• model (clusterer): Takes in a fitted clusterer.
• X (arr): Training set features.

Silhouette plot

Measures and plots how close each point in one cluster is to points in the neighboring clusters. The thickness of the clusters corresponds to the cluster size. The vertical line represents the average silhouette score of all the points.

Silhouette coefficients near +1 indicate that the sample is far away from the neighboring clusters. A value of 0 indicates that the sample is on or close to the decision boundary between two neighboring clusters, and negative values indicate that those samples might have been assigned to the wrong cluster.

You want all silhouette cluster scores to be above average (past the red line) and as close to 1 as possible. You also prefer cluster sizes that reflect the underlying patterns in the data.

wandb.sklearn.plot_silhouette(model, X_train, ['spam', 'not spam'])

• model (clusterer): Takes in a fitted clusterer.
• X (arr): Training set features.
• cluster_labels (list): Names for cluster labels. Makes plots easier to read by replacing cluster indexes with corresponding names.

Outlier candidates plot

Measures a datapoint’s influence on regression model through Cook’s distance. Instances with heavily skewed influences could be outliers. Useful for outlier detection.

wandb.sklearn.plot_outlier_candidates(model, X, y)

• model (regressor): Takes in a fitted classifier.
• X (arr): Training set features.
• y (arr): Training set labels.

Residuals plot

Measures and plots the predicted target values (y-axis) versus the difference between actual and predicted target values (x-axis), as well as the distribution of the residual error.

The residuals of a well-fit model should be randomly distributed because good models account for most phenomena in a data set, except for random error.

wandb.sklearn.plot_residuals(model, X, y)

• model (regressor): Takes in a fitted classifier.
• X (arr): Training set features.
• y (arr): Training set labels.

If you have any questions, ask them in the Slack community.

Example

Run in colab: A simple notebook to get you started.