python - Calculate sklearn.roc_auc_score for multi-class

Python - Calculate sklearn.roc_auc_score for multi-class

The roc_auc_score function in scikit-learn is designed for binary classification problems. However, you can use the roc_auc_score function in combination with the OneVsRestClassifier or OneVsOneClassifier to extend it to multi-class classification problems.

Here's an example using the OneVsRestClassifier:

from sklearn.model_selection import train_test_split from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.multiclass import OneVsRestClassifier from sklearn.metrics import roc_auc_score # Generate a multi-class dataset X, y = make_classification(n_samples=1000, n_features=20, n_classes=3, random_state=42) # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create a multi-class classifier using OneVsRest classifier = OneVsRestClassifier(LogisticRegression(random_state=42)) # Train the classifier classifier.fit(X_train, y_train) # Predict probabilities on the test set y_prob = classifier.predict_proba(X_test) # Calculate ROC AUC score for each class roc_auc = roc_auc_score(y_test, y_prob, multi_class='ovr') print(f"Multi-class ROC AUC Score: {roc_auc}")

In this example:

  • make_classification is used to generate a multi-class dataset.
  • The dataset is split into training and testing sets.
  • OneVsRestClassifier is applied to a binary classifier (LogisticRegression in this case) to create a multi-class classifier.
  • The classifier is trained using the training data.
  • Probabilities are predicted for each class on the test set using predict_proba.
  • roc_auc_score is used to calculate the ROC AUC score for each class in a one-vs-rest (ovr) fashion.

You can adjust the classifier and dataset according to your specific problem. Keep in mind that the choice of the binary classifier may impact the performance of the overall multi-class model.

Examples

  1. "Python sklearn.roc_auc_score multi-class example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.preprocessing import label_binarize from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a multi-label dataset for demonstration X, y = make_multilabel_classification(n_samples=1000, n_classes=3, n_labels=2, random_state=42) # Binarize the labels y_bin = label_binarize(y, classes=[0, 1, 2]) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y_bin, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for each class y_probs = clf.predict_proba(X_test) # Calculate ROC AUC score for multi-class roc_auc = roc_auc_score(y_test, y_probs, average='macro')
    • Description: Demonstrates the calculation of ROC AUC score for a multi-class classification problem using a logistic regression classifier.

  2. "sklearn.roc_auc_score micro-average example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a binary classification dataset for demonstration X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for positive class y_probs = clf.predict_proba(X_test)[:, 1] # Calculate ROC AUC score (micro-average) roc_auc = roc_auc_score(y_test, y_probs, average='micro')
    • Description: Illustrates the calculation of ROC AUC score using micro-average for a binary classification problem.

  3. "Python sklearn.roc_auc_score weighted average example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a binary classification dataset for demonstration X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for positive class y_probs = clf.predict_proba(X_test)[:, 1] # Calculate ROC AUC score (weighted average) roc_auc = roc_auc_score(y_test, y_probs, average='weighted')
    • Description: Shows the calculation of ROC AUC score using weighted average for a binary classification problem.

  4. "Python sklearn.roc_auc_score per-class example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a binary classification dataset for demonstration X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for positive class y_probs = clf.predict_proba(X_test)[:, 1] # Calculate ROC AUC score for each class roc_auc_per_class = roc_auc_score(y_test, y_probs, average=None)
    • Description: Computes the ROC AUC score separately for each class in a binary classification problem.

  5. "Python sklearn.roc_auc_score multi-label example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a multi-label dataset for demonstration X, y = make_multilabel_classification(n_samples=1000, n_classes=3, n_labels=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for each label y_probs = clf.predict_proba(X_test) # Calculate ROC AUC score for multi-label roc_auc_multilabel = roc_auc_score(y_test, y_probs, average='macro', multi_class='ovr')
    • Description: Calculates ROC AUC score for a multi-label classification problem using a logistic regression classifier.

  6. "Python sklearn.roc_auc_score per-label example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a multi-label dataset for demonstration X, y = make_multilabel_classification(n_samples=1000, n_classes=3, n_labels=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for each label y_probs = clf.predict_proba(X_test) # Calculate ROC AUC score for each label roc_auc_per_label = roc_auc_score(y_test, y_probs, average=None, multi_class='ovr')
    • Description: Computes the ROC AUC score separately for each label in a multi-label classification problem.

  7. "Python sklearn.roc_auc_score by-label example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a multi-label dataset for demonstration X, y = make_multilabel_classification(n_samples=1000, n_classes=3, n_labels=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for each label y_probs = clf.predict_proba(X_test) # Calculate ROC AUC score by label roc_auc_by_label = roc_auc_score(y_test, y_probs.T, average=None)
    • Description: Calculates the ROC AUC score for each label independently in a multi-label classification problem.

  8. "Python sklearn.roc_auc_score weighted multi-class example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a binary classification dataset for demonstration X, y = make_classification(n_samples=1000, n_features=20, n_classes=3, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for each class y_probs = clf.predict_proba(X_test) # Calculate weighted ROC AUC score for multi-class roc_auc_weighted = roc_auc_score(y_test, y_probs, average='weighted', multi_class='ovr')
    • Description: Demonstrates the calculation of weighted ROC AUC score for a multi-class classification problem.

  9. "Python sklearn.roc_auc_score sample-weighted example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a binary classification dataset for demonstration X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Predict probabilities for positive class y_probs = clf.predict_proba(X_test)[:, 1] # Generate sample weights (for demonstration) sample_weights = np.random.rand(len(y_test)) # Calculate ROC AUC score with sample weights roc_auc_sample_weighted = roc_auc_score(y_test, y_probs, sample_weight=sample_weights)
    • Description: Shows the calculation of ROC AUC score with sample weights for a binary classification problem.

  10. "Python sklearn.roc_auc_score decision function example"

    • Code:
      from sklearn.metrics import roc_auc_score from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Generate a binary classification dataset for demonstration X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Fit a classifier (e.g., Logistic Regression) clf = LogisticRegression() clf.fit(X_train, y_train) # Obtain decision function scores decision_scores = clf.decision_function(X_test) # Calculate ROC AUC score using decision function scores roc_auc_decision_function = roc_auc_score(y_test, decision_scores)
    • Description: Calculates ROC AUC score using decision function scores for a binary classification problem.

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