secure_learning.metrics module
Provides classification and regression metrics.
- secure_learning.metrics.accuracy_score(y_real, y_pred)[source]
Computes the accuracy of the predicted labels. Accuracy is computed as the ratio of all correctly predicted labels over the number of predicted labels.
- Parameters:
y_real (
List[SecureFixedPoint]) – Real labels (-/+ 1)y_pred (
List[SecureFixedPoint]) – Predicted labels (-/+ 1)
- Return type:
SecureFixedPoint- Returns:
Accuracy of given predictions
- secure_learning.metrics.adj_r2_score(y_real, y_pred, n_features)[source]
Adjusted R-squared value for given predicted and real target values.
- Parameters:
y_real (
List[SecureFixedPoint]) – Real target valuesy_pred (
List[SecureFixedPoint]) – Predicted target valuesn_features (
int) – Number of features
- Return type:
SecureFixedPoint- Returns:
Adjusted R-squared value
- secure_learning.metrics.f1_score(y_real, y_pred, pos_label)[source]
F1-score for given predicted and real target labels.
- Parameters:
y_real (
List[SecureFixedPoint]) – Real labels (-/+ 1)y_pred (
List[SecureFixedPoint]) – Predicted labels (-/+ 1)pos_label (
int) – Label to compute f1 score of
- Raises:
SecureLearnValueError – pos_label value must be either -1 or 1
- Return type:
SecureFixedPoint- Returns:
F1 score
- secure_learning.metrics.mean_squared_error(y_real, y_pred)[source]
Compute residual mean of squares. Residual mean of squares equals the mean of squares of deviations between predicted and real values.
- Parameters:
y_real (
List[SecureFixedPoint]) – Real labels (-/+ 1)y_pred (
List[SecureFixedPoint]) – Predicted labels (-/+ 1)
- Return type:
SecureFixedPoint- Returns:
Residual mean of squares
- secure_learning.metrics.mean_squared_model(y_real)[source]
Compute explained mean of squares. Explained sum of squares equals the mean of squares of deviations from the mean.
- Parameters:
y_real (
List[SecureFixedPoint]) – Input- Return type:
SecureFixedPoint- Returns:
Explained mean of squares
- secure_learning.metrics.precision_score(y_real, y_pred, pos_label)[source]
Computes the precision of the predicted labels of category pos_label. Precision is computed as the ratio of all correctly predicted pos_label over the number of predicted pos_label. This is an indication how precise the predictions of pos_label are: given prediction pos_label, how likely is it that the true label is pos_label.
- Parameters:
y_real (
List[SecureFixedPoint]) – Real labels (-/+ 1)y_pred (
List[SecureFixedPoint]) – Predicted labels (-/+ 1)pos_label (
int) – Label (value) to compute precision of
- Raises:
SecureLearnValueError – pos_label value must be either -1 or 1
- Return type:
SecureFixedPoint- Returns:
Precision of given predictions
- secure_learning.metrics.r2_score(y_real, y_pred)[source]
R-squared value for given predicted and real target values.
- Parameters:
y_real (
List[SecureFixedPoint]) – Real target valuesy_pred (
List[SecureFixedPoint]) – Predicted target values
- Return type:
SecureFixedPoint- Returns:
R-squared value
- secure_learning.metrics.recall_score(y_real, y_pred, pos_label)[source]
Computes the recall of the predicted labels of category pos_label. Recall is computed as the ratio of all correctly predicted pos_label over the number of real pos_label. This is an indication how many actual pos_label we misclassified.
- Parameters:
y_real (
List[SecureFixedPoint]) – Real labels (-/+ 1)y_pred (
List[SecureFixedPoint]) – Predicted labels (-/+ 1)pos_label (
int) – Label (value) to compute recall of
- Raises:
SecureLearnValueError – pos_label value must be either -1 or 1
- Return type:
SecureFixedPoint- Returns:
Recall of given predictions