ml module
- btQuant.ml.anomalyScore(trees, X)[source]
Compute anomaly scores from isolation forest.
- Parameters:
trees – list of isolation trees
X – features to score
- Returns:
anomaly scores (higher = more anomalous)
- btQuant.ml.decisionTree(X, y, maxDepth=5)[source]
Decision tree classifier using Gini impurity.
- Parameters:
X – features (n_samples, n_features)
y – target labels
maxDepth – maximum tree depth
- Returns:
tree structure
- Return type:
dict
- btQuant.ml.gradientBoosting(X, y, nEstimators=100, learningRate=0.1, maxDepth=3)[source]
Gradient boosting regressor.
- Parameters:
X – features
y – target values
nEstimators – number of boosting rounds
learningRate – learning rate (shrinkage)
maxDepth – maximum tree depth
- Returns:
predict function
- btQuant.ml.isolationForest(X, nTrees=100, maxSamples=None, maxDepth=10)[source]
Isolation forest for anomaly detection.
- Parameters:
X – features (n_samples, n_features)
nTrees – number of trees
maxSamples – samples per tree (default all)
maxDepth – maximum tree depth
- Returns:
list of isolation trees
- btQuant.ml.kmeans(X, k=3, maxIters=100, tol=0.0001)[source]
K-means clustering.
- Parameters:
X – features (n_samples, n_features)
k – number of clusters
maxIters – maximum iterations
tol – convergence tolerance
- Returns:
centroids, labels
- btQuant.ml.knn(XTrain, yTrain, XTest, k=3)[source]
K-nearest neighbors classifier.
- Parameters:
XTrain – training features
yTrain – training labels
XTest – test features
k – number of neighbors
- Returns:
predicted labels
- btQuant.ml.lda(X, y, nComponents=None)[source]
Linear discriminant analysis.
- Parameters:
X – features
y – class labels
nComponents – number of components
- Returns:
transformed data, eigenvalues, eigenvectors
- btQuant.ml.logisticRegression(X, y, learningRate=0.01, nIters=1000)[source]
Logistic regression classifier.
- Parameters:
X – features
y – binary labels (0/1)
learningRate – learning rate
nIters – number of iterations
- Returns:
weights, bias, predict function
- btQuant.ml.naiveBayes(XTrain, yTrain, XTest)[source]
Gaussian naive Bayes classifier.
- Parameters:
XTrain – training features
yTrain – training labels
XTest – test features
- Returns:
predicted labels
- btQuant.ml.pca(X, nComponents=None)[source]
Principal component analysis.
- Parameters:
X – features (n_samples, n_features)
nComponents – number of components to keep
- Returns:
transformed data, eigenvalues, eigenvectors
- btQuant.ml.predictTree(tree, X)[source]
Predict using a regression or decision tree.
- Parameters:
tree – tree structure from regressionTree or decisionTree
X – features to predict (n_samples, n_features)
- Returns:
predictions array