QuantumClassifier

QuantumClassifier Parameters:

Core Parameters:

• nqubits: Set[int]
• Description: Set of qubit indices, where each value must be greater than 0.

• Validation: Ensures that all elements are integers > 0.

• randomstate: int

• Description: Seed value for random number generation.

• Default: 1234

• predictions: bool

• Description: Flag to determine if predictions are enabled.
• Default: False

Model Structure Parameters:

• numPredictors: int
• Description: Number of predictors used in the QNN with bagging.
• Constraints: Must be greater than 0.

• Default: 10

• numLayers: int

• Description: Number of layers in the Quantum Neural Networks.
• Constraints: Must be greater than 0.
• Default: 5

Set-Based Configuration Parameters:

• classifiers: Set[Model]
• Description: Set of classifier models.
• Constraints: Must contain at least one classifier.
• Default: {Model.ALL}

• Options: {Model.QNN, Model.QNNBAG, Model.MPSQNN, Model.QSVM, Model.FastQSVM, Model.MPSQSVM, Model.QKNN, Model.FastQKNN, Model.MPSQKNN}

• ansatzs: Set[Ansatzs]

• Description: Set of quantum ansatz configurations.
• Constraints: Must contain at least one ansatz.
• Default: {Ansatzs.ALL}

• Options: {Ansatzs.HCZRX, Ansatzs.TREE_TENSOR, Ansatzs.TWO_LOCAL, Ansatzs.HARDWARE_EFFICIENT, Ansatzs.ANNULAR}

• embeddings: Set[Embedding]

• Description: Set of embedding strategies.
• Constraints: Must contain at least one embedding.
• Default: {Embedding.ALL}

• Options: {Embedding.RX, Embedding.RY, Embedding.RZ, Embedding.ZZ, Embedding.ZZ_LOCAL, Embedding.AMP, Embedding.DENSE_ANGLE, Embedding.HIGHER_ORDER}

• features: Set[float]

• Description: Set of feature values (must be between 0 and 1).
• Constraints: Values > 0 and <= 1.
• Default: {0.3, 0.5, 0.8}

Training Parameters:

• learningRate: float
• Description: Learning rate for optimization.
• Constraints: Must be greater than 0.

• Default: 0.01

• epochs: int

• Description: Number of training epochs.
• Constraints: Must be greater than 0.

• Default: 100

• batchSize: int

• Description: Size of each batch during training.
• Constraints: Must be greater than 0.
• Default: 8

Threshold and Sampling:

• threshold: int
• Description: Decision threshold for parallelization, if the model is bigger than this threshold it will use GPU.
• Constraints: Must be greater than 0.

• Default: 16

• maxSamples: float

• Description: Maximum proportion of samples to be used from the dataset characteristics.
• Constraints: Between 0 and 1.
• Default: 1.0

Logging and Metrics:

• verbose: bool
• Description: Flag for detailed output during training.
• Default: False

QSVM / FastQSVM / MPSQSVM hyperparameters:

• svmC: float
• Description: Regularization strength for precomputed-kernel SVMs.

• Default: 1.0

• svmClassWeight: object

• Description: Class weights passed to the underlying SVC.

• Default: None

• svmTol: float

• Description: Optimization tolerance for the SVC solver.

• Default: 1e-3

• svmCacheSize: int

• Description: Cache size in MB for the SVC solver.

• Default: 200

• svmMaxIter: int

• Description: Maximum number of iterations for the SVC solver.

• Default: -1

• svmShrinking: bool

• Description: Enables or disables the shrinking heuristic.

• Default: True

• svmProbability: bool

• Description: Enables probability calibration in SVC.

• Default: False

• svmRandomState: int

• Description: Random seed used by the SVC solver. If omitted, the experiment seed is reused.

• Default: None

• svmDecisionFunctionShape: str

• Description: Multiclass decision function shape for SVC.

• Default: "ovr"

• svmBreakTies: bool

• Description: Enables tie-breaking for multiclass SVC predictions.

• Default: False

• svmVerbose: bool

• Description: Enables verbose output from libsvm.

• Default: False

• customMetric: Optional[Callable]

• Description: User-defined metric function for evaluation.
• Validation:
  • Function must accept y_true and y_pred as the first two arguments.
  • Must return a scalar value (int or float).
  • Function execution is validated with dummy arguments.
• Default: None

Custom Preprocessors:

• customImputerNum: Optional[Any]
• Description: Custom numeric data imputer.
• Validation:
  • Must be an object with fit, transform, and optionally fit_transform methods.
  • Validated with dummy data.

• Default: None

• customImputerCat: Optional[Any]

• Description: Custom categorical data imputer.
• Validation:
  • Must be an object with fit, transform, and optionally fit_transform methods.
  • Validated with dummy data.
• Default: None

Functions:

fit

fit(self, X, y, test_size=0.4, showTable=True)

Fits classification algorithms to X and y using a hold-out approach. Predicts and scores on a test set determined by test_size.

Parameters:

• X: Input features (DataFrame or compatible format).
• y: Target labels (must be numeric, e.g., via LabelEncoder or OrdinalEncoder).
• test_size: Proportion of the dataset to use as the test set. Default is 0.4.
• showTable: Display a table with results. Default is True.

Behavior:

• Validates the compatibility of input dimensions.
• Automatically applies PCA transformation for incompatible dimensions.
• Requires all categories to be present in training data.

repeated_cross_validation

repeated_cross_validation(self, X, y, n_splits=10, n_repeats=5, showTable=True)

Performs repeated cross-validation on the dataset using the specified splits and repeats.

Parameters:

• X: Input features (DataFrame or compatible format).
• y: Target labels (must be numeric).
• n_splits: Number of folds for splitting the dataset. Default is 10.
• n_repeats: Number of times cross-validation is repeated. Default is 5.
• showTable: Display a table with results. Default is True.

Behavior:

• Uses RepeatedStratifiedKFold for generating splits.
• Aggregates results from multiple train-test splits.

leave_one_out

leave_one_out(self, X, y, showTable=True)

Performs leave-one-out cross-validation on the dataset.

Parameters:

• X: Input features (DataFrame or compatible format).
• y: Target labels (must be numeric).
• showTable: Display a table with results. Default is True.

Behavior:

• Uses LeaveOneOut for generating train-test splits.
• Evaluates the model on each split and aggregates results.