Source code for eqc_models.ml.decomposition

# (C) Quantum Computing Inc., 2024.
# Import libs
import os
import sys
import time
import datetime
import json
import warnings
from functools import wraps
import numpy as np
from qci_client import QciClient
from eqc_models import QuadraticModel
from eqc_models.solvers.qciclient import (
    Dirac3CloudSolver,
    Dirac3ContinuousCloudSolver,
    Dirac1CloudSolver,
)
class DecompBase(QuadraticModel):
    """An Base class for decomposition algorithms.
    
    Parameters
    ----------
    
    relaxation_schedule: Relaxation schedule used by Dirac-3; default:
    2.
    
    num_samples: Number of samples used by Dirac-3; default: 1.
    """
    
    def __init__(
        self,
        relaxation_schedule=2,
        num_samples=1,
    ):
        super(self).__init__(None, None, None)
        self.relaxation_schedule = relaxation_schedule
        self.num_samples = num_samples
    def _get_hamiltonian(
        self,
        X: np.array,
    ):
        pass
    def _set_model(self, J, C, sum_constraint):
        # Set hamiltonians
        self._C = C
        self._J = J
        self._H = C, J
        self._sum_constraint = sum_constraint
        # Set upper_bound
        num_variables = C.shape[0]
        self.upper_bound = sum_constraint * np.ones((num_variables,))
        return
    def _solve(self):
        solver = Dirac3ContinuousCloudSolver()
        response = solver.solve(
            self,
            relaxation_schedule=self.relaxation_schedule,
            solution_precision=1,
            sum_constraint=self._sum_constraint,
            num_samples=self.num_samples,
        )
        sol = response["results"]["solutions"][0]
        return sol, response
    def _solve_d1_test(self):
        qubo = self._J
        # Make sure matrix is symmetric to machine precision
        qubo = 0.5 * (qubo + qubo.transpose())
        # Instantiate
        qci = QciClient()
        # Create json objects
        qubo_json = {
            "file_name": "qubo_tutorial.json",
            "file_config": {
                "qubo": {"data": qubo, "num_variables": qubo.shape[0]},
            },
        }
        response_json = qci.upload_file(file=qubo_json)
        qubo_file_id = response_json["file_id"]
        # Setup job json
        job_params = {
            "device_type": "dirac-1",
            "alpha": 1.0,
            "num_samples": 20,
        }
        job_json = qci.build_job_body(
            job_type="sample-qubo",
            job_params=job_params,
            qubo_file_id=qubo_file_id,
            job_name="tutorial_eqc1",
            job_tags=["tutorial_eqc1"],
        )
        print(job_json)
        # Run the job
        job_response_json = qci.process_job(
            job_body=job_json,
        )
        print(job_response_json)
        results = job_response_json["results"]
        energies = results["energies"]
        samples = results["solutions"]
        if True:
            print("Energies:", energies)
        sol = np.array(samples[0])
        print(sol)
        return sol
    def fit(self, X):
        pass
    def transform(self, X: np.array):
        pass
    def fit_transform(self, X):
        pass
    def get_dynamic_range(self):
        C = self._C
        J = self._J
        if C is None:
            return
        if J is None:
            return
        absc = np.abs(C)
        absj = np.abs(J)
        minc = np.min(absc[absc > 0])
        maxc = np.max(absc)
        minj = np.min(absj[absj > 0])
        maxj = np.max(absj)
        minval = min(minc, minj)
        maxval = max(maxc, maxj)
        return 10 * np.log10(maxval / minval)
[docs]
class PCA(DecompBase):
    """
    An implementation of Principal component analysis (PCA) that
    uses QCi's Dirac-3.
    Linear dimensionality reduction using Singular Value
    Decomposition of the data to project it to a lower dimensional
    space.
    Parameters
    ----------
    
    n_components: Number of components to keep; if n_components is not
    set all components are kept; default: None.
    
    relaxation_schedule: Relaxation schedule used by Dirac-3; default:
    2.
    
    num_samples: Number of samples used by Dirac-3; default: 1.
    Examples
    -----------
    
    >>> from sklearn import datasets
    >>> iris = datasets.load_iris()
    >>> X = iris.data
    >>> from sklearn.preprocessing import StandardScaler
    >>> scaler = StandardScaler()
    >>> X = scaler.fit_transform(X)
    >>> from eqc_models.ml.decomposition import PCA
    >>> from contextlib import redirect_stdout
    >>> import io
    >>> f = io.StringIO()
    >>> with redirect_stdout(f):
    ...    obj = PCA(
    ...        n_components=4,
    ...        relaxation_schedule=2,
    ...        num_samples=1,
    ...    )
    ...    X_pca = obj.fit_transform(X)
    
    """
    
    def __init__(
        self,
        n_components=None,
        relaxation_schedule=2,
        num_samples=1,
    ):
        self.n_components = n_components
        self.relaxation_schedule = relaxation_schedule
        self.num_samples = num_samples
        self.X = None
        self.X_pca = None
    def _get_hamiltonian(
        self,
        X: np.array,
    ):
        num_records = X.shape[0]
        num_features = X.shape[1]
        J = -np.matmul(X.transpose(), X)
        assert J.shape[0] == num_features
        assert J.shape[1] == num_features
        C = np.zeros((num_features, 1))
        return J, C
    def _get_first_component(self, X):
        J, C = self._get_hamiltonian(X)
        assert J.shape[0] == J.shape[1], "Inconsistent hamiltonian size!"
        assert J.shape[0] == C.shape[0], "Inconsistent hamiltonian size!"
        self._set_model(J, C, 1.0)
        sol, response = self._solve()
        assert len(sol) == C.shape[0], "Inconsistent solution size!"
        fct = np.linalg.norm(sol)
        if fct > 0:
            fct = 1.0 / fct
        v0 = fct * np.array(sol)
        v0 = v0.reshape((v0.shape[0], 1))
        lambda0 = np.matmul(np.matmul(v0.transpose(), -J), v0)[0][0]
        assert lambda0 >= 0, "Unexpected negative eigenvalue!"
        fct = np.sqrt(lambda0)
        if fct > 0:
            fct = 1.0 / fct
        u0 = fct * np.matmul(X, v0)
        u0 = u0.reshape(-1)
        fct = np.linalg.norm(u0)
        if fct > 0:
            fct = 1.0 / fct
        u0 = fct * u0
        return u0, response
[docs]
    def fit(self, X):
        """
        Build a PCA object from the training set X.
        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The training input samples.
        Returns
        -------
        responses. 
        A dirct containing Dirac responses.
        """
        num_features = X.shape[1]
        if self.n_components is None:
            n_components = num_features
        else:
            n_components = self.n_components
        n_components = min(n_components, num_features)
        self.X = X.copy()
        self.X_pca = []
        resp_hash = {}
        for i in range(n_components):
            u, resp = self._get_first_component(X)
            self.X_pca.append(u)
            u = u.reshape((u.shape[0], 1))
            X = X - np.matmul(
                u,
                np.matmul(u.transpose(), X),
            )
            assert X.shape == self.X.shape, "Inconsistent size!"
            resp_hash["component_%d_response" % (i + 1)] = resp
            
        self.X_pca = np.array(self.X_pca).transpose()
        assert self.X_pca.shape[0] == self.X.shape[0]
        assert self.X_pca.shape[1] == n_components
        return resp_hash
[docs]
    def transform(self, X: np.array):
        """
        Apply dimensionality reduction to X.
        X is projected on the first principal components previously extracted
        from a training set.
        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
        New data, where `n_samples` is the number of samples
        and `n_features` is the number of features.
        Returns
        -------
        X_new : array-like of shape (n_samples, n_components)
        Projection of X in the first principal components, where `n_samples`
        is the number of samples and `n_components` is the number of the components.
        """
        if self.X is None:
            return
        return self.X_pca
[docs]
    def fit_transform(self, X):
        """
        Fit the model with X and apply the dimensionality reduction on X.
        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
        Training data, where `n_samples` is the number of samples
        and `n_features` is the number of features.
        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components)
        Transformed values.
        """
        self.fit(X)
        return self.transform(X)