Symbolic regression

This module contains the implementation of the symbolic regression methods

Symbolic Regression

helpers

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auc_from_scores_labels

def auc_from_scores_labels(
    scores, labels
):

Call self as a function.

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compare_theta_corr

def compare_theta_corr(
    theta, X, C, method:str='pearson', use_upper:bool=True
):

Call self as a function.

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spearman_rho

def spearman_rho(
    a, b
):

Call self as a function.

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cosine_sim

def cosine_sim(
    a, b, eps:float=1e-12
):

Call self as a function.

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pearson_between_vectors

def pearson_between_vectors(
    a, b, eps:float=1e-12
):

Call self as a function.

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flatten_upper

def flatten_upper(
    mat
):

Call self as a function.

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empirical_corr_matrix

def empirical_corr_matrix(
    X, centered:bool=True
):

Call self as a function.

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curved_edge

def curved_edge(
    ax, x1, y1, x2, y2, curvature:float=0.2, plot_kwargs:VAR_KEYWORD
):

Draw a curved (quadratic Bézier) edge between (x1,y1) and (x2,y2). curvature > 0 bends left, curvature < 0 bends right.

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loss_SR3

def loss_SR3(
    alpha, model, dataset, vk, G, L1_reg, options
):

loss used for 2BC with SR3

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derivative_loss_alpha_multi_vk

def derivative_loss_alpha_multi_vk(
    tree, X, options, vk, G
):

loss projecting and aligning the gradiant Args: dy: (S, N) gradients of tree w.r.t inputs vk: (S, N, m) projector vectors per sample (m projectors) G: (S, m) target projected gradients per sample Returns MSE between normalized projected dy and normalized G (averaged over m).

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loss_SR2

def loss_SR2(
    alpha, model, dataset, G, L1_reg, options
):

loss used for the 2BC witb SR2

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derivative_loss_alpha_multi

def derivative_loss_alpha_multi(
    tree, X, options, G
):

loss aligning the gradiant

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loss_SR1

def loss_SR1(
    alpha, model, X, Y, L1_reg, options
):

loss used for the 2BC witb SR1

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class_loss

def class_loss(
    tree, X, options, Y
):

classification loss used for the 2BC witb SR1

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TwoBodyModel

def TwoBodyModel(
    pairs, key, add_constant:bool=False
):

Initialize self. See help(type(self)) for accurate signature.

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FFNN_theta_to_mu

def FFNN_theta_to_mu(
    hidden_dim:int, num_layers:int, parent:Union=<flax.linen.module._Sentinel object at 0x7f9be9751700>,
    name:Optional=None
)->None:

simple feed forward net from theta to mu1

SR class

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SymbolicRegression

def SymbolicRegression(
    dataset:Dataset, cluster_idx_in:Array, objective:str, type_of_vk:Optional=None,
    cluster_idx_out:Optional=None, # only needed for SR1 if not specified, full/in
    search_space:str='2_body_correlator', # ansatz or genetic
    add_constant:bool=False, shift_data:bool=True, VAE_model:Optional=None, # needed for SR2,3
    VAE_params:Optional=None, # needed for SR2,3
    mu_cluster:Optional=None, # needed for SR2,3
    idx_mu_cluster:Optional=None, # needed for SR2,3
):

Wrapper with the SR methods to be used on top of the representation learned by the cpVAE for quantum

Args:

dataset: Dataset object
cluster_idx_in: coord. specifying the location of the cluster we analyse in parameter (theta) space
objective: SR1, SR2 or SR3
cluster_idx_out: coord. specifying the location of the cluster we analyse in parameter (theta) space (only used in SR1)
search_space: 2_body_correlator or genetic
add_constant: if True, add a constant term to the model
shift_data: if the symbolic function takes direcly the dataset.data or if {0,1}->{-1,1} before

for SR2,3 also need:

VAE_model: the VAE model
VAE_params: its params
mu_cluster: the value of the latent variable accrooss theta space where the cluster appear (for now, only one mu)
idx_mu_cluster: index of the latent variable where the cluster appear (for now, only one)