from typing import Union, List, Optional
import numpy as np
from gmr import GMM
from pandas import DataFrame
from bamt.utils.MathUtils import component
from .base import BaseNode
from .schema import MixtureGaussianParams
[docs]
class MixtureGaussianNode(BaseNode):
"""
Main class for Mixture Gaussian Node
"""
def __init__(self, name):
super(MixtureGaussianNode, self).__init__(name)
self.type = "MixtureGaussian"
[docs]
def fit_parameters(self, data: DataFrame) -> MixtureGaussianParams:
"""
Train params for Mixture Gaussian Node
"""
parents = self.disc_parents + self.cont_parents
if not parents:
n_comp = int(
(
component(data, [self.name], "aic")
+ component(data, [self.name], "bic")
)
/ 2
) # component(data, [node], 'LRTS')#
# n_comp = 3
gmm = GMM(n_components=n_comp).from_samples(
np.transpose([data[self.name].values]),
n_iter=500,
init_params="kmeans++",
)
means = gmm.means.tolist()
cov = gmm.covariances.tolist()
# weigts = np.transpose(gmm.to_responsibilities(np.transpose([data[node].values])))
w = gmm.priors.tolist() # []
# for row in weigts:
# w.append(np.mean(row))
return {"mean": means, "coef": w, "covars": cov}
if parents:
if not self.disc_parents and self.cont_parents:
nodes = [self.name] + self.cont_parents
new_data = data[nodes]
new_data.reset_index(inplace=True, drop=True)
n_comp = int(
(
component(new_data, nodes, "aic")
+ component(new_data, nodes, "bic")
)
/ 2
) # component(new_data, nodes, 'LRTS')#
# n_comp = 3
gmm = GMM(n_components=n_comp).from_samples(
new_data[nodes].values, n_iter=500, init_params="kmeans++"
)
means = gmm.means.tolist()
cov = gmm.covariances.tolist()
# weigts = np.transpose(gmm.to_responsibilities(new_data[nodes].values))
w = gmm.priors.tolist() # []
# for row in weigts:
# w.append(np.mean(row))
return {"mean": means, "coef": w, "covars": cov}
[docs]
@staticmethod
def get_dist(node_info, pvals):
mean = node_info["mean"]
covariance = node_info["covars"]
w = node_info["coef"]
n_comp = len(node_info["coef"])
if n_comp != 0:
if pvals:
indexes = [i for i in range(1, len(pvals) + 1)]
if not np.isnan(np.array(pvals)).all():
gmm = GMM(
n_components=n_comp,
priors=w,
means=mean,
covariances=covariance,
)
cond_gmm = gmm.condition(indexes, [pvals])
return cond_gmm.means, cond_gmm.covariances, cond_gmm.priors
else:
return np.nan, np.nan, np.nan
else:
gmm = GMM(
n_components=n_comp, priors=w, means=mean, covariances=covariance
)
return gmm.means, gmm.covariances, gmm.priors
else:
return np.nan, np.nan, np.nan
[docs]
def choose(
self, node_info: MixtureGaussianParams, pvals: List[Union[str, float]]
) -> Optional[float]:
"""
Func to get value from current node
node_info: nodes info from distributions
pvals: parent values
Return value from MixtureGaussian node
"""
mean, covariance, w = self.get_dist(node_info, pvals)
n_comp = len(w)
gmm = GMM(
n_components=n_comp,
priors=w,
means=mean,
covariances=covariance,
)
return gmm.sample(1)[0][0]
[docs]
@staticmethod
def predict(
node_info: MixtureGaussianParams, pvals: List[Union[str, float]]
) -> Optional[float]:
"""
Func to get prediction from current node
node_info: nodes info from distributions
pvals: parent values
Return value from MixtureGaussian node
"""
mean = node_info["mean"]
covariance = node_info["covars"]
w = node_info["coef"]
n_comp = len(node_info["coef"])
if n_comp != 0:
if pvals:
indexes = [i for i in range(1, len(pvals) + 1)]
if not np.isnan(np.array(pvals)).all():
gmm = GMM(
n_components=n_comp,
priors=w,
means=mean,
covariances=covariance,
)
sample = gmm.predict(indexes, [pvals])[0][0]
else:
sample = np.nan
else:
# gmm = GMM(n_components=n_comp, priors=w, means=mean, covariances=covariance)
sample = 0
for ind, wi in enumerate(w):
sample += wi * mean[ind][0]
else:
sample = np.nan
return sample