def regression_maker(j, x, y):
level = 0 if j % 100 == 0 else 2
utils.Debug.allprint(base_regression.PROGRESS_STR.format(gn=genes[j],
i=j,
total=G),
level=level)
data = sklearn_regression.sklearn_gene(x, utils.scale_vector(y),
copy.copy(model))
data['ind'] = j
return j, data
def regression_maker(j, x, y, pp, weights):
level = 0 if j % 100 == 0 else 2
utils.Debug.allprint(base_regression.PROGRESS_STR.format(gn=genes[j],
i=j,
total=G),
level=level)
data = bayes_stats.bbsr(x, utils.scale_vector(y), pp[j, :].flatten(),
weights[j, :].flatten(), nS)
data['ind'] = j
return j, data
def regression_maker(j):
level = 0 if j % 100 == 0 else 2
utils.Debug.allprint(base_regression.PROGRESS_STR.format(gn=self.genes[j], i=j, total=self.G), level=level)
data = stars_model_select(self.X.values,
utils.scale_vector(self.Y.get_gene_data(j, force_dense=True, flatten=True)),
self.alphas,
method=self.method,
num_subsamples=self.num_subsamples,
random_seed=self.random_seed,
**self.params)
data['ind'] = j
return data
def regression_maker(j):
level = 0 if j % 100 == 0 else 2
utils.Debug.allprint(base_regression.PROGRESS_STR.format(
gn=self.genes[j], i=j, total=self.G),
level=level)
data = elastic_net(
self.X.values,
utils.scale_vector(
self.Y.get_gene_data(j, force_dense=True).flatten()),
self.params)
data['ind'] = j
return data
def regression_maker(j):
level = 0 if j % 100 == 0 else 2
utils.Debug.allprint(base_regression.PROGRESS_STR.format(
gn=self.genes[j], i=j, total=self.G),
level=level)
data = sklearn_gene(self.X.values,
utils.scale_vector(
self.Y.get_gene_data(j,
force_dense=True,
flatten=True)),
copy.copy(self.model),
min_coef=self.min_coef)
data['ind'] = j
return data
def regression_maker(j, x, y):
level = 0 if j % 100 == 0 else 2
utils.Debug.allprint(base_regression.PROGRESS_STR.format(gn=genes[j],
i=j,
total=G),
level=level)
data = stability_selection.stars_model_select(
x,
utils.scale_vector(y),
alphas,
num_subsamples=num_subsamples,
method=method,
random_seed=random_seed,
**params)
data['ind'] = j
return j, data
def regression_maker(j):
level = 0 if j % 100 == 0 else 2
utils.Debug.allprint(base_regression.PROGRESS_STR.format(
gn=self.genes[j], i=j, total=self.G),
level=level)
data = bayes_stats.bbsr(
self.X.values,
utils.scale_vector(
self.Y.get_gene_data(j, force_dense=True, flatten=True)),
self.pp.iloc[j, :].values.flatten(),
self.weights_mat.iloc[j, :].values.flatten(),
self.nS,
ordinary_least_squares=self.ols_only)
data['ind'] = j
return data