RunasSudo/scipy-yli
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Style fixups

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RunasSudo 2022-10-14 20:08:01 +11:00
parent 255ad100fd
commit a753761675
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
4 changed files with 5 additions and 11 deletions
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2
yli/__init__.py
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@ -14,7 +14,7 @@
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from .bayes_factors import BayesFactor, bayesfactor_afbf
from .bayes_factors import bayesfactor_afbf
from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
from .fs import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
from .regress import PenalisedLogit, regress, vif

4
yli/bayes_factors.py
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@ -15,9 +15,7 @@
# along with this program. If not, see <https://www.gnu.org/licenses/>.
class BayesFactor:
"""
A Bayes factor
"""
"""A Bayes factor"""
def __init__(self, factor, num_symbol, num_desc, denom_symbol, denom_desc):
self.factor = factor

4
yli/distributions.py
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@ -284,9 +284,7 @@ transformed_dist = transformed_gen(name='transformed')
ConfidenceInterval = collections.namedtuple('ConfidenceInterval', ['lower', 'upper'])
def hdi(distribution, level=0.95):
"""
Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)
"""
"""Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)"""
# For a given lower limit, we can compute the corresponding 95% interval
def interval_width(lower):

6
yli/sig_tests.py
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@ -188,9 +188,7 @@ class PearsonChiSquaredResult:
self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False))
def chi2(df, dep, ind, *, nan_policy='warn'):
"""
Perform a Pearson chi-squared test
"""
"""Perform a Pearson chi-squared test"""
# Check for/clean NaNs
df = check_nan(df[[ind, dep]], nan_policy)
@ -209,7 +207,7 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
if ct.shape == (2,2):
# 2x2 table
# Use statsmodels to get OR andRR
# Use statsmodels to get OR and RR
smct = sm.stats.Table2x2(np.flip(ct.to_numpy()), shift_zeros=False)
result = smct.test_nominal_association()