def test_visualization(self):
res = ols(formula="x1 + x2 + x3 + x4",
table=self.y, metadata=self.x)
res.fit()
ols_summary(self.results, res, tree=self.t2)
fp = os.path.join(self.results, 'pvalues.csv')
self.assertTrue(os.path.exists(fp))
fp = os.path.join(self.results, 'coefficients.csv')
self.assertTrue(os.path.exists(fp))
fp = os.path.join(self.results, 'predicted.csv')
self.assertTrue(os.path.exists(fp))
fp = os.path.join(self.results, 'residuals.csv')
self.assertTrue(os.path.exists(fp))
index_fp = os.path.join(self.results, 'index.html')
self.assertTrue(os.path.exists(index_fp))
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<h1>Simplicial Linear Regression Summary</h1>',
html)
self.assertIn('<th>Coefficients</th>\n', html)
self.assertIn('<th>Predicted Balances</th>\n', html)
self.assertIn('<th>Residuals</th>\n', html)
def test_visualization(self):
res = ols(formula="x1 + x2 + x3 + x4",
table=self.y, metadata=self.x)
res.fit()
ols_summary(self.results, res, tree=self.t2)
fp = os.path.join(self.results, 'pvalues.csv')
self.assertTrue(os.path.exists(fp))
fp = os.path.join(self.results, 'coefficients.csv')
self.assertTrue(os.path.exists(fp))
fp = os.path.join(self.results, 'predicted.csv')
self.assertTrue(os.path.exists(fp))
fp = os.path.join(self.results, 'residuals.csv')
self.assertTrue(os.path.exists(fp))
index_fp = os.path.join(self.results, 'index.html')
self.assertTrue(os.path.exists(index_fp))
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<h1>Simplicial Linear Regression Summary</h1>',
html)
self.assertIn('<th>Coefficients</th>\n', html)
self.assertIn('<th>Predicted Balances</th>\n', html)
self.assertIn('<th>Residuals</th>\n', html)
def ols_regression(output_dir: str,
table: pd.DataFrame, tree: skbio.TreeNode,
metadata: Metadata, formula: str) -> None:
res = ols(table=table, metadata=metadata._dataframe,
formula=formula)
res.fit()
ols_summary(output_dir, res, tree)
def ols_regression(output_dir: str, table: pd.DataFrame, tree: skbio.TreeNode,
metadata: Metadata, formula: str) -> None:
if np.any(table.var(axis=0) == 0):
message = ('Detected zero variance balances - '
'double check your table for unobserved features.')
raise UserWarning(message)
res = ols(table=table, metadata=metadata.to_dataframe(), formula=formula)
res.fit()
ols_summary(output_dir, res, tree)
