From 34dbf81153a1869edccae2c1bd0ef84d814cab80 Mon Sep 17 00:00:00 2001 From: Nirmayi Date: Mon, 29 Jul 2024 09:01:24 +0200 Subject: [PATCH] minor uodates --- src/control_methods/random_proportions/script.py | 1 - src/metrics/r2/config.vsh.yaml | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/src/control_methods/random_proportions/script.py b/src/control_methods/random_proportions/script.py index 4331122..3ebf1ac 100644 --- a/src/control_methods/random_proportions/script.py +++ b/src/control_methods/random_proportions/script.py @@ -16,7 +16,6 @@ print('Reading input files', flush=True) input_single_cell = ad.read_h5ad(par['input_single_cell']) input_spatial_masked = ad.read_h5ad(par['input_spatial_masked']) -input_solution = ad.read_h5ad(par['input_solution']) print('Generate predictions', flush=True) label_distribution = input_single_cell.obs["cell_type"].value_counts() diff --git a/src/metrics/r2/config.vsh.yaml b/src/metrics/r2/config.vsh.yaml index 73efeed..631f0aa 100644 --- a/src/metrics/r2/config.vsh.yaml +++ b/src/metrics/r2/config.vsh.yaml @@ -10,7 +10,7 @@ info: R2, or the “coefficient of determination”, reports the fraction of the true proportion values' variance that can be explained by the predicted proportion values. The best score, and upper bound, is 1.0. There is no fixed lower bound for the metric. The uniform/non-weighted average across all cell types/states is used to summarise performance. By default, cases resulting in a score of NaN (perfect predictions) or -Inf (imperfect predictions) are replaced with 1.0 (perfect predictions) or 0.0 (imperfect predictions) respectively. reference: miles2005rsquared documentation_url: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html - repository_url: https://github.com/scikit-learn/scikit-learn/tree/5c4aa5d0d90ba66247d675d4c3fc2fdfba3c39ff + repository_url: https://github.com/scikit-learn/scikit-learn min: -inf max: 1 maximize: true