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Copy pathcellevaluation.py
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146 lines (133 loc) · 8.72 KB
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import argparse
import os
def parse_args():
parser = argparse.ArgumentParser(description="Evaluate results")
# General arguments
parser.add_argument("--vis_label_data", type=str, default="OC1_all.csv",
help="Count data of Images, linked with Patient IDs")
parser.add_argument("--figure_dir", type=str, default="figures/",
help="Path to save images to")
# Preprocess arguments
parser.add_argument("--merge", action="store_true", default=False,
help="Wether or not to merge predictions of models in embed_dir")
parser.add_argument("--embed_to_h5ad", action="store_true", default=False,
help="Wether or not to create an sc.AnnData obj and save it to out for predictions")
parser.add_argument("--performance_metrics", action="store_true", default=False,
help="Wether or not to calculate performance metrics of stored sc.AnnData")
parser.add_argument("--h5ad_dir", type=str, default="out/",
help="Path to h5ad object")
parser.add_argument("--vis_name_pattern", type=str, default="model_[0-9]_all.h5ad",
help="Pattern of .5ad objects in 'h5ad_dir' which should be evaluated")
parser.add_argument("--num_subgraphs_per_graph", type=int, default=0,
help="Number of subgraphs per graph to create to evaluate niches")
parser.add_argument("--num_hops_per_subgraph", type=int, default=[], nargs='*',
help="Number of hops per subgraphs to create to evaluate niches")
parser.add_argument("--sum_by_graph", action="store_true", default=False,
help="Wether or not 'vis_label_data' is label data or cell measurement data: ROI or Image key")
parser.add_argument("--do_clustering_metrics", action="store_true", default=False,
help="Wether or not to calculate metrics which need to cluster predicted expression")
parser.add_argument("--do_pathway_metrics", action="store_true", default=False,
help="Wether or not to calculate agreement between enricht CollecTRI, PROGENy and msigdb between true and actual counts base don true counts clusterin")
parser.add_argument("--do_performance_metrics", action="store_true", default=False,
help="Wether or not to save metrics per markers/cell/niche")
# Visualize Expression
parser.add_argument("--visualize_expression", action="store_true", default=False,
help="Wether or not to visualize predicted sc expression")
parser.add_argument("--has_expr_data", action="store_true", default=False,
help="Wether or not true Single Cell expression data is in measurements.csv")
parser.add_argument("--raw_subset_dir", type=str, default="TMA1_preprocessed",
help="How the subdir in raw/ and processed/ is called, used when --has_expr_data is given")
parser.add_argument("--processed_subset_dir", type=str, default="TMA1_preprocessed",
help="Subset directory of processed/ and raw/ of data")
parser.add_argument("--embed_dir", type=str, default="out/",
help="Path to predicted single cell data per Graph/Image, if --merge is called this should be the parent directory containing all to be merged prediction folders, resulting in os.path.join(args['embed_dir'], 'mean') containing merged predictions")
parser.add_argument("--vis_select_cells", type=int, default=0,
help="Number of cells to perform dim reduction on. If 0, then all cells get reduced")
parser.add_argument("--vis_name", type=str, default="_cells",
help="Name added to figures name, saves processed data as NAME.h5ad") #alo for visualize image
# Visualize Image
parser.add_argument("--visualize_image", action="store_true", default=False,
help="Wether or not to Visualize an Image")
parser.add_argument("--vis_name_og", type=str, default="",
help="Name of NAME.h5ad containing TRUE single-cell counts(used for validation data)")
parser.add_argument("--vis_img_raw_subset_dir", type=str, default="TMA1_preprocessed",
help="Name of raw/ subsetdir which contains .tiff Images to visualize")
parser.add_argument("--name_tiff", type=str, default="027-2B27.tiff",
help="Name of .tiff Image to visualize")
parser.add_argument("--figure_img_dir", type=str, default="figures/",
help="Path to output figures to")
parser.add_argument("--vis_protein", type=str, default="",
help="Proteins to visualize Expression over Image of, seperated by ,; . converts to space")
parser.add_argument("--vis_img_xcoords", nargs='+', type=int, default=[0,0],
help="Image x coords, smaller first")
parser.add_argument("--vis_img_ycoords", nargs='+', type=int, default=[0,0],
help="Image y coords, smaller first")
parser.add_argument("--vis_all_channels", action="store_true", default=False,
help="Wether or not to visualize all Image channels on their own")
parser.add_argument("--size", type=int, default=25,
help="Size of dots as cells to be plotted")
# Visualize Model Run
parser.add_argument("--visualize_model_run", action="store_true", default=False,
help="Wether or not to Visualize statistics of model run")
parser.add_argument("--model_path", type=str, default="out/models/ROI.pt",
help="Path and name of model save")
parser.add_argument("--output_model_name", type=str, default="ROI",
help="Name of model in figures")
parser.add_argument("--figure_model_dir", type=str, default="figures/",
help="Path to output figures to")
parser.add_argument("--is_cs", action="store_true", default=False,
help="Wether or not Cosine Similarity is used or Contrast Loss")
return parser.parse_args()
def main(**args):
if args['merge']:
from src.utils.utils import merge
merge(args['embed_dir'])
args['embed_dir'] = os.path.join(args['embed_dir'], 'mean')
if args['embed_to_h5ad']:
from src.utils.create_h5ad import createH5AD
createH5AD(path=args['h5ad_dir'],
processed_dir=args['processed_subset_dir'],
embed_dir=args['embed_dir'],
label_data=args['vis_label_data'],
name=args['vis_name'])
if args['performance_metrics']:
from src.explain.Metrics import Metrics
Metrics(path=args['h5ad_dir'],
target=args['vis_label_data'],
name=args['vis_name_pattern'],
num_subgraphs_per_graph=args['num_subgraphs_per_graph'],
num_hops_per_subgraph=args['num_hops_per_subgraph'],
out=args['figure_dir'],
sum_by_graph=args['sum_by_graph'],
do_clustering_metrics=args['do_clustering_metrics'],
do_pathway_metrics=args['do_pathway_metrics'],
do_performance_metrics=args['do_performance_metrics'])
if args['visualize_expression']:
from src.explain.VisualizeExpression import visualizeExpression
visualizeExpression(processed_dir=args['processed_subset_dir'],
embed_dir=args['embed_dir'],
label_data=args['vis_label_data'],
figure_dir=args['figure_dir'],
name=args['vis_name'],
select_cells=args['vis_select_cells'])
if args['has_expr_data']:
from src.explain.correlation import correlation
correlation(raw_subset_dir=args['raw_subset_dir'],
figure_dir=args['figure_dir'],
vis_name=args['vis_name'])
if args['visualize_image']:
from src.explain.VisualizeImage import visualizeImage
visualizeImage(raw_subset_dir=args['vis_img_raw_subset_dir'],
name_tiff=args['name_tiff'],
figure_dir=args['figure_img_dir'],
vis_name=args['vis_name'],
args=args)
if args['visualize_model_run']:
from src.utils.per_epoch_metrics import epochMetrics
epochMetrics(model_path=args['model_path'],
figure_dir=args['figure_model_dir'],
is_cs=args['is_cs'],
name=args['output_model_name'])
if __name__ == '__main__':
args = vars(parse_args())
main(**args)