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https://github.com/chanzuckerberg/cellxgene.git
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3ebbb0ccbf
* move common code into server, update tests and makefile remove backend directory, refactor update smoke tests
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
import sys
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import argparse
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import random
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import time
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import numpy as np
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import server.common.compute.diffexp_generic as diffexp_generic
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from server.common.config.app_config import AppConfig
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from server.data_common.matrix_loader import MatrixDataLoader
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def main():
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parser = argparse.ArgumentParser("A command to test diffexp")
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parser.add_argument("dataset", help="name of a dataset to load")
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parser.add_argument("-na", "--numA", type=int, help="number of rows in group A")
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parser.add_argument("-nb", "--numB", type=int, help="number of rows in group B")
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parser.add_argument("-va", "--varA", help="obs variable:value to use for group A")
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parser.add_argument("-vb", "--varB", help="obs variable:value to use for group B")
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parser.add_argument("-t", "--trials", default=1, type=int, help="number of trials")
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parser.add_argument("-a", "--alg", choices=("default", "generic"), default="default", help="algorithm to use")
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parser.add_argument("-s", "--show", default=False, action="store_true", help="show the results")
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parser.add_argument(
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"-n", "--new-selection", default=False, action="store_true", help="change the selection between each trial"
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)
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parser.add_argument("--seed", default=1, type=int, help="set the random seed")
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args = parser.parse_args()
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app_config = AppConfig()
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app_config.update_server_config(single_dataset__datapath=args.dataset)
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app_config.update_server_config(app__verbose=True)
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app_config.complete_config()
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loader = MatrixDataLoader(args.dataset)
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adaptor = loader.open(app_config)
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random.seed(args.seed)
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np.random.seed(args.seed)
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rows = adaptor.get_shape()[0]
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if args.numA:
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filterA = random.sample(range(rows), args.numA)
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elif args.varA:
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vname, vval = args.varA.split(":")
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filterA = get_filter_from_obs(adaptor, vname, vval)
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else:
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print("must supply numA or varA")
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sys.exit(1)
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if args.numB:
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filterB = random.sample(range(rows), args.numB)
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elif args.varB:
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vname, vval = args.varB.split(":")
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filterB = get_filter_from_obs(adaptor, vname, vval)
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else:
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print("must supply numB or varB")
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sys.exit(1)
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for i in range(args.trials):
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if args.new_selection:
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if args.numA:
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filterA = random.sample(range(rows), args.numA)
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if args.numB:
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filterB = random.sample(range(rows), args.numB)
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maskA = np.zeros(rows, dtype=bool)
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maskA[filterA] = True
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maskB = np.zeros(rows, dtype=bool)
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maskB[filterB] = True
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t1 = time.time()
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if args.alg == "default":
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results = adaptor.compute_diffexp_ttest(maskA, maskB)
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elif args.alg == "generic":
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results = diffexp_generic.diffexp_ttest(adaptor, maskA, maskB)
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t2 = time.time()
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print("TIME=", t2 - t1)
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if args.show:
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for res in results:
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print(res)
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def get_filter_from_obs(adaptor, obsname, obsval):
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attrs = adaptor.get_obs_columns()
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if obsname not in attrs:
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print(f"Unknown obs attr {obsname}: expected on of {attrs}")
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sys.exit(1)
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obsvals = adaptor.query_obs_array(obsname)[:]
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obsval = type(obsvals[0])(obsval)
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vfilter = np.where(obsvals == obsval)[0]
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if len(vfilter) == 0:
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u = np.unique(obsvals)
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print(f"Unknown value in variable {obsname}:{obsval}: expected one of {list(u)}")
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sys.exit(1)
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return vfilter
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if __name__ == "__main__":
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main()
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