Comments (3)
hi @Chengwei94 ,
can you report what are the rest of the parameters for the prepare and solve method?
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Hi, heres the snipppet I am running. I have tried both the temporal and spatial temporal problem. The joint attr is the output of a topic model where theres 50 dimension.
from jax.config import config; config.update("jax_enable_x64", True)
stp = TemporalProblem(adata)
stp = stp.score_genes_for_marginals(
gene_set_proliferation="mouse", gene_set_apoptosis="mouse"
)
adata.obs['time'] = adata.obs.timepoint.astype('str').str[1:].astype('float32').astype('category')
stp = stp.prepare(
time_key="time",
# spatial_key="spatial",
joint_attr="X_stamp",
cost = 'euclidean',
# cost = {"x": "hellinger", "y": "hellinger", "xy":"hellinger"},
device = "cpu",
# callback="local-pca",
)
stp = stp.solve(initializer="rank2", rank = 500)
Im using float64 in jax, as I encoutered an error when im using float32
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hi @Chengwei94 sorry for late reply, yes the Euclidean cost is expected to take more memory than the squared euclidean. All the costs except the squared euclidean are less memory efficient cause they need to be factorized differently in the low rank cased, so a higher memory requirement is expected. What is the motivation to use euclidean instead of squared euclidean? and would you be able to try with a smaller rank value (e.g. 300) and on GPU? thanks!
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Related Issues (20)
- Buggy Test: test_gw_problem gives joint_attr HOT 1
- Buggy Test: `scale_cost` given to prepare in tests/problems/time/test_mixins.py HOT 1
- check the convergence flag
- change links to papers
- adapt low rank gamma default for linear problems HOT 1
- describe birth death process better HOT 1
- Extract the cells in the trace HOT 4
- adapt user guide
- Refactor: Check if both x and x_callback is given
- Refactor: Maybe GW should inherit from FGW instead of vice versa HOT 2
- check for failing tests HOT 1
- check for unrecognized in `solve` methods
- check for warnings thrown by us
- Add Elastic Costs again or asses if they are necessary or not HOT 2
- Plotting Tests Fail HOT 1
- `.impute` consumes too much memory HOT 2
- output the raw cell transition data without aggregation HOT 6
- Full rank initializers
- bug when using tp.compute_interpolated_distance HOT 7
- Questions about patient manifold.
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