Dataframe effect size #52
Conversation
Codecov Report
@@ Coverage Diff @@
## main #52 +/- ##
==========================================
+ Coverage 93.25% 94.75% +1.49%
==========================================
Files 3 4 +1
Lines 267 343 +76
Branches 53 67 +14
==========================================
+ Hits 249 325 +76
Misses 18 18
📣 We’re building smart automated test selection to slash your CI/CD build times. Learn more |
|
@jeromekelleher |
tstrait/simulate_effect_size.py
Outdated
|
|
||
|
|
||
| def sim_traits(ts, num_causal, model, alpha=0, random_seed=None): | ||
| """Rnadomly selects causal sites from the inputted tree sequence data, and simulates |
tstrait/simulate_effect_size.py
Outdated
| import tstrait | ||
|
|
||
|
|
||
| class EffectSizeSimulator: |
There was a problem hiding this comment.
Perhaps TraitSimulator would be clearer?
tstrait/simulate_effect_size.py
Outdated
| simulation. The tree sequence data must include a mutation. | ||
| :type ts: tskit.TreeSequence | ||
| :param num_causal: Number of causal sites that will be chosen randomly. It must be | ||
| a positive integer that is greater than the number of sites in the tree sequence |
tstrait/simulate_effect_size.py
Outdated
| data=[causal_site_array, causal_state_array, beta_array] | ||
| ).T | ||
| effect_size_df = effect_size_df.set_axis( | ||
| ["SiteID", "CausalState", "EffectSize"], axis="columns" |
There was a problem hiding this comment.
Looks good - any particular reason for CamelCasing the column names? I would tend to see them as variables, so would use site_id, causal_state etc
tstrait/simulate_effect_size.py
Outdated
| return effect_size_df | ||
|
|
||
|
|
||
| def sim_traits(ts, num_causal, model, alpha=0, random_seed=None): |
There was a problem hiding this comment.
I guess an issue here is that we're simulating a single trait. Should we call this function sim_trait then?
tests/test_sim_trait.py
Outdated
| alpha=alpha, | ||
| random_seed=random_seed, | ||
| ) | ||
| assert sim_result.shape[0] == num_causal |
There was a problem hiding this comment.
These tests aren't very strong, and there's a lot of duplicated code. You could delegate most of this to a method
def check_dimensions(self, df, num_causal):
assert len(df) == num_causal
# etc
tests/test_sim_trait.py
Outdated
| ): | ||
| tstrait.sim_traits(ts=ts, num_causal=1, model=model, alpha=1, random_seed=1) | ||
|
|
||
| @pytest.mark.parametrize("num_ind", [1, 2, np.array([5])[0]]) |
There was a problem hiding this comment.
There's really no point in taking the cross product of these parameters - they don't interact and all you're testing is whether you raise a ValueError. It's just a waste of time and electricity!
|
will also need to add pandas to setup.cfg install requires |
|
@jeromekelleher |
Generate a pandas dataframe to output simulated effect sizes
Created a function to obtain dataframe based on effect size simulation.