Evaluators Tutorial¶
– On evaluation –
In this tutorial, we will cover different approaches to model evaluation. Let’s start by investigating perturb-lib evaluators:
[1]:
import perturb_lib as plib
plib.list_evaluators()
[1]:
['MAE', 'Pearson', 'R2', 'RMSE']
Let us focus on standard RMSE evaluator for example:
[2]:
evaluator_id = "RMSE"
evaluator = plib.load_evaluator(evaluator_id)
plib.describe_evaluator(evaluator_id)
[2]:
'Root-mean-square error (RMSE).'
Let us now get datasets and train a sample model.
[5]:
plib_data = plib.load_plibdata(contexts_ids=["HumanCellLine_K562_10xChromium3-scRNA-seq_Replogle22"])
traindata, _, testdata = plib.split_plibdata_3fold(
plib_data, context_ids="HumanCellLine_K562_10xChromium3-scRNA-seq_Replogle22"
)
model = plib.load_model(
"Catboost",
model_args={
"learning_rate": 0.75,
"random_seed": 11,
"iterations": 1000,
"task_type": "CPU",
},
)
model.fit(traindata)
16:10:50 | INFO | Loading training data into RAM..
16:10:51 | INFO | ReactomePathways.gmt.zip found in cache.
16:10:51 | INFO | Reducing embedding to 20 dimensions using PCA.
16:10:51 | INFO | Embedding training data of size 12344962..
16:10:51 | INFO | Creating X matrix
16:10:54 | INFO | Creating y vector
16:10:54 | INFO | Fitting CatBoostRegressor on data of shape (12344962, 41)..
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16:16:51 | INFO | Model fitting completed
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We can now evaluate_model results as follows:
[6]:
evaluator.evaluate(model.predict(testdata), testdata)
[6]:
0.17545009567806363
Introducing new evaluators is also trivial:
[6]:
@plib.register_evaluator
class CoolEvaluator(plib.PlibEvaluatorMixin):
def evaluate(self, predictions, true_values, context_adata=None) -> float:
return 0.0
[7]:
"CoolEvaluator" in plib.list_evaluators()
[7]:
True