def generic_compute_shaps(fasta_files, genotype, classifier, n_samples, verb):
"""
Given a genotype file and/or a collection of possibly gzipped FASTA files as well as a
phenotrex classifier, collect genotype information from both, get SHAP information about the
genotypes using the classifier, and return a finished ShapHandler object as well as the list
of GenotypeRecords created.
"""
if not len(fasta_files) and genotype is None:
raise RuntimeError(
'Must either supply FASTA file(s) or single genotype file for prediction.')
if len(fasta_files):
grs_from_fasta = fastas_to_grs(fasta_files, n_threads=None, verb=verb)
else:
grs_from_fasta = []
grs_from_file = load_genotype_file(genotype) if genotype is not None else []
gr = grs_from_fasta + grs_from_file
model = load_classifier(filename=classifier, verb=verb)
sh = ShapHandler.from_clf(model)
try:
fs, sv, bv = model.get_shap(gr, n_samples=n_samples)
except TypeError:
raise RuntimeError('This TrexClassifier is not capable of generating SHAP explanations.')
sh.add_feature_data(sample_names=[x.identifier for x in gr],
features=fs, shaps=sv, base_value=bv)
return sh, gr
def get_weights(classifier, out):
"""
Write the feature weights of a classifier to a flat file.
"""
from phenotrex.io.flat import write_weights_file
from phenotrex.io.serialization import load_classifier
clf = load_classifier(filename=classifier, verb=True)
weights = clf.get_feature_weights()
write_weights_file(weights_file=out, weights=weights)
def test_predict_from_fasta(self, trait_name, classifier_type, fasta_files):
model_path = MODELS_PATH/trait_name/f'{trait_name}.{classifier_type.lower()}.pkl'
with TemporaryDirectory() as tmpdir:
tmp_path = Path(tmpdir)/'model.pkl'
summary_path = Path(tmpdir)/'summary.tsv' if classifier_type == 'XGB' else None
per_sample_path = Path(tmpdir)/'per_sample.tsv' if classifier_type == 'XGB' else None
clf = load_classifier(model_path)
save_classifier(clf, tmp_path)
pred = predict(
fasta_files=fasta_files, classifier=tmp_path,
out_explain_summary=summary_path,
out_explain_per_sample=per_sample_path
)
assert summary_path is None or summary_path.is_file()
assert per_sample_path is None or per_sample_path.is_file()
print(pred)
def test_get_shap(self, trait_name, classifier_type):
"""
Get ShapHandler and SHAP data from classifier and genotype file.
:param trait_name:
:param classifier_type:
:return:
"""
full_path_genotype = FLAT_PATH / trait_name / f"{trait_name}.genotype"
full_path_phenotype = FLAT_PATH / trait_name / f"{trait_name}.phenotype"
training_records, genotype, phenotype, group = load_training_files(
genotype_file=full_path_genotype,
phenotype_file=full_path_phenotype,
verb=True)
tr = training_records[:3]
model_path = MODELS_PATH / trait_name / f'{trait_name}.{classifier_type.lower()}.pkl'
clf = load_classifier(model_path, verb=True)
sh = ShapHandler.from_clf(clf)
fs, sv, bv = clf.get_shap(tr, n_samples=50)
return tr, sh, fs, sv, bv
def predict(input_files: List[str],
classifier: str,
min_proba=0.0,
verb=True) -> pd.DataFrame:
if not len(input_files):
raise RuntimeError('Must supply input file(s) for prediction.')
fasta_files, genotype_files = _determine_file_types(input_files)
gr = []
if fasta_files:
gr += fastas_to_grs(fasta_files, n_threads=None, verb=verb)
for f in genotype_files:
gr += load_genotype_file(f)
model = load_classifier(filename=classifier, verb=verb)
preds, probas = model.predict(X=gr)
translate_output = {
trait_id: trait_sign
for trait_sign, trait_id in DEFAULT_TRAIT_SIGN_MAPPING.items()
}
out = {}
for record, result, probability in zip(gr, preds, probas):
if probability[result] < min_proba:
result_disp = np.nan
else:
result_disp = translate_output[result]
out[record.identifier] = {
'Trait Present': result_disp,
'Confidence': str(round(probability[result], 4))
}
df = pd.DataFrame.from_dict(out).T
df.index.name = 'Genome'
df = df.reset_index()
df['Trait Name'] = model.trait_name
df['Feature Type'] = model.feature_type
df['Model File'] = Path(classifier).name
df = df[[
'Genome', 'Model File', 'Feature Type', 'Trait Name', 'Trait Present',
'Confidence'
]]
return df
def predict(fasta_files=tuple(),
genotype=None,
classifier=None,
min_proba=0.5,
out_explain_per_sample=None,
out_explain_summary=None,
shap_n_samples=None,
n_max_explained_features=None,
verb=False):
"""
Predict phenotype from a set of (possibly gzipped) DNA or protein FASTA files
or a single genotype file. Optionally, compute SHAP explanations individually and/or summarily
for the predicted samples.
NB: Genotype computation is highly expensive and performed on the fly on FASTA files.
For increased speed when predicting multiple phenotypes, create a .genotype file to reuse
with the command `compute-genotype`.
NB: As opposed to XGB models where they are trivially available, computing SHAP explanations
on SVM models entails training a model-agnostic KernelExplainer which is highly costly (dozens
to hundreds of seconds per sample if using a somewhat reasonable value for `shap_n_samples`).
:param fasta_files: An iterable of fasta file paths
:param genotype: A genotype file path
:param classifier: A pickled classifier file path
:param out_explain_per_sample: Where to save the most influential features by SHAP for each
predicted sample.
:param out_explain_summary: Where to save the SHAP summary of the predictions.
:param shap_n_samples: The n_samples parameter -
only used by models which incorporate a `shap.KernelExplainer`.
:param n_max_explained_features: How many of the most influential features by SHAP to consider.
:param verb: Whether to show progress of fasta file annotation.
"""
if not len(fasta_files) and genotype is None:
raise RuntimeError(
'Must supply FASTA file(s) and/or single genotype file for prediction.'
)
if len(fasta_files):
grs_from_fasta = fastas_to_grs(fasta_files, n_threads=None, verb=verb)
else:
grs_from_fasta = []
grs_from_file = load_genotype_file(
genotype) if genotype is not None else []
gr = grs_from_fasta + grs_from_file
model = load_classifier(filename=classifier, verb=verb)
if out_explain_per_sample is not None or out_explain_summary is not None:
try:
fs, sv, bv = model.get_shap(gr, n_samples=shap_n_samples)
except TypeError:
raise RuntimeError(
'This TrexClassifier is not capable of generating SHAP explanations.'
)
sh = ShapHandler.from_clf(model)
sh.add_feature_data(sample_names=[x.identifier for x in gr],
features=fs,
shaps=sv,
base_value=bv)
if out_explain_per_sample is not None:
shap_df = pd.concat([
sh.get_shap_force(x.identifier,
n_max_features=n_max_explained_features)
for x in gr
],
axis=0)
shap_df.to_csv(out_explain_per_sample, sep='\t', index=False)
if out_explain_summary is not None:
sum_df = sh.get_shap_summary(n_max_explained_features)
sum_df.to_csv(out_explain_summary, sep='\t', index=False)
preds, probas = model.predict(X=gr)
translate_output = {
trait_id: trait_sign
for trait_sign, trait_id in DEFAULT_TRAIT_SIGN_MAPPING.items()
}
print(f"# Trait: {model.trait_name}")
print("Identifier\tTrait present\tConfidence")
for record, result, probability in zip(gr, preds, probas):
if probability[result] < min_proba:
result_disp = "N/A"
else:
result_disp = translate_output[result]
print(
f"{record.identifier}\t{result_disp}\t{str(round(probability[result], 4))}"
)
def test_ml_load(self):
td = self.get_training_data()
xgb = load_classifier(MODELS_PATH/trait_name/f'{trait_name}.xgb.pkl')
preds = xgb.predict(td)
assert preds is not None
