class LCV:
"""Wrapper for SciKitLearn linear_model.ElasticNetCV to help with model optimization"""
def __init__(self, x='numpy_array', y='predictor', sample_labels=None,
test_split=.2, sk_lassocv_kwargs=None, regression_site_labels=None, test_samples=None,
output_name=None, output_directory=None):
assert isinstance(sk_lassocv_kwargs, dict)
assert isinstance(sample_labels, list)
assert isinstance(regression_site_labels, list)
self.x = x
self.y = y
self.test_split = test_split
self.la_kwargs = sk_lassocv_kwargs
# test_container/ train_container [array, outcomes, labels]
self.test_container = [[], [], []]
self.train_container = [[], [], []]
self.la_model = None
self.sample_labels = sample_labels
self.regression_site_labels = regression_site_labels
self.model_stats = None
self.input_test_samples = test_samples
self.output_name = output_name
self.output_directory = output_directory
self.run()
def run(self):
self.set_test_samples()
if self.input_test_samples:
self.test_split = 0.1
if self.test_split == 0:
self.test_container = self.train_container
self.fit_model()
self.get_model_stats()
self.model_output()
def set_test_samples(self):
if self.input_test_samples:
test_samples = self.input_test_samples
else:
test_size = int(round(len(self.y) * self.test_split, 0))
test_samples = random.sample(self.sample_labels, test_size)
# test_container/ validation_container [test_array, test_outcomes, labels]
for count, info in enumerate(zip(self.x, self.y, self.sample_labels)):
if info[2] in test_samples:
self.test_container[0].append(info[0])
self.test_container[1].append(info[1])
self.test_container[2].append(info[2])
else:
self.train_container[0].append(info[0])
self.train_container[1].append(info[1])
self.train_container[2].append(info[2])
self.test_container[0] = np.asarray(self.test_container[0])
self.test_container[1] = np.asarray(self.test_container[1])
self.train_container[0] = np.asarray(self.train_container[0])
self.train_container[1] = np.asarray(self.train_container[1])
def fit_model(self):
self.la_model = LassoCV(**self.la_kwargs).fit(self.train_container[0], self.train_container[1])
def get_model_stats(self):
regression_sites = []
for site in zip(self.regression_site_labels, list(self.la_model.coef_)):
if not math.isclose(site[1], 0):
regression_sites.append(site[0])
model_score = self.la_model.score(self.test_container[0], self.test_container[1])
predicted_values = self.la_model.predict(self.test_container[0])
self.model_stats = (regression_sites, model_score, predicted_values)
def model_output(self):
"""
"""
kwarg_pair = ['Test Split:%s\n' % str(self.test_split)]
for key, value in self.la_model.get_params().items():
kwarg_pair.append('%s:%s' % (key, str(value)))
output_path = '%s%s' % (self.output_directory, self.output_name)
joblib.dump(self.la_model, output_path + '.model')
out = open(output_path + '.model_info.txt', 'w')
out.write('%s\n' % self.output_name)
out.write('Model Score (R^2) = %s\n' % str(self.model_stats[1]))
out.write('%s\n' % '\t'.join(kwarg_pair))
out.write('Test Samples \t%s\n' % '\t'.join(stc(self.test_container[2])))
out.write('Test Samples Predicted Values \t%s\n' % '\t'.join(stc(self.model_stats[2])))
out.write('Test Samples Actual Values \t%s\n' % '\t'.join(stc(self.test_container[1])))
out.write('Training Samples \t%s\n' % '\t'.join(stc(self.train_container[2])))
out.write('Training Samples Actual Values \t%s\n' % '\t'.join(stc(self.train_container[1])))
out.write('Regression Sites \t%s\n' % '\t'.join(stc(self.model_stats[0])))
out.close()
