def test_pivot_data(self):
json_obj = {"file_list": [self.processed_run_path], "run_list": [1]}
json_string = json.dumps(json_obj)
df_to_pca = pivot_data(
json_string, "discharge_capacity", "voltage", self.cycles_to_pca
)
self.assertEqual(df_to_pca.shape, (len(self.cycles_to_pca), 1000))
def test_get_pca_embeddings(self):
json_obj = {"file_list": [self.processed_run_path], "run_list": [1]}
json_string = json.dumps(json_obj)
df_to_pca = pivot_data(json_string, "discharge_capacity", "voltage",
self.cycles_to_test)
pca_embeddings = self.pc.get_pca_embeddings(df_to_pca)
self.assertEqual(pca_embeddings.shape,
(len(self.cycles_to_test), self.pc.n_components))
def test_get_reconstruction_errors(self):
json_obj = {"file_list": [self.processed_run_path], "run_list": [1]}
json_string = json.dumps(json_obj)
df_to_pca = pivot_data(json_string, "discharge_capacity", "voltage",
self.cycles_to_test)
reconstruction_errors, outliers = self.pc.get_reconstruction_error_outliers(
df_to_pca, threshold=1.5)
self.assertAlmostEqual(reconstruction_errors[0], 0.002553278, places=8)
self.assertTrue(outliers[0])
def test_get_pca_reconstruction(self):
"""
Method to inverse transform PCA embeddings to reconstruct data
"""
json_obj = {"file_list": [self.processed_run_path], "run_list": [1]}
json_string = json.dumps(json_obj)
df_to_pca = pivot_data(json_string, "discharge_capacity", "voltage",
self.cycles_to_test)
pca_embeddings = self.pc.get_pca_embeddings(df_to_pca)
pca_reconstruction = self.pc.get_pca_reconstruction(pca_embeddings)
self.assertEqual(pca_reconstruction.shape,
(len(self.cycles_to_test), 1000))
