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How to use get_embedding_id method in autotest

Best Python code snippet using autotest_python

get_data.py

Source:get_data.py

...34		# Validation/testing data35		for container, indices in [ (val_data, val_indices), (test_data, test_indices) ]:36			for m, sampling_percent in indices:37				for sampling in all_samplers:38					container[0].append(get_embedding_id(task, 'complete_data', 0))39					container[1].append(get_embedding_id(task, sampling, sampling_percent))40					container[2].append(task_map[task])41					container[3].append(metric_map[m])42					container[4].append(43						count_performance_retained(results[task][m][sampling_percent][sampling], m, scaled = False)44					)45		# Training data46		for m, sampling_percent in train_indices:47			y = [ count_performance_retained(48				results[task][m][sampling_percent][sampling], m, scaled = False49			) for sampling in all_samplers ]50			# Pointwise51			for at, sampling in enumerate(all_samplers):52				if y[at] in [ INF, -INF ]: continue53				train_data_pointwise[0].append(get_embedding_id(task, 'complete_data', 0))54				train_data_pointwise[1].append(get_embedding_id(task, sampling, sampling_percent))55				train_data_pointwise[2].append(task_map[task])56				train_data_pointwise[3].append(metric_map[m])57				train_data_pointwise[4].append(y[at])58			# Pairwise59			for i in range(len(all_samplers)):60				for j in range(i+1, len(all_samplers)):61					if y[i] in [ INF, -INF ]: continue62					if y[j] in [ INF, -INF ]: continue63					if y[i] == y[j]: continue64					if y[i] > y[j]: better, lower = i, j65					else: better, lower = j, i66					train_data_pairwise[0].append(get_embedding_id(task, 'complete_data', 0))67					train_data_pairwise[1].append(get_embedding_id(task, all_samplers[better], sampling_percent))68					train_data_pairwise[2].append(get_embedding_id(task, all_samplers[lower], sampling_percent))69					train_data_pairwise[3].append(task_map[task])70					train_data_pairwise[4].append(metric_map[m])71	save_obj([ train_data_pointwise, val_data, test_data ], TRAINING_DATA_PATH(dataset, "pointwise"))72	save_obj([ train_data_pairwise, val_data, test_data ], TRAINING_DATA_PATH(dataset, "pairwise"))73def get_results(dataset):74	PATH = CACHED_KENDALL_TAU_PATH(dataset)75	if os.path.exists(PATH + ".pkl"): return load_obj(PATH)76	loop = tqdm(77		total = len(scenarios) * ((len(svp_methods) * len(sampling_svp)) + len(sampling_kinds)) * \78		len(methods_to_compare) * len(percent_rns_options)79	)80	y = {}81	for task, metrics_to_return in scenarios:82		...

postgresql.py

Source:postgresql.py

...22            return row['id']23        except psycopg2.errors.UniqueViolation:24            self.get_connection().rollback()25            # We anyway will return the ID of already saved embedding26            return self.get_embedding_id(recognizer, digest)27        finally:28            cur.close()29    def get_embeddings(self, recognizer) -> List[PersonEmbedding]:30        cur = self.get_connection().cursor()31        sql = "SELECT id, person, embedding, tags FROM embeddings WHERE recognizer = %s"32        cur.execute(sql, (recognizer,))33        return [PersonEmbedding(r['id'], r['person'], np.array(r['embedding']), r['tags']) for r in cur.fetchall()]34    def get_embedding(self, embedding_id: str) -> dict:35        cur = self.get_connection().cursor()36        sql = "SELECT * FROM embeddings WHERE id = %s"37        cur.execute(sql, (embedding_id,))38        return cur.fetchone()39    def get_embedding_id(self, recognizer, digest) -> Optional[str]:40        cur = self.get_connection().cursor()41        cur.execute(42            "SELECT id FROM embeddings WHERE recognizer = %s AND digest = %s", (recognizer, digest),43        )44        row = cur.fetchone()45        return str(row['id']) if row else None46    def get_connection(self) -> psycopg2.extensions.connection:47        if self.conn is None:48            self.conn = self.connect()49        return self.conn50    def connect(self) -> psycopg2.extensions.connection:...

test_embeddings.py

Source:test_embeddings.py

...5        db = EmbeddingsDatabase(data_dir=_data_dir)6        embedding_name: str = 'r-16'7        embedding_id: int = db.add_embedding(embedding_name)8        assert embedding_id in list(db.list_ready_embedding_ids())9        assert embedding_id == db.get_embedding_id(embedding_name)10        new_embedding_id: int = db.push_new_embedding_version(embedding_name)11        assert new_embedding_id != embedding_id12        assert new_embedding_id in list(db.list_ready_embedding_ids())13        assert embedding_id not in list(db.list_ready_embedding_ids())14        assert new_embedding_id == db.get_embedding_id(embedding_name)15        assert isinstance(db.get_embedding(new_embedding_id), Embedding)16        other_embedding_id: int = db.get_embedding_id('r-32')17        url = 'data:,This is a test sentence'18        emb_vectors = list(db.get_url_vectors(url=url))19        assert len(emb_vectors) == 220        assert all(embedding_id in [new_embedding_id, other_embedding_id] for embedding_id, v in emb_vectors)21        db.print_embeddings()22        del db23        # Let's reconnect.24        db = EmbeddingsDatabase(data_dir=_data_dir)...

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