Files
cellxgene/server/test/unit/common/test_writable_annotation.py
Madison Dunitz 2689d8d2c0 Create hosted user annotations [1685] (#1726)
* add function to retrieve latest annotation from db, db updates

* read and write tiledb arrays

* adding tests
2020-08-13 19:07:17 -05:00

288 lines
12 KiB
Python

import json
from os import path, listdir
import unittest
from unittest.mock import MagicMock, patch
import tiledb
from flask import Flask
import server.test.unit.decode_fbs as decode_fbs
import shutil
import numpy as np
import pandas as pd
from server.common.rest import schema_get_helper, annotations_put_fbs_helper
from server.db.cellxgene_orm import CellxGeneDataset, Annotation
from server.test import data_with_tmp_annotations, make_fbs, data_with_tmp_tiledb_annotations
from server.data_common.matrix_loader import MatrixDataType
from server.common.errors import AnnotationCategoryNameError
class auth(object):
def get_user_id():
return "1234"
class WritableTileDBStoredAnnotationTest(unittest.TestCase):
def setUp(self):
self.user_id = '1234'
self.data, self.tmp_dir, self.annotations = data_with_tmp_tiledb_annotations(MatrixDataType.H5AD)
self.data.dataset_config.user_annotations = self.annotations
self.db = self.annotations.db
self.n_rows = self.data.get_shape()[0]
self.test_dict = {
"cat_A": pd.Series(["label_A"] * self.n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * self.n_rows, dtype="category"),
}
self.fbs = make_fbs(self.test_dict)
self.df = pd.DataFrame(self.test_dict)
self.app = Flask('fake_app')
self.app.__setattr__("auth", auth)
def tearDown(self):
shutil.rmtree(self.tmp_dir)
def annotation_put_fbs(self, fbs):
annotations_put_fbs_helper(self.data, fbs)
res = json.dumps({"status": "OK"})
return res
def test_category_name_throws_errors_for_categories_that_cant_be_converted_to_filenames(self):
with self.app.test_request_context():
bad_category_names = make_fbs(
{
"cat_A": pd.Series(["label_A"] * self.n_rows, dtype="category"),
"cat/B": pd.Series(["label_B"] * self.n_rows, dtype="category"),
}
)
with self.assertRaises(AnnotationCategoryNameError):
self.annotation_put_fbs(bad_category_names)
def test_convert_to_pandas__converts_tiledb_to_pandas_df(self):
with self.app.test_request_context():
self.annotations.write_labels(self.df, self.data)
dataset_id = self.db.query([CellxGeneDataset], [CellxGeneDataset.name == self.data.get_location()])[0].id
annotation = self.db.query_for_most_recent(
Annotation,
[Annotation.user_id == self.user_id, Annotation.dataset_id == str(dataset_id)]
)
# retrieve tiledb array
df = tiledb.open(annotation.tiledb_uri)
self.assertEqual(type(df), tiledb.array.SparseArray)
# convert to pandas df
pandas_df = self.annotations.convert_to_pandas_df(df)
self.assertEqual(type(pandas_df), pd.DataFrame)
def test_write_labels_creates_a_dataset_if_it_doesnt_exist(self):
with self.app.test_request_context():
new_name = 'new_dataset/location'
self.data.get_location = MagicMock(return_value=new_name)
num_datasets = len(self.db.query([CellxGeneDataset]))
self.annotation_put_fbs(self.fbs)
more_datasets = len(self.db.query([CellxGeneDataset]))
self.assertGreater(more_datasets, num_datasets)
self.assertGreater(len(self.db.query([CellxGeneDataset], [CellxGeneDataset.name == new_name])), 0)
def test_write_labels_links_to_existing_dataset(self):
with self.app.test_request_context():
# add dataset to to db
self.annotation_put_fbs(self.fbs)
num_datasets = len(self.db.query([CellxGeneDataset]))
# create another annotation with the same dataset
self.annotation_put_fbs(self.fbs)
same_num_datasets = len(self.db.query([CellxGeneDataset]))
self.assertEqual(num_datasets, same_num_datasets)
def test_read_labels_returns_pandas_df(self):
with self.app.test_request_context():
self.annotation_put_fbs(self.fbs)
pandas_df = self.annotations.read_labels(self.data)
self.assertEqual(type(pandas_df), pd.DataFrame)
def test_read_labels_returns_df_matching_original(self):
with self.app.test_request_context():
self.annotation_put_fbs(self.fbs)
pandas_df = self.annotations.read_labels(self.data)
self.assertEqual(pandas_df.shape, (self.n_rows, 2))
self.assertEqual(set(pandas_df.columns), {"cat_A", "cat_B"})
self.assertTrue(self.data.original_obs_index.equals(pandas_df.index))
self.assertTrue(np.all(pandas_df["cat_A"] == ["label_A"] * self.n_rows))
self.assertTrue(np.all(pandas_df["cat_B"] == ["label_B"] * self.n_rows))
def test_error_checks(self):
# verify that the expected errors are generated
with self.app.test_request_context():
n_rows = self.data.get_shape()[0]
fbs_bad = make_fbs({"louvain": pd.Series(["undefined"] * n_rows, dtype="category")})
# ensure we catch attempt to overwrite non-writable data
with self.assertRaises(KeyError):
self.annotation_put_fbs(fbs_bad)
@patch('server.common.annotations.hosted_tiledb.current_app')
def test_write_labels_stores_df_as_tiledb_array(self, mock_user_id):
mock_user_id.auth.get_user_id.return_value = '1234'
self.annotations.write_labels(self.df, self.data)
# get uri
dataset_id = self.db.query([CellxGeneDataset], [CellxGeneDataset.name == self.data.get_location()])[0].id
annotation = self.db.query_for_most_recent(
Annotation,
[Annotation.user_id == '1234', Annotation.dataset_id == str(dataset_id)]
)
df = tiledb.open(annotation.tiledb_uri)
self.assertEqual(type(df), tiledb.array.SparseArray)
class WritableAnnotationTest(unittest.TestCase):
def setUp(self):
self.data, self.tmp_dir, self.annotations = data_with_tmp_annotations(MatrixDataType.H5AD)
self.data.dataset_config.user_annotations = self.annotations
def tearDown(self):
shutil.rmtree(self.tmp_dir)
def annotation_put_fbs(self, fbs):
annotations_put_fbs_helper(self.data, fbs)
res = json.dumps({"status": "OK"})
return res
def test_error_checks(self):
# verify that the expected errors are generated
n_rows = self.data.get_shape()[0]
fbs_bad = make_fbs({"louvain": pd.Series(["undefined"] * n_rows, dtype="category")})
# ensure we catch attempt to overwrite non-writable data
with self.assertRaises(KeyError):
self.annotation_put_fbs(fbs_bad)
def test_write_to_file(self):
# verify the file is written as expected
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
self.assertTrue(path.exists(self.annotations.output_file))
df = pd.read_csv(self.annotations.output_file, index_col=0, header=0, comment="#")
self.assertEqual(df.shape, (n_rows, 2))
self.assertEqual(set(df.columns), {"cat_A", "cat_B"})
self.assertTrue(self.data.original_obs_index.equals(df.index))
self.assertTrue(np.all(df["cat_A"] == ["label_A"] * n_rows))
self.assertTrue(np.all(df["cat_B"] == ["label_B"] * n_rows))
# verify complete overwrite on second attempt, AND rotation occurs
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A1"] * n_rows, dtype="category"),
"cat_C": pd.Series(["label_C"] * n_rows, dtype="category"),
}
)
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
self.assertTrue(path.exists(self.annotations.output_file))
df = pd.read_csv(self.annotations.output_file, index_col=0, header=0, comment="#")
self.assertEqual(set(df.columns), {"cat_A", "cat_C"})
self.assertTrue(np.all(df["cat_A"] == ["label_A1"] * n_rows))
self.assertTrue(np.all(df["cat_C"] == ["label_C"] * n_rows))
# rotation
name, ext = path.splitext(self.annotations.output_file)
backup_dir = f"{name}-backups"
self.assertTrue(path.isdir(backup_dir))
found_files = listdir(backup_dir)
self.assertEqual(len(found_files), 1)
def test_file_rotation_to_max_9(self):
# verify we stop rotation at 9
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
for i in range(0, 11):
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
name, ext = path.splitext(self.annotations.output_file)
backup_dir = f"{name}-backups"
self.assertTrue(path.isdir(backup_dir))
found_files = listdir(backup_dir)
self.assertTrue(len(found_files) <= 9)
def test_put_get_roundtrip(self):
# verify that OBS PUTs (annotation_put_fbs) are accessible via
# GET (annotation_to_fbs_matrix)
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
# put
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
# get
labels = self.annotations.read_labels(None)
fbsAll = self.data.annotation_to_fbs_matrix("obs", None, labels)
schema = schema_get_helper(self.data)
annotations = decode_fbs.decode_matrix_FBS(fbsAll)
obs_index_col_name = schema["annotations"]["obs"]["index"]
self.assertEqual(annotations["n_rows"], n_rows)
self.assertEqual(annotations["n_cols"], 7)
self.assertIsNone(annotations["row_idx"])
self.assertEqual(
annotations["col_idx"],
[obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain", "cat_A", "cat_B"],
)
col_idx = annotations["col_idx"]
self.assertEqual(annotations["columns"][col_idx.index("cat_A")], ["label_A"] * n_rows)
self.assertEqual(annotations["columns"][col_idx.index("cat_B")], ["label_B"] * n_rows)
# verify the schema was updated
all_col_schema = {c["name"]: c for c in schema["annotations"]["obs"]["columns"]}
self.assertEqual(
all_col_schema["cat_A"],
{"name": "cat_A", "type": "categorical", "categories": ["label_A"], "writable": True},
)
self.assertEqual(
all_col_schema["cat_B"],
{"name": "cat_B", "type": "categorical", "categories": ["label_B"], "writable": True},
)
def test_config(self):
features = self.data.get_features(self.annotations)
# test each for singular presence and accuracy of available flag
def check_feature(method, path, available):
feature = list(
filter(lambda f: f.method == method and f.path == path and f.available == available, features)
)
self.assertIsNotNone(feature)
self.assertEqual(len(feature), 1)
check_feature("POST", "/cluster/", False)
check_feature("POST", "/diffexp/", self.data.dataset_config.diffexp__enable)
check_feature("GET", "/layout/obs", True)
check_feature("PUT", "/layout/obs", self.data.dataset_config.embeddings__enable_reembedding)
check_feature("PUT", "/annotations/obs", True)