class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists(['name', 'id', 'fid', 'isMale', 'scale', 'birth'], datatypes('string', 'int64', 'float64', 'boolean', 'decimal', 'datetime')) self.schema = df_schema_to_odps_schema(schema) table_name = 'pyodps_test_engine_table' self.odps.delete_table(table_name, if_exists=True) self.table = self.odps.create_table( name='pyodps_test_engine_table', schema=self.schema) self.expr = CollectionExpr(_source_data=self.table, _schema=schema) self.engine = ODPSEngine(self.odps) class FakeBar(object): def update(self, *args, **kwargs): pass self.faked_bar = FakeBar() def _gen_data(self, rows=None, data=None, nullable_field=None, value_range=None): if data is None: data = [] for _ in range(rows): record = [] for t in self.schema.types: method = getattr(self, '_gen_random_%s' % t.name) if t.name == 'bigint': record.append(method(value_range=value_range)) else: record.append(method()) data.append(record) if nullable_field is not None: j = self.schema._name_indexes[nullable_field] for i, l in enumerate(data): if i % 2 == 0: data[i][j] = None self.odps.write_table(self.table, 0, [self.table.new_record(values=d) for d in data]) return data def testTunnelCases(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) expr = self.expr.name.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) res = self.engine._handle_cases(self.expr, self.faked_bar) result = self._get_result(res) self.assertEqual(data, result) expr = self.expr['name', self.expr.id.rename('new_id')] res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual([it[:2] for it in data], result) table_name = 'pyodps_test_engine_partitioned' self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[df.name == data[0][0]]['fid', 'id'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[df.name == data[0][0]]['fid', 'id'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name', 'id']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) def testAsync(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.id.sum() res = self.engine.execute(expr, async=True) self.assertNotEqual(res.instance.status, Instance.Status.TERMINATED) res.wait() self.assertEqual(sum(it[1] for it in data), res.fetch()) def testBase(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10]['name', lambda x: x.id] result = self._get_result(self.engine.execute(expr).values) self.assertEqual(len([it for it in data if it[1] < 10]), len(result)) if len(result) > 0: self.assertEqual(2, len(result[0])) expr = self.expr[Scalar(3).rename('const'), self.expr.id, (self.expr.id + 1).rename('id2')] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual([c.name for c in res.columns], ['const', 'id', 'id2']) self.assertTrue(all(it[0] == 3 for it in result)) self.assertEqual(len(data), len(result)) self.assertEqual([it[1]+1 for it in data], [it[2] for it in result]) expr = self.expr.sort('id')[:5] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) expr = self.expr.sort('id')[:5] # test do not use tunnel res = self.engine.execute(expr, use_tunnel=False) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) def testElement(self): data = self._gen_data(5, nullable_field='name') fields = [ self.expr.name.isnull().rename('name1'), self.expr.name.notnull().rename('name2'), self.expr.name.fillna('test').rename('name3'), self.expr.id.isin([1, 2, 3]).rename('id1'), self.expr.id.isin(self.expr.fid.astype('int')).rename('id2'), self.expr.id.notin([1, 2, 3]).rename('id3'), self.expr.id.notin(self.expr.fid.astype('int')).rename('id4'), self.expr.id.between(self.expr.fid, 3).rename('id5'), self.expr.name.fillna('test').switch('test', 'test' + self.expr.name.fillna('test'), 'test2', 'test2' + self.expr.name.fillna('test'), default=self.expr.name).rename('name4'), self.expr.id.cut([100, 200, 300], labels=['xsmall', 'small', 'large', 'xlarge'], include_under=True, include_over=True).rename('id6') ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual(len([it for it in data if it[0] is None]), len([it[0] for it in result if it[0]])) self.assertEqual(len([it[0] for it in data if it[0] is not None]), len([it[1] for it in result if it[1]])) self.assertEqual([(it[0] if it[0] is not None else 'test') for it in data], [it[2] for it in result]) self.assertEqual([(it[1] in (1, 2, 3)) for it in data], [it[3] for it in result]) fids = [int(it[2]) for it in data] self.assertEqual([(it[1] in fids) for it in data], [it[4] for it in result]) self.assertEqual([(it[1] not in (1, 2, 3)) for it in data], [it[5] for it in result]) self.assertEqual([(it[1] not in fids) for it in data], [it[6] for it in result]) self.assertEqual([(it[2] <= it[1] <= 3) for it in data], [it[7] for it in result]) self.assertEqual([to_str('testtest' if it[0] is None else it[0]) for it in data], [to_str(it[8]) for it in result]) def get_val(val): if val <= 100: return 'xsmall' elif 100 < val <= 200: return 'small' elif 200 < val <= 300: return 'large' else: return 'xlarge' self.assertEqual([to_str(get_val(it[1])) for it in data], [to_str(it[9]) for it in result]) def testArithmetic(self): data = self._gen_data(5, value_range=(-1000, 1000)) fields = [ (self.expr.id + 1).rename('id1'), (self.expr.fid - 1).rename('fid1'), (self.expr.scale * 2).rename('scale1'), (self.expr.scale + self.expr.id).rename('scale2'), (self.expr.id / 2).rename('id2'), (self.expr.id ** -2).rename('id3'), abs(self.expr.id).rename('id4'), (~self.expr.id).rename('id5'), (-self.expr.fid).rename('fid2'), (~self.expr.isMale).rename('isMale1'), (-self.expr.isMale).rename('isMale2'), (self.expr.id // 2).rename('id6'), (self.expr.birth + day(1).rename('birth1')), (self.expr.birth - (self.expr.birth - millisecond(10))).rename('birth2'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[0] for it in result]) self.assertAlmostEqual([it[2] - 1 for it in data], [it[1] for it in result]) self.assertEqual([it[4] * 2 for it in data], [it[2] for it in result]) self.assertEqual([it[4] + it[1] for it in data], [it[3] for it in result]) self.assertAlmostEqual([float(it[1]) / 2 for it in data], [it[4] for it in result]) self.assertEqual([int(it[1] ** -2) for it in data], [it[5] for it in result]) self.assertEqual([abs(it[1]) for it in data], [it[6] for it in result]) self.assertEqual([~it[1] for it in data], [it[7] for it in result]) self.assertAlmostEqual([-it[2] for it in data], [it[8] for it in result]) self.assertEqual([not it[3] for it in data], [it[9] for it in result]) self.assertEqual([it[1] // 2 for it in data], [it[11] for it in result]) self.assertEqual([it[5] + timedelta(days=1) for it in data], [it[12] for it in result]) self.assertEqual([10] * len(data), [it[13] for it in result]) def testMath(self): data = self._gen_data(5, value_range=(1, 90)) import numpy as np methods_to_fields = [ (np.sin, self.expr.id.sin()), (np.cos, self.expr.id.cos()), (np.tan, self.expr.id.tan()), (np.sinh, self.expr.id.sinh()), (np.cosh, self.expr.id.cosh()), (np.tanh, self.expr.id.tanh()), (np.log, self.expr.id.log()), (np.log2, self.expr.id.log2()), (np.log10, self.expr.id.log10()), (np.log1p, self.expr.id.log1p()), (np.exp, self.expr.id.exp()), (np.expm1, self.expr.id.expm1()), (np.arccosh, self.expr.id.arccosh()), (np.arcsinh, self.expr.id.arcsinh()), (np.arctanh, self.expr.id.arctanh()), (np.arctan, self.expr.id.arctan()), (np.sqrt, self.expr.id.sqrt()), (np.abs, self.expr.id.abs()), (np.ceil, self.expr.id.ceil()), (np.floor, self.expr.id.floor()), (np.trunc, self.expr.id.trunc()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[1]) for it in data] second = [it[i] for it in result] self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): if np.isnan(it1) and np.isnan(it2): continue self.assertAlmostEqual(it1, it2) def testString(self): data = self._gen_data(5) methods_to_fields = [ (lambda s: s.capitalize(), self.expr.name.capitalize()), (lambda s: data[0][0] in s, self.expr.name.contains(data[0][0], regex=False)), (lambda s: s.count(data[0][0]), self.expr.name.count(data[0][0])), (lambda s: s.endswith(data[0][0]), self.expr.name.endswith(data[0][0])), (lambda s: s.startswith(data[0][0]), self.expr.name.startswith(data[0][0])), (lambda s: s.find(data[0][0]), self.expr.name.find(data[0][0])), (lambda s: s.rfind(data[0][0]), self.expr.name.rfind(data[0][0])), (lambda s: s.replace(data[0][0], 'test'), self.expr.name.replace(data[0][0], 'test')), (lambda s: s[0], self.expr.name.get(0)), (lambda s: len(s), self.expr.name.len()), (lambda s: s.ljust(10), self.expr.name.ljust(10)), (lambda s: s.ljust(20, '*'), self.expr.name.ljust(20, fillchar='*')), (lambda s: s.rjust(10), self.expr.name.rjust(10)), (lambda s: s.rjust(20, '*'), self.expr.name.rjust(20, fillchar='*')), (lambda s: s * 4, self.expr.name.repeat(4)), (lambda s: s[2: 10: 2], self.expr.name.slice(2, 10, 2)), (lambda s: s[-5: -1], self.expr.name.slice(-5, -1)), (lambda s: s.title(), self.expr.name.title()), (lambda s: s.rjust(20, '0'), self.expr.name.zfill(20)), (lambda s: s.isalnum(), self.expr.name.isalnum()), (lambda s: s.isalpha(), self.expr.name.isalpha()), (lambda s: s.isdigit(), self.expr.name.isdigit()), (lambda s: s.isspace(), self.expr.name.isspace()), (lambda s: s.isupper(), self.expr.name.isupper()), (lambda s: s.istitle(), self.expr.name.istitle()), (lambda s: to_str(s).isnumeric(), self.expr.name.isnumeric()), (lambda s: to_str(s).isdecimal(), self.expr.name.isdecimal()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[0]) for it in data] second = [it[i] for it in result] self.assertEqual(first, second) def testApply(self): data = self._gen_data(5) def my_func(row): return row.name, expr = self.expr['name', 'id'].apply(my_func, axis=1, names='name') res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([r[0] for r in result], [r[0] for r in data]) def my_func2(row): yield len(row.name) yield row.id expr = self.expr['name', 'id'].apply(my_func2, axis=1, names='cnt', types='int') res = self.engine.execute(expr) result = self._get_result(res) def gen_expected(data): for r in data: yield len(r[0]) yield r[1] self.assertEqual([r[0] for r in result], [r for r in gen_expected(data)]) def testDatetime(self): data = self._gen_data(5) import pandas as pd methods_to_fields = [ (lambda s: list(s.birth.dt.year.values), self.expr.birth.year), (lambda s: list(s.birth.dt.month.values), self.expr.birth.month), (lambda s: list(s.birth.dt.day.values), self.expr.birth.day), (lambda s: list(s.birth.dt.hour.values), self.expr.birth.hour), (lambda s: list(s.birth.dt.minute.values), self.expr.birth.minute), (lambda s: list(s.birth.dt.second.values), self.expr.birth.second), (lambda s: list(s.birth.dt.weekofyear.values), self.expr.birth.weekofyear), (lambda s: list(s.birth.dt.dayofweek.values), self.expr.birth.dayofweek), (lambda s: list(s.birth.dt.weekday.values), self.expr.birth.weekday), (lambda s: list(s.birth.dt.date.values), self.expr.birth.date), (lambda s: list(s.birth.dt.strftime('%Y%d')), self.expr.birth.strftime('%Y%d')) ] fields = [it[1].rename('birth'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) def conv(v): if isinstance(v, pd.Timestamp): return v.to_datetime().date() else: return v second = [conv(it[i]) for it in result] self.assertEqual(first, second) def testSortDistinct(self): data = [ ['name1', 4, None, None, None, None], ['name2', 2, None, None, None, None], ['name1', 4, None, None, None, None], ['name1', 3, None, None, None, None], ] self._gen_data(data=data) expr = self.expr.sort(['name', -self.expr.id]).distinct(['name', lambda x: x.id + 1])[:50] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 3) expected = [ ['name1', 5], ['name1', 4], ['name2', 3] ] self.assertEqual(expected, result) def testGroupbyAggregation(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name', 'id'])[lambda x: x.fid.min() * 2 < 8] \ .agg(self.expr.fid.max() + 1, new_id=self.expr.id.sum()) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 5.1, 6], ['name2', 2, 4.5, 2] ] result = sorted(result, key=lambda k: k[0]) self.assertEqual(expected, result) field = self.expr.groupby('name').sort(['id', -self.expr.fid]).row_number() expr = self.expr['name', 'id', 'fid', field] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 4.1, 1], ['name1', 3, 2.2, 2], ['name1', 4, 5.3, 3], ['name1', 4, 4.2, 4], ['name2', 2, 3.5, 1], ] result = sorted(result, key=lambda k: (k[0], k[1], -k[2])) self.assertEqual(expected, result) expr = self.expr.name.value_counts()[:25] expected = [ ['name1', 4], ['name2', 1] ] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.name.topk(25) res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name').count() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expected = [ ['name1', 2], ['name2', 1] ] expr = self.expr.groupby('name').id.nunique() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expr = self.expr[self.expr['id'] > 2].name.value_counts()[:25] expected = [ ['name1', 4] ] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) def testJoinGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name1', 4, -1], ['name2', 1, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) expr = self.expr.join(expr2, on='name')[self.expr] expr = expr.groupby('id').agg(expr.fid.sum()) res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd expected = pd.DataFrame(data, columns=self.expr.schema.names).groupby('id').agg({'fid': 'sum'})\ .reset_index().values.tolist() for it in zip(sorted(expected, key=lambda it: it[0]), sorted(result, key=lambda it: it[0])): self.assertAlmostEqual(it[0][0], it[1][0]) self.assertAlmostEqual(it[0][1], it[1][1]) def testFilterGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name']).agg(id=self.expr.id.max())[lambda x: x.id > 3] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 1) expected = [ ['name1', 4] ] self.assertEqual(expected, result) def testWindowRewrite(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr[self.expr.id - self.expr.id.mean() < 10][ [lambda x: x.id - x.id.max()]][[lambda x: x.id - x.id.min()]][lambda x: x.id - x.id.std() > 0] res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) expected = df.id - df.id.max() expected = expected - expected.min() expected = list(expected[expected - expected.std() > 0]) self.assertEqual(expected, [it[0] for it in result]) def testReduction(self): data = self._gen_data(rows=5, value_range=(-100, 100)) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) methods_to_fields = [ (lambda s: df.id.mean(), self.expr.id.mean()), (lambda s: len(df), self.expr.count()), (lambda s: df.id.var(ddof=0), self.expr.id.var(ddof=0)), (lambda s: df.id.std(ddof=0), self.expr.id.std(ddof=0)), (lambda s: df.id.median(), self.expr.id.median()), (lambda s: df.id.sum(), self.expr.id.sum()), (lambda s: df.id.min(), self.expr.id.min()), (lambda s: df.id.max(), self.expr.id.max()), (lambda s: df.isMale.min(), self.expr.isMale.min()), (lambda s: df.name.max(), self.expr.name.max()), (lambda s: df.birth.max(), self.expr.birth.max()), (lambda s: df.isMale.sum(), self.expr.isMale.sum()), (lambda s: df.isMale.any(), self.expr.isMale.any()), (lambda s: df.isMale.all(), self.expr.isMale.all()), (lambda s: df.name.nunique(), self.expr.name.nunique()), ] fields = [it[1].rename('f'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result][0] if isinstance(first, float): self.assertAlmostEqual(first, second) else: self.assertEqual(first, second) def testMapReduceByApplyDistributeSort(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) def mapper(row): for word in row[0].split(): yield word, 1 class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.word elif self._curr != row.word: yield (self._curr, self._cnt) self._curr = row.word self._cnt = 0 self._cnt += row.count def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].apply( mapper, axis=1, names=['word', 'count'], types=['string', 'int']) expr = expr.groupby('word').sort('word').apply( reducer, names=['word', 'count'], types=['string', 'int']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testMapReduce(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output(['word', 'cnt'], ['string', 'int']) def mapper(row): for word in row[0].split(): yield word, 1 @output(['word', 'cnt'], ['string', 'int']) def reducer(keys): cnt = [0, ] def h(row, done): cnt[0] += row[1] if done: yield keys[0], cnt[0] return h expr = self.expr['name', ].map_reduce(mapper, reducer, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) @output(['word', 'cnt'], ['string', 'int']) class reducer2(object): def __init__(self, keys): self.cnt = 0 def __call__(self, row, done): self.cnt += row.cnt if done: yield row.word, self.cnt expr = self.expr['name', ].map_reduce(mapper, reducer2, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testDistributeSort(self): data = [ ['name', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output_names('name', 'id') @output_types('string', 'int') class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.name elif self._curr != row.name: yield (self._curr, self._cnt) self._curr = row.name self._cnt = 0 self._cnt += 1 def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].groupby('name').sort('name').apply(reducer) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 2], ['name', 3]] self.assertEqual(sorted(expected), sorted(result)) def testJoin(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name1', 4, -1], ['name2', 1, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr.join(expr2)['name', 'id2'] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 5) expected = [ [to_str('name1'), 4], [to_str('name2'), 1] ] self.assertTrue(all(it in expected for it in result)) expr = self.expr.join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 2) expected = [to_str('name1'), 4] self.assertTrue(all(it == expected for it in result)) finally: table2.drop() def testUnion(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name3', 5, -1], ['name4', 6, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr['name', 'id'].distinct().union(expr2[expr2.id2.rename('id'), 'name']) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 4], ['name1', 3], ['name2', 2], ['name3', 5], ['name4', 6] ] result = sorted(result) expected = sorted(expected) self.assertEqual(len(result), len(expected)) for e, r in zip(result, expected): self.assertEqual([to_str(t) for t in e], [to_str(t) for t in r]) finally: table2.drop() def testPersist(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) table_name = 'pyodps_test_engine_persist_table' try: df = self.engine.persist(self.expr, table_name) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, result) finally: self.odps.delete_table(table_name, if_exists=True) try: schema = Schema.from_lists(self.schema.names, self.schema.types, ['ds'], ['string']) self.odps.create_table(table_name, schema) df = self.engine.persist(self.expr, table_name, partition='ds=today', create_partition=True) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, [d[:-1] for d in result]) finally: self.odps.delete_table(table_name, if_exists=True) try: self.engine.persist(self.expr, table_name, partitions=['name']) t = self.odps.get_table(table_name) self.assertEqual(2, len(list(t.partitions))) with t.open_reader(partition='name=name1', reopen=True) as r: self.assertEqual(4, r.count) with t.open_reader(partition='name=name2', reopen=True) as r: self.assertEqual(1, r.count) finally: self.odps.delete_table(table_name, if_exists=True) def teardown(self): self.table.drop()
class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists( ['name', 'id', 'fid', 'isMale', 'scale', 'birth'], datatypes('string', 'int64', 'float64', 'boolean', 'decimal', 'datetime')) self.schema = df_schema_to_odps_schema(schema) table_name = 'pyodps_test_engine_table' self.odps.delete_table(table_name, if_exists=True) self.table = self.odps.create_table(name='pyodps_test_engine_table', schema=self.schema) self.expr = CollectionExpr(_source_data=self.table, _schema=schema) self.engine = ODPSEngine(self.odps) class FakeBar(object): def update(self, *args, **kwargs): pass self.faked_bar = FakeBar() def _gen_data(self, rows=None, data=None, nullable_field=None, value_range=None): if data is None: data = [] for _ in range(rows): record = [] for t in self.schema.types: method = getattr(self, '_gen_random_%s' % t.name) if t.name == 'bigint': record.append(method(value_range=value_range)) else: record.append(method()) data.append(record) if nullable_field is not None: j = self.schema._name_indexes[nullable_field] for i, l in enumerate(data): if i % 2 == 0: data[i][j] = None self.odps.write_table(self.table, 0, [self.table.new_record(values=d) for d in data]) return data def testTunnelCases(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) expr = self.expr.name.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) res = self.engine._handle_cases(self.expr, self.faked_bar) result = self._get_result(res) self.assertEqual(data, result) expr = self.expr['name', self.expr.id.rename('new_id')] res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual([it[:2] for it in data], result) table_name = 'pyodps_test_engine_partitioned' self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[df.name == data[0][0]]['fid', 'id'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[df.name == data[0][0]]['fid', 'id'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) df = self.engine.persist(self.expr, table_name, partitions=['name', 'id']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'].count() expr = self.engine._pre_process(expr) res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[(df.name == data[0][0]) & (df.id == data[0][1])]['fid', 'ismale'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) def testAsync(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.id.sum() res = self.engine.execute(expr, async=True) self.assertNotEqual(res.instance.status, Instance.Status.TERMINATED) res.wait() self.assertEqual(sum(it[1] for it in data), res.fetch()) def testBase(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10]['name', lambda x: x.id] result = self._get_result(self.engine.execute(expr).values) self.assertEqual(len([it for it in data if it[1] < 10]), len(result)) if len(result) > 0: self.assertEqual(2, len(result[0])) expr = self.expr[Scalar(3).rename('const'), self.expr.id, (self.expr.id + 1).rename('id2')] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual([c.name for c in res.columns], ['const', 'id', 'id2']) self.assertTrue(all(it[0] == 3 for it in result)) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[2] for it in result]) expr = self.expr.sort('id')[:5] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) expr = self.expr.sort('id')[:5] # test do not use tunnel res = self.engine.execute(expr, use_tunnel=False) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) def testElement(self): data = self._gen_data(5, nullable_field='name') fields = [ self.expr.name.isnull().rename('name1'), self.expr.name.notnull().rename('name2'), self.expr.name.fillna('test').rename('name3'), self.expr.id.isin([1, 2, 3]).rename('id1'), self.expr.id.isin(self.expr.fid.astype('int')).rename('id2'), self.expr.id.notin([1, 2, 3]).rename('id3'), self.expr.id.notin(self.expr.fid.astype('int')).rename('id4'), self.expr.id.between(self.expr.fid, 3).rename('id5'), self.expr.name.fillna('test').switch( 'test', 'test' + self.expr.name.fillna('test'), 'test2', 'test2' + self.expr.name.fillna('test'), default=self.expr.name).rename('name4'), self.expr.id.cut([100, 200, 300], labels=['xsmall', 'small', 'large', 'xlarge'], include_under=True, include_over=True).rename('id6') ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual(len([it for it in data if it[0] is None]), len([it[0] for it in result if it[0]])) self.assertEqual(len([it[0] for it in data if it[0] is not None]), len([it[1] for it in result if it[1]])) self.assertEqual([(it[0] if it[0] is not None else 'test') for it in data], [it[2] for it in result]) self.assertEqual([(it[1] in (1, 2, 3)) for it in data], [it[3] for it in result]) fids = [int(it[2]) for it in data] self.assertEqual([(it[1] in fids) for it in data], [it[4] for it in result]) self.assertEqual([(it[1] not in (1, 2, 3)) for it in data], [it[5] for it in result]) self.assertEqual([(it[1] not in fids) for it in data], [it[6] for it in result]) self.assertEqual([(it[2] <= it[1] <= 3) for it in data], [it[7] for it in result]) self.assertEqual( [to_str('testtest' if it[0] is None else it[0]) for it in data], [to_str(it[8]) for it in result]) def get_val(val): if val <= 100: return 'xsmall' elif 100 < val <= 200: return 'small' elif 200 < val <= 300: return 'large' else: return 'xlarge' self.assertEqual([to_str(get_val(it[1])) for it in data], [to_str(it[9]) for it in result]) def testArithmetic(self): data = self._gen_data(5, value_range=(-1000, 1000)) fields = [ (self.expr.id + 1).rename('id1'), (self.expr.fid - 1).rename('fid1'), (self.expr.scale * 2).rename('scale1'), (self.expr.scale + self.expr.id).rename('scale2'), (self.expr.id / 2).rename('id2'), (self.expr.id**-2).rename('id3'), abs(self.expr.id).rename('id4'), (~self.expr.id).rename('id5'), (-self.expr.fid).rename('fid2'), (~self.expr.isMale).rename('isMale1'), (-self.expr.isMale).rename('isMale2'), (self.expr.id // 2).rename('id6'), (self.expr.birth + day(1).rename('birth1')), (self.expr.birth - (self.expr.birth - millisecond(10))).rename('birth2'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[0] for it in result]) self.assertAlmostEqual([it[2] - 1 for it in data], [it[1] for it in result]) self.assertEqual([it[4] * 2 for it in data], [it[2] for it in result]) self.assertEqual([it[4] + it[1] for it in data], [it[3] for it in result]) self.assertAlmostEqual([float(it[1]) / 2 for it in data], [it[4] for it in result]) self.assertEqual([int(it[1]**-2) for it in data], [it[5] for it in result]) self.assertEqual([abs(it[1]) for it in data], [it[6] for it in result]) self.assertEqual([~it[1] for it in data], [it[7] for it in result]) self.assertAlmostEqual([-it[2] for it in data], [it[8] for it in result]) self.assertEqual([not it[3] for it in data], [it[9] for it in result]) self.assertEqual([it[1] // 2 for it in data], [it[11] for it in result]) self.assertEqual([it[5] + timedelta(days=1) for it in data], [it[12] for it in result]) self.assertEqual([10] * len(data), [it[13] for it in result]) def testMath(self): data = self._gen_data(5, value_range=(1, 90)) import numpy as np methods_to_fields = [ (np.sin, self.expr.id.sin()), (np.cos, self.expr.id.cos()), (np.tan, self.expr.id.tan()), (np.sinh, self.expr.id.sinh()), (np.cosh, self.expr.id.cosh()), (np.tanh, self.expr.id.tanh()), (np.log, self.expr.id.log()), (np.log2, self.expr.id.log2()), (np.log10, self.expr.id.log10()), (np.log1p, self.expr.id.log1p()), (np.exp, self.expr.id.exp()), (np.expm1, self.expr.id.expm1()), (np.arccosh, self.expr.id.arccosh()), (np.arcsinh, self.expr.id.arcsinh()), (np.arctanh, self.expr.id.arctanh()), (np.arctan, self.expr.id.arctan()), (np.sqrt, self.expr.id.sqrt()), (np.abs, self.expr.id.abs()), (np.ceil, self.expr.id.ceil()), (np.floor, self.expr.id.floor()), (np.trunc, self.expr.id.trunc()), ] fields = [ it[1].rename('id' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[1]) for it in data] second = [it[i] for it in result] self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): if np.isnan(it1) and np.isnan(it2): continue self.assertAlmostEqual(it1, it2) def testString(self): data = self._gen_data(5) methods_to_fields = [ (lambda s: s.capitalize(), self.expr.name.capitalize()), (lambda s: data[0][0] in s, self.expr.name.contains(data[0][0], regex=False)), (lambda s: s.count(data[0][0]), self.expr.name.count(data[0][0])), (lambda s: s.endswith(data[0][0]), self.expr.name.endswith(data[0][0])), (lambda s: s.startswith(data[0][0]), self.expr.name.startswith(data[0][0])), (lambda s: s.find(data[0][0]), self.expr.name.find(data[0][0])), (lambda s: s.rfind(data[0][0]), self.expr.name.rfind(data[0][0])), (lambda s: s.replace(data[0][0], 'test'), self.expr.name.replace(data[0][0], 'test')), (lambda s: s[0], self.expr.name.get(0)), (lambda s: len(s), self.expr.name.len()), (lambda s: s.ljust(10), self.expr.name.ljust(10)), (lambda s: s.ljust(20, '*'), self.expr.name.ljust(20, fillchar='*')), (lambda s: s.rjust(10), self.expr.name.rjust(10)), (lambda s: s.rjust(20, '*'), self.expr.name.rjust(20, fillchar='*')), (lambda s: s * 4, self.expr.name.repeat(4)), (lambda s: s[2:10:2], self.expr.name.slice(2, 10, 2)), (lambda s: s[-5:-1], self.expr.name.slice(-5, -1)), (lambda s: s.title(), self.expr.name.title()), (lambda s: s.rjust(20, '0'), self.expr.name.zfill(20)), (lambda s: s.isalnum(), self.expr.name.isalnum()), (lambda s: s.isalpha(), self.expr.name.isalpha()), (lambda s: s.isdigit(), self.expr.name.isdigit()), (lambda s: s.isspace(), self.expr.name.isspace()), (lambda s: s.isupper(), self.expr.name.isupper()), (lambda s: s.istitle(), self.expr.name.istitle()), (lambda s: to_str(s).isnumeric(), self.expr.name.isnumeric()), (lambda s: to_str(s).isdecimal(), self.expr.name.isdecimal()), ] fields = [ it[1].rename('id' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[0]) for it in data] second = [it[i] for it in result] self.assertEqual(first, second) def testApply(self): data = self._gen_data(5) def my_func(row): return row.name, expr = self.expr['name', 'id'].apply(my_func, axis=1, names='name') res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([r[0] for r in result], [r[0] for r in data]) def my_func2(row): yield len(row.name) yield row.id expr = self.expr['name', 'id'].apply(my_func2, axis=1, names='cnt', types='int') res = self.engine.execute(expr) result = self._get_result(res) def gen_expected(data): for r in data: yield len(r[0]) yield r[1] self.assertEqual([r[0] for r in result], [r for r in gen_expected(data)]) def testDatetime(self): data = self._gen_data(5) import pandas as pd methods_to_fields = [ (lambda s: list(s.birth.dt.year.values), self.expr.birth.year), (lambda s: list(s.birth.dt.month.values), self.expr.birth.month), (lambda s: list(s.birth.dt.day.values), self.expr.birth.day), (lambda s: list(s.birth.dt.hour.values), self.expr.birth.hour), (lambda s: list(s.birth.dt.minute.values), self.expr.birth.minute), (lambda s: list(s.birth.dt.second.values), self.expr.birth.second), (lambda s: list(s.birth.dt.weekofyear.values), self.expr.birth.weekofyear), (lambda s: list(s.birth.dt.dayofweek.values), self.expr.birth.dayofweek), (lambda s: list(s.birth.dt.weekday.values), self.expr.birth.weekday), (lambda s: list(s.birth.dt.date.values), self.expr.birth.date), (lambda s: list(s.birth.dt.strftime('%Y%d')), self.expr.birth.strftime('%Y%d')) ] fields = [ it[1].rename('birth' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) def conv(v): if isinstance(v, pd.Timestamp): return v.to_datetime().date() else: return v second = [conv(it[i]) for it in result] self.assertEqual(first, second) def testSortDistinct(self): data = [ ['name1', 4, None, None, None, None], ['name2', 2, None, None, None, None], ['name1', 4, None, None, None, None], ['name1', 3, None, None, None, None], ] self._gen_data(data=data) expr = self.expr.sort(['name', -self.expr.id ]).distinct(['name', lambda x: x.id + 1])[:50] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 3) expected = [['name1', 5], ['name1', 4], ['name2', 3]] self.assertEqual(expected, result) def testGroupbyAggregation(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name', 'id'])[lambda x: x.fid.min() * 2 < 8] \ .agg(self.expr.fid.max() + 1, new_id=self.expr.id.sum()) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 3, 5.1, 6], ['name2', 2, 4.5, 2]] result = sorted(result, key=lambda k: k[0]) self.assertEqual(expected, result) field = self.expr.groupby('name').sort(['id', -self.expr.fid]).row_number() expr = self.expr['name', 'id', 'fid', field] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 4.1, 1], ['name1', 3, 2.2, 2], ['name1', 4, 5.3, 3], ['name1', 4, 4.2, 4], ['name2', 2, 3.5, 1], ] result = sorted(result, key=lambda k: (k[0], k[1], -k[2])) self.assertEqual(expected, result) expr = self.expr.name.value_counts()[:25] expected = [['name1', 4], ['name2', 1]] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.name.topk(25) res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name').count() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expected = [['name1', 2], ['name2', 1]] expr = self.expr.groupby('name').id.nunique() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expr = self.expr[self.expr['id'] > 2].name.value_counts()[:25] expected = [['name1', 4]] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) def testJoinGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name1', 4, -1], ['name2', 1, -2]] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) expr = self.expr.join(expr2, on='name')[self.expr] expr = expr.groupby('id').agg(expr.fid.sum()) res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd expected = pd.DataFrame(data, columns=self.expr.schema.names).groupby('id').agg({'fid': 'sum'})\ .reset_index().values.tolist() for it in zip(sorted(expected, key=lambda it: it[0]), sorted(result, key=lambda it: it[0])): self.assertAlmostEqual(it[0][0], it[1][0]) self.assertAlmostEqual(it[0][1], it[1][1]) def testFilterGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby( ['name']).agg(id=self.expr.id.max())[lambda x: x.id > 3] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 1) expected = [['name1', 4]] self.assertEqual(expected, result) def testWindowRewrite(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr[self.expr.id - self.expr.id.mean() < 10][[ lambda x: x.id - x.id.max() ]][[lambda x: x.id - x.id.min()]][lambda x: x.id - x.id.std() > 0] res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) expected = df.id - df.id.max() expected = expected - expected.min() expected = list(expected[expected - expected.std() > 0]) self.assertEqual(expected, [it[0] for it in result]) def testReduction(self): data = self._gen_data(rows=5, value_range=(-100, 100)) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) methods_to_fields = [ (lambda s: df.id.mean(), self.expr.id.mean()), (lambda s: len(df), self.expr.count()), (lambda s: df.id.var(ddof=0), self.expr.id.var(ddof=0)), (lambda s: df.id.std(ddof=0), self.expr.id.std(ddof=0)), (lambda s: df.id.median(), self.expr.id.median()), (lambda s: df.id.sum(), self.expr.id.sum()), (lambda s: df.id.min(), self.expr.id.min()), (lambda s: df.id.max(), self.expr.id.max()), (lambda s: df.isMale.min(), self.expr.isMale.min()), (lambda s: df.name.max(), self.expr.name.max()), (lambda s: df.birth.max(), self.expr.birth.max()), (lambda s: df.isMale.sum(), self.expr.isMale.sum()), (lambda s: df.isMale.any(), self.expr.isMale.any()), (lambda s: df.isMale.all(), self.expr.isMale.all()), (lambda s: df.name.nunique(), self.expr.name.nunique()), ] fields = [ it[1].rename('f' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result][0] if isinstance(first, float): self.assertAlmostEqual(first, second) else: self.assertEqual(first, second) def testMapReduceByApplyDistributeSort(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) def mapper(row): for word in row[0].split(): yield word, 1 class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.word elif self._curr != row.word: yield (self._curr, self._cnt) self._curr = row.word self._cnt = 0 self._cnt += row.count def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].apply(mapper, axis=1, names=['word', 'count'], types=['string', 'int']) expr = expr.groupby('word').sort('word').apply(reducer, names=['word', 'count'], types=['string', 'int']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testMapReduce(self): data = [ ['name key', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key name', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output(['word', 'cnt'], ['string', 'int']) def mapper(row): for word in row[0].split(): yield word, 1 @output(['word', 'cnt'], ['string', 'int']) def reducer(keys): cnt = [ 0, ] def h(row, done): cnt[0] += row[1] if done: yield keys[0], cnt[0] return h expr = self.expr['name', ].map_reduce(mapper, reducer, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) @output(['word', 'cnt'], ['string', 'int']) class reducer2(object): def __init__(self, keys): self.cnt = 0 def __call__(self, row, done): self.cnt += row.cnt if done: yield row.word, self.cnt expr = self.expr['name', ].map_reduce(mapper, reducer2, group='word') res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 3], ['name', 4]] self.assertEqual(sorted(result), sorted(expected)) def testDistributeSort(self): data = [ ['name', 4, 5.3, None, None, None], ['name', 2, 3.5, None, None, None], ['key', 4, 4.2, None, None, None], ['name', 3, 2.2, None, None, None], ['key', 3, 4.1, None, None, None], ] self._gen_data(data=data) @output_names('name', 'id') @output_types('string', 'int') class reducer(object): def __init__(self): self._curr = None self._cnt = 0 def __call__(self, row): if self._curr is None: self._curr = row.name elif self._curr != row.name: yield (self._curr, self._cnt) self._curr = row.name self._cnt = 0 self._cnt += 1 def close(self): if self._curr is not None: yield (self._curr, self._cnt) expr = self.expr['name', ].groupby('name').sort('name').apply(reducer) res = self.engine.execute(expr) result = self._get_result(res) expected = [['key', 2], ['name', 3]] self.assertEqual(sorted(expected), sorted(result)) def testJoin(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name1', 4, -1], ['name2', 1, -2]] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr.join(expr2)['name', 'id2'] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 5) expected = [[to_str('name1'), 4], [to_str('name2'), 1]] self.assertTrue(all(it in expected for it in result)) expr = self.expr.join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 2) expected = [to_str('name1'), 4] self.assertTrue(all(it == expected for it in result)) finally: table2.drop() def testUnion(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name3', 5, -1], ['name4', 6, -2]] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr['name', 'id'].distinct().union( expr2[expr2.id2.rename('id'), 'name']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 4], ['name1', 3], ['name2', 2], ['name3', 5], ['name4', 6]] result = sorted(result) expected = sorted(expected) self.assertEqual(len(result), len(expected)) for e, r in zip(result, expected): self.assertEqual([to_str(t) for t in e], [to_str(t) for t in r]) finally: table2.drop() def testPersist(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) table_name = 'pyodps_test_engine_persist_table' try: df = self.engine.persist(self.expr, table_name) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, result) finally: self.odps.delete_table(table_name, if_exists=True) try: schema = Schema.from_lists(self.schema.names, self.schema.types, ['ds'], ['string']) self.odps.create_table(table_name, schema) df = self.engine.persist(self.expr, table_name, partition='ds=today', create_partition=True) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, [d[:-1] for d in result]) finally: self.odps.delete_table(table_name, if_exists=True) try: self.engine.persist(self.expr, table_name, partitions=['name']) t = self.odps.get_table(table_name) self.assertEqual(2, len(list(t.partitions))) with t.open_reader(partition='name=name1', reopen=True) as r: self.assertEqual(4, r.count) with t.open_reader(partition='name=name2', reopen=True) as r: self.assertEqual(1, r.count) finally: self.odps.delete_table(table_name, if_exists=True) def teardown(self): self.table.drop()
class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists(['name', 'id', 'fid', 'isMale', 'scale', 'birth'], datatypes('string', 'int64', 'float64', 'boolean', 'decimal', 'datetime')) self.schema = df_schema_to_odps_schema(schema) table_name = 'pyodps_test_engine_table' self.odps.delete_table(table_name, if_exists=True) self.table = self.odps.create_table( name='pyodps_test_engine_table', schema=self.schema) self.expr = CollectionExpr(_source_data=self.table, _schema=schema) self.engine = ODPSEngine(self.odps) class FakeBar(object): def update(self, *args, **kwargs): pass self.faked_bar = FakeBar() def _gen_random_bigint(self, value_range=None): return random.randint(*(value_range or types.bigint._bounds)) def _gen_random_string(self, max_length=15): gen_letter = lambda: letters[random.randint(0, 51)] return to_str(''.join([gen_letter() for _ in range(random.randint(1, 15))])) def _gen_random_double(self): return random.uniform(-2**32, 2**32) def _gen_random_datetime(self): return datetime.fromtimestamp(random.randint(0, int(time.time()))) def _gen_random_boolean(self): return random.uniform(-1, 1) > 0 def _gen_random_decimal(self): return Decimal(str(self._gen_random_double())) def _gen_data(self, rows=None, data=None, nullable_field=None, value_range=None): if data is None: data = [] for _ in range(rows): record = [] for t in self.schema.types: method = getattr(self, '_gen_random_%s' % t.name) if t.name == 'bigint': record.append(method(value_range=value_range)) else: record.append(method()) data.append(record) if nullable_field is not None: j = self.schema._name_indexes[nullable_field] for i, l in enumerate(data): if i % 2 == 0: data[i][j] = None self.odps.write_table(self.table, 0, [self.table.new_record(values=d) for d in data]) return data def _get_result(self, res): if isinstance(res, ResultFrame): res = res.values try: import pandas if isinstance(res, pandas.DataFrame): return [list(it) for it in res.values] else: return res except ImportError: return res def testTunnelCases(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) expr = self.expr.name.count() res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual(10, result) res = self.engine._handle_cases(self.expr, self.faked_bar) result = self._get_result(res) self.assertEqual(data, result) expr = self.expr['name', self.expr.id.rename('new_id')] res = self.engine._handle_cases(expr, self.faked_bar) result = self._get_result(res) self.assertEqual([it[:2] for it in data], result) table_name = 'pyodps_test_engine_partitioned' self.odps.delete_table(table_name, if_exists=True) df = self.expr.persist(table_name, partitions=['name']) try: expr = df.count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertIsNone(res) expr = df[df.name == data[0][0]]['fid', 'id'].count() res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(res, 0) expr = df[df.name == data[0][0]]['fid', 'id'] res = self.engine._handle_cases(expr, self.faked_bar) self.assertGreater(len(res), 0) finally: self.odps.delete_table(table_name, if_exists=True) def testBase(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10]['name', lambda x: x.id] result = self._get_result(self.engine.execute(expr).values) self.assertEqual(len([it for it in data if it[1] < 10]), len(result)) if len(result) > 0: self.assertEqual(2, len(result[0])) expr = self.expr[Scalar(3).rename('const'), self.expr.id, (self.expr.id + 1).rename('id2')] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual([c.name for c in res.columns], ['const', 'id', 'id2']) self.assertTrue(all(it[0] == 3 for it in result)) self.assertEqual(len(data), len(result)) self.assertEqual([it[1]+1 for it in data], [it[2] for it in result]) expr = self.expr.sort('id')[:5] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual(sorted(data, key=lambda it: it[1])[:5], result) def testElement(self): data = self._gen_data(5, nullable_field='name') fields = [ self.expr.name.isnull().rename('name1'), self.expr.name.notnull().rename('name2'), self.expr.name.fillna('test').rename('name3'), self.expr.id.isin([1, 2, 3]).rename('id1'), self.expr.id.isin(self.expr.fid.astype('int')).rename('id2'), self.expr.id.notin([1, 2, 3]).rename('id3'), self.expr.id.notin(self.expr.fid.astype('int')).rename('id4'), self.expr.id.between(self.expr.fid, 3).rename('id5'), self.expr.name.fillna('test').switch('test', 'test' + self.expr.name.fillna('test'), 'test2', 'test2' + self.expr.name.fillna('test'), default=self.expr.name).rename('name4'), self.expr.id.cut([100, 200, 300], labels=['xsmall', 'small', 'large', 'xlarge'], include_under=True, include_over=True).rename('id6') ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual(len([it for it in data if it[0] is None]), len([it[0] for it in result if it[0]])) self.assertEqual(len([it[0] for it in data if it[0] is not None]), len([it[1] for it in result if it[1]])) self.assertEqual([(it[0] if it[0] is not None else 'test') for it in data], [it[2] for it in result]) self.assertEqual([(it[1] in (1, 2, 3)) for it in data], [it[3] for it in result]) fids = [int(it[2]) for it in data] self.assertEqual([(it[1] in fids) for it in data], [it[4] for it in result]) self.assertEqual([(it[1] not in (1, 2, 3)) for it in data], [it[5] for it in result]) self.assertEqual([(it[1] not in fids) for it in data], [it[6] for it in result]) self.assertEqual([(it[2] <= it[1] <= 3) for it in data], [it[7] for it in result]) self.assertEqual([to_str('testtest' if it[0] is None else it[0]) for it in data], [to_str(it[8]) for it in result]) def get_val(val): if val <= 100: return 'xsmall' elif 100 < val <= 200: return 'small' elif 200 < val <= 300: return 'large' else: return 'xlarge' self.assertEqual([to_str(get_val(it[1])) for it in data], [to_str(it[9]) for it in result]) def testArithmetic(self): data = self._gen_data(5, value_range=(-1000, 1000)) fields = [ (self.expr.id + 1).rename('id1'), (self.expr.fid - 1).rename('fid1'), (self.expr.scale * 2).rename('scale1'), (self.expr.scale + self.expr.id).rename('scale2'), (self.expr.id / 2).rename('id2'), (self.expr.id ** -2).rename('id3'), abs(self.expr.id).rename('id4'), (~self.expr.id).rename('id5'), (-self.expr.fid).rename('fid2'), (~self.expr.isMale).rename('isMale1'), (-self.expr.isMale).rename('isMale2'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[0] for it in result]) self.assertAlmostEqual([it[2] - 1 for it in data], [it[1] for it in result]) self.assertEqual([it[4] * 2 for it in data], [it[2] for it in result]) self.assertEqual([it[4] + it[1] for it in data], [it[3] for it in result]) self.assertAlmostEqual([float(it[1]) / 2 for it in data], [it[4] for it in result]) self.assertEqual([int(it[1] ** -2) for it in data], [it[5] for it in result]) self.assertEqual([abs(it[1]) for it in data], [it[6] for it in result]) self.assertEqual([~it[1] for it in data], [it[7] for it in result]) self.assertAlmostEqual([-it[2] for it in data], [it[8] for it in result]) self.assertEqual([not it[3] for it in data], [it[9] for it in result]) # TODO: test the datetime add and substract def testMath(self): data = self._gen_data(5, value_range=(1, 90)) import numpy as np methods_to_fields = [ (np.sin, self.expr.id.sin()), (np.cos, self.expr.id.cos()), (np.tan, self.expr.id.tan()), (np.sinh, self.expr.id.sinh()), (np.cosh, self.expr.id.cosh()), (np.tanh, self.expr.id.tanh()), (np.log, self.expr.id.log()), (np.log2, self.expr.id.log2()), (np.log10, self.expr.id.log10()), (np.log1p, self.expr.id.log1p()), (np.exp, self.expr.id.exp()), (np.expm1, self.expr.id.expm1()), (np.arccosh, self.expr.id.arccosh()), (np.arcsinh, self.expr.id.arcsinh()), (np.arctanh, self.expr.id.arctanh()), (np.arctan, self.expr.id.arctan()), (np.sqrt, self.expr.id.sqrt()), (np.abs, self.expr.id.abs()), (np.ceil, self.expr.id.ceil()), (np.floor, self.expr.id.floor()), (np.trunc, self.expr.id.trunc()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[1]) for it in data] second = [it[i] for it in result] self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): if np.isnan(it1) and np.isnan(it2): continue self.assertAlmostEqual(it1, it2) def testString(self): data = self._gen_data(5) methods_to_fields = [ (lambda s: s.capitalize(), self.expr.name.capitalize()), (lambda s: data[0][0] in s, self.expr.name.contains(data[0][0], regex=False)), (lambda s: s.count(data[0][0]), self.expr.name.count(data[0][0])), (lambda s: s.endswith(data[0][0]), self.expr.name.endswith(data[0][0])), (lambda s: s.startswith(data[0][0]), self.expr.name.startswith(data[0][0])), (lambda s: s.find(data[0][0]), self.expr.name.find(data[0][0])), (lambda s: s.rfind(data[0][0]), self.expr.name.rfind(data[0][0])), (lambda s: s.replace(data[0][0], 'test'), self.expr.name.replace(data[0][0], 'test')), (lambda s: s[0], self.expr.name.get(0)), (lambda s: len(s), self.expr.name.len()), (lambda s: s.ljust(10), self.expr.name.ljust(10)), (lambda s: s.ljust(20, '*'), self.expr.name.ljust(20, fillchar='*')), (lambda s: s.rjust(10), self.expr.name.rjust(10)), (lambda s: s.rjust(20, '*'), self.expr.name.rjust(20, fillchar='*')), (lambda s: s * 4, self.expr.name.repeat(4)), (lambda s: s[2: 10: 2], self.expr.name.slice(2, 10, 2)), (lambda s: s[-5: -1], self.expr.name.slice(-5, -1)), (lambda s: s.title(), self.expr.name.title()), (lambda s: s.rjust(20, '0'), self.expr.name.zfill(20)), (lambda s: s.isalnum(), self.expr.name.isalnum()), (lambda s: s.isalpha(), self.expr.name.isalpha()), (lambda s: s.isdigit(), self.expr.name.isdigit()), (lambda s: s.isspace(), self.expr.name.isspace()), (lambda s: s.isupper(), self.expr.name.isupper()), (lambda s: s.istitle(), self.expr.name.istitle()), (lambda s: to_str(s).isnumeric(), self.expr.name.isnumeric()), (lambda s: to_str(s).isdecimal(), self.expr.name.isdecimal()), ] fields = [it[1].rename('id'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = [method(it[0]) for it in data] second = [it[i] for it in result] self.assertEqual(first, second) def testDatetime(self): data = self._gen_data(5) import pandas as pd methods_to_fields = [ (lambda s: list(s.birth.dt.year.values), self.expr.birth.year), (lambda s: list(s.birth.dt.month.values), self.expr.birth.month), (lambda s: list(s.birth.dt.day.values), self.expr.birth.day), (lambda s: list(s.birth.dt.hour.values), self.expr.birth.hour), (lambda s: list(s.birth.dt.minute.values), self.expr.birth.minute), (lambda s: list(s.birth.dt.second.values), self.expr.birth.second), (lambda s: list(s.birth.dt.weekofyear.values), self.expr.birth.weekofyear), (lambda s: list(s.birth.dt.dayofweek.values), self.expr.birth.dayofweek), (lambda s: list(s.birth.dt.weekday.values), self.expr.birth.weekday), ] fields = [it[1].rename('birth'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result] self.assertEqual(first, second) def testSortDistinct(self): data = [ ['name1', 4, None, None, None, None], ['name2', 2, None, None, None, None], ['name1', 4, None, None, None, None], ['name1', 3, None, None, None, None], ] self._gen_data(data=data) expr = self.expr.sort(['name', -self.expr.id]).distinct(['name', lambda x: x.id + 1])[:50] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 3) expected = [ ['name1', 5], ['name1', 4], ['name2', 3] ] self.assertEqual(expected, result) def testGroupbyAggregation(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name', 'id'])[lambda x: x.fid.min() * 2 < 8] \ .agg(self.expr.fid.max() + 1, new_id=self.expr.id.sum()) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 5.1, 6], ['name2', 2, 4.5, 2] ] result = sorted(result, key=lambda k: k[0]) self.assertEqual(expected, result) field = self.expr.groupby('name').sort(['id', -self.expr.fid]).row_number() expr = self.expr['name', 'id', 'fid', field] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 4.1, 1], ['name1', 3, 2.2, 2], ['name1', 4, 5.3, 3], ['name1', 4, 4.2, 4], ['name2', 2, 3.5, 1], ] result = sorted(result, key=lambda k: (k[0], k[1], -k[2])) self.assertEqual(expected, result) expr = self.expr.name.value_counts()[:25] expected = [ ['name1', 4], ['name2', 1] ] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.name.topk(25) res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name').count() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) def testFilterGroupby(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr.groupby(['name']).agg(id=self.expr.id.max())[lambda x: x.id > 3] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 1) expected = [ ['name1', 4] ] self.assertEqual(expected, result) def testWindowRewrite(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) expr = self.expr[self.expr.id - self.expr.id.mean() < 10][ [lambda x: x.id - x.id.max()]][[lambda x: x.id - x.id.min()]][lambda x: x.id - x.id.std() > 0] # FIXME compiling too slow res = self.engine.execute(expr) result = self._get_result(res) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) expected = df.id - df.id.max() expected = expected - expected.min() expected = list(expected[expected - expected.std() > 0]) self.assertEqual(expected, [it[0] for it in result]) def testReduction(self): data = self._gen_data(rows=5, value_range=(-100, 100)) import pandas as pd df = pd.DataFrame(data, columns=self.schema.names) methods_to_fields = [ (lambda s: df.id.mean(), self.expr.id.mean()), (lambda s: len(df), self.expr.count()), (lambda s: df.id.var(ddof=0), self.expr.id.var(ddof=0)), (lambda s: df.id.std(ddof=0), self.expr.id.std(ddof=0)), (lambda s: df.id.median(), self.expr.id.median()), (lambda s: df.id.sum(), self.expr.id.sum()), (lambda s: df.id.min(), self.expr.id.min()), (lambda s: df.id.max(), self.expr.id.max()), (lambda s: df.isMale.min(), self.expr.isMale.min()), (lambda s: df.name.max(), self.expr.name.max()), (lambda s: df.birth.max(), self.expr.birth.max()), (lambda s: df.name.sum(), self.expr.name.sum()), (lambda s: df.isMale.sum(), self.expr.isMale.sum()), ] fields = [it[1].rename('f'+str(i)) for i, it in enumerate(methods_to_fields)] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) df = pd.DataFrame(data, columns=self.schema.names) for i, it in enumerate(methods_to_fields): method = it[0] first = method(df) second = [it[i] for it in result][0] self.assertAlmostEqual(first, second) def testJoin(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name1', 4, -1], ['name2', 1, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr.join(expr2)['name', 'id2'] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 5) expected = [ [to_str('name1'), 4], [to_str('name2'), 1] ] self.assertTrue(all(it in expected for it in result)) expr = self.expr.join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 2) expected = [to_str('name1'), 4] self.assertTrue(all(it == expected for it in result)) finally: table2.drop() def testUnion(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = 'pyodps_test_engine_table2' self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [ ['name3', 5, -1], ['name4', 6, -2] ] self.odps.write_table(table2, 0, [table2.new_record(values=d) for d in data2]) try: expr = self.expr['name', 'id'].distinct().union(expr2[expr2.id2.rename('id'), 'name']) res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 4], ['name1', 3], ['name2', 2], ['name3', 5], ['name4', 6] ] result = sorted(result) expected = sorted(expected) self.assertEqual(len(result), len(expected)) for e, r in zip(result, expected): self.assertEqual([to_str(t) for t in e], [to_str(t) for t in r]) finally: table2.drop() def testPersist(self): data = [ ['name1', 4, 5.3, None, None, None], ['name2', 2, 3.5, None, None, None], ['name1', 4, 4.2, None, None, None], ['name1', 3, 2.2, None, None, None], ['name1', 3, 4.1, None, None, None], ] self._gen_data(data=data) table_name = 'pyodps_test_engine_persist_table' try: df = self.expr.persist(table_name) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, result) finally: self.odps.delete_table(table_name, if_exists=True) try: self.expr.persist(table_name, partitions=['name']) t = self.odps.get_table(table_name) self.assertEqual(2, len(list(t.partitions))) with t.open_reader(partition='name=name1', reopen=True) as r: self.assertEqual(4, r.count) with t.open_reader(partition='name=name2', reopen=True) as r: self.assertEqual(1, r.count) finally: self.odps.delete_table(table_name, if_exists=True) def teardown(self): self.table.drop()
class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists(['name', 'id'], datatypes('string', 'int64')) table = MockTable(name='pyodps_test_expr_table', schema=schema) self.expr = CollectionExpr(_source_data=table, _schema=schema) schema2 = Schema.from_lists(['name2', 'id2'], datatypes('string', 'int64')) table2 = MockTable(name='pyodps_test_expr_table2', schema=schema2) self.expr2 = CollectionExpr(_source_data=table2, _schema=schema2) def _lines_eq(self, expected, actual): self.assertSequenceEqual( [to_str(line.rstrip()) for line in expected.split('\n')], [to_str(line.rstrip()) for line in actual.split('\n')]) def testProjectionFormatter(self): expr = self.expr['name', self.expr.id.rename('new_id')].new_id.astype( 'float32') self._lines_eq(EXPECTED_PROJECTION_FORMAT, repr(expr)) def testFilterFormatter(self): expr = self.expr[(self.expr.name != 'test') & (self.expr.id > 100)] self._lines_eq(EXPECTED_FILTER_FORMAT, repr(expr)) def testSliceFormatter(self): expr = self.expr[:100] self._lines_eq(EXPECTED_SLICE_FORMAT, repr(expr)) expr = self.expr[5:100:3] self._lines_eq(EXPECTED_SLICE_WITH_START_STEP_FORMAT, repr(expr)) def testArithmeticFormatter(self): expr = self.expr d = -(expr['id']) + 20.34 - expr['id'] + float(20) * expr['id'] \ - expr['id'] / 4.9 + 40 // 2 + expr['id'] // 1.2 try: self._lines_eq(EXPECTED_ARITHMETIC_FORMAT, repr(d)) except AssertionError as e: left = [ to_str(line.rstrip()) for line in EXPECTED_ARITHMETIC_FORMAT.split('\n') ] right = [to_str(line.rstrip()) for line in repr(d).split('\n')] self.assertEqual(len(left), len(right)) for l, r in zip(left, right): try: self.assertEqual(l, r) except AssertionError: try: self.assertAlmostEqual(float(l), float(r)) except: raise e def testSortFormatter(self): expr = self.expr.sort(['name', -self.expr.id]) self._lines_eq(EXPECTED_SORT_FORMAT, repr(expr)) def testDistinctFormatter(self): expr = self.expr.distinct(['name', self.expr.id + 1]) self._lines_eq(EXPECTED_DISTINCT_FORMAT, repr(expr)) def testGroupbyFormatter(self): expr = self.expr.groupby(['name', 'id']).agg(new_id=self.expr.id.sum()) self._lines_eq(EXPECTED_GROUPBY_FORMAT, repr(expr)) grouped = self.expr.groupby(['name']) expr = grouped.mutate(grouped.row_number(sort='id')) self._lines_eq(EXPECTED_MUTATE_FORMAT, repr(expr)) expr = self.expr.groupby(['name', 'id']).count() self._lines_eq(EXPECTED_GROUPBY_COUNT_FORMAT, repr(expr)) def testReductionFormatter(self): expr = self.expr.groupby(['id']).id.std() self._lines_eq(EXPECTED_REDUCTION_FORMAT, repr(expr)) expr = self.expr.id.mean() self._lines_eq(EXPECTED_REDUCTION_FORMAT2, repr(expr)) expr = self.expr.count() self._lines_eq(EXPECTED_REDUCTION_FORMAT3, repr(expr)) def testWindowFormatter(self): expr = self.expr.groupby(['name']).sort(-self.expr.id).name.rank() self._lines_eq(EXPECTED_WINDOW_FORMAT1, repr(expr)) expr = self.expr.groupby(['id']).id.cummean(preceding=10, following=5, unique=True) self._lines_eq(EXPECTED_WINDOW_FORMAT2, repr(expr)) def testElementFormatter(self): expr = self.expr.name.contains('test') self._lines_eq(EXPECTED_STRING_FORMAT, repr(expr)) expr = self.expr.id.between(1, 3) self._lines_eq(EXPECTED_ELEMENT_FORMAT, repr(expr)) expr = self.expr.name.astype('datetime').strftime('%Y') self._lines_eq(EXPECTED_DATETIME_FORMAT, repr(expr)) expr = self.expr.id.switch(3, self.expr.name, 4, self.expr.name + 'abc', default=self.expr.name + 'test') self._lines_eq(EXPECTED_SWITCH_FORMAT, repr(expr)) def testJoinFormatter(self): expr = self.expr.join(self.expr2, ('name', 'name2')) self._lines_eq(EXPECTED_JOIN_FORMAT, repr(expr)) def testAstypeFormatter(self): expr = self.expr.id.astype('float') self._lines_eq(EXPECTED_CAST_FORMAT, repr(expr))
class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists( ['name', 'id', 'fid', 'isMale', 'birth', 'scale'][:5], datatypes('string', 'int64', 'float64', 'boolean', 'datetime', 'decimal')[:5]) self.schema = df_schema_to_odps_schema(schema) table_name = tn('pyodps_test_%s' % str(uuid.uuid4()).replace('-', '_')) self.odps.delete_table(table_name, if_exists=True) self.table = self.odps.create_table(name=table_name, schema=self.schema) self.expr = CollectionExpr(_source_data=self.table, _schema=schema) self.engine = SeahawksEngine(self.odps) class FakeBar(object): def update(self, *args, **kwargs): pass def inc(self, *args, **kwargs): pass def status(self, *args, **kwargs): pass self.faked_bar = FakeBar() def teardown(self): self.table.drop() def _gen_data(self, rows=None, data=None, nullable_field=None, value_range=None): if data is None: data = [] for _ in range(rows): record = [] for t in self.schema.types: method = getattr(self, '_gen_random_%s' % t.name) if t.name == 'bigint': record.append(method(value_range=value_range)) else: record.append(method()) data.append(record) if nullable_field is not None: j = self.schema._name_indexes[nullable_field] for i, l in enumerate(data): if i % 2 == 0: data[i][j] = None self.odps.write_table(self.table, 0, data) return data def testAsync(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr.id.sum() future = self.engine.execute(expr, async=True) self.assertFalse(future.done()) res = future.result() self.assertEqual(sum(it[1] for it in data), res) def testCache(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10].cache() cnt = expr.count() dag = self.engine.compile(expr) self.assertEqual(len(dag.nodes()), 2) res = self.engine.execute(cnt) self.assertEqual(len([it for it in data if it[1] < 10]), res) self.assertTrue(context.is_cached(expr)) table = context.get_cached(expr) self.assertIsInstance(table, SeahawksTable) def testBatch(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10].cache() expr1 = expr.id.sum() expr2 = expr.id.mean() dag = self.engine.compile([expr1, expr2]) self.assertEqual(len(dag.nodes()), 3) self.assertEqual(sum(len(v) for v in dag._graph.values()), 2) expect1 = sum(d[1] for d in data if d[1] < 10) length = len([d[1] for d in data if d[1] < 10]) expect2 = (expect1 / float(length)) if length > 0 else 0.0 res = self.engine.execute([expr1, expr2], n_parallel=2) self.assertEqual(res[0], expect1) self.assertAlmostEqual(res[1], expect2) self.assertTrue(context.is_cached(expr)) # test async and timeout expr = self.expr[self.expr.id < 10] expr1 = expr.id.sum() expr2 = expr.id.mean() fs = self.engine.execute([expr, expr1, expr2], n_parallel=2, async=True, timeout=1) self.assertEqual(len(fs), 3) self.assertEqual(fs[1].result(), expect1) self.assertAlmostEqual(fs[2].result(), expect2) self.assertTrue(context.is_cached(expr)) def testBase(self): data = self._gen_data(10, value_range=(-1000, 1000)) expr = self.expr[self.expr.id < 10]['name', lambda x: x.id] result = self._get_result(self.engine.execute(expr).values) self.assertEqual(len([it for it in data if it[1] < 10]), len(result)) if len(result) > 0: self.assertEqual(2, len(result[0])) expr = self.expr[Scalar(3).rename('const'), self.expr.id, (self.expr.id + 1).rename('id2')] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertEqual([c.name for c in res.columns], ['const', 'id', 'id2']) self.assertTrue(all(it[0] == 3 for it in result)) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[2] for it in result]) expr = self.expr.sort('id')[:5] res = self.engine.execute(expr) result = self._get_result(res.values) self.assertListAlmostEqual( sorted(data, key=lambda it: it[1])[:5], [r[:-1] + [r[-1].replace(tzinfo=None)] for r in result], only_float=False, delta=.001) expr = self.expr[:1].filter(lambda x: x.name == data[1][0]) res = self.engine.execute(expr) self.assertEqual(len(res), 0) def testChinese(self): data = [ ['中文', 4, 5.3, None, None], ['\'中文2', 2, 3.5, None, None], ] self._gen_data(data=data) expr = self.expr.filter(self.expr.name == '中文') res = self.engine.execute(expr) self.assertEqual(len(res), 1) expr = self.expr.filter(self.expr.name == '\'中文2') res = self.engine.execute(expr) self.assertEqual(len(res), 1) expr = self.expr.filter(self.expr.name == u'中文') res = self.engine.execute(expr) self.assertEqual(len(res), 1) def testElement(self): data = self._gen_data(5, nullable_field='name') fields = [ self.expr.name.isnull().rename('name1'), self.expr.name.notnull().rename('name2'), self.expr.name.fillna('test').rename('name3'), self.expr.id.isin([1, 2, 3]).rename('id1'), self.expr.id.isin(self.expr.fid.astype('int')).rename('id2'), self.expr.id.notin([1, 2, 3]).rename('id3'), self.expr.id.notin(self.expr.fid.astype('int')).rename('id4'), self.expr.id.between(self.expr.fid, 3).rename('id5'), self.expr.name.fillna('test').switch( 'test', 'test' + self.expr.name.fillna('test'), 'test2', 'test2' + self.expr.name.fillna('test'), default=self.expr.name).rename('name4'), self.expr.name.fillna('test').switch('test', 1, 'test2', 2).rename('name5'), self.expr.id.cut([100, 200, 300], labels=['xsmall', 'small', 'large', 'xlarge'], include_under=True, include_over=True).rename('id6'), self.expr.id.between(self.expr.fid, 3, inclusive=False).rename('id7'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual(len([it for it in data if it[0] is None]), len([it[0] for it in result if it[0]])) self.assertEqual(len([it[0] for it in data if it[0] is not None]), len([it[1] for it in result if it[1]])) self.assertEqual([(it[0] if it[0] is not None else 'test') for it in data], [it[2] for it in result]) self.assertEqual([(it[1] in (1, 2, 3)) for it in data], [it[3] for it in result]) fids = [int(it[2]) for it in data] self.assertEqual([(it[1] in fids) for it in data], [it[4] for it in result]) self.assertEqual([(it[1] not in (1, 2, 3)) for it in data], [it[5] for it in result]) self.assertEqual([(it[1] not in fids) for it in data], [it[6] for it in result]) self.assertEqual([(it[2] <= it[1] <= 3) for it in data], [it[7] for it in result]) self.assertEqual( [to_str('testtest' if it[0] is None else it[0]) for it in data], [to_str(it[8]) for it in result]) self.assertEqual([to_str(1 if it[0] is None else None) for it in data], [to_str(it[9]) for it in result]) def get_val(val): if val <= 100: return 'xsmall' elif 100 < val <= 200: return 'small' elif 200 < val <= 300: return 'large' else: return 'xlarge' self.assertEqual([to_str(get_val(it[1])) for it in data], [to_str(it[10]) for it in result]) self.assertEqual([(it[2] < it[1] < 3) for it in data], [it[11] for it in result]) def testArithmetic(self): data = self._gen_data(5, value_range=(-1000, 1000)) fields = [ (self.expr.id + 1).rename('id1'), (self.expr.fid - 1).rename('fid1'), (self.expr.id / 2).rename('id2'), (self.expr.id**2).rename('id3'), abs(self.expr.id).rename('id4'), (~self.expr.id).rename('id5'), (-self.expr.fid).rename('fid2'), (~self.expr.isMale).rename('isMale1'), (-self.expr.isMale).rename('isMale2'), (self.expr.id // 2).rename('id6'), (self.expr.id % 2).rename('id7'), ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(data), len(result)) self.assertEqual([it[1] + 1 for it in data], [it[0] for it in result]) self.assertListAlmostEqual([it[2] - 1 for it in data], [it[1] for it in result], delta=.001) self.assertListAlmostEqual([float(it[1]) / 2 for it in data], [it[2] for it in result], delta=.001) self.assertEqual([int(it[1]**2) for it in data], [it[3] for it in result]) self.assertEqual([abs(it[1]) for it in data], [it[4] for it in result]) self.assertEqual([~it[1] for it in data], [it[5] for it in result]) self.assertListAlmostEqual([-it[2] for it in data], [it[6] for it in result], delta=.001) self.assertEqual([not it[3] for it in data], [it[7] for it in result]) self.assertEqual([it[1] // 2 for it in data], [it[9] for it in result]) self.assertEqual([it[1] % 2 for it in data], [it[10] for it in result]) def testMath(self): # TODO: test sinh, cosh..., and acosh, asinh... data = self._gen_data(5, value_range=(1, 90)) if hasattr(math, 'expm1'): expm1 = math.expm1 else: expm1 = lambda x: 2 * math.exp(x / 2.0) * math.sinh(x / 2.0) methods_to_fields = [ (math.sin, self.expr.id.sin()), (math.cos, self.expr.id.cos()), (math.tan, self.expr.id.tan()), (math.log, self.expr.id.log()), (lambda v: math.log(v, 2), self.expr.id.log2()), (math.log10, self.expr.id.log10()), (math.log1p, self.expr.id.log1p()), (math.exp, self.expr.id.exp()), (expm1, self.expr.id.expm1()), (math.atan, self.expr.id.arctan()), (math.sqrt, self.expr.id.sqrt()), (abs, self.expr.id.abs()), (math.ceil, self.expr.id.ceil()), (math.floor, self.expr.id.floor()), (math.trunc, self.expr.id.trunc()), ] fields = [ it[1].rename('id' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): mt = it[0] def method(v): try: return mt(v) except ValueError: return float('nan') first = [method(it[1]) for it in data] second = [it[i] for it in result] self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): not_valid = lambda x: \ x is None or (isinstance(x, float) and (math.isnan(x) or math.isinf(x))) if not_valid(it1) and not_valid(it2): continue if isinstance(it1, float) and it1 > 1.0e15: scale = 0.1**(int(math.log10(it1)) - 15) self.assertAlmostEqual(it1 * scale, it2 * scale, delta=8) else: self.assertAlmostEqual(it1, it2, delta=2) def testString(self): data = self._gen_data(5) methods_to_fields = [ (lambda s: s.capitalize(), self.expr.name.capitalize()), (lambda s: data[0][0] in s, self.expr.name.contains(data[0][0], regex=False)), (lambda s: s[0] + '|' + str(s[1]), self.expr.name.cat(self.expr.id.astype('string'), sep='|')), (lambda s: s.endswith(data[0][0]), self.expr.name.endswith(data[0][0])), (lambda s: s.startswith(data[0][0]), self.expr.name.startswith(data[0][0])), (lambda s: s.replace(data[0][0], 'test'), self.expr.name.replace(data[0][0], 'test', regex=False)), (lambda s: s[0], self.expr.name.get(0)), (lambda s: len(s), self.expr.name.len()), (lambda s: s.ljust(10), self.expr.name.ljust(10)), (lambda s: s.ljust(20, '*'), self.expr.name.ljust(20, fillchar='*')), (lambda s: s.rjust(10), self.expr.name.rjust(10)), (lambda s: s.rjust(20, '*'), self.expr.name.rjust(20, fillchar='*')), (lambda s: s * 4, self.expr.name.repeat(4)), (lambda s: s[1:], self.expr.name.slice(1)), (lambda s: s[1:6], self.expr.name.slice(1, 6)), (lambda s: s.title(), self.expr.name.title()), (lambda s: s.rjust(20, '0'), self.expr.name.zfill(20)), ] fields = [ it[1].rename('id' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] if i != 2: first = [method(it[0]) for it in data] else: # cat first = [method(it) for it in data] second = [it[i] for it in result] self.assertEqual(first, second) def testDatetime(self): data = self._gen_data(5) def date_value(sel): if isinstance(sel, six.string_types): fun = lambda v: getattr(v, sel) else: fun = sel col_id = [ idx for idx, col in enumerate(self.schema.names) if col == 'birth' ][0] return [fun(row[col_id]) for row in data] methods_to_fields = [ (partial(date_value, 'year'), self.expr.birth.year), (partial(date_value, 'month'), self.expr.birth.month), (partial(date_value, 'day'), self.expr.birth.day), (partial(date_value, 'hour'), self.expr.birth.hour), (partial(date_value, 'minute'), self.expr.birth.minute), (partial(date_value, 'second'), self.expr.birth.second), (partial(date_value, lambda d: d.isocalendar()[1]), self.expr.birth.weekofyear), (partial(date_value, lambda d: d.weekday()), self.expr.birth.dayofweek), (partial(date_value, lambda d: d.weekday()), self.expr.birth.weekday), (partial(date_value, lambda d: time.mktime(d.timetuple())), self.expr.birth.unix_timestamp), (partial( date_value, lambda d: datetime.combine(d.date(), datetime.min.time())), self.expr.birth.date), ] fields = [ it[1].rename('birth' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = method() try: import pandas as pd def conv(v): if isinstance(v, pd.Timestamp): v = v.to_datetime() if isinstance(v, datetime): return v.replace(tzinfo=None) return v except ImportError: conv = lambda v: v second = [conv(it[i]) for it in result] self.assertEqual(first, second) def testSortDistinct(self): data = [ ['name1', 4, None, None, None], ['name2', 2, None, None, None], ['name1', 4, None, None, None], ['name1', 3, None, None, None], ] self._gen_data(data=data) expr = self.expr.sort(['name', -self.expr.id ]).distinct(['name', lambda x: x.id + 1])[:50] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 3) expected = [['name1', 5], ['name1', 4], ['name2', 3]] self.assertEqual(sorted(expected), sorted(result)) def testPivotTable(self): data = [['name1', 1, 1.0, True, None], ['name1', 1, 5.0, True, None], ['name1', 2, 2.0, True, None], ['name2', 1, 3.0, False, None], ['name2', 3, 4.0, False, None]] self._gen_data(data=data) expr = self.expr expr1 = expr.pivot_table(rows='name', values='fid') res = self.engine.execute(expr1) result = self._get_result(res) expected = [ ['name1', 8.0 / 3], ['name2', 3.5], ] self.assertListAlmostEqual(sorted(result), sorted(expected), only_float=False) expr2 = expr.pivot_table(rows='name', values='fid', aggfunc=['mean', 'sum']) res = self.engine.execute(expr2) result = self._get_result(res) expected = [ ['name1', 8.0 / 3, 8.0], ['name2', 3.5, 7.0], ] self.assertEqual(res.schema.names, ['name', 'fid_mean', 'fid_sum']) self.assertListAlmostEqual(sorted(result), sorted(expected), only_float=False) expr5 = expr.pivot_table(rows='id', values='fid', columns='name', aggfunc=['mean', 'sum']) expr6 = expr5['name1_fid_mean', expr5.groupby(Scalar(1)).sort('name1_fid_mean'). name1_fid_mean.astype('float').cumsum()] k = lambda x: list(0 if it is None else it for it in x) expected = [[2, 2], [3, 5], [None, 5]] res = self.engine.execute(expr6) result = self._get_result(res) self.assertEqual(sorted(result, key=k), sorted(expected, key=k)) expr3 = expr.pivot_table(rows='id', values='fid', columns='name', fill_value=0).distinct() res = self.engine.execute(expr3) result = self._get_result(res) expected = [ [2, 0, 2.0], [3, 4.0, 0], [1, 3.0, 3.0], ] self.assertEqual(res.schema.names, ['id', 'name2_fid_mean', 'name1_fid_mean']) self.assertEqual(result, expected) expr7 = expr.pivot_table(rows='id', values='fid', columns='name', aggfunc=['mean', 'sum']).cache() self.assertEqual(len(self.engine.execute(expr7)), 3) expr8 = self.expr.pivot_table(rows='id', values='fid', columns='name') self.assertEqual(len(self.engine.execute(expr8)), 3) self.assertNotIsInstance(expr8.schema, DynamicSchema) expr9 = (expr8['name1_fid_mean'] - expr8['name2_fid_mean']).rename('substract') self.assertEqual(len(self.engine.execute(expr9)), 3) expr10 = expr8.distinct() self.assertEqual(len(self.engine.execute(expr10)), 3) def testGroupbyAggregation(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] self._gen_data(data=data) field = self.expr.groupby('name').sort(['id', -self.expr.fid]).row_number() expr = self.expr['name', 'id', 'fid', field] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 3, 4.1, 1], ['name1', 3, 2.2, 2], ['name1', 4, 5.3, 3], ['name1', 4, 4.2, 4], ['name2', 2, 3.5, 1], ] result = sorted(result, key=lambda k: (k[0], k[1], -k[2])) self.assertEqual(expected, result) expr = self.expr.name.value_counts(dropna=True)[:25] expected = [['name1', 4], ['name2', 1]] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.name.topk(25) res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name').count() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(sorted([it[1:] for it in expected]), sorted(result)) expected = [['name1', 2], ['name2', 1]] expr = self.expr.groupby('name').id.nunique() res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual([it[1:] for it in expected], result) expr = self.expr[self.expr['id'] > 2].name.value_counts()[:25] expected = [['name1', 4]] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr.groupby('name', Scalar(1).rename('constant')) \ .agg(id=self.expr.id.sum()) expected = [['name1', 1, 14], ['name2', 1, 2]] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(expected, result) expr = self.expr[:1] expr = expr.groupby('name').agg(expr.id.sum()) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 4]] self.assertEqual(expected, result) def testProjectionGroupbyFilter(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] self._gen_data(data=data) df = self.expr.copy() df['id'] = df.id + 1 df2 = df.groupby('name').agg( id=df.id.sum())[lambda x: x.name == 'name2'] expected = [['name2', 3]] res = self.engine.execute(df2) result = self._get_result(res) self.assertEqual(expected, result) def testJoinGroupby(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = tn('pyodps_test_engine_table2') self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name1', 4, -1], ['name2', 1, -2]] self.odps.write_table(table2, 0, data2) expr = self.expr.join(expr2, on='name')[self.expr] expr = expr.groupby('id').agg(expr.fid.sum()) res = self.engine.execute(expr) result = self._get_result(res) id_idx = [ idx for idx, col in enumerate(self.expr.schema.names) if col == 'id' ][0] fid_idx = [ idx for idx, col in enumerate(self.expr.schema.names) if col == 'fid' ][0] expected = [[k, sum( v[fid_idx] for v in row)] for k, row in itertools.groupby( sorted(data, key=lambda r: r[id_idx]), lambda r: r[id_idx])] for it in zip(sorted(expected, key=lambda it: it[0]), sorted(result, key=lambda it: it[0])): self.assertAlmostEqual(it[0][0], it[1][0]) self.assertAlmostEqual(it[0][1], it[1][1]) def testFilterGroupby(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] self._gen_data(data=data) expr = self.expr.groupby( ['name']).agg(id=self.expr.id.max())[lambda x: x.id > 3] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 1) expected = [['name1', 4]] self.assertEqual(expected, result) def testWindowFunction(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 6.1, None, None], ] self._gen_data(data=data) expr = self.expr.groupby('name').id.cumsum() res = self.engine.execute(expr) result = self._get_result(res) expected = [[14]] * 4 + [[2]] self.assertEqual(sorted(expected), sorted(result)) expr = self.expr.groupby('name').sort('fid').id.cummax() res = self.engine.execute(expr) result = self._get_result(res) expected = [[3], [4], [4], [4], [2]] self.assertEqual(sorted(expected), sorted(result)) expr = self.expr[ self.expr.groupby('name', 'id').sort('fid').id.cummean(), ] res = self.engine.execute(expr) result = self._get_result(res) expected = [[3], [3], [4], [4], [2]] self.assertEqual(sorted(expected), sorted(result)) expr = self.expr.groupby('name').mutate( id2=lambda x: x.id.cumcount(), fid2=lambda x: x.fid.cummin(sort='id')) res = self.engine.execute(expr['name', 'id2', 'fid2']) result = self._get_result(res) expected = [ ['name1', 4, 2.2], ['name1', 4, 2.2], ['name1', 4, 2.2], ['name1', 4, 2.2], ['name2', 1, 3.5], ] self.assertEqual(sorted(expected), sorted(result)) expr = self.expr[ self.expr.id, self.expr.groupby('name').rank('id'), self.expr.groupby('name').dense_rank('fid', ascending=False), self.expr.groupby('name'). row_number(sort=['id', 'fid'], ascending=[True, False]), self.expr.groupby('name').percent_rank('id'), ] res = self.engine.execute(expr) result = self._get_result(res) expected = [[4, 3, 2, 3, float(2) / 3], [2, 1, 1, 1, 0.0], [4, 3, 3, 4, float(2) / 3], [3, 1, 4, 2, float(0) / 3], [3, 1, 1, 1, float(0) / 3]] [ self.assertListAlmostEqual(l, r) for l, r in zip(sorted(expected), sorted(result)) ] expr = self.expr[ self.expr.id, self.expr.groupby('name').id. lag(offset=3, default=0, sort=['id', 'fid']).rename('id2'), self.expr.groupby('name').id.lead(offset=1, default=-1, sort=['id', 'fid'], ascending=[False, False] ).rename('id3'), ] res = self.engine.execute(expr) result = self._get_result(res) expected = [[4, 3, 4], [2, 0, -1], [4, 0, 3], [3, 0, -1], [3, 0, 3]] self.assertEqual(sorted(expected), sorted(result)) def testWindowRewrite(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] self._gen_data(data=data) expr = self.expr[self.expr.id - self.expr.id.mean() < 10][[ lambda x: x.id - x.id.max() ]][[lambda x: x.id - x.id.min()]][lambda x: x.id - x.id.std() > 0] res = self.engine.execute(expr) result = self._get_result(res) id_idx = [ idx for idx, col in enumerate(self.expr.schema.names) if col == 'id' ][0] expected = [r[id_idx] for r in data] maxv = max(expected) expected = [v - maxv for v in expected] minv = min(expected) expected = [v - minv for v in expected] meanv = sum(expected) * 1.0 / len(expected) meanv2 = sum([v**2 for v in expected]) * 1.0 / len(expected) std = math.sqrt(meanv2 - meanv**2) expected = [v for v in expected if v > std] self.assertEqual(expected, [it[0] for it in result]) def testReduction(self): data = self._gen_data(rows=5, value_range=(-100, 100)) def stats(col, func): col_idx = [ idx for idx, cn in enumerate(self.expr.schema.names) if cn == col ][0] return func([r[col_idx] for r in data]) def var(vct, ddof=0): meanv = mean(vct) meanv2 = mean([v**2 for v in vct]) return (meanv2 - meanv**2) * len(vct) / (len(vct) - ddof) def moment(vct, order, central=False, absolute=False): abs_fun = abs if absolute else lambda x: x if central: m = mean(vct) return mean([abs_fun(v - m)**order for v in vct]) else: return mean([abs_fun(v)**order for v in vct]) def skew(vct): n = len(vct) return moment(vct, 3, central=True) / (std( vct, 1)**3) * (n**2) / (n - 1) / (n - 2) def kurtosis(vct): n = len(vct) m4 = moment(vct, 4, central=True) m2 = var(vct, 0) return 1.0 / (n - 2) / (n - 3) * ((n * n - 1.0) * m4 / m2**2 - 3 * (n - 1)**2) mean = lambda v: sum(v) * 1.0 / len(v) std = lambda v, ddof=0: math.sqrt(var(v, ddof)) nunique = lambda v: len(set(v)) cat = lambda v: len([it for it in v if it is not None]) methods_to_fields = [ (partial(stats, 'id', mean), self.expr.id.mean()), (partial(len, data), self.expr.count()), (partial(stats, 'id', var), self.expr.id.var(ddof=0)), (partial(stats, 'id', lambda x: var(x, 1)), self.expr.id.var(ddof=1)), (partial(stats, 'id', std), self.expr.id.std(ddof=0)), (partial(stats, 'id', lambda x: moment(x, 3, central=True)), self.expr.id.moment(3, central=True)), (partial(stats, 'id', skew), self.expr.id.skew()), (partial(stats, 'id', kurtosis), self.expr.id.kurtosis()), (partial(stats, 'id', sum), self.expr.id.sum()), (partial(stats, 'id', min), self.expr.id.min()), (partial(stats, 'id', max), self.expr.id.max()), (partial(stats, 'isMale', min), self.expr.isMale.min()), (partial(stats, 'isMale', sum), self.expr.isMale.sum()), (partial(stats, 'isMale', any), self.expr.isMale.any()), (partial(stats, 'isMale', all), self.expr.isMale.all()), (partial(stats, 'name', nunique), self.expr.name.nunique()), (partial(stats, 'name', cat), self.expr.name.cat(sep='|')), (partial(stats, 'id', lambda x: len(x)), self.expr.id.count()), ] fields = [ it[1].rename('f' + str(i)) for i, it in enumerate(methods_to_fields) ] expr = self.expr[fields] res = self.engine.execute(expr) result = self._get_result(res) for i, it in enumerate(methods_to_fields): method = it[0] first = method() second = [it[i] for it in result][0] if i == len(methods_to_fields) - 2: # cat second = len(second.split('|')) if isinstance(first, float): self.assertAlmostEqual(first, second) else: if first != second: pass self.assertEqual(first, second) def testJoin(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = tn('pyodps_test_engine_table2') self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name1', 4, -1], ['name2', 1, -2]] self.odps.write_table(table2, 0, data2) try: expr = self.expr.join(expr2)['name', 'id2'] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 5) expected = [[to_str('name1'), 4], [to_str('name2'), 1]] self.assertTrue(all(it in expected for it in result)) expr = self.expr.join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(len(result), 2) expected = [to_str('name1'), 4] self.assertTrue(all(it == expected for it in result)) expr = self.expr.left_join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 4], ['name2', None], ['name1', 4], ['name1', None], ['name1', None]] self.assertEqual(len(result), 5) self.assertTrue(all(it in expected for it in result)) expr = self.expr.right_join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 4], ['name1', 4], [None, 1], ] self.assertEqual(len(result), 3) self.assertTrue(all(it in expected for it in result)) expr = self.expr.outer_join(expr2, on=['name', ('id', 'id2')])[self.expr.name, expr2.id2] res = self.engine.execute(expr) result = self._get_result(res) expected = [ ['name1', 4], ['name1', 4], ['name2', None], ['name1', None], ['name1', None], [None, 1], ] self.assertEqual(len(result), 6) self.assertTrue(all(it in expected for it in result)) grouped = self.expr.groupby('name').agg( new_id=self.expr.id.sum()).cache() self.engine.execute(self.expr.join(grouped, on='name')) expr = self.expr.join(expr2, on=[ 'name', ('id', 'id2') ])[lambda x: x.groupby(Scalar(1)).sort('name').row_number(), ] self.engine.execute(expr) finally: table2.drop() def testUnion(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] schema2 = Schema.from_lists(['name', 'id2', 'id3'], [types.string, types.bigint, types.bigint]) table_name = tn('pyodps_test_engine_table2') self.odps.delete_table(table_name, if_exists=True) table2 = self.odps.create_table(name=table_name, schema=schema2) expr2 = CollectionExpr(_source_data=table2, _schema=odps_schema_to_df_schema(schema2)) self._gen_data(data=data) data2 = [['name3', 5, -1], ['name4', 6, -2]] self.odps.write_table(table2, 0, data2) try: expr = self.expr['name', 'id'].distinct().union( expr2[expr2.id2.rename('id'), 'name']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 4], ['name1', 3], ['name2', 2], ['name3', 5], ['name4', 6]] result = sorted(result) expected = sorted(expected) self.assertEqual(len(result), len(expected)) for e, r in zip(result, expected): self.assertEqual([to_str(t) for t in e], [to_str(t) for t in r]) finally: table2.drop() def testScaleValue(self): data = [ ['name1', 4, 5.3], ['name2', 2, 3.5], ['name1', 4, 4.2], ['name1', 3, 2.2], ['name1', 3, 4.1], ] schema = Schema.from_lists(['name', 'id', 'fid'], [types.string, types.bigint, types.double]) table_name = tn('pyodps_test_engine_scale_table') self.odps.delete_table(table_name, if_exists=True) table = self.odps.create_table(name=table_name, schema=schema) self.odps.write_table(table_name, 0, data) expr_input = CollectionExpr(_source_data=table, _schema=odps_schema_to_df_schema(schema)) expr = expr_input.min_max_scale(columns=['fid']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 4, 1.0], ['name2', 2, 0.41935483870967744], ['name1', 4, 0.6451612903225807], ['name1', 3, 0.0], ['name1', 3, 0.6129032258064515]] result = sorted(result) expected = sorted(expected) for first, second in zip(result, expected): self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): self.assertAlmostEqual(it1, it2) expr = expr_input.std_scale(columns=['fid']) res = self.engine.execute(expr) result = self._get_result(res) expected = [['name1', 4, 1.4213602653434203], ['name2', 2, -0.3553400663358544], ['name1', 4, 0.3355989515394193], ['name1', 3, -1.6385125281042194], ['name1', 3, 0.23689337755723686]] result = sorted(result) expected = sorted(expected) for first, second in zip(result, expected): self.assertEqual(len(first), len(second)) for it1, it2 in zip(first, second): self.assertAlmostEqual(it1, it2) def testPersist(self): data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] self._gen_data(data=data) table_name = tn('pyodps_test_engine_persist_seahawks_table') try: df = self.engine.persist(self.expr, table_name) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, result) finally: self.odps.delete_table(table_name, if_exists=True) try: schema = Schema.from_lists(self.schema.names, self.schema.types, ['ds'], ['string']) self.odps.create_table(table_name, schema) df = self.engine.persist(self.expr, table_name, partition='ds=today', create_partition=True) res = self.engine.execute(df) result = self._get_result(res) self.assertEqual(len(result), 5) self.assertEqual(data, [r[:-1] for r in result]) finally: self.odps.delete_table(table_name, if_exists=True) try: self.engine.persist(self.expr, table_name, partitions=['name']) t = self.odps.get_table(table_name) self.assertEqual(2, len(list(t.partitions))) with t.open_reader(partition='name=name1', reopen=True) as r: self.assertEqual(4, r.count) with t.open_reader(partition='name=name2', reopen=True) as r: self.assertEqual(1, r.count) finally: self.odps.delete_table(table_name, if_exists=True) def testMakeKV(self): from odps import types as odps_types data = [ ['name1', 1.0, 3.0, None, 10.0, None, None], ['name1', None, 3.0, 5.1, None, None, None], ['name1', 7.1, None, None, None, 8.2, None], ['name2', None, 1.2, 1.5, None, None, None], ['name2', None, 1.0, None, None, None, 1.1], ] kv_cols = ['k1', 'k2', 'k3', 'k5', 'k7', 'k9'] schema = Schema.from_lists(['name'] + kv_cols, [odps_types.string] + [odps_types.double] * 6) table_name = tn('pyodps_test_engine_make_kv') self.odps.delete_table(table_name, if_exists=True) table = self.odps.create_table(name=table_name, schema=schema) expr = CollectionExpr(_source_data=table, _schema=odps_schema_to_df_schema(schema)) try: self.odps.write_table(table, 0, data) expr1 = expr.to_kv(columns=kv_cols, kv_delim='=') res = self.engine.execute(expr1) result = self._get_result(res) expected = [ ['name1', 'k1=1,k2=3,k5=10'], ['name1', 'k2=3,k3=5.1'], ['name1', 'k1=7.1,k7=8.2'], ['name2', 'k2=1.2,k3=1.5'], ['name2', 'k2=1,k9=1.1'], ] self.assertListEqual(result, expected) finally: table.drop() def testFilterOrder(self): table_name = tn('pyodps_test_division_error') self.odps.delete_table(table_name, if_exists=True) table = self.odps.create_table(table_name, 'divided bigint, divisor bigint', lifecycle=1) try: self.odps.write_table(table_name, [[2, 0], [1, 1], [1, 2], [5, 1], [5, 0]]) df = CollectionExpr(_source_data=table, _schema=odps_schema_to_df_schema(table.schema)) fdf = df[df.divisor > 0] ddf = fdf[(fdf.divided / fdf.divisor).rename('result'), ] expr = ddf[ddf.result > 1] res = self.engine.execute(expr) result = self._get_result(res) self.assertEqual(result, [[ 5, ]]) finally: table.drop() def testAXFException(self): import sqlalchemy data = [ ['name1', 4, 5.3, None, None], ['name2', 2, 3.5, None, None], ['name1', 4, 4.2, None, None], ['name1', 3, 2.2, None, None], ['name1', 3, 4.1, None, None], ] self._gen_data(data=data) table_name = tn('pyodps_test_engine_axf_seahawks_table') try: schema = Schema.from_lists(self.schema.names, self.schema.types, ['ds'], ['string']) self.odps.create_table(table_name, schema) df = self.engine.persist(self.expr, table_name, partition='ds=today', create_partition=True) with self.assertRaises(sqlalchemy.exc.DatabaseError): self.engine.execute(df.input) finally: self.odps.delete_table(table_name, if_exists=True)
class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists(["name", "id"], datatypes("string", "int64")) table = MockTable(name="pyodps_test_expr_table", schema=schema) self.expr = CollectionExpr(_source_data=table, _schema=schema) schema2 = Schema.from_lists(["name2", "id2"], datatypes("string", "int64")) table2 = MockTable(name="pyodps_test_expr_table2", schema=schema2) self.expr2 = CollectionExpr(_source_data=table2, _schema=schema2) def _lines_eq(self, expected, actual): self.assertSequenceEqual( [to_str(line.rstrip()) for line in expected.split("\n")], [to_str(line.rstrip()) for line in actual.split("\n")], ) def testProjectionFormatter(self): expr = self.expr["name", self.expr.id.rename("new_id")].new_id.astype("float32") self._lines_eq(EXPECTED_PROJECTION_FORMAT, repr(expr)) def testFilterFormatter(self): expr = self.expr[(self.expr.name != "test") & (self.expr.id > 100)] self._lines_eq(EXPECTED_FILTER_FORMAT, repr(expr)) def testSliceFormatter(self): expr = self.expr[:100] self._lines_eq(EXPECTED_SLICE_FORMAT, repr(expr)) expr = self.expr[5:100:3] self._lines_eq(EXPECTED_SLICE_WITH_START_STEP_FORMAT, repr(expr)) def testArithmeticFormatter(self): expr = self.expr d = -(expr["id"]) + 20.34 - expr["id"] + float(20) * expr["id"] - expr["id"] / 4.9 + 40 // 2 + expr["id"] // 1.2 try: self._lines_eq(EXPECTED_ARITHMETIC_FORMAT, repr(d)) except AssertionError as e: left = [to_str(line.rstrip()) for line in EXPECTED_ARITHMETIC_FORMAT.split("\n")] right = [to_str(line.rstrip()) for line in repr(d).split("\n")] self.assertEqual(len(left), len(right)) for l, r in zip(left, right): try: self.assertEqual(l, r) except AssertionError: try: self.assertAlmostEqual(float(l), float(r)) except: raise e def testSortFormatter(self): expr = self.expr.sort(["name", -self.expr.id]) self._lines_eq(EXPECTED_SORT_FORMAT, repr(expr)) def testDistinctFormatter(self): expr = self.expr.distinct(["name", self.expr.id + 1]) self._lines_eq(EXPECTED_DISTINCT_FORMAT, repr(expr)) def testGroupbyFormatter(self): expr = self.expr.groupby(["name", "id"]).agg(new_id=self.expr.id.sum()) self._lines_eq(EXPECTED_GROUPBY_FORMAT, repr(expr)) grouped = self.expr.groupby(["name"]) expr = grouped.mutate(grouped.row_number()) self._lines_eq(EXPECTED_MUTATE_FORMAT, repr(expr)) expr = self.expr.groupby(["name", "id"]).count() self._lines_eq(EXPECTED_GROUPBY_COUNT_FORMAT, repr(expr)) def testReductionFormatter(self): expr = self.expr.groupby(["id"]).id.std() self._lines_eq(EXPECTED_REDUCTION_FORMAT, repr(expr)) expr = self.expr.id.mean() self._lines_eq(EXPECTED_REDUCTION_FORMAT2, repr(expr)) expr = self.expr.count() self._lines_eq(EXPECTED_REDUCTION_FORMAT3, repr(expr)) def testWindowFormatter(self): expr = self.expr.groupby(["name"]).sort(-self.expr.id).name.rank() self._lines_eq(EXPECTED_WINDOW_FORMAT1, repr(expr)) expr = self.expr.groupby(["id"]).id.cummean(preceding=10, following=5, unique=True) self._lines_eq(EXPECTED_WINDOW_FORMAT2, repr(expr)) def testElementFormatter(self): expr = self.expr.name.contains("test") self._lines_eq(EXPECTED_STRING_FORMAT, repr(expr)) expr = self.expr.id.between(1, 3) self._lines_eq(EXPECTED_ELEMENT_FORMAT, repr(expr)) expr = self.expr.name.astype("datetime").strftime("%Y") self._lines_eq(EXPECTED_DATETIME_FORMAT, repr(expr)) expr = self.expr.id.switch(3, self.expr.name, 4, self.expr.name + "abc", default=self.expr.name + "test") self._lines_eq(EXPECTED_SWITCH_FORMAT, repr(expr)) def testJoinFormatter(self): expr = self.expr.join(self.expr2, ("name", "name2")) self._lines_eq(EXPECTED_JOIN_FORMAT, repr(expr)) def testAstypeFormatter(self): expr = self.expr.id.astype("float") self._lines_eq(EXPECTED_CAST_FORMAT, repr(expr))