CLN: Removed all occurances of goal_time and enabled linting for benchmarks

asv_bench/benchmarks/algorithms.py

@@ -15,8 +15,6 @@
 
 class Factorize(object):
 
-    goal_time = 0.2
-
     params = [True, False]
     param_names = ['sort']
 
@@ -38,8 +36,6 @@ def time_factorize_string(self, sort):
 
 class Duplicated(object):
 
-    goal_time = 0.2
-
     params = ['first', 'last', False]
     param_names = ['keep']
 
@@ -61,8 +57,6 @@ def time_duplicated_string(self, keep):
 
 class DuplicatedUniqueIndex(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**5
         self.idx_int_dup = pd.Int64Index(np.arange(N * 5))
@@ -75,8 +69,6 @@ def time_duplicated_unique_int(self):
 
 class Match(object):
 
-    goal_time = 0.2
-
     def setup(self):
         self.uniques = tm.makeStringIndex(1000).values
         self.all = self.uniques.repeat(10)
@@ -88,8 +80,6 @@ def time_match_string(self):
 
 class Hashing(object):
 
-    goal_time = 0.2
-
     def setup_cache(self):
         N = 10**5
 

asv_bench/benchmarks/attrs_caching.py

@@ -8,8 +8,6 @@
 
 class DataFrameAttributes(object):
 
-    goal_time = 0.2
-
     def setup(self):
         self.df = DataFrame(np.random.randn(10, 6))
         self.cur_index = self.df.index
@@ -23,8 +21,6 @@ def time_set_index(self):
 
 class CacheReadonly(object):
 
-    goal_time = 0.2
-
     def setup(self):
 
         class Foo:

asv_bench/benchmarks/binary_ops.py

@@ -9,8 +9,6 @@
 
 class Ops(object):
 
-    goal_time = 0.2
-
     params = [[True, False], ['default', 1]]
     param_names = ['use_numexpr', 'threads']
 
@@ -42,8 +40,6 @@ def teardown(self, use_numexpr, threads):
 
 class Ops2(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**3
         self.df = DataFrame(np.random.randn(N, N))
@@ -81,8 +77,6 @@ def time_frame_float_mod(self):
 
 class Timeseries(object):
 
-    goal_time = 0.2
-
     params = [None, 'US/Eastern']
     param_names = ['tz']
 
@@ -109,8 +103,6 @@ def time_timestamp_ops_diff_with_shift(self, tz):
 
 class AddOverflowScalar(object):
 
-    goal_time = 0.2
-
     params = [1, -1, 0]
     param_names = ['scalar']
 
@@ -124,8 +116,6 @@ def time_add_overflow_scalar(self, scalar):
 
 class AddOverflowArray(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**6
         self.arr = np.arange(N)

asv_bench/benchmarks/categoricals.py

@@ -14,8 +14,6 @@
 
 class Concat(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**5
         self.s = pd.Series(list('aabbcd') * N).astype('category')
@@ -32,8 +30,6 @@ def time_union(self):
 
 class Constructor(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**5
         self.categories = list('abcde')
@@ -75,8 +71,6 @@ def time_from_codes_all_int8(self):
 
 class ValueCounts(object):
 
-    goal_time = 0.2
-
     params = [True, False]
     param_names = ['dropna']
 
@@ -91,8 +85,6 @@ def time_value_counts(self, dropna):
 
 class Repr(object):
 
-    goal_time = 0.2
-
     def setup(self):
         self.sel = pd.Series(['s1234']).astype('category')
 
@@ -102,8 +94,6 @@ def time_rendering(self):
 
 class SetCategories(object):
 
-    goal_time = 0.2
-
     def setup(self):
         n = 5 * 10**5
         arr = ['s%04d' % i for i in np.random.randint(0, n // 10, size=n)]
@@ -115,8 +105,6 @@ def time_set_categories(self):
 
 class Rank(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**5
         ncats = 100
@@ -154,8 +142,6 @@ def time_rank_int_cat_ordered(self):
 
 class Isin(object):
 
-    goal_time = 0.2
-
     params = ['object', 'int64']
     param_names = ['dtype']
 
@@ -195,8 +181,6 @@ def time_categorical_series_is_monotonic_decreasing(self):
 
 class Contains(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**5
         self.ci = tm.makeCategoricalIndex(N)
@@ -212,7 +196,6 @@ def time_categorical_contains(self):
 
 class CategoricalSlicing(object):
 
-    goal_time = 0.2
     params = ['monotonic_incr', 'monotonic_decr', 'non_monotonic']
     param_names = ['index']
 

asv_bench/benchmarks/ctors.py

@@ -5,8 +5,6 @@
 
 class SeriesConstructors(object):
 
-    goal_time = 0.2
-
     param_names = ["data_fmt", "with_index"]
     params = [[lambda x: x,
                list,
@@ -30,8 +28,6 @@ def time_series_constructor(self, data_fmt, with_index):
 
 class SeriesDtypesConstructors(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**4
         self.arr = np.random.randn(N, N)
@@ -54,8 +50,6 @@ def time_dtindex_from_index_with_series(self):
 
 class MultiIndexConstructor(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**4
         self.iterables = [tm.makeStringIndex(N), range(20)]

asv_bench/benchmarks/eval.py

@@ -8,8 +8,6 @@
 
 class Eval(object):
 
-    goal_time = 0.2
-
     params = [['numexpr', 'python'], [1, 'all']]
     param_names = ['engine', 'threads']
 
@@ -41,8 +39,6 @@ def teardown(self, engine, threads):
 
 class Query(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 10**6
         halfway = (N // 2) - 1

asv_bench/benchmarks/frame_ctor.py

@@ -10,8 +10,6 @@
 
 class FromDicts(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N, K = 5000, 50
         self.index = tm.makeStringIndex(N)
@@ -45,8 +43,6 @@ def time_nested_dict_int64(self):
 
 class FromSeries(object):
 
-    goal_time = 0.2
-
     def setup(self):
         mi = MultiIndex.from_product([range(100), range(100)])
         self.s = Series(np.random.randn(10000), index=mi)
@@ -57,7 +53,6 @@ def time_mi_series(self):
 
 class FromDictwithTimestamp(object):
 
-    goal_time = 0.2
     params = [Nano(1), Hour(1)]
     param_names = ['offset']
 
@@ -74,7 +69,6 @@ def time_dict_with_timestamp_offsets(self, offset):
 
 class FromRecords(object):
 
-    goal_time = 0.2
     params = [None, 1000]
     param_names = ['nrows']
 
@@ -89,8 +83,6 @@ def time_frame_from_records_generator(self, nrows):
 
 class FromNDArray(object):
 
-    goal_time = 0.2
-
     def setup(self):
         N = 100000
         self.data = np.random.randn(N)