TEST-#5014: Simplify adding new ASV benchmarks (#5015)

Signed-off-by: Myachev <anatoly.myachev@intel.com>
modin-project · Sep 24, 2022 · ff3425d · ff3425d
1 parent 0a2c0de
commit ff3425d
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Showing 9 changed files with 90 additions and 132 deletions.
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -470,7 +470,7 @@ jobs:
 
               # check pure pandas
               MODIN_ASV_USE_IMPL=pandas asv run --quick --strict --show-stderr --launch-method=spawn \
-                -b ^benchmarks -b ^io -b ^scalability | tee benchmarks.log
+                -b ^benchmarks -b ^io | tee benchmarks.log
 
               # HDK: ERR_OUT_OF_CPU_MEM: Not enough host memory to execute the query (MODIN#4270)
               # just disable test for testing - it works well in a machine with more memory

diff --git a/asv_bench/benchmarks/benchmarks.py b/asv_bench/benchmarks/benchmarks.py
@@ -19,7 +19,6 @@
 # define `MODIN_ASV_USE_IMPL` env var to choose library for using in performance
 # measurements
 
-import modin.pandas as pd
 import numpy as np
 
 from .utils import (
@@ -30,7 +29,6 @@
     random_string,
     random_columns,
     random_booleans,
-    ASV_USE_IMPL,
     GROUPBY_NGROUPS,
     IMPL,
     execute,
@@ -44,7 +42,6 @@ class BaseTimeGroupBy:
     def setup(self, shape, ngroups=5, groupby_ncols=1):
         ngroups = translator_groupby_ngroups(ngroups, shape)
         self.df, self.groupby_columns = generate_dataframe(
-            ASV_USE_IMPL,
             "int",
             *shape,
             RAND_LOW,
@@ -122,12 +119,8 @@ class TimeJoin:
     ]
 
     def setup(self, shapes, how, sort):
-        self.df1 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[0], RAND_LOW, RAND_HIGH
-        )
-        self.df2 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[1], RAND_LOW, RAND_HIGH
-        )
+        self.df1 = generate_dataframe("int", *shapes[0], RAND_LOW, RAND_HIGH)
+        self.df2 = generate_dataframe("int", *shapes[1], RAND_LOW, RAND_HIGH)
 
     def time_join(self, shapes, how, sort):
         # join dataframes on index to get the predictable shape
@@ -143,12 +136,8 @@ class TimeMerge:
     ]
 
     def setup(self, shapes, how, sort):
-        self.df1 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[0], RAND_LOW, RAND_HIGH
-        )
-        self.df2 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[1], RAND_LOW, RAND_HIGH
-        )
+        self.df1 = generate_dataframe("int", *shapes[0], RAND_LOW, RAND_HIGH)
+        self.df2 = generate_dataframe("int", *shapes[1], RAND_LOW, RAND_HIGH)
 
     def time_merge(self, shapes, how, sort):
         # merge dataframes by index to get the predictable shape
@@ -168,15 +157,11 @@ class TimeConcat:
     ]
 
     def setup(self, shapes, how, axis):
-        self.df1 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[0], RAND_LOW, RAND_HIGH
-        )
-        self.df2 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[1], RAND_LOW, RAND_HIGH
-        )
+        self.df1 = generate_dataframe("int", *shapes[0], RAND_LOW, RAND_HIGH)
+        self.df2 = generate_dataframe("int", *shapes[1], RAND_LOW, RAND_HIGH)
 
     def time_concat(self, shapes, how, axis):
-        execute(IMPL[ASV_USE_IMPL].concat([self.df1, self.df2], axis=axis, join=how))
+        execute(IMPL.concat([self.df1, self.df2], axis=axis, join=how))
 
 
 class TimeAppend:
@@ -187,12 +172,8 @@ class TimeAppend:
     ]
 
     def setup(self, shapes, sort):
-        self.df1 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[0], RAND_LOW, RAND_HIGH
-        )
-        self.df2 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[1], RAND_LOW, RAND_HIGH
-        )
+        self.df1 = generate_dataframe("int", *shapes[0], RAND_LOW, RAND_HIGH)
+        self.df2 = generate_dataframe("int", *shapes[1], RAND_LOW, RAND_HIGH)
         if sort:
             self.df1.columns = self.df1.columns[::-1]
 
@@ -209,12 +190,8 @@ class TimeBinaryOp:
     ]
 
     def setup(self, shapes, binary_op, axis):
-        self.df1 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[0], RAND_LOW, RAND_HIGH
-        )
-        self.df2 = generate_dataframe(
-            ASV_USE_IMPL, "int", *shapes[1], RAND_LOW, RAND_HIGH
-        )
+        self.df1 = generate_dataframe("int", *shapes[0], RAND_LOW, RAND_HIGH)
+        self.df2 = generate_dataframe("int", *shapes[1], RAND_LOW, RAND_HIGH)
         self.op = getattr(self.df1, binary_op)
 
     def time_binary_op(self, shapes, binary_op, axis):
@@ -242,9 +219,7 @@ def get_loc(df, loc, axis, item_length):
         )
 
     def setup(self, shape, item_length, loc, is_equal_indices):
-        self.df = generate_dataframe(
-            ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH
-        ).copy()
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH).copy()
         self.loc, self.iloc = self.get_loc(
             self.df, loc, item_length=item_length, axis=1
         )
@@ -297,7 +272,7 @@ class TimeArithmetic:
     ]
 
     def setup(self, shape, axis):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
 
     def time_sum(self, shape, axis):
         execute(self.df.sum(axis=axis))
@@ -351,7 +326,7 @@ class TimeSortValues:
     ]
 
     def setup(self, shape, columns_number, ascending_list):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
         self.columns = random_columns(self.df.columns, columns_number)
         self.ascending = (
             random_booleans(columns_number)
@@ -372,7 +347,7 @@ class TimeDrop:
     ]
 
     def setup(self, shape, axis, drop_ncols):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
         drop_count = (
             int(len(self.df.axes[axis]) * drop_ncols)
             if isinstance(drop_ncols, float)
@@ -392,7 +367,7 @@ class TimeHead:
     ]
 
     def setup(self, shape, head_count):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
         self.head_count = (
             int(head_count * len(self.df.index))
             if isinstance(head_count, float)
@@ -411,7 +386,7 @@ class TimeTail:
     ]
 
     def setup(self, shape, tail_count):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
         self.tail_count = (
             int(tail_count * len(self.df.index))
             if isinstance(tail_count, float)
@@ -430,7 +405,7 @@ class TimeExplode:
 
     def setup(self, shape):
         self.df = generate_dataframe(
-            ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH, gen_unique_key=True
+            "int", *shape, RAND_LOW, RAND_HIGH, gen_unique_key=True
         )
 
     def time_explode(self, shape):
@@ -446,16 +421,15 @@ class TimeFillnaSeries:
     ]
 
     def setup(self, value_type, shape, limit):
-        pd = IMPL[ASV_USE_IMPL]
-        self.series = gen_nan_data(ASV_USE_IMPL, *shape)
+        self.series = gen_nan_data(*shape)
 
         if value_type == "scalar":
             self.value = 18.19
         elif value_type == "dict":
             self.value = {k: k * 1.23 for k in range(shape[0])}
         elif value_type == "Series":
-            self.value = pd.Series(
-                [k * 1.23 for k in range(shape[0])], index=pd.RangeIndex(shape[0])
+            self.value = IMPL.Series(
+                [k * 1.23 for k in range(shape[0])], index=IMPL.RangeIndex(shape[0])
             )
         else:
             assert False
@@ -479,25 +453,24 @@ class TimeFillnaDataFrame:
     ]
 
     def setup(self, value_type, shape, limit):
-        pd = IMPL[ASV_USE_IMPL]
-        self.df = gen_nan_data(ASV_USE_IMPL, *shape)
+        self.df = gen_nan_data(*shape)
         columns = self.df.columns
 
         if value_type == "scalar":
             self.value = 18.19
         elif value_type == "dict":
             self.value = {k: i * 1.23 for i, k in enumerate(columns)}
         elif value_type == "Series":
-            self.value = pd.Series(
+            self.value = IMPL.Series(
                 [i * 1.23 for i in range(len(columns))], index=columns
             )
         elif value_type == "DataFrame":
-            self.value = pd.DataFrame(
+            self.value = IMPL.DataFrame(
                 {
                     k: [i + j * 1.23 for j in range(shape[0])]
                     for i, k in enumerate(columns)
                 },
-                index=pd.RangeIndex(shape[0]),
+                index=IMPL.RangeIndex(shape[0]),
                 columns=columns,
             )
         else:
@@ -517,7 +490,6 @@ class BaseTimeValueCounts:
     def setup(self, shape, ngroups=5, subset=1):
         ngroups = translator_groupby_ngroups(ngroups, shape)
         self.df, self.subset = generate_dataframe(
-            ASV_USE_IMPL,
             "int",
             *shape,
             RAND_LOW,
@@ -585,11 +557,11 @@ class TimeIndexing:
     }
 
     def setup(self, shape, indexer_type):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
         trigger_import(self.df)
 
         self.indexer = self.indexer_getters[indexer_type](self.df)
-        if isinstance(self.indexer, (pd.Series, pd.DataFrame)):
+        if isinstance(self.indexer, (IMPL.Series, IMPL.DataFrame)):
             # HACK: Triggering `dtypes` meta-data computation in advance,
             # so it won't affect the `loc/iloc` time:
             self.indexer.dtypes
@@ -611,7 +583,7 @@ class TimeIndexingColumns:
     params = [get_benchmark_shapes("TimeIndexing")]
 
     def setup(self, shape):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
         trigger_import(self.df)
         self.numeric_indexer = [0, 1]
         self.labels_indexer = self.df.columns[self.numeric_indexer].tolist()
@@ -631,10 +603,12 @@ class TimeMultiIndexing:
     params = [get_benchmark_shapes("TimeMultiIndexing")]
 
     def setup(self, shape):
-        df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
 
-        index = pd.MultiIndex.from_product([df.index[: shape[0] // 2], ["bar", "foo"]])
-        columns = pd.MultiIndex.from_product(
+        index = IMPL.MultiIndex.from_product(
+            [df.index[: shape[0] // 2], ["bar", "foo"]]
+        )
+        columns = IMPL.MultiIndex.from_product(
             [df.columns[: shape[1] // 2], ["buz", "fuz"]]
         )
 
@@ -661,10 +635,10 @@ class TimeResetIndex:
     ]
 
     def setup(self, shape, drop, level):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
 
         if level:
-            index = pd.MultiIndex.from_product(
+            index = IMPL.MultiIndex.from_product(
                 [self.df.index[: shape[0] // 2], ["bar", "foo"]],
                 names=["level_1", "level_2"],
             )
@@ -683,7 +657,7 @@ class TimeAstype:
     ]
 
     def setup(self, shape, dtype, astype_ncolumns):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
         if astype_ncolumns == "all":
             self.astype_arg = dtype
         elif astype_ncolumns == "one":
@@ -702,7 +676,7 @@ class TimeDescribe:
     ]
 
     def setup(self, shape):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
 
     def time_describe(self, shape):
         execute(self.df.describe())
@@ -715,7 +689,7 @@ class TimeProperties:
     ]
 
     def setup(self, shape):
-        self.df = generate_dataframe(ASV_USE_IMPL, "int", *shape, RAND_LOW, RAND_HIGH)
+        self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
 
     def time_shape(self, shape):
         return self.df.shape