From f0926e517cc4238896624fe99b8a0c0a6e2369d0 Mon Sep 17 00:00:00 2001 From: dougbrn Date: Wed, 8 May 2024 18:19:32 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20lincc-fr?= =?UTF-8?q?ameworks/nested-pandas@d29961271437036edb7cd3f77eff2a8935ee38c7?= =?UTF-8?q?=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...rks.AssignSingleDfToNestedSeries.peakmem_run.json | 2 +- ...hmarks.AssignSingleDfToNestedSeries.time_run.json | 2 +- .../benchmarks.NestedFrameAddNested.peakmem_run.json | 2 +- .../benchmarks.NestedFrameAddNested.time_run.json | 2 +- .../benchmarks.NestedFrameQuery.peakmem_run.json | 2 +- .../benchmarks.NestedFrameQuery.time_run.json | 2 +- .../benchmarks.NestedFrameReduce.peakmem_run.json | 2 +- .../benchmarks.NestedFrameReduce.time_run.json | 2 +- ...marks.ReassignHalfOfNestedSeries.peakmem_run.json | 2 +- ...nchmarks.ReassignHalfOfNestedSeries.time_run.json | 2 +- 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newline at end of file +[[86, 85975040.0], [87, 85970944.0], [90, 85979136.0], [93, 85848064.0]] \ No newline at end of file diff --git a/graphs/summary/benchmarks.NestedFrameAddNested.time_run.json b/graphs/summary/benchmarks.NestedFrameAddNested.time_run.json index fd42e58..8c604e6 100644 --- a/graphs/summary/benchmarks.NestedFrameAddNested.time_run.json +++ b/graphs/summary/benchmarks.NestedFrameAddNested.time_run.json @@ -1 +1 @@ -[[86, 0.008688522500001739], [87, 0.008596525500003338], [91, 0.008661456500000497]] \ No newline at end of file +[[86, 0.008688522500001739], [87, 0.008596525500003338], [90, 0.008661456500000497], [93, 0.008388538249995747]] \ No newline at end of file diff --git a/graphs/summary/benchmarks.NestedFrameQuery.peakmem_run.json b/graphs/summary/benchmarks.NestedFrameQuery.peakmem_run.json index 67d65a3..baea8ee 100644 --- a/graphs/summary/benchmarks.NestedFrameQuery.peakmem_run.json +++ b/graphs/summary/benchmarks.NestedFrameQuery.peakmem_run.json @@ -1 +1 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b/graphs/summary/benchmarks.NestedFrameReduce.peakmem_run.json @@ -1 +1 @@ -[[86, 88236032.0], [87, 88231936.0], [91, 88371200.0]] \ No newline at end of file +[[86, 88236032.0], [87, 88231936.0], [90, 88371200.0], [93, 88240128.0]] \ No newline at end of file diff --git a/graphs/summary/benchmarks.NestedFrameReduce.time_run.json b/graphs/summary/benchmarks.NestedFrameReduce.time_run.json index 4b46c3a..4231c90 100644 --- a/graphs/summary/benchmarks.NestedFrameReduce.time_run.json +++ b/graphs/summary/benchmarks.NestedFrameReduce.time_run.json @@ -1 +1 @@ -[[86, 0.0053287602500020625], [87, 0.0053333822500007955], [91, 0.005301326000001438]] \ No newline at end of file +[[86, 0.0053287602500020625], [87, 0.0053333822500007955], [90, 0.005301326000001438], [93, 0.005278603749999888]] \ No newline at end of file diff --git a/graphs/summary/benchmarks.ReassignHalfOfNestedSeries.peakmem_run.json b/graphs/summary/benchmarks.ReassignHalfOfNestedSeries.peakmem_run.json index 81a027a..eaceedb 100644 --- a/graphs/summary/benchmarks.ReassignHalfOfNestedSeries.peakmem_run.json +++ b/graphs/summary/benchmarks.ReassignHalfOfNestedSeries.peakmem_run.json @@ -1 +1 @@ -[[10, 337645568.0], [16, 335147008.0], [18, 336084992.0], [20, 347787264.0], [21, 322420736.0], [22, 334610432.0], [28, 343216128.0], [33, 335106048.0], [39, 337678336.0], [40, 337629184.0], [50, 336134144.0], [53, 334491648.0], [55, 339996672.0], [56, 333393920.0], [61, 341368832.0], [63, 341438464.0], [64, 334606336.0], [67, 338046976.0], [69, 338726912.0], [78, 282021888.0], [80, 279302144.0], [86, 277057536.0], [87, 277372928.0], [91, 269115392.0]] \ No newline at end of file +[[10, 337645568.0], [16, 335147008.0], [18, 336084992.0], [20, 347787264.0], [21, 322420736.0], [22, 334610432.0], [28, 343216128.0], [33, 335106048.0], [39, 337678336.0], [40, 337629184.0], [50, 336134144.0], [53, 334491648.0], [55, 339996672.0], [56, 333393920.0], [61, 341368832.0], [63, 341438464.0], [64, 334606336.0], [67, 338046976.0], 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"53": 1712947498000, "55": 1712954496000, "56": 1713199935000, "61": 1713379089000, "63": 1713393190000, "64": 1713484795000, "67": 1713908591000, "69": 1714753113000, "78": 1714767489000, "80": 1714822400000, "86": 1715020010000, "87": 1715094688000, "91": 1715103155000}, "params": {"machine": ["gh-runner"], "python": ["3.10"], "Cython": [""], "build": [""], "packaging": [""], "branch": ["HEAD"]}, "graph_param_list": [{"machine": "gh-runner", "python": "3.10", "Cython": "", "build": "", "packaging": "", "branch": "HEAD"}], "benchmarks": {"benchmarks.AssignSingleDfToNestedSeries.peakmem_run": {"code": "class AssignSingleDfToNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "name": "benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "6106a8befbead9252b00aef6a759a13bb8654dc82f15c86b5bb6da7262f4571c"}, "benchmarks.AssignSingleDfToNestedSeries.time_run": {"code": "class AssignSingleDfToNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "min_run_count": 2, "name": "benchmarks.AssignSingleDfToNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "561679277ad2275813ce0c17ad97dbe31f41b12cdb6533009f12ae1f7b057caf", "warmup_time": -1}, "benchmarks.NestedFrameAddNested.peakmem_run": {"code": "class NestedFrameAddNested:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "name": "benchmarks.NestedFrameAddNested.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "7222660c05e164eeabe42a9ea1f18045af2e407dd1e1bb788010eaad7294ba85"}, "benchmarks.NestedFrameAddNested.time_run": {"code": "class NestedFrameAddNested:\n def time_run(self):\n \"\"\"Benchmark the runtime of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "min_run_count": 2, "name": "benchmarks.NestedFrameAddNested.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "51982ce9982d11255339dc387081a0db496f1856294c479e72d65e2a4f5fb506", "warmup_time": -1}, "benchmarks.NestedFrameQuery.peakmem_run": {"code": "class NestedFrameQuery:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameQuery.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "cdc2254d41009fb5e493b99ed753b9d677a385388103705b16f1b486c03217c2"}, "benchmarks.NestedFrameQuery.time_run": {"code": "class NestedFrameQuery:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameQuery.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "e9944ed6698261a36de55f71c8228b4fcaa8a50b56657fd8132fc6488238f484", "warmup_time": -1}, "benchmarks.NestedFrameReduce.peakmem_run": {"code": "class NestedFrameReduce:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameReduce.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "0dc3aac5f09ea8525a745669cff6fca764fabb7cbe35db18f7d46f7377bfea12"}, "benchmarks.NestedFrameReduce.time_run": {"code": "class NestedFrameReduce:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameReduce.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "5259b45d6e432e2b3eb25050a9581ac63703fbbae1e1fd98cc2fe7b83a9e8b95", "warmup_time": -1}, "benchmarks.ReassignHalfOfNestedSeries.peakmem_run": {"code": "class ReassignHalfOfNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "name": "benchmarks.ReassignHalfOfNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "567fe640051453cd164d11ee74a8aff8c06e2b89a239133ddcc020d9fe426a55"}, "benchmarks.ReassignHalfOfNestedSeries.time_run": {"code": "class ReassignHalfOfNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "min_run_count": 2, "name": "benchmarks.ReassignHalfOfNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "9d18ab926df92e4580e7dcbb3760d4206342c672b1e879d7e5a0a3542904b9e4", "warmup_time": -1}}, "machines": {"gh-runner": {"machine": "gh-runner", "version": 1}}, "tags": {}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file +{"project": "nested-pandas", "project_url": "https://github.com/lincc-frameworks/nested-pandas", "show_commit_url": "https://github.com/lincc-frameworks/nested-pandas/commit/", "hash_length": 8, "revision_to_hash": {"0": "70b520ee45f14a642519b413399edc584795095d", "1": "d8ea9bda0fcb5d729f3ca55b91df33652a409323", "3": "48c6798c90b202dfd3eb325b2e56ac972ab3219a", "4": "bc320bce49750cd3fcb6278e0a060ed57e62bd77", "6": "b78b3b61ce9619716dc74f50bd2dc9994f12123c", "10": "05cc195f6037872a3ac0d49081d89b7374f5cb41", "16": "a1d41363e2f60a4689d512e9d7965b526946d551", "18": "dfb9139c39c4b5c695a8e296098ccfb2f9bc2ec5", "20": "9ca3810ef2729f910f999bd88dd15003f1bf7021", "21": "e04f558b879a6bfd9ae916691fb15fb5f3df8666", "22": "70afe87150f7221c858c90f4125398e4e502f8fc", "28": "00be4642a5dbe446ab85f018fe64ead72bf190a2", "33": "136f2c50571728c795c6e8a08d0df4036353f4d6", "39": "e43b187addfaa16b89d974bee67b59d61ed4017d", "40": "b822a14532f190820039018fc9165af45bd5ec4f", "50": "d63cee9f605c04ae9d8b3c512f4eb98a9fbbe9dd", "53": "39acb912b9fcb12cbcc77e3726ae059e5cd2a7b8", "55": "e5ca3df612c3945db77d580e19e93a1078c62cec", "56": "1818c57531e60865f09f858a874f7cacfcf018ef", "61": "3aff79e5dddbf3a6207585559f353f3985e9c506", "63": "ac4032f7664c77fcda34e78f998d079ab4885d13", "64": "025ad9620df22f45e86e346c98aa225dd9684dd2", "67": "659b63966afb34338e389ab3f858080f18282ced", "69": "6d09b2346519c1c9144b2e016daaf96fc68ab827", "78": "109c16c82efcb3b33fc770e552d6e0d1e021c61f", "80": "49b03ffbb439392f4771a2ad4c94dd7c26808d79", "86": "f2d488f1ba2883e105c6e7b3fba907f3bc6aef42", "87": "ab5fc4c8176e01e7858630117170317d54ca42e1", "90": "c5ef5a57a8bb50709a0030b8c19e59212d5241af", "93": "d29961271437036edb7cd3f77eff2a8935ee38c7"}, "revision_to_date": {"0": 1712002112000, "1": 1712002749000, "3": 1712158683000, "4": 1712158691000, "6": 1712160074000, "10": 1712169058000, "16": 1712252119000, "18": 1712253001000, "20": 1712254078000, "21": 1712261028000, "22": 1712264456000, "28": 1712681232000, "33": 1712692068000, "39": 1712773245000, "40": 1712773256000, "50": 1712865526000, "53": 1712947498000, "55": 1712954496000, "56": 1713199935000, "61": 1713379089000, "63": 1713393190000, "64": 1713484795000, "67": 1713908591000, "69": 1714753113000, "78": 1714767489000, "80": 1714822400000, "86": 1715020010000, "87": 1715094688000, "90": 1715103155000, "93": 1715192273000}, "params": {"machine": ["gh-runner"], "python": ["3.10"], "Cython": [""], "build": [""], "packaging": [""], "branch": ["HEAD"]}, "graph_param_list": [{"machine": "gh-runner", "python": "3.10", "Cython": "", "build": "", "packaging": "", "branch": "HEAD"}], "benchmarks": {"benchmarks.AssignSingleDfToNestedSeries.peakmem_run": {"code": "class AssignSingleDfToNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "name": "benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "6106a8befbead9252b00aef6a759a13bb8654dc82f15c86b5bb6da7262f4571c"}, "benchmarks.AssignSingleDfToNestedSeries.time_run": {"code": "class AssignSingleDfToNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n self.new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we\n # need to order by field name here for backwards compatibility.\n dtype=NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()}),\n )", "min_run_count": 2, "name": "benchmarks.AssignSingleDfToNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "561679277ad2275813ce0c17ad97dbe31f41b12cdb6533009f12ae1f7b057caf", "warmup_time": -1}, "benchmarks.NestedFrameAddNested.peakmem_run": {"code": "class NestedFrameAddNested:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "name": "benchmarks.NestedFrameAddNested.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "7222660c05e164eeabe42a9ea1f18045af2e407dd1e1bb788010eaad7294ba85"}, "benchmarks.NestedFrameAddNested.time_run": {"code": "class NestedFrameAddNested:\n def time_run(self):\n \"\"\"Benchmark the runtime of adding a nested layer\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n # use provided seed, \"None\" acts as if no seed is provided\n randomstate = np.random.RandomState(seed=1)\n \n # Generate base data\n base_data = {\"a\": randomstate.random(self.n_base), \"b\": randomstate.random(self.n_base) * 2}\n self.base_nf = NestedFrame(data=base_data)\n \n layer_data = {\n \"t\": randomstate.random(self.layer_size * self.n_base) * 20,\n \"flux\": randomstate.random(self.layer_size * self.n_base) * 100,\n \"band\": randomstate.choice([\"r\", \"g\"], size=self.layer_size * self.n_base),\n \"index\": np.arange(self.layer_size * self.n_base) % self.n_base,\n }\n self.layer_nf = NestedFrame(data=layer_data).set_index(\"index\")", "min_run_count": 2, "name": "benchmarks.NestedFrameAddNested.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "51982ce9982d11255339dc387081a0db496f1856294c479e72d65e2a4f5fb506", "warmup_time": -1}, "benchmarks.NestedFrameQuery.peakmem_run": {"code": "class NestedFrameQuery:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameQuery.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "cdc2254d41009fb5e493b99ed753b9d677a385388103705b16f1b486c03217c2"}, "benchmarks.NestedFrameQuery.time_run": {"code": "class NestedFrameQuery:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the two queries\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameQuery.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "e9944ed6698261a36de55f71c8228b4fcaa8a50b56657fd8132fc6488238f484", "warmup_time": -1}, "benchmarks.NestedFrameReduce.peakmem_run": {"code": "class NestedFrameReduce:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "name": "benchmarks.NestedFrameReduce.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "0dc3aac5f09ea8525a745669cff6fca764fabb7cbe35db18f7d46f7377bfea12"}, "benchmarks.NestedFrameReduce.time_run": {"code": "class NestedFrameReduce:\n def time_run(self):\n \"\"\"Benchmark the runtime of applying the reduce function\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment\"\"\"\n self.nf = datasets.generate_data(self.n_base, self.n_nested)", "min_run_count": 2, "name": "benchmarks.NestedFrameReduce.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "5259b45d6e432e2b3eb25050a9581ac63703fbbae1e1fd98cc2fe7b83a9e8b95", "warmup_time": -1}, "benchmarks.ReassignHalfOfNestedSeries.peakmem_run": {"code": "class ReassignHalfOfNestedSeries:\n def peakmem_run(self):\n \"\"\"Benchmark the memory usage of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "name": "benchmarks.ReassignHalfOfNestedSeries.peakmem_run", "param_names": [], "params": [], "type": "peakmemory", "unit": "bytes", "version": "567fe640051453cd164d11ee74a8aff8c06e2b89a239133ddcc020d9fe426a55"}, "benchmarks.ReassignHalfOfNestedSeries.time_run": {"code": "class ReassignHalfOfNestedSeries:\n def time_run(self):\n \"\"\"Benchmark the runtime of changing a single nested series element.\"\"\"\n self.run()\n\n def setup(self):\n \"\"\"Set up the benchmark environment.\"\"\"\n # When we had NestedExtentionArray inheriting ArrowExtentionArray, it sorted the fields, so we need to\n # order by field name here for backwards compatibility.\n dtype = NestedDtype.from_fields({\"band\": pa.string(), \"flux\": pa.float64(), \"time\": pa.float64()})\n original_df = pd.DataFrame(\n {\n \"time\": np.linspace(0, 1, self.n_sources),\n \"flux\": np.arange(self.n_sources, dtype=np.float64),\n \"band\": np.full_like(\"sdssu\", self.n_sources),\n }\n )\n self.series = pd.Series(\n [original_df] * self.n_objects,\n dtype=dtype,\n )\n \n new_df = pd.DataFrame(\n {\n \"time\": np.arange(self.n_sources, dtype=np.float64),\n \"flux\": np.linspace(0, 1, self.n_sources),\n \"band\": np.full_like(\"lsstg\", self.n_sources),\n }\n )\n self.new_series = pd.Series([new_df] * (self.n_objects // 2), dtype=dtype)", "min_run_count": 2, "name": "benchmarks.ReassignHalfOfNestedSeries.time_run", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "9d18ab926df92e4580e7dcbb3760d4206342c672b1e879d7e5a0a3542904b9e4", "warmup_time": -1}}, "machines": {"gh-runner": {"machine": "gh-runner", "version": 1}}, "tags": {}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file diff --git a/info.json b/info.json index a736e40..f44003b 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.1", - "timestamp": 1715103373020 + "timestamp": 1715192371364 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index ddb6544..9ee7d54 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"regressions": [["benchmarks.AssignSingleDfToNestedSeries.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.time_run.json", {}, null, 0.034723282499996344, 0.006260144666669021, [[null, 78, 0.006260144666669021, 0.034723282499996344]]], ["benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json", {}, null, 256495616.0, 215633920.0, [[null, 78, 215633920.0, 256495616.0]]]]} \ No newline at end of file +{"regressions": [["benchmarks.AssignSingleDfToNestedSeries.time_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.time_run.json", {}, null, 0.03398099700000046, 0.006260144666669021, [[null, 78, 0.006260144666669021, 0.03398099700000046]]], ["benchmarks.AssignSingleDfToNestedSeries.peakmem_run", "graphs/Cython/branch-HEAD/build/machine-gh-runner/packaging/python-3.10/benchmarks.AssignSingleDfToNestedSeries.peakmem_run.json", {}, null, 256983040.0, 215633920.0, [[null, 78, 215633920.0, 256983040.0]]]]} \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index 3b1ae2c..3587c3e 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,8 +1,8 @@ -tag:nested-pandas.asv,1970-01-01:/42329e0ff67915ad3ca1780e822a054176e6cab641b28d1ed890f8d28222bd1cAirspeed Velocitynested-pandas performance regressions2024-05-07T15:14:36Ztag:nested-pandas.asv,2024-05-03:/eb4a3566c42a993e1b46a453dc2359aa50f94f2dd38c656bc38fee9802720670454.67% benchmarks.AssignSingleDfToNestedSeries.time_run2024-05-07T15:14:36Z<a href="index.html#benchmarks.AssignSingleDfToNestedSeries.time_run?commits=109c16c82efcb3b33fc770e552d6e0d1e021c61f">454.67% regression</a> on 2024-05-03 20:18:09 in commit <a href="https://github.com/lincc-frameworks/nested-pandas/commit/109c16c82efcb3b33fc770e552d6e0d1e021c61f">109c16c8</a>.<br> - New value: 34.7ms, old value: 6.26ms.<br> - Latest value: 34.7ms (454.67% worse - than best value 6.26ms).tag:nested-pandas.asv,2024-05-03:/f78c071dfb5f7e712acfb54ad13cd2af02c44ebbe5cd17e783b61ac64b14a38518.95% benchmarks.AssignSingleDfToNestedSeries.peakmem_run2024-05-07T15:14:35Z<a href="index.html#benchmarks.AssignSingleDfToNestedSeries.peakmem_run?commits=109c16c82efcb3b33fc770e552d6e0d1e021c61f">18.95% regression</a> on 2024-05-03 20:18:09 in commit <a href="https://github.com/lincc-frameworks/nested-pandas/commit/109c16c82efcb3b33fc770e552d6e0d1e021c61f">109c16c8</a>.<br> - New value: 256M, old value: 216M.<br> - Latest value: 256M (18.95% worse +tag:nested-pandas.asv,1970-01-01:/42329e0ff67915ad3ca1780e822a054176e6cab641b28d1ed890f8d28222bd1cAirspeed Velocitynested-pandas performance regressions2024-05-07T15:14:36Ztag:nested-pandas.asv,2024-05-03:/eb4a3566c42a993e1b46a453dc2359aa50f94f2dd38c656bc38fee9802720670442.81% benchmarks.AssignSingleDfToNestedSeries.time_run2024-05-07T15:14:36Z<a href="index.html#benchmarks.AssignSingleDfToNestedSeries.time_run?commits=109c16c82efcb3b33fc770e552d6e0d1e021c61f">442.81% regression</a> on 2024-05-03 20:18:09 in commit <a href="https://github.com/lincc-frameworks/nested-pandas/commit/109c16c82efcb3b33fc770e552d6e0d1e021c61f">109c16c8</a>.<br> + New value: 34.0ms, old value: 6.26ms.<br> + Latest value: 34.0ms (442.81% worse + than best value 6.26ms).tag:nested-pandas.asv,2024-05-03:/f78c071dfb5f7e712acfb54ad13cd2af02c44ebbe5cd17e783b61ac64b14a38519.18% benchmarks.AssignSingleDfToNestedSeries.peakmem_run2024-05-07T15:14:35Z<a href="index.html#benchmarks.AssignSingleDfToNestedSeries.peakmem_run?commits=109c16c82efcb3b33fc770e552d6e0d1e021c61f">19.18% regression</a> on 2024-05-03 20:18:09 in commit <a href="https://github.com/lincc-frameworks/nested-pandas/commit/109c16c82efcb3b33fc770e552d6e0d1e021c61f">109c16c8</a>.<br> + New value: 257M, old value: 216M.<br> + Latest value: 257M (19.18% worse than best value 216M). \ No newline at end of file