Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Timeseries and calc_temporal_pattern #88

Merged
merged 16 commits into from
Oct 28, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions thicket/stats/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@
from .scoring import score_bhattacharyya
from .scoring import score_hellinger
from .preference import preference
from .calc_temporal_pattern import calc_temporal_pattern
from .distance import bhattacharyya_distance
from .distance import hellinger_distance

Expand Down
77 changes: 77 additions & 0 deletions thicket/stats/calc_temporal_pattern.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,77 @@
# Copyright 2022 Lawrence Livermore National Security, LLC and other
# Thicket Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: MIT

import pandas as pd
import numpy as np

from ..utils import verify_thicket_structures


def calc_temporal_pattern(thicket, columns=None):
"""Calculate the associated temporal pattern with the passed in columns.

Designed to take in a timeseries thicket, and append two columns to the
aggregated statistics (statsframe) table for the temporal pattern calculated on each node over time.

The two additional columns include the _temporal_score, and the _pattern associated with that score.
The score assigns a value between 0 and 1 based on how drastically the values change over time

Arguments:
thicket (thicket): timeseries Thicket object
columns (list): List of numeric columns to calculate temporal pattern.
Note, if using a columnar joined thicket a list of tuples must be passed in
with the format (column index, column name).

Returns:
(list): returns a list of output statsframe column names
"""
if columns is None:
raise ValueError(
"To see a list of valid columns, run 'Thicket.performance_cols'."
)

if "iteration" not in thicket.dataframe.index.names:
raise ValueError(
"Must have a timeseries thicket with iteration as an index level"
)

verify_thicket_structures(thicket.dataframe, index=["node"], columns=columns)

output_column_names = []

for column in columns:
if not pd.api.types.is_numeric_dtype(thicket.dataframe[column]):
raise ValueError("Column data type must be numeric")
pattern_col = []
score_col = []
# for any node that has temporal values we can calculate the pattern per node
for node, node_df in thicket.dataframe.groupby(level=0):
# if the node has any nans, pattern is none
if node_df[column].isna().values.any():
pattern = "none"
score = np.nan
else:
values = node_df[column]
score = 1 - (sum(values) / (max(values) * len(values)))
if score < 0.2:
pattern = "constant"
elif score >= 0.2 and score < 0.4:
pattern = "phased"
elif score >= 0.4 and score < 0.6:
pattern = "dynamic"
else:
pattern = "sporadic"
pattern_col.append(pattern)
score_col.append(score)

# add the new columns to the statsframe and output list
pattern_column_name = column + "_pattern"
score_column_name = column + "_temporal_score"
thicket.statsframe.dataframe[pattern_column_name] = pattern_col
thicket.statsframe.dataframe[score_column_name] = score_col
output_column_names.append(pattern_column_name)
output_column_names.append(score_column_name)

return output_column_names
36 changes: 36 additions & 0 deletions thicket/tests/conftest.py
Original file line number Diff line number Diff line change
Expand Up @@ -262,3 +262,39 @@ def literal_thickets():
thickets = [tk, tk2, tk3]

return thickets


@pytest.fixture
def example_timeseries(data_dir, tmpdir):
"""Timeseries Caliper file"""
cali_timeseries_dir = os.path.join(data_dir, "example-timeseries")
cali_file = os.path.join(cali_timeseries_dir, "timeseries.cali")

shutil.copy(cali_file, str(tmpdir))
tmpfile = os.path.join(str(tmpdir), "timeseries.cali")

return tmpfile


@pytest.fixture
def example_timeseries_cxx(data_dir, tmpdir):
"""Timeseries Caliper file"""
cali_timeseries_dir = os.path.join(data_dir, "example-timeseries")
cali_file = os.path.join(cali_timeseries_dir, "cxx.cali")

shutil.copy(cali_file, str(tmpdir))
tmpfile = os.path.join(str(tmpdir), "cxx.cali")

return tmpfile


@pytest.fixture
def mem_power_timeseries(data_dir, tmpdir):
"""Timeseries Caliper file"""
cali_timeseries_dir = os.path.join(data_dir, "example-timeseries")
cali_file = os.path.join(cali_timeseries_dir, "mem_power_timeseries.cali")

shutil.copy(cali_file, str(tmpdir))
tmpfile = os.path.join(str(tmpdir), "mem_power_timeseries.cali")

return tmpfile
65 changes: 65 additions & 0 deletions thicket/tests/data/example-timeseries/cxx.cali
Original file line number Diff line number Diff line change
@@ -0,0 +1,65 @@
__rec=node,id=21,attr=10,data=1612,parent=3
__rec=node,id=92,attr=8,data=caliper.config,parent=21
__rec=node,id=89,attr=10,data=1612,parent=1
__rec=node,id=90,attr=8,data=iterations,parent=89
__rec=node,id=22,attr=8,data=cali.caliper.version,parent=21
__rec=node,id=23,attr=22,data=2.10.0-dev
__rec=node,id=91,attr=90,data=100,parent=23
__rec=node,id=93,attr=92,data=,parent=91
__rec=node,id=30,attr=10,data=85,parent=5
__rec=node,id=67,attr=8,data=min#time.duration.ns,parent=30
__rec=node,id=68,attr=8,data=max#time.duration.ns,parent=30
__rec=node,id=69,attr=8,data=sum#time.duration.ns,parent=30
__rec=node,id=70,attr=8,data=avg#time.duration.ns,parent=30
__rec=node,id=32,attr=10,data=85,parent=2
__rec=node,id=85,attr=8,data=count,parent=32
__rec=node,id=86,attr=8,data=aggregate.slot,parent=32
__rec=node,id=26,attr=10,data=2133,parent=1
__rec=node,id=27,attr=8,data=loop.iterations,parent=26
__rec=node,id=31,attr=8,data=timeseries.starttime,parent=30
__rec=node,id=33,attr=8,data=timeseries.snapshot,parent=32
__rec=ctx,ref=93,attr=67=68=69=70=85=86=27=31=33,data=448713.000000=448713.000000=448713.000000=448713.000000=1=0=0=1689118709.135139=0
__rec=node,id=18,attr=10,data=276,parent=3
__rec=node,id=20,attr=8,data=region,parent=18
__rec=node,id=94,attr=20,data=main
__rec=ctx,ref=94=93,attr=67=68=69=70=85=86=27=31=33,data=59851.000000=59851.000000=59851.000000=59851.000000=1=1=0=1689118709.135139=0
__rec=node,id=95,attr=20,data=init,parent=94
__rec=node,id=42,attr=8,data=min#region.count,parent=30
__rec=node,id=43,attr=8,data=max#region.count,parent=30
__rec=node,id=44,attr=8,data=sum#region.count,parent=30
__rec=node,id=45,attr=8,data=avg#region.count,parent=30
__rec=ctx,ref=95=93,attr=42=43=44=45=67=68=69=70=85=86=27=31=33,data=1.000000=1.000000=1.000000=1.000000=12219.000000=12219.000000=12219.000000=12219.000000=1=2=0=1689118709.135139=0
__rec=node,id=34,attr=10,data=2133,parent=5
__rec=node,id=35,attr=8,data=timeseries.duration,parent=34
__rec=ctx,ref=94=93,attr=27=31=33=35=31,data=0=1689118709.135139=0=0.001398=1689118709.136537
__rec=node,id=28,attr=10,data=85,parent=1
__rec=node,id=29,attr=8,data=loop.start_iteration,parent=28
__rec=ctx,ref=94=93,attr=67=68=69=70=85=86=27=29=31=33,data=200739.000000=200739.000000=200739.000000=200739.000000=1=0=20=0=1689118709.136537=1
__rec=node,id=19,attr=8,data=loop,parent=18
__rec=node,id=103,attr=19,data=mainloop,parent=94
__rec=ctx,ref=103=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=19.000000=1.000000=3306.000000=13928.000000=282447.000000=4787.237288=59=1=20=0=1689118709.136537=1
__rec=node,id=104,attr=20,data=foo,parent=103
__rec=ctx,ref=104=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=141457.000000=219005.000000=3281358.000000=164067.900000=20=2=20=0=1689118709.136537=1
__rec=node,id=12,attr=10,data=84,parent=6
__rec=node,id=16,attr=8,data=class.iteration,parent=12
__rec=node,id=96,attr=16,data=true,parent=1
__rec=node,id=97,attr=10,data=21,parent=96
__rec=node,id=98,attr=8,data=iteration#mainloop,parent=97
__rec=ctx,ref=103=93,attr=27=29=31=33=35=31=98,data=20=0=1689118709.136537=1=0.003734=1689118709.140271=19
__rec=ctx,ref=103=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=3238.000000=83328.000000=357604.000000=5960.066667=60=0=20=20=1689118709.140271=2
__rec=ctx,ref=104=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=159474.000000=209917.000000=3364016.000000=168200.800000=20=1=20=20=1689118709.140271=2
__rec=ctx,ref=103=93,attr=27=29=31=33=35=31=98,data=20=20=1689118709.140271=2=0.003722=1689118709.143993=39
__rec=ctx,ref=103=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=3213.000000=84229.000000=402162.000000=6702.700000=60=0=20=40=1689118709.143993=3
__rec=ctx,ref=104=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=159545.000000=335990.000000=3522373.000000=176118.650000=20=1=20=40=1689118709.143993=3
__rec=ctx,ref=103=93,attr=27=29=31=33=35=31=98,data=20=40=1689118709.143993=3=0.003928=1689118709.147921=59
__rec=ctx,ref=103=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=3342.000000=96083.000000=348946.000000=5815.766667=60=0=20=60=1689118709.147921=4
__rec=ctx,ref=104=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=136880.000000=173199.000000=3203448.000000=160172.400000=20=1=20=60=1689118709.147921=4
__rec=ctx,ref=103=93,attr=27=29=31=33=35=31=98,data=20=60=1689118709.147921=4=0.003560=1689118709.151481=79
__rec=ctx,ref=103=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=3308.000000=112210.000000=407532.000000=6792.200000=60=0=20=80=1689118709.151481=5
__rec=ctx,ref=104=93,attr=42=43=44=45=67=68=69=70=85=86=27=29=31=33,data=1.000000=1.000000=20.000000=1.000000=156252.000000=715191.000000=4120824.000000=206041.200000=20=1=20=80=1689118709.151481=5
__rec=ctx,ref=103=93,attr=27=29=31=33=35=31=98,data=20=80=1689118709.151481=5=0.004522=1689118709.156003=99
__rec=ctx,ref=103=93,attr=42=43=44=45=67=68=69=70=85=86=27=31=33,data=1.000000=1.000000=1.000000=1.000000=114486.000000=114486.000000=114486.000000=114486.000000=1=0=0=1689118709.156003=6
__rec=ctx,ref=103=93,attr=27=31=33=35=31,data=0=1689118709.156003=6=0.000119=1689118709.156122
__rec=node,id=24,attr=8,data=cali.channel,parent=21
__rec=node,id=25,attr=24,data=default
__rec=globals,ref=93=25
34 changes: 34 additions & 0 deletions thicket/tests/data/example-timeseries/mem_power_timeseries.cali
Original file line number Diff line number Diff line change
@@ -0,0 +1,34 @@
__rec=node,id=26,attr=10,data=2125,parent=1
__rec=node,id=27,attr=8,data=loop.iterations,parent=26
__rec=node,id=30,attr=10,data=2061,parent=2
__rec=node,id=31,attr=8,data=memstat.vmsize,parent=30
__rec=node,id=32,attr=8,data=memstat.vmrss,parent=30
__rec=node,id=33,attr=8,data=memstat.data,parent=30
__rec=node,id=36,attr=10,data=2133,parent=2
__rec=node,id=37,attr=8,data=variorum.val.power_node_watts,parent=36
__rec=node,id=38,attr=8,data=variorum.power_node_watts,parent=36
__rec=node,id=18,attr=10,data=268,parent=3
__rec=node,id=20,attr=8,data=region,parent=18
__rec=node,id=41,attr=20,data=main
__rec=node,id=21,attr=10,data=1612,parent=3
__rec=node,id=22,attr=8,data=cali.caliper.version,parent=21
__rec=node,id=23,attr=22,data=2.10.0
__rec=ctx,ref=41=23,attr=27=31=32=33=37=38,data=0=923=679=537=389=194
__rec=node,id=28,attr=10,data=77,parent=1
__rec=node,id=29,attr=8,data=loop.start_iteration,parent=28
__rec=node,id=19,attr=8,data=loop,parent=18
__rec=node,id=45,attr=19,data=lulesh.cycle,parent=41
__rec=node,id=12,attr=10,data=76,parent=6
__rec=node,id=16,attr=8,data=class.iteration,parent=12
__rec=node,id=42,attr=16,data=true,parent=1
__rec=node,id=43,attr=10,data=13,parent=42
__rec=node,id=44,attr=8,data=iteration#lulesh.cycle,parent=43
__rec=ctx,ref=45=23,attr=27=29=31=32=33=37=38=44,data=20=0=1000=743=614=391=390=19
__rec=ctx,ref=45=23,attr=27=29=31=32=33=37=38=44,data=20=20=1000=743=614=392=391=39
__rec=ctx,ref=45=23,attr=27=29=31=32=33=37=38=44,data=20=40=1000=743=614=392=392=59
__rec=ctx,ref=45=23,attr=27=29=31=32=33=37=38=44,data=20=60=1000=743=614=392=392=79
__rec=ctx,ref=45=23,attr=27=29=31=32=33=37=38=44,data=20=80=1000=743=614=398=395=99
__rec=ctx,ref=45=23,attr=27=31=32=33=37=38,data=0=1000=743=614=398=398
__rec=node,id=24,attr=8,data=cali.channel,parent=21
__rec=node,id=25,attr=24,data=default
__rec=globals,ref=23=25
Loading
Loading