mirror of https://github.com/mongodb/mongo
102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
# Copyright (C) 2022-present MongoDB, Inc.
|
|
#
|
|
# This program is free software: you can redistribute it and/or modify
|
|
# it under the terms of the Server Side Public License, version 1,
|
|
# as published by MongoDB, Inc.
|
|
#
|
|
# This program is distributed in the hope that it will be useful,
|
|
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
# Server Side Public License for more details.
|
|
#
|
|
# You should have received a copy of the Server Side Public License
|
|
# along with this program. If not, see
|
|
# <http://www.mongodb.com/licensing/server-side-public-license>.
|
|
#
|
|
# As a special exception, the copyright holders give permission to link the
|
|
# code of portions of this program with the OpenSSL library under certain
|
|
# conditions as described in each individual source file and distribute
|
|
# linked combinations including the program with the OpenSSL library. You
|
|
# must comply with the Server Side Public License in all respects for
|
|
# all of the code used other than as permitted herein. If you modify file(s)
|
|
# with this exception, you may extend this exception to your version of the
|
|
# file(s), but you are not obligated to do so. If you do not wish to do so,
|
|
# delete this exception statement from your version. If you delete this
|
|
# exception statement from all source files in the program, then also delete
|
|
# it in the license file.
|
|
#
|
|
"""Calibrate QSN nodes."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import experiment as exp
|
|
import matplotlib.pyplot as plt
|
|
import pandas as pd
|
|
import statsmodels.api as sm
|
|
from config import QsNodeCalibrationConfig, QuerySolutionCalibrationConfig
|
|
from cost_estimator import estimate
|
|
from database_instance import DatabaseInstance
|
|
from sklearn.linear_model import LinearRegression
|
|
|
|
__all__ = ["calibrate"]
|
|
|
|
|
|
async def calibrate(config: QuerySolutionCalibrationConfig, database: DatabaseInstance):
|
|
"""Main entry-point for QSN calibration."""
|
|
|
|
if not config.enabled:
|
|
return {}
|
|
|
|
df = await exp.load_calibration_data(database, config.input_collection_name)
|
|
noout_df = exp.remove_outliers(df, 0.0, 0.90)
|
|
qsn_df = exp.extract_qsn_nodes(noout_df)
|
|
result = {}
|
|
for node_config in config.nodes:
|
|
result[node_config.type] = calibrate_node(qsn_df, config, node_config)
|
|
return result
|
|
|
|
|
|
def calibrate_node(
|
|
qsn_df: pd.DataFrame,
|
|
config: QuerySolutionCalibrationConfig,
|
|
node_config: QsNodeCalibrationConfig,
|
|
):
|
|
qsn_node_df = qsn_df[qsn_df.node_type == node_config.type]
|
|
if node_config.filter_function is not None:
|
|
qsn_node_df = node_config.filter_function(qsn_node_df)
|
|
|
|
if node_config.variables_override is None:
|
|
variables = ["n_processed"]
|
|
else:
|
|
variables = node_config.variables_override
|
|
y = qsn_node_df["execution_time"]
|
|
X = qsn_node_df[variables]
|
|
|
|
X = sm.add_constant(X)
|
|
|
|
def fit(X, y):
|
|
nnls = LinearRegression(positive=True, fit_intercept=False)
|
|
model = nnls.fit(X, y)
|
|
return (model.coef_, model.predict)
|
|
|
|
model = estimate(fit, X.to_numpy(), y.to_numpy(), config.test_size, config.trace)
|
|
# plot regression and save to file
|
|
if model.predict:
|
|
fig, ax = plt.subplots()
|
|
ax.scatter(qsn_node_df[variables], y, label="Executions")
|
|
ax.plot(
|
|
qsn_node_df[variables],
|
|
model.predict(X),
|
|
linewidth=3,
|
|
color="tab:orange",
|
|
label="Linear Regression",
|
|
)
|
|
ax.set(
|
|
xlabel="Number of documents",
|
|
ylabel="Execution time (ns)",
|
|
title=f"Regression for {node_config.type}",
|
|
)
|
|
ax.legend()
|
|
fig.savefig(f"{node_config.type}.png")
|
|
return model
|