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81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
# Copyright (C) 2022-present MongoDB, Inc.
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the Server Side Public License, version 1,
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# as published by MongoDB, Inc.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# Server Side Public License for more details.
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#
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# You should have received a copy of the Server Side Public License
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# along with this program. If not, see
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# <http://www.mongodb.com/licensing/server-side-public-license>.
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#
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# As a special exception, the copyright holders give permission to link the
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# code of portions of this program with the OpenSSL library under certain
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# conditions as described in each individual source file and distribute
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# linked combinations including the program with the OpenSSL library. You
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# must comply with the Server Side Public License in all respects for
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# all of the code used other than as permitted herein. If you modify file(s)
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# with this exception, you may extend this exception to your version of the
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# file(s), but you are not obligated to do so. If you do not wish to do so,
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# delete this exception statement from your version. If you delete this
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# exception statement from all source files in the program, then also delete
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# it in the license file.
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#
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"""Calibrate ABT nodes."""
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from __future__ import annotations
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import experiment as exp
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import pandas as pd
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import statsmodels.api as sm
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from config import AbtCalibratorConfig, AbtNodeCalibrationConfig
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from cost_estimator import estimate
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from database_instance import DatabaseInstance
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from sklearn.linear_model import LinearRegression
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__all__ = ["calibrate"]
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async def calibrate(config: AbtCalibratorConfig, database: DatabaseInstance):
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"""Main entry-point for ABT calibration."""
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if not config.enabled:
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return {}
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df = await exp.load_calibration_data(database, config.input_collection_name)
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noout_df = exp.remove_outliers(df, 0.0, 0.90)
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abt_df = exp.extract_abt_nodes(noout_df)
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result = {}
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for node_config in config.nodes:
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result[node_config.type] = calibrate_node(abt_df, config, node_config)
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return result
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def calibrate_node(
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abt_df: pd.DataFrame, config: AbtCalibratorConfig, node_config: AbtNodeCalibrationConfig
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):
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abt_node_df = abt_df[abt_df.abt_type == node_config.type]
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if node_config.filter_function is not None:
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abt_node_df = node_config.filter_function(abt_node_df)
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# pylint: disable=invalid-name
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if node_config.variables_override is None:
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variables = ["n_processed"]
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else:
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variables = node_config.variables_override
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y = abt_node_df["execution_time"]
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X = abt_node_df[variables]
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X = sm.add_constant(X)
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def fit(X, y):
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nnls = LinearRegression(positive=True, fit_intercept=False)
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model = nnls.fit(X, y)
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return (model.coef_, model.predict)
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return estimate(fit, X.to_numpy(), y.to_numpy(), config.test_size, config.trace)
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