OilfieldToolsNet/ResInsight
4
0
Fork 0
mirror of https://github.com/OPM/ResInsight.git synced 2025-02-25 18:55:39 -06:00
Code Issues Packages Projects Releases Wiki Activity

#10292 Regression Analysis: add forecasting

Browse Source 
Fixes #10292.
This commit is contained in:
Kristian Bendiksen
2023-06-02 12:22:44 +02:00
parent bcc00adea1
commit 3a81cea65d
4 changed files with 230 additions and 51 deletions
Download Patch File Download Diff File Expand all files Collapse all files

196
ApplicationLibCode/ProjectDataModel/Summary/RimSummaryRegressionAnalysisCurve.cpp
View File

@@ -18,6 +18,9 @@
#include "RimSummaryRegressionAnalysisCurve.h"
#include "RiaQDateTimeTools.h"
#include "RiaTimeTTools.h"
#include "cafPdmUiLineEditor.h"
#include "cafPdmUiTextEditor.h"
@@ -28,6 +31,8 @@
#include "PolynominalRegression.hpp"
#include "PowerFitRegression.hpp"
#include <QDateTime>
#include <cmath>
#include <vector>
@@ -46,6 +51,16 @@ void caf::AppEnum<RimSummaryRegressionAnalysisCurve::RegressionType>::setUp()
addItem( RimSummaryRegressionAnalysisCurve::RegressionType::LOGISTIC, "LOGISTIC", "Logistic" );
setDefault( RimSummaryRegressionAnalysisCurve::RegressionType::LINEAR );
}
template <>
void caf::AppEnum<RimSummaryRegressionAnalysisCurve::ForecastUnit>::setUp()
{
addItem( RimSummaryRegressionAnalysisCurve::ForecastUnit::DAYS, "DAYS", "Days" );
addItem( RimSummaryRegressionAnalysisCurve::ForecastUnit::MONTHS, "MONTHS", "Months" );
addItem( RimSummaryRegressionAnalysisCurve::ForecastUnit::YEARS, "YEARS", "Years" );
setDefault( RimSummaryRegressionAnalysisCurve::ForecastUnit::YEARS );
}
}; // namespace caf
//--------------------------------------------------------------------------------------------------
@@ -56,6 +71,9 @@ RimSummaryRegressionAnalysisCurve::RimSummaryRegressionAnalysisCurve()
CAF_PDM_InitObject( "Regression Analysis Curve", ":/SummaryCurve16x16.png" );
CAF_PDM_InitFieldNoDefault( &m_regressionType, "RegressionType", "Type" );
CAF_PDM_InitField( &m_forecastForward, "ForecastForward", 0, "Forward" );
CAF_PDM_InitField( &m_forecastBackward, "ForecastBackward", 0, "Backward" );
CAF_PDM_InitFieldNoDefault( &m_forecastUnit, "ForecastUnit", "Unit" );
CAF_PDM_InitField( &m_polynominalDegree, "PolynominalDegree", 3, "Degree" );
CAF_PDM_InitFieldNoDefault( &m_expressionText, "ExpressionText", "Expression" );
@@ -128,87 +146,56 @@ std::tuple<std::vector<time_t>, std::vector<double>, QString>
{
if ( values.empty() || timeSteps.empty() ) return { timeSteps, values, "" };
auto convertToDouble = []( const std::vector<time_t>& timeSteps )
{
std::vector<double> doubleVector( timeSteps.size() );
std::transform( timeSteps.begin(),
timeSteps.end(),
doubleVector.begin(),
[]( const auto& timeVal ) { return static_cast<double>( timeVal ); } );
return doubleVector;
};
auto convertToTimeT = []( const std::vector<double>& timeSteps )
{
std::vector<time_t> tVector( timeSteps.size() );
std::transform( timeSteps.begin(),
timeSteps.end(),
tVector.begin(),
[]( const auto& timeVal ) { return static_cast<time_t>( timeVal ); } );
return tVector;
};
auto filterValues = []( const std::vector<double>& timeSteps, const std::vector<double>& values )
{
std::vector<double> filteredTimeSteps;
std::vector<double> filteredValues;
for ( size_t i = 0; i < timeSteps.size(); i++ )
{
if ( timeSteps[i] > 0.0 && values[i] > 0.0 )
{
filteredTimeSteps.push_back( timeSteps[i] );
filteredValues.push_back( values[i] );
}
}
return std::make_pair( filteredTimeSteps, filteredValues );
};
std::vector<double> timeStepsD = convertToDouble( timeSteps );
std::vector<time_t> outputTimeSteps = getOutputTimeSteps( timeSteps, m_forecastBackward(), m_forecastForward(), m_forecastUnit() );
std::vector<double> outputTimeStepsD = convertToDouble( outputTimeSteps );
if ( m_regressionType == RegressionType::LINEAR )
{
regression::LinearRegression linearRegression;
linearRegression.fit( timeStepsD, values );
std::vector<double> predictedValues = linearRegression.predict( timeStepsD );
return { timeSteps, predictedValues, generateRegressionText( linearRegression ) };
std::vector<double> predictedValues = linearRegression.predict( outputTimeStepsD );
return { outputTimeSteps, predictedValues, generateRegressionText( linearRegression ) };
}
else if ( m_regressionType == RegressionType::POLYNOMINAL )
{
regression::PolynominalRegression polynominalRegression;
polynominalRegression.fit( timeStepsD, values, m_polynominalDegree );
std::vector<double> predictedValues = polynominalRegression.predict( timeStepsD );
return { timeSteps, predictedValues, generateRegressionText( polynominalRegression ) };
std::vector<double> predictedValues = polynominalRegression.predict( outputTimeStepsD );
return { outputTimeSteps, predictedValues, generateRegressionText( polynominalRegression ) };
}
else if ( m_regressionType == RegressionType::POWER_FIT )
{
auto [filteredTimeSteps, filteredValues] = filterValues( timeStepsD, values );
auto [filteredTimeSteps, filteredValues] = getPositiveValues( timeStepsD, values );
regression::PowerFitRegression powerFitRegression;
powerFitRegression.fit( filteredTimeSteps, filteredValues );
std::vector<double> predictedValues = powerFitRegression.predict( filteredTimeSteps );
return { convertToTimeT( filteredTimeSteps ), predictedValues, generateRegressionText( powerFitRegression ) };
std::vector<double> predictedValues = powerFitRegression.predict( outputTimeStepsD );
return { convertToTimeT( outputTimeStepsD ), predictedValues, generateRegressionText( powerFitRegression ) };
}
else if ( m_regressionType == RegressionType::EXPONENTIAL )
{
auto [filteredTimeSteps, filteredValues] = filterValues( timeStepsD, values );
auto [filteredTimeSteps, filteredValues] = getPositiveValues( timeStepsD, values );
regression::ExponentialRegression exponentialRegression;
exponentialRegression.fit( filteredTimeSteps, filteredValues );
std::vector<double> predictedValues = exponentialRegression.predict( filteredTimeSteps );
return { convertToTimeT( filteredTimeSteps ), predictedValues, generateRegressionText( exponentialRegression ) };
std::vector<double> predictedValues = exponentialRegression.predict( outputTimeStepsD );
return { convertToTimeT( outputTimeStepsD ), predictedValues, generateRegressionText( exponentialRegression ) };
}
else if ( m_regressionType == RegressionType::LOGARITHMIC )
{
auto [filteredTimeSteps, filteredValues] = filterValues( timeStepsD, values );
auto [filteredTimeSteps, filteredValues] = getPositiveValues( timeStepsD, values );
regression::LogarithmicRegression logarithmicRegression;
logarithmicRegression.fit( filteredTimeSteps, filteredValues );
std::vector<double> predictedValues = logarithmicRegression.predict( filteredTimeSteps );
return { convertToTimeT( filteredTimeSteps ), predictedValues, generateRegressionText( logarithmicRegression ) };
std::vector<double> predictedValues = logarithmicRegression.predict( outputTimeStepsD );
return { convertToTimeT( outputTimeStepsD ), predictedValues, generateRegressionText( logarithmicRegression ) };
}
else if ( m_regressionType == RegressionType::LOGISTIC )
{
regression::LogisticRegression logisticRegression;
logisticRegression.fit( timeStepsD, values );
std::vector<double> predictedValues = logisticRegression.predict( timeStepsD );
return { timeSteps, predictedValues, generateRegressionText( logisticRegression ) };
std::vector<double> predictedValues = logisticRegression.predict( outputTimeStepsD );
return { convertToTimeT( outputTimeStepsD ), predictedValues, generateRegressionText( logisticRegression ) };
}
return { timeSteps, values, "" };
@@ -231,6 +218,11 @@ void RimSummaryRegressionAnalysisCurve::defineUiOrdering( QString uiConfigName,
regressionCurveGroup->add( &m_expressionText );
caf::PdmUiGroup* forecastingGroup = uiOrdering.addNewGroup( "Forecasting" );
forecastingGroup->add( &m_forecastForward );
forecastingGroup->add( &m_forecastBackward );
forecastingGroup->add( &m_forecastUnit );
RimSummaryCurve::defineUiOrdering( uiConfigName, uiOrdering );
}
@@ -242,7 +234,8 @@ void RimSummaryRegressionAnalysisCurve::fieldChangedByUi( const caf::PdmFieldHan
const QVariant& newValue )
{
RimSummaryCurve::fieldChangedByUi( changedField, oldValue, newValue );
if ( changedField == &m_regressionType || changedField == &m_polynominalDegree )
if ( changedField == &m_regressionType || changedField == &m_polynominalDegree || changedField == &m_forecastBackward ||
changedField == &m_forecastForward || changedField == &m_forecastUnit )
{
loadAndUpdateDataAndPlot();
}
@@ -265,6 +258,14 @@ void RimSummaryRegressionAnalysisCurve::defineEditorAttribute( const caf::PdmFie
lineEditorAttr->validator = new QIntValidator( 1, 50, nullptr );
}
}
else if ( field == &m_forecastForward || field == &m_forecastBackward )
{
if ( auto* lineEditorAttr = dynamic_cast<caf::PdmUiLineEditorAttribute*>( attribute ) )
{
// Block negative forecast
lineEditorAttr->validator = new QIntValidator( 0, 50, nullptr );
}
}
else if ( field == &m_expressionText )
{
auto myAttr = dynamic_cast<caf::PdmUiTextEditorAttribute*>( attribute );
@@ -382,3 +383,96 @@ QString RimSummaryRegressionAnalysisCurve::generateRegressionText( const regress
// TODO: Display more parameters here.
return "";
}
//--------------------------------------------------------------------------------------------------
///
//--------------------------------------------------------------------------------------------------
void RimSummaryRegressionAnalysisCurve::appendTimeSteps( std::vector<time_t>& destinationTimeSteps, const std::set<QDateTime>& sourceTimeSteps )
{
for ( const QDateTime& t : sourceTimeSteps )
destinationTimeSteps.push_back( RiaTimeTTools::fromQDateTime( t ) );
}
//--------------------------------------------------------------------------------------------------
///
//--------------------------------------------------------------------------------------------------
std::vector<time_t> RimSummaryRegressionAnalysisCurve::getOutputTimeSteps( const std::vector<time_t>& timeSteps,
int forecastBackward,
int forecastForward,
ForecastUnit forecastUnit )
{
auto getTimeSpan = []( int value, ForecastUnit unit )
{
if ( unit == ForecastUnit::YEARS ) return DateTimeSpan( value, 0, 0 );
if ( unit == ForecastUnit::MONTHS ) return DateTimeSpan( 0, value, 0 );
CAF_ASSERT( unit == ForecastUnit::DAYS );
return DateTimeSpan( 0, 0, value );
};
int numDates = 50;
std::vector<time_t> outputTimeSteps;
if ( forecastBackward > 0 )
{
QDateTime firstTimeStepInData = RiaQDateTimeTools::fromTime_t( timeSteps.front() );
QDateTime forecastStartTimeStep = RiaQDateTimeTools::subtractSpan( firstTimeStepInData, getTimeSpan( forecastBackward, forecastUnit ) );
auto forecastTimeSteps =
RiaQDateTimeTools::createEvenlyDistributedDatesInInterval( forecastStartTimeStep, firstTimeStepInData, numDates );
appendTimeSteps( outputTimeSteps, forecastTimeSteps );
}
outputTimeSteps.insert( std::end( outputTimeSteps ), std::begin( timeSteps ), std::end( timeSteps ) );
if ( forecastForward > 0 )
{
QDateTime lastTimeStepInData = RiaQDateTimeTools::fromTime_t( timeSteps.back() );
QDateTime forecastEndTimeStep = RiaQDateTimeTools::addSpan( lastTimeStepInData, getTimeSpan( forecastForward, forecastUnit ) );
auto forecastTimeSteps = RiaQDateTimeTools::createEvenlyDistributedDatesInInterval( lastTimeStepInData, forecastEndTimeStep, numDates );
appendTimeSteps( outputTimeSteps, forecastTimeSteps );
}
return outputTimeSteps;
}
//--------------------------------------------------------------------------------------------------
///
//--------------------------------------------------------------------------------------------------
std::vector<double> RimSummaryRegressionAnalysisCurve::convertToDouble( const std::vector<time_t>& timeSteps )
{
std::vector<double> doubleVector( timeSteps.size() );
std::transform( timeSteps.begin(),
timeSteps.end(),
doubleVector.begin(),
[]( const auto& timeVal ) { return static_cast<double>( timeVal ); } );
return doubleVector;
}
//--------------------------------------------------------------------------------------------------
///
//--------------------------------------------------------------------------------------------------
std::vector<time_t> RimSummaryRegressionAnalysisCurve::convertToTimeT( const std::vector<double>& timeSteps )
{
std::vector<time_t> tVector( timeSteps.size() );
std::transform( timeSteps.begin(), timeSteps.end(), tVector.begin(), []( const auto& timeVal ) { return static_cast<time_t>( timeVal ); } );
return tVector;
}
//--------------------------------------------------------------------------------------------------
///
//--------------------------------------------------------------------------------------------------
std::pair<std::vector<double>, std::vector<double>>
RimSummaryRegressionAnalysisCurve::getPositiveValues( const std::vector<double>& timeSteps, const std::vector<double>& values )
{
std::vector<double> filteredTimeSteps;
std::vector<double> filteredValues;
for ( size_t i = 0; i < timeSteps.size(); i++ )
{
if ( timeSteps[i] > 0.0 && values[i] > 0.0 )
{
filteredTimeSteps.push_back( timeSteps[i] );
filteredValues.push_back( values[i] );
}
}
return std::make_pair( filteredTimeSteps, filteredValues );
}

20
ApplicationLibCode/ProjectDataModel/Summary/RimSummaryRegressionAnalysisCurve.h
View File

@@ -57,6 +57,13 @@ public:
LOGISTIC
};
enum class ForecastUnit
{
DAYS,
MONTHS,
YEARS,
};
RimSummaryRegressionAnalysisCurve();
~RimSummaryRegressionAnalysisCurve() override;
@@ -67,6 +74,8 @@ public:
// X Axis functions
std::vector<double> valuesX() const override;
std::vector<time_t> timeStepsX() const override;
static std::vector<time_t>
getOutputTimeSteps( const std::vector<time_t>& timeSteps, int forecastBackward, int forecastForward, ForecastUnit forecastUnit );
private:
void onLoadDataAndUpdate( bool updateParentPlot ) override;
@@ -82,6 +91,12 @@ private:
std::tuple<std::vector<time_t>, std::vector<double>, QString> computeRegressionCurve( const std::vector<time_t>& timeSteps,
const std::vector<double>& values ) const;
static std::vector<double> convertToDouble( const std::vector<time_t>& timeSteps );
static std::vector<time_t> convertToTimeT( const std::vector<double>& timeSteps );
static std::pair<std::vector<double>, std::vector<double>> getPositiveValues( const std::vector<double>& timeSteps,
const std::vector<double>& values );
static QString generateRegressionText( const regression::LinearRegression& reg );
static QString generateRegressionText( const regression::PolynominalRegression& reg );
static QString generateRegressionText( const regression::PowerFitRegression& reg );
@@ -91,9 +106,14 @@ private:
static QString formatDouble( double v );
static void appendTimeSteps( std::vector<time_t>& destinationTimeSteps, const std::set<QDateTime>& sourceTimeSteps );
caf::PdmField<caf::AppEnum<RegressionType>> m_regressionType;
caf::PdmField<int> m_polynominalDegree;
caf::PdmField<QString> m_expressionText;
caf::PdmField<int> m_forecastForward;
caf::PdmField<int> m_forecastBackward;
caf::PdmField<caf::AppEnum<ForecastUnit>> m_forecastUnit;
std::vector<double> m_valuesX;
std::vector<time_t> m_timeStepsX;

1
ApplicationLibCode/UnitTests/CMakeLists_files.cmake
View File

@@ -94,6 +94,7 @@ set(SOURCE_GROUP_SOURCE_FILES
${CMAKE_CURRENT_LIST_DIR}/RigWellLogCurveData-Test.cpp
${CMAKE_CURRENT_LIST_DIR}/RimWellLogCalculatedCurve-Test.cpp
${CMAKE_CURRENT_LIST_DIR}/RifReaderFmuRft-Test.cpp
${CMAKE_CURRENT_LIST_DIR}/RimSummaryRegressionAnalysisCurve-Test.cpp
)
if(RESINSIGHT_ENABLE_GRPC)

64
ApplicationLibCode/UnitTests/RimSummaryRegressionAnalysisCurve-Test.cpp Normal file
View File

@@ -0,0 +1,64 @@
/////////////////////////////////////////////////////////////////////////////////
//
// Copyright (C) 2023- Equinor ASA
//
// ResInsight is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// ResInsight 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 GNU General Public License at <http://www.gnu.org/licenses/gpl.html>
// for more details.
//
#include "gtest/gtest.h"
#include "RiaQDateTimeTools.h"
#include "RiaTimeTTools.h"
#include "RimSummaryRegressionAnalysisCurve.h"
#include <QDateTime>
//--------------------------------------------------------------------------------------------------
///
//--------------------------------------------------------------------------------------------------
TEST( RimSummaryRegressionAnalysisCurve, getOutputTimeStepsNoForecast )
{
const std::vector<time_t> timeSteps = { 100000 };
const std::vector<time_t> output =
RimSummaryRegressionAnalysisCurve::getOutputTimeSteps( timeSteps, 0, 0, RimSummaryRegressionAnalysisCurve::ForecastUnit::MONTHS );
ASSERT_EQ( timeSteps, output );
}
TEST( RimSummaryRegressionAnalysisCurve, getOutputTimeStepsForwardForecast )
{
QDateTime dt = RiaQDateTimeTools::fromYears( 2020 );
const std::vector<time_t> timeSteps = { RiaTimeTTools::fromQDateTime( dt ) };
int forecastBackward = 0;
int forecastForward = 1;
const std::vector<time_t> output =
RimSummaryRegressionAnalysisCurve::getOutputTimeSteps( timeSteps,
forecastBackward,
forecastForward,
RimSummaryRegressionAnalysisCurve::ForecastUnit::YEARS );
ASSERT_EQ( output.size(), 51u );
// First output value should be the original value in time steps
ASSERT_EQ( timeSteps[0], output[0] );
QDateTime oneYearLater = dt.addYears( 1 );
for ( size_t i = 1; i < output.size(); i++ )
{
auto d = RiaQDateTimeTools::fromTime_t( output[i] );
ASSERT_FALSE( RiaQDateTimeTools::lessThan( d, dt ) );
ASSERT_TRUE( RiaQDateTimeTools::lessThan( d, oneYearLater ) );
}
//
}