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db.getCollection('dataset_imports').find({'dataView': {$exists: true}}).forEach(function(item) | ||
{ | ||
item.dataView.isPrivate = false; | ||
db.getCollection('dataset_imports').save(item); | ||
}) | ||
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db.getCollection('dictionaries').find().forEach(function(item) | ||
{ | ||
for(i = 0; i != item.dataviews.length; ++i) | ||
{ | ||
var dataView = item.dataviews[i]; | ||
dataView.isPrivate = false; | ||
} | ||
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for(i = 0; i != item.filters.length; ++i) | ||
{ | ||
var filter = item.filters[i]; | ||
filter.isPrivate = false; | ||
} | ||
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db.getCollection('dictionaries').save(item); | ||
}) |
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db.getCollection('dataset_imports').updateMany({concreteClass : "models.RedCapDataSetImport"}, | ||
{ $set: { "eventNames": [] } } | ||
) |
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db.getCollection('dictionaries').find().forEach(function(item) | ||
{ | ||
for(i = 0; i != item.dataviews.length; ++i) | ||
{ | ||
var dataView = item.dataviews[i] | ||
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if (dataView.useOptimizedRepoChartCalcMethod) { | ||
dataView.generationMethod = "Repo and Full Data" | ||
} else { | ||
dataView.generationMethod = "Auto" | ||
} | ||
delete dataView.useOptimizedRepoChartCalcMethod | ||
} | ||
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db.getCollection('dictionaries').save(item); | ||
}) | ||
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db.getCollection('dataset_imports').find({'dataView': {$exists: true}}).forEach(function(item){ | ||
var dataView = item.dataView | ||
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if (dataView.useOptimizedRepoChartCalcMethod) { | ||
dataView.generationMethod = "Repo and Full Data" | ||
} else { | ||
dataView.generationMethod = "Auto" | ||
} | ||
delete dataView.useOptimizedRepoChartCalcMethod | ||
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db.getCollection('dataset_imports').save(item); | ||
}) |
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db.getCollection('dataset_settings').update({}, | ||
{$unset:{tranSMARTVisitFieldName:"", tranSMARTReplacements: ""}}, | ||
{multi: true} | ||
) | ||
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db.getCollection('dictionaries').find().forEach(function(item) | ||
{ | ||
var toUpdate = false | ||
for(i = 0; i != item.dataviews.length; ++i) | ||
{ | ||
for(j = 0; j != item.dataviews[i].widgetSpecs.length; ++j) | ||
{ | ||
var widgetSpec = item.dataviews[i].widgetSpecs[j]; | ||
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switch(widgetSpec.concreteClass) { | ||
case "models.CorrelationWidgetSpec" : | ||
toUpdate = true; | ||
widgetSpec.correlationType = "Pearson"; | ||
break; | ||
} | ||
} | ||
} | ||
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if (toUpdate) { | ||
db.getCollection('dictionaries').save(item); | ||
} | ||
}) | ||
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db.getCollection('dictionaries').find().forEach(function(item) | ||
{ | ||
var toUpdate = false | ||
for(i = 0; i != item.dataviews.length; ++i) | ||
{ | ||
for(j = 0; j != item.dataviews[i].widgetSpecs.length; ++j) | ||
{ | ||
var widgetSpec = item.dataviews[i].widgetSpecs[j]; | ||
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switch(widgetSpec.concreteClass) { | ||
case "models.TemplateHtmlWidgetSpec": | ||
widgetSpec.concreteClass = "models.CustomHtmlWidgetSpec"; | ||
break; | ||
} | ||
} | ||
} | ||
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if (toUpdate) { | ||
db.getCollection('dictionaries').save(item); | ||
} | ||
}) |
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db.getCollection('classifications').find().forEach(function(item) | ||
{ | ||
var name = item.concreteClass.substr(item.concreteClass.lastIndexOf(".") + 1) | ||
var newClassName = "org.incal.spark_ml.models.classification." + name | ||
item.concreteClass = newClassName | ||
db.getCollection('classifications').save(item); | ||
}) | ||
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db.getCollection('regressions').find().forEach(function(item) | ||
{ | ||
var name = item.concreteClass.substr(item.concreteClass.lastIndexOf(".") + 1) | ||
var newClassName = "org.incal.spark_ml.models.regression." + name | ||
item.concreteClass = newClassName | ||
db.getCollection('regressions').save(item); | ||
}) |
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db.getCollection('dictionaries').find({'classificationResults': {$exists: true}}).forEach(function(item) | ||
{ | ||
var toUpdate = false | ||
for(i = 0; i != item.classificationResults.length; ++i) | ||
{ | ||
var result = item.classificationResults[i]; | ||
if (result["setting-trainingTestingSplit"]) { | ||
result["setting-trainingTestSplitRatio"] = result["setting-trainingTestingSplit"] | ||
delete result["setting-trainingTestingSplit"]; | ||
toUpdate = true; | ||
} | ||
} | ||
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if (toUpdate) { | ||
db.getCollection('dictionaries').save(item); | ||
} | ||
}) | ||
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db.getCollection('dictionaries').find({'regressionResults': {$exists: true}}).forEach(function(item) | ||
{ | ||
var toUpdate = false | ||
for(i = 0; i != item.regressionResults.length; ++i) | ||
{ | ||
var result = item.regressionResults[i]; | ||
if (result["setting-trainingTestingSplit"]) { | ||
result["setting-trainingTestSplitRatio"] = result["setting-trainingTestingSplit"] | ||
delete result["setting-trainingTestingSplit"]; | ||
toUpdate = true; | ||
} | ||
} | ||
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if (toUpdate) { | ||
db.getCollection('dictionaries').save(item); | ||
} | ||
}) | ||
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function replace(result, prefix, newPrefix, name) { | ||
const oldName = prefix + "-" + name | ||
const newName = newPrefix + "-" + name | ||
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if (result[oldName]) { | ||
print(oldName + " -> " + newName) | ||
result[newName] = result[oldName] | ||
delete result[oldName]; | ||
} | ||
} | ||
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db.getCollection('dictionaries').find({'classificationResults': {$exists: true}}).forEach(function(item) | ||
{ | ||
var toUpdate = false | ||
for(i = 0; i != item.classificationResults.length; ++i) | ||
{ | ||
var result = item.classificationResults[i]; | ||
// io spec | ||
replace(result, "setting", "runSpec-ioSpec", "inputFieldNames") | ||
replace(result, "setting", "runSpec-ioSpec", "outputFieldName") | ||
replace(result, "setting", "runSpec-ioSpec", "filterId") | ||
replace(result, "setting", "runSpec-ioSpec", "replicationFilterId") | ||
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// ml model | ||
replace(result, "setting", "runSpec", "mlModelId") | ||
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// learning setting | ||
replace(result, "setting", "runSpec-learningSetting", "featuresNormalizationType") | ||
replace(result, "setting", "runSpec-learningSetting", "featuresSelectionNum") | ||
replace(result, "setting", "runSpec-learningSetting", "pcaDims") | ||
replace(result, "setting", "runSpec-learningSetting", "trainingTestSplitRatio") | ||
replace(result, "setting", "runSpec-learningSetting", "samplingRatios") | ||
replace(result, "setting", "runSpec-learningSetting", "repetitions") | ||
replace(result, "setting", "runSpec-learningSetting", "crossValidationFolds") | ||
replace(result, "setting", "runSpec-learningSetting", "crossValidationEvalMetric") | ||
replace(result, "setting", "runSpec-learningSetting", "binCurvesNumBins") | ||
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toUpdate = true; | ||
} | ||
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if (toUpdate) { | ||
db.getCollection('dictionaries').save(item); | ||
} | ||
}) | ||
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db.getCollection('dictionaries').find({'regressionResults': {$exists: true}}).forEach(function(item) | ||
{ | ||
var toUpdate = false | ||
for(i = 0; i != item.regressionResults.length; ++i) | ||
{ | ||
var result = item.regressionResults[i]; | ||
// io spec | ||
replace(result, "setting", "runSpec-ioSpec", "inputFieldNames") | ||
replace(result, "setting", "runSpec-ioSpec", "outputFieldName") | ||
replace(result, "setting", "runSpec-ioSpec", "filterId") | ||
replace(result, "setting", "runSpec-ioSpec", "replicationFilterId") | ||
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// ml model | ||
replace(result, "setting", "runSpec", "mlModelId") | ||
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// learning setting | ||
replace(result, "setting", "runSpec-learningSetting", "featuresNormalizationType") | ||
replace(result, "setting", "runSpec-learningSetting", "outputNormalizationType") | ||
replace(result, "setting", "runSpec-learningSetting", "pcaDims") | ||
replace(result, "setting", "runSpec-learningSetting", "trainingTestSplitRatio") | ||
replace(result, "setting", "runSpec-learningSetting", "repetitions") | ||
replace(result, "setting", "runSpec-learningSetting", "crossValidationFolds") | ||
replace(result, "setting", "runSpec-learningSetting", "crossValidationEvalMetric") | ||
replace(result, "setting", "runSpec-learningSetting", "collectOutputs") | ||
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if (!result["runSpec-learningSetting-collectOutputs"]) { | ||
result["runSpec-learningSetting-collectOutputs"] = false | ||
} | ||
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toUpdate = true; | ||
} | ||
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if (toUpdate) { | ||
db.getCollection('dictionaries').save(item); | ||
} | ||
}) | ||
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db.getCollection('dictionaries').find({}).forEach(function(item) | ||
{ | ||
var toUpdate = false; | ||
if (item.classificationResults) { | ||
for(i = 0; i != item.classificationResults.length; ++i) | ||
{ | ||
item.classificationResults[i].concreteClass = "org.incal.spark_ml.models.result.StandardClassificationResult"; | ||
toUpdate = true | ||
} | ||
} | ||
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if (item.regressionResults) { | ||
for(i = 0; i != item.regressionResults.length; ++i) | ||
{ | ||
item.regressionResults[i].concreteClass = "org.incal.spark_ml.models.result.StandardRegressionResult"; | ||
toUpdate = true | ||
} | ||
} | ||
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if (toUpdate) { | ||
db.getCollection('dictionaries').save(item); | ||
} | ||
}) |
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#!/bin/bash | ||
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echo " ______ __ | ||
/ \ | \ | ||
| ######\ ____| ## ______ | ||
| ##__| ## / ## | \ | ||
| ## ##| ####### \######\\ | ||
| ########| ## | ## / ## | ||
| ## | ##| ##__| ##| ####### | ||
| ## | ## \## ## \## ## | ||
\## \## \####### \####### | ||
--------------------------------- | ||
MIGRATION ASSISTANT | ||
--------------------------------- | ||
" | ||
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" | ||
bold=$(tput bold) | ||
normal=$(tput sgr0) | ||
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CUR_DIR=$SCRIPT_DIR/../ | ||
APP_CONF_FILE=$CUR_DIR/conf/application.conf | ||
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if [ ! -f $APP_CONF_FILE ]; | ||
then | ||
echo -e "\n>>> Something is wrong. This script was not run from an Ada installation dir.\n" | ||
exit 1 | ||
fi | ||
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APP_VERSION_LINE=$(grep -F "app.version" $APP_CONF_FILE) | ||
APP_VERSION=${APP_VERSION_LINE#*= } | ||
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echo -e "Current Ada version is ${bold}$APP_VERSION${normal}.\n" | ||
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if [[ $APP_VERSION < "0.7.0" ]]; | ||
then | ||
echo -e ">>> This script can be used only for Ada versions 0.7.0 and above.\n" | ||
exit 1 | ||
fi | ||
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PREV_DIR_CORRECT="" | ||
while [[ $PREV_DIR_CORRECT != "y" && $PREV_DIR_CORRECT != "Y" ]]; | ||
do | ||
# PREV_DIR="USER INPUT" | ||
read -p "Enter the (root) dir of your previous Ada installation: " PREV_DIR | ||
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if [ -d $PREV_DIR ]; | ||
then | ||
PREV_APP_CONF_FILE=$PREV_DIR/conf/application.conf | ||
if [ -f $PREV_APP_CONF_FILE ]; | ||
then | ||
PREV_APP_VERSION_LINE=$(grep -F "app.version" $PREV_APP_CONF_FILE) | ||
PREV_APP_VERSION=${PREV_APP_VERSION_LINE#*= } | ||
echo -e "\n>>> Found the app version ${bold}$PREV_APP_VERSION${normal} in the dir '$PREV_DIR'.\n" | ||
if [[ $APP_VERSION > $PREV_APP_VERSION ]]; | ||
then | ||
# PREV_DIR_CORRECT="USER INPUT" | ||
read -p "Is it correct [y/n]? " PREV_DIR_CORRECT | ||
else | ||
echo -e ">>> Cannot continue. Your (current) version must be newer than the source one.\n" | ||
fi | ||
else | ||
echo -e "\n>>> Cannot find an application config. The dir '$PREV_DIR' does not seem to belong to an Ada instalation.\n" | ||
fi | ||
else | ||
echo -e "\n>>> The dir '$PREV_DIR' does not exist.\n" | ||
fi | ||
done | ||
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# Configuration files | ||
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echo -e "\n>>> Copying configuration files...\n" | ||
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cp $PREV_DIR/conf/custom.conf $CUR_DIR/conf/custom.conf | ||
cp $PREV_DIR/bin/set_env.sh $CUR_DIR/bin/set_env.sh | ||
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PREV_TEMP_LINE=$(grep -F "ADA_TEMP=" $PREV_DIR/bin/runme) | ||
PREV_MEM_LINE=$(grep -F "ADA_MEM=" $PREV_DIR/bin/runme) | ||
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sed "s|ADA_TEMP=.*|${PREV_TEMP_LINE}|g" $CUR_DIR/bin/runme > $CUR_DIR/bin/runme_x | ||
sed "s|ADA_MEM=.*|${PREV_MEM_LINE}|g" $CUR_DIR/bin/runme_x > $CUR_DIR/bin/runme | ||
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rm $CUR_DIR/bin/runme_x | ||
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# Copy extra folders, e.g. dataImports, and images | ||
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cd $PREV_DIR | ||
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echo -e ">>> Searching for extra sub dirs to copy..." | ||
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for i in $(ls -d */); | ||
do | ||
SUB_DIR=${i%%/}; | ||
if [[ $SUB_DIR != "bin" && $SUB_DIR != "conf" && $SUB_DIR != "lib" && $SUB_DIR != "share" ]]; | ||
then | ||
echo -e "\nCopying '$SUB_DIR'..." | ||
cp -r $PREV_DIR/$SUB_DIR $CUR_DIR/$SUB_DIR | ||
fi | ||
done | ||
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# Mongo | ||
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echo -e "\n>>> ${bold}Warning: DB migration has to be done manually. Please execute all the db-update scripts from > $PREV_APP_VERSION to $APP_VERSION${normal}.\n" | ||
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echo -e ">>> Ada migration has been successfully finished!\n" | ||
|
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echo "---------------------------------" | ||
echo -e "\n[[EnJoY Ada Discovery Analytics. Visit us at https://ada-discovery.org. Bye]]\n" |
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