Examples with DataRow com.ibm.cohort.datarow.model.DataRow used on opensource projects

Example 1 with DataRow

use of com.ibm.cohort.datarow.model.DataRow in project quality-measure-and-cohort-service by Alvearie.

the class AnyColumnFunctions method AnyColumnRegex.

public static Object AnyColumnRegex(Object object, String regex) {
    DataRow dataRow = (DataRow) object;
    StringMatcher matcher = new RegexStringMatcher(regex);
    return dataRow.getFieldNames().stream().filter(matcher).map(dataRow::getValue).collect(Collectors.toList());
}

Example 2 with DataRow

use of com.ibm.cohort.datarow.model.DataRow in project quality-measure-and-cohort-service by Alvearie.

the class AnyColumnFunctions method AnyColumn.

public static Object AnyColumn(Object object, String fieldPrefix) {
    DataRow dataRow = (DataRow) object;
    StringMatcher matcher = new PrefixStringMatcher(fieldPrefix);
    return dataRow.getFieldNames().stream().filter(matcher).map(dataRow::getValue).collect(Collectors.toList());
}

Example 3 with DataRow

use of com.ibm.cohort.datarow.model.DataRow in project quality-measure-and-cohort-service by Alvearie.

the class SparkCqlEvaluator method evaluate.

/**
 * Evaluate the input CQL for a single context + data pair.
 *
 * @param libraryProvider Library provider providing CQL/ELM content
 * @param termProvider    Terminology provider providing terminology resources
 * @param funProvider     External function provider providing static CQL functions
 * @param contextName     Context name corresponding to the library context key
 *                        currently under evaluation.
 * @param resultsSchema   StructType containing the schema data for the output table
 *                        that will be created.
 * @param rowsByContext   Data for a single evaluation context
 * @param dataTypeAliases Mapping of data type to abstract type
 * @param perContextAccum Spark accumulator that tracks each individual context
 *                        evaluation
 * @param errorAccum      Spark accumulator that tracks CQL evaluation errors
 * @param batchRunTime    Single unified timestamp for all contexts
 * @return Evaluation results for all expressions evaluated keyed by the context
 *         ID. Expression names are automatically namespaced according to the
 *         library name to avoid issues arising for expression names matching
 *         between libraries (e.g. LibraryName.ExpressionName).
 * @throws Exception on general failure including CQL library loading issues
 */
protected Iterator<Tuple2<Object, Row>> evaluate(CqlLibraryProvider libraryProvider, CqlTerminologyProvider termProvider, ExternalFunctionProvider funProvider, String contextName, StructType resultsSchema, Tuple2<Object, List<Row>> rowsByContext, Map<String, String> dataTypeAliases, LongAccumulator perContextAccum, CollectionAccumulator<EvaluationError> errorAccum, ZonedDateTime batchRunTime) throws Exception {
    // Convert the Spark objects to the cohort Java model
    List<DataRow> datarows = rowsByContext._2().stream().map(getDataRowFactory()).collect(Collectors.toList());
    Map<String, List<Object>> dataByDataType = new HashMap<>();
    for (DataRow datarow : datarows) {
        String dataType = (String) datarow.getValue(ContextRetriever.SOURCE_FACT_IDX);
        List<Object> mappedRows = dataByDataType.computeIfAbsent(dataType, x -> new ArrayList<>());
        mappedRows.add(datarow);
        if (dataTypeAliases.containsKey(dataType)) {
            String mappedType = dataTypeAliases.get(dataType);
            List<Object> aliasedRows = dataByDataType.computeIfAbsent(mappedType, x -> new ArrayList<>());
            aliasedRows.add(datarow);
        }
    }
    DataRowRetrieveProvider retrieveProvider = new DataRowRetrieveProvider(dataByDataType, termProvider);
    CqlDataProvider dataProvider = new DataRowDataProvider(getDataRowClass(), retrieveProvider);
    CqlEvaluator evaluator = new CqlEvaluator().setLibraryProvider(libraryProvider).setDataProvider(dataProvider).setTerminologyProvider(termProvider).setExternalFunctionProvider(funProvider);
    CqlEvaluationRequests requests = getFilteredJobSpecificationWithIds();
    SparkOutputColumnEncoder columnEncoder = getSparkOutputColumnEncoder();
    return evaluate(rowsByContext, contextName, resultsSchema, evaluator, requests, columnEncoder, perContextAccum, errorAccum, batchRunTime);
}

Example 4 with DataRow

use of com.ibm.cohort.datarow.model.DataRow in project quality-measure-and-cohort-service by Alvearie.

the class AnyColumnFunctionsTest method testAnyColumnRegexNoMatches.

@Test
public void testAnyColumnRegexNoMatches() {
    DataRow row = spy(DataRow.class);
    Set<String> allFields = new HashSet<>();
    allFields.add("matchingField1");
    allFields.add("matchingField2");
    allFields.add("nonMatchingField");
    doReturn(allFields).when(row).getFieldNames();
    doReturn("matchingValue1").when(row).getValue("matchingField1");
    doReturn("matchingValue2").when(row).getValue("matchingField2");
    doReturn("nonMatchingValue").when(row).getValue("nonMatchingField");
    String regex = "prefix[0-9]+";
    List<Object> actual = (List<Object>) AnyColumnFunctions.AnyColumnRegex(row, regex);
    assertThat(actual, empty());
}

Example 5 with DataRow

use of com.ibm.cohort.datarow.model.DataRow in project quality-measure-and-cohort-service by Alvearie.

the class AnyColumnFunctionsTest method testAnyColumnHasMatches.

@Test
public void testAnyColumnHasMatches() {
    String matchingField1 = "matchingField1";
    String matchingField2 = "matchingField2";
    String nonMatchingField = "nonMatchingField";
    String expectedValue1 = "matchingValue1";
    String expectedValue2 = "matchingValue2";
    DataRow row = spy(DataRow.class);
    Set<String> allFields = new HashSet<>();
    allFields.add(matchingField1);
    allFields.add(matchingField2);
    allFields.add(nonMatchingField);
    doReturn(allFields).when(row).getFieldNames();
    doReturn(expectedValue1).when(row).getValue(matchingField1);
    doReturn(expectedValue2).when(row).getValue(matchingField2);
    doReturn("nonMatchingValue").when(row).getValue(nonMatchingField);
    String prefix = "matchingField";
    List<Object> actual = (List<Object>) AnyColumnFunctions.AnyColumn(row, prefix);
    assertThat(actual, containsInAnyOrder(expectedValue1, expectedValue2));
}