public void WeirdColumnTest()
{
Stream weirdStream = TestUtils.RetrieveResource(weirdColumnResource);
var weirdConfig = GetWeirdConfig();
CCRecordSet recordSet = CCRecordSet.FromStream(weirdStream, weirdConfig);
var nbc = ClassifierFactory.GetClassifierByName <string>("NaiveBayesClassifier");
nbc.Train(recordSet.Select(rec => new KeyValuePair <string, string>(rec.Description, rec.PredictedValues["Fish"])).ToList());
Assert.AreEqual(nbc.Categorize("Seattle Lounge").Category, "Trout");
}
public void TestRecordSetFromCsv()
{
Stream s = TestUtils.RetrieveResource(fullChargeList);
CCRecordSet recordSet = CCRecordSet.FromStream(s, config);
recordSet.SerializeToFile(serializationOutput);
Assert.IsTrue(File.Exists(serializationOutput));
CCRecordSet newRecordSet = CCRecordSet.FromFile(serializationOutput, config);
CollectionAssert.AreEquivalent(recordSet.ToList(), newRecordSet.ToList());
}
public void DuplicateDetectionTest()
{
var recordReader = new CsvRecordReader("USBank");
Stream stream = TestUtils.RetrieveResource(fullChargeList);
CCRecordSet recordSet = CCRecordSet.FromStream(stream, config);
var duplicateRecord = new List <CCRecord>()
{
recordSet.ToArray()[0]
};
Updater.ClassifyAndUpdate(recordSet, duplicateRecord, recordReader, config);
}
public void TestGrouping()
{
Stream s = TestUtils.RetrieveResource(fullChargeList);
CCRecordSet recordSet = CCRecordSet.FromStream(s, config);
var periodSpec = Selectors.periodSpecifiers[Consts.PERIOD_SPECIFIER_MONTH];
var aggregation = Selectors.aggreations[Consts.AGGREGATION_AVG];
var predictedColumns = TestUtils.GetMockC3PredictedColumns();
var report = Transforms.GetPeriodSummary(recordSet.ToDataTable(), periodSpec, predictedColumns[0], aggregation);
var expectedHeaders = new DateTime[] { DateTime.Parse("1/1/2004 12:00:00 AM"), DateTime.Parse("2/1/2004 12:00:00 AM"), DateTime.Parse("3/1/2004 12:00:00 AM"), DateTime.Parse("4/1/2004 12:00:00 AM") };
var actualHeaders = report.AsEnumerable().Select(row => row["Period start"]).Cast <DateTime>().ToArray();
CollectionAssert.AreEqual(expectedHeaders, actualHeaders);
TestUtils.AssertApproximatelyEqual((decimal)report.Rows[0][1], 65.73384615m);
TestUtils.AssertApproximatelyEqual((decimal)report.Rows[1][2], 44.94272727m);
TestUtils.AssertApproximatelyEqual((decimal)report.Rows[2][3], 32.17625m);
TestUtils.AssertApproximatelyEqual((decimal)report.Rows[3][4], 111.4819697m);
}
public void NaiveBayesClassificationIntegrationTest()
{
Stream oldRecordsStream = TestUtils.RetrieveResource(fullChargeList);
CCRecordSet records = CCRecordSet.FromStream(oldRecordsStream, config);
var nbc = ClassifierFactory.GetClassifierByName <string>("NaiveBayesClassifier");
var trainingData = records
.Select(rec => new KeyValuePair <string, string>(rec.Description, rec.PredictedValues["Category"]))
.ToList();
nbc.Train(trainingData);
Assert.AreEqual(nbc.Categorize("Trader Joe's").Category, "GROC");
Assert.AreEqual(nbc.Categorize("Shell Oil 27440482209 Seattle Wa").Category, "TRANS");
trainingData = records
.Select(rec => new KeyValuePair <string, string>(rec.Description, rec.PredictedValues["Owner"]))
.ToList();
nbc = ClassifierFactory.GetClassifierByName <string>("NaiveBayesClassifier");
nbc.Train(trainingData);
Assert.AreEqual(nbc.Categorize("Radio Shack 00133652 Knoxville").Category, "Bob");
}
public void TestRecordSetWithInvalidValues()
{
Stream s = TestUtils.RetrieveResource(invalidValueResource);
CCRecordSet recordSet = CCRecordSet.FromStream(s, config);
}