/// <summary>
/// Given a DataFrame, df, ensure any column with the same name has matching
/// attributes (i.e. ColumnType)
/// </summary>
/// <param name="df">The DataFrame whose columns dictate the attributes of
/// this DataFrame.</param>
public void MatchColumns(DataFrame df, bool caseSensitive = false)
{
if (Columns == null || Columns.Count == 0)
{
return;
}
foreach (DataFrameColumn column in Columns)
{
DataFrameColumn other = df.FindColumn(column.Header, caseSensitive);
if (other != null)
{
column.ColumnType = other.ColumnType;
column.CopyFactors(other);
column.CopyBins(other);
}
}
}
static void TitanicDemo()
{
WriteH1("Testing");
#region Dummy data importer
//WriteH2("Dummy Data");
//DataImporterDummy di = new DataImporterDummy();
//DataFrame df = new DataFrame(di);
//df.Load(String.Empty, true, true);
//Console.WriteLine(df.TotalColumns);
//foreach(string h in df.Headers)
//{
// Console.WriteLine(h);
//}
#endregion
#region CSV data importer
WriteH2("CSV Data (Titanic)");
DataImporterCSV di_csv = new DataImporterCSV();
DataExporterCSV de_csv = new DataExporterCSV();
DataFrame df_train = new DataFrame(di_csv, de_csv);
DataFrame df_test = new DataFrame(di_csv, de_csv);
df_train.Load(@"c:\temp\titanic.csv", true, "Survived");
df_test.Load(@"c:\temp\titanic_test.csv", true);
Console.WriteLine($"Total Columns (training data): {df_train.TotalColumns}");
Console.WriteLine($"Total Columns (testing data): {df_test.TotalColumns}");
// Change the type of some of the training columns
df_train.SetColumnType("pclass", DataFrameColumnType.Factors);
df_train.SetColumnType("sex", DataFrameColumnType.Factors);
df_train.SetColumnType("age", DataFrameColumnType.Bins);
//df_train["age"].SetBins(new double[] { 0.0, 18.0, 100.0 });
df_train["age"].SetBins(new double[] { 0.0, 15.0, 25.0, 30.0, 40.0, 50.0, 55.0, 65.0, 75.0, 100.0 });
df_train["age"].EmptyValue = 30.27; // Average value of known ages
df_train.SetColumnType("fare", DataFrameColumnType.Double);
df_train.SetColumnType("sibsp", DataFrameColumnType.Double);
df_train.SetColumnType("parch", DataFrameColumnType.Double);
df_train.CreateDataColumn("CabinLetter", GetCabinLetter);
df_test.CreateDataColumn("CabinLetter", GetCabinLetter); // For now, we have to explicitly set both training set
// and test set separately.
df_train.SetColumnType("CabinLetter", DataFrameColumnType.Factors);
df_train.SetColumnType("survived", DataFrameColumnType.Double);
// Try and match the types in the testing set
df_test.MatchColumns(df_train);
Console.WriteLine($"df_train hasResults? {df_train.HasResults}. df_test hasResults? {df_test.HasResults}");
// Start calculations
Matrix Xtrain = df_train.ExportFeatures();
Matrix ytrain = df_train.ExportResults();
Matrix Xtest = df_test.ExportFeatures();
// Try Logistic Regression
double[] labels = new double[] { 0.0, 1.0 };
Matrix lr_theta = LogisticRegression.OneVsAll(Xtrain, ytrain, labels, 0.1, 1000);
Matrix lr_prediction = LogisticRegression.PredictOneVsAll(lr_theta, Xtest);
int input_layer_size = Xtrain.Columns;
int output_layer_size = labels.Length;
int hidden_layer_size = (input_layer_size + output_layer_size) / 2;
Matrix[] nn_theta = NeuralNetwork.Train(Xtrain, ytrain, input_layer_size, hidden_layer_size, labels, 0.1, 1000);
Matrix nn_prediction = NeuralNetwork.Predict(nn_theta[0], nn_theta[1], Xtest);
// Exporting
DataFrame df_lr_export = df_test;
DataFrame df_nn_export = df_test;
DataFrameColumn col_lr_results = new DataFrameColumn(df_lr_export, lr_prediction, 0);
DataFrameColumn col_nn_results = new DataFrameColumn(df_nn_export, nn_prediction, 0);
col_lr_results.Header = col_nn_results.Header = "Survived";
df_lr_export.Save(@"c:\temp\lr_results.csv");
df_nn_export.Save(@"c:\temp\nn_results.csv");
#endregion
}