private static InferenceResult InferTextFileColumnTypesCore(IHostEnvironment env, IMultiStreamSource fileSource, Arguments args, IChannel ch)
{
Contracts.AssertValue(ch);
ch.AssertValue(env);
ch.AssertValue(fileSource);
ch.AssertValue(args);
if (args.ColumnCount == 0)
{
ch.Error("Too many empty columns for automatic inference.");
return(InferenceResult.Fail());
}
if (args.ColumnCount >= SmartColumnsLim)
{
ch.Error("Too many columns for automatic inference.");
return(InferenceResult.Fail());
}
// Read the file as the specified number of text columns.
var textLoaderArgs = new TextLoader.Arguments
{
Column = new[] { TextLoader.Column.Parse(string.Format("C:TX:0-{0}", args.ColumnCount - 1)) },
Separator = args.Separator,
AllowSparse = args.AllowSparse,
AllowQuoting = args.AllowQuote,
};
var idv = TextLoader.ReadFile(env, textLoaderArgs, fileSource);
idv = idv.Take(args.MaxRowsToRead);
// Read all the data into memory.
// List items are rows of the dataset.
var data = new List <ReadOnlyMemory <char>[]>();
using (var cursor = idv.GetRowCursor(col => true))
{
int columnIndex;
bool found = cursor.Schema.TryGetColumnIndex("C", out columnIndex);
Contracts.Assert(found);
var colType = cursor.Schema.GetColumnType(columnIndex);
Contracts.Assert(colType.ItemType.IsText);
ValueGetter <VBuffer <ReadOnlyMemory <char> > > vecGetter = null;
ValueGetter <ReadOnlyMemory <char> > oneGetter = null;
bool isVector = colType.IsVector;
if (isVector)
{
vecGetter = cursor.GetGetter <VBuffer <ReadOnlyMemory <char> > >(columnIndex);
}
else
{
Contracts.Assert(args.ColumnCount == 1);
oneGetter = cursor.GetGetter <ReadOnlyMemory <char> >(columnIndex);
}
VBuffer <ReadOnlyMemory <char> > line = default;
ReadOnlyMemory <char> tsValue = default;
while (cursor.MoveNext())
{
if (isVector)
{
vecGetter(ref line);
Contracts.Assert(line.Length == args.ColumnCount);
var values = new ReadOnlyMemory <char> [args.ColumnCount];
line.CopyTo(values);
data.Add(values);
}
else
{
oneGetter(ref tsValue);
var values = new[] { tsValue };
data.Add(values);
}
}
}
if (data.Count < 2)
{
ch.Error("Too few rows ({0}) for automatic inference.", data.Count);
return(InferenceResult.Fail());
}
var cols = new IntermediateColumn[args.ColumnCount];
for (int i = 0; i < args.ColumnCount; i++)
{
cols[i] = new IntermediateColumn(data.Select(x => x[i]).ToArray(), i);
}
foreach (var expert in GetExperts())
{
expert.Apply(cols);
}
Contracts.Check(cols.All(x => x.SuggestedType != null), "Column type inference must be conclusive");
// Aggregating header signals.
int suspect = 0;
var usedNames = new HashSet <string>();
for (int i = 0; i < args.ColumnCount; i++)
{
if (cols[i].HasHeader == true)
{
if (usedNames.Add(cols[i].RawData[0].ToString()))
{
suspect++;
}
else
{
// duplicate value in the first column is a strong signal that this is not a header
suspect -= args.ColumnCount;
}
}
else if (cols[i].HasHeader == false)
{
suspect--;
}
}
// REVIEW: Why not use this for column names as well?
TextLoader.Arguments fileArgs;
bool hasHeader;
if (TextLoader.FileContainsValidSchema(env, fileSource, out fileArgs))
{
hasHeader = fileArgs.HasHeader;
}
else
{
hasHeader = suspect > 0;
}
// suggest names
var names = new List <string>();
usedNames.Clear();
foreach (var col in cols)
{
string name0;
string name;
name0 = name = SuggestName(col, hasHeader);
int i = 0;
while (!usedNames.Add(name))
{
name = string.Format("{0}_{1:00}", name0, i++);
}
names.Add(name);
}
var outCols =
cols.Select((x, i) => new Column(x.ColumnId, names[i], x.SuggestedType)).ToArray();
var numerics = outCols.Count(x => x.ItemType.IsNumber);
ch.Info("Detected {0} numeric and {1} text columns.", numerics, outCols.Length - numerics);
if (hasHeader)
{
ch.Info("Generated column names from the file header.");
}
return(InferenceResult.Success(outCols, hasHeader, cols.Select(col => col.RawData).ToArray()));
}
private static InferenceResult InferTextFileColumnTypesCore(MLContext context, IMultiStreamSource fileSource, Arguments args)
{
if (args.ColumnCount == 0)
{
// too many empty columns for automatic inference
return(InferenceResult.Fail());
}
if (args.ColumnCount >= SmartColumnsLim)
{
// too many columns for automatic inference
return(InferenceResult.Fail());
}
// read the file as the specified number of text columns
var textLoaderOptions = new TextLoader.Options
{
Columns = new[] { new TextLoader.Column("C", DataKind.String, 0, args.ColumnCount - 1) },
Separators = new[] { args.Separator },
AllowSparse = args.AllowSparse,
AllowQuoting = args.AllowQuote,
};
var textLoader = context.Data.CreateTextLoader(textLoaderOptions);
var idv = textLoader.Load(fileSource);
idv = context.Data.TakeRows(idv, args.MaxRowsToRead);
// read all the data into memory.
// list items are rows of the dataset.
var data = new List <ReadOnlyMemory <char>[]>();
using (var cursor = idv.GetRowCursor(idv.Schema))
{
var column = cursor.Schema.GetColumnOrNull("C").Value;
var colType = column.Type;
ValueGetter <VBuffer <ReadOnlyMemory <char> > > vecGetter = null;
ValueGetter <ReadOnlyMemory <char> > oneGetter = null;
bool isVector = colType.IsVector();
if (isVector)
{
vecGetter = cursor.GetGetter <VBuffer <ReadOnlyMemory <char> > >(column);
}
else
{
oneGetter = cursor.GetGetter <ReadOnlyMemory <char> >(column);
}
VBuffer <ReadOnlyMemory <char> > line = default;
ReadOnlyMemory <char> tsValue = default;
while (cursor.MoveNext())
{
if (isVector)
{
vecGetter(ref line);
var values = new ReadOnlyMemory <char> [args.ColumnCount];
line.CopyTo(values);
data.Add(values);
}
else
{
oneGetter(ref tsValue);
var values = new[] { tsValue };
data.Add(values);
}
}
}
if (data.Count < 2)
{
// too few rows for automatic inference
return(InferenceResult.Fail());
}
var cols = new IntermediateColumn[args.ColumnCount];
for (int i = 0; i < args.ColumnCount; i++)
{
cols[i] = new IntermediateColumn(data.Select(x => x[i]).ToArray(), i);
}
foreach (var expert in GetExperts())
{
expert.Apply(cols);
}
// Aggregating header signals.
int suspect = 0;
var usedNames = new HashSet <string>();
for (int i = 0; i < args.ColumnCount; i++)
{
if (cols[i].HasHeader == true)
{
if (usedNames.Add(cols[i].RawData[0].ToString()))
{
suspect++;
}
else
{
// duplicate value in the first column is a strong signal that this is not a header
suspect -= args.ColumnCount;
}
}
else if (cols[i].HasHeader == false)
{
suspect--;
}
}
// suggest names
usedNames.Clear();
foreach (var col in cols)
{
string name0;
string name;
name0 = name = SuggestName(col, args.HasHeader);
int i = 0;
while (!usedNames.Add(name))
{
name = string.Format("{0}_{1:00}", name0, i++);
}
col.Name = name;
}
// validate & retrieve label column
var labelColumn = GetAndValidateLabelColumn(args, cols);
// if label column has all Boolean values, set its type as Boolean
if (labelColumn.HasAllBooleanValues())
{
labelColumn.SuggestedType = BooleanDataViewType.Instance;
}
var outCols = cols.Select(x => new Column(x.ColumnId, x.Name, x.SuggestedType)).ToArray();
return(InferenceResult.Success(outCols, args.HasHeader, cols.Select(col => col.RawData).ToArray()));
}
