@@ -115,9 +115,7 @@ If the schema of the data is not known at compile time, or too cumbersome, you c
115115var mlContext = new MLContext ();
116116
117117// Create the reader: define the data columns and where to find them in the text file.
118- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
119- {
120- Column = new [] {
118+ var reader = mlContext .Data .TextReader (new [] {
121119 // A boolean column depicting the 'label'.
122120 new TextLoader .Column (" IsOver50K" , DataKind .BL , 0 ),
123121 // Three text columns.
@@ -126,8 +124,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
126124 new TextLoader .Column (" MaritalStatus" , DataKind .TX , 3 )
127125 },
128126 // First line of the file is a header, not a data row.
129- HasHeader = true
130- } );
127+ hasHeader : true
128+ );
131129
132130// Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
133131var data = reader .Read (dataPath );
@@ -175,19 +173,17 @@ The code is very similar using the dynamic API:
175173var mlContext = new MLContext ();
176174
177175// Create the reader: define the data columns and where to find them in the text file.
178- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
179- {
180- Column = new [] {
176+ var reader = mlContext .Data .TextReader (new [] {
181177 // A boolean column depicting the 'label'.
182- new TextLoader .Column (" IsOver50k " , DataKind .BL , 0 ),
178+ new TextLoader .Column (" IsOver50K " , DataKind .BL , 0 ),
183179 // Three text columns.
184180 new TextLoader .Column (" Workclass" , DataKind .TX , 1 ),
185181 new TextLoader .Column (" Education" , DataKind .TX , 2 ),
186182 new TextLoader .Column (" MaritalStatus" , DataKind .TX , 3 )
187183 },
188184 // First line of the file is a header, not a data row.
189- HasHeader = true
190- } );
185+ hasHeader : true
186+ );
191187
192188var data = reader .Read (exampleFile1 , exampleFile2 );
193189```
@@ -365,19 +361,17 @@ You can also use the dynamic API to create the equivalent of the previous pipeli
365361var mlContext = new MLContext ();
366362
367363// Create the reader: define the data columns and where to find them in the text file.
368- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
369- {
370- Column = new [] {
364+ var reader = mlContext .Data .TextReader (new [] {
371365 // A boolean column depicting the 'label'.
372- new TextLoader .Column (" IsOver50k " , DataKind .BL , 0 ),
366+ new TextLoader .Column (" IsOver50K " , DataKind .BL , 0 ),
373367 // Three text columns.
374368 new TextLoader .Column (" Workclass" , DataKind .TX , 1 ),
375369 new TextLoader .Column (" Education" , DataKind .TX , 2 ),
376370 new TextLoader .Column (" MaritalStatus" , DataKind .TX , 3 )
377371 },
378372 // First line of the file is a header, not a data row.
379- HasHeader = true
380- } );
373+ hasHeader : true
374+ );
381375
382376// Start creating our processing pipeline. For now, let's just concatenate all the text columns
383377// together into one.
@@ -482,20 +476,18 @@ var mlContext = new MLContext();
482476
483477// Step one: read the data as an IDataView.
484478// First, we define the reader: specify the data columns and where to find them in the text file.
485- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
486- {
487- Column = new [] {
479+ var reader = mlContext .Data .TextReader (new [] {
488480 // We read the first 11 values as a single float vector.
489481 new TextLoader .Column (" FeatureVector" , DataKind .R4 , 0 , 10 ),
490482
491483 // Separately, read the target variable.
492484 new TextLoader .Column (" Target" , DataKind .R4 , 11 ),
493485 },
494486 // First line of the file is a header, not a data row.
495- HasHeader = true ,
487+ hasHeader : true ,
496488 // Default separator is tab, but we need a semicolon.
497- Separator = " ; "
498- } );
489+ separatorChar : ';'
490+ );
499491
500492// Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
501493var trainData = reader .Read (trainDataPath );
@@ -653,9 +645,7 @@ var mlContext = new MLContext();
653645
654646// Step one: read the data as an IDataView.
655647// First, we define the reader: specify the data columns and where to find them in the text file.
656- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
657- {
658- Column = new [] {
648+ var reader = mlContext .Data .TextReader (new [] {
659649 new TextLoader .Column (" SepalLength" , DataKind .R4 , 0 ),
660650 new TextLoader .Column (" SepalWidth" , DataKind .R4 , 1 ),
661651 new TextLoader .Column (" PetalLength" , DataKind .R4 , 2 ),
@@ -664,8 +654,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
664654 new TextLoader .Column (" Label" , DataKind .TX , 4 ),
665655 },
666656 // Default separator is tab, but the dataset has comma.
667- Separator = " , "
668- } );
657+ separatorChar : ','
658+ );
669659
670660// Retrieve the training data.
671661var trainData = reader .Read (irisDataPath );
@@ -952,17 +942,15 @@ You can achieve the same results using the dynamic API.
952942var mlContext = new MLContext ();
953943
954944// Define the reader: specify the data columns and where to find them in the text file.
955- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
956- {
957- Column = new [] {
945+ var reader = mlContext .Data .TextReader (new [] {
958946 // The four features of the Iris dataset will be grouped together as one Features column.
959947 new TextLoader .Column (" Features" , DataKind .R4 , 0 , 3 ),
960948 // Label: kind of iris.
961949 new TextLoader .Column (" Label" , DataKind .TX , 4 ),
962950 },
963951 // Default separator is tab, but the dataset has comma.
964- Separator = " , "
965- } );
952+ separatorChar : ','
953+ );
966954
967955// Read the training data.
968956var trainData = reader .Read (dataPath );
@@ -1073,9 +1061,8 @@ You can achieve the same results using the dynamic API.
10731061var mlContext = new MLContext ();
10741062
10751063// Define the reader: specify the data columns and where to find them in the text file.
1076- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
1077- {
1078- Column = new [] {
1064+ var reader = mlContext .Data .TextReader (new []
1065+ {
10791066 new TextLoader .Column (" Label" , DataKind .BL , 0 ),
10801067 // We will load all the categorical features into one vector column of size 8.
10811068 new TextLoader .Column (" CategoricalFeatures" , DataKind .TX , 1 , 8 ),
@@ -1084,8 +1071,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
10841071 // Let's also separately load the 'Workclass' column.
10851072 new TextLoader .Column (" Workclass" , DataKind .TX , 1 ),
10861073 },
1087- HasHeader = true
1088- } );
1074+ hasHeader : true
1075+ );
10891076
10901077// Read the data.
10911078var data = reader .Read (dataPath );
@@ -1207,14 +1194,13 @@ You can achieve the same results using the dynamic API.
12071194var mlContext = new MLContext ();
12081195
12091196// Define the reader: specify the data columns and where to find them in the text file.
1210- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
1211- {
1212- Column = new [] {
1197+ var reader = mlContext .Data .TextReader (new []
1198+ {
12131199 new TextLoader .Column (" IsToxic" , DataKind .BL , 0 ),
12141200 new TextLoader .Column (" Message" , DataKind .TX , 1 ),
12151201 },
1216- HasHeader = true
1217- } );
1202+ hasHeader : true
1203+ );
12181204
12191205// Read the data.
12201206var data = reader .Read (dataPath );
@@ -1330,9 +1316,8 @@ var mlContext = new MLContext();
13301316
13311317// Step one: read the data as an IDataView.
13321318// First, we define the reader: specify the data columns and where to find them in the text file.
1333- var reader = mlContext .Data .TextReader (new TextLoader .Arguments
1334- {
1335- Column = new [] {
1319+ var reader = mlContext .Data .TextReader (new []
1320+ {
13361321 // We read the first 11 values as a single float vector.
13371322 new TextLoader .Column (" SepalLength" , DataKind .R4 , 0 ),
13381323 new TextLoader .Column (" SepalWidth" , DataKind .R4 , 1 ),
@@ -1342,8 +1327,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
13421327 new TextLoader .Column (" Label" , DataKind .TX , 4 ),
13431328 },
13441329 // Default separator is tab, but the dataset has comma.
1345- Separator = " , "
1346- } );
1330+ separatorChar : ','
1331+ );
13471332
13481333// Read the data.
13491334var data = reader .Read (dataPath );
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