7eaddd9a54
ML - устранение неточностей и багов ML - оптимизация производительности DecisionTreeRegressor.Fit, RandomForestRegressor.Fit ML - тесты
64 lines
1.7 KiB
ObjectPascal
64 lines
1.7 KiB
ObjectPascal
unit TestHelpers;
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interface
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uses MLABC;
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procedure Check(cond: boolean; msg: string);
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procedure CheckRaises(action: procedure; msg: string);
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procedure CheckSchemaMatchesColumns(df: DataFrame);
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procedure CheckSchemaMatchesColumns(df: DataFrame; expectedCats: array of boolean);
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procedure CheckProbabilityRowsSumToOne(m: Matrix; eps: real := 1e-9);
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implementation
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procedure Check(cond: boolean; msg: string);
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begin
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if not cond then
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raise new Exception(msg);
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end;
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procedure CheckRaises(action: procedure; msg: string);
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begin
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var raised := false;
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try
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action();
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except
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on e: Exception do
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raised := true;
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end;
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Check(raised, msg);
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end;
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procedure CheckSchemaMatchesColumns(df: DataFrame);
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begin
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Check(df.Schema.ColumnCount = df.ColumnCount, 'Schema/column count mismatch');
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for var i := 0 to df.ColumnCount - 1 do
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begin
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Check(df.Schema.NameAt(i) = df.GetColumn(i).Info.Name, $'Name mismatch at {i}');
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Check(df.Schema.ColumnTypeAt(i) = df.GetColumn(i).Info.ColType, $'Type mismatch at {i}');
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Check(df.GetColumn(i).RowCount = df.RowCount, $'RowCount mismatch at column {i}');
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end;
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end;
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procedure CheckSchemaMatchesColumns(df: DataFrame; expectedCats: array of boolean);
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begin
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CheckSchemaMatchesColumns(df);
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Check(expectedCats.Length = df.ColumnCount, 'Expected categorical flags length mismatch');
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for var i := 0 to df.ColumnCount - 1 do
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Check(df.Schema.IsCategoricalAt(i) = expectedCats[i], $'Categorical mismatch at {i}');
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end;
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procedure CheckProbabilityRowsSumToOne(m: Matrix; eps: real);
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begin
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for var i := 0 to m.RowCount - 1 do
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begin
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var s := 0.0;
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for var j := 0 to m.ColCount - 1 do
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s += m[i, j];
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Check(Abs(s - 1.0) < eps, $'Probability row {i} must sum to 1');
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end;
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end;
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end.
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